diff --git a/Dev/SygilDetector.ipynb b/Dev/SygilDetector.ipynb index e2b55ba..8e74853 100644 --- a/Dev/SygilDetector.ipynb +++ b/Dev/SygilDetector.ipynb @@ -1,8 +1,20 @@ { "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sigil Detection\n", + "\n", + "**OLD Notebook: the final technique used is in notebook 2**\n", + "\n", + "This notebook generates synthetic data and test ideas for sigil detection (detection of the shape the player draws on the screen)" + ] + }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -15,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -29,32 +41,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.figure(1, figsize=(5,5))\n", "\n", @@ -63,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -188,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -211,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -234,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -259,58 +248,9 @@ }, { "cell_type": "code", - "execution_count": 191, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "points = sample_points(tri_points, 100)\n", "\n", @@ -335,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -367,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -415,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -438,29 +378,9 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "89\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "model_points, n_inliers, model = ransac_njit(points, triangle_model, 2, success_rate=0.9, p_inlier=0.2)\n", "print(n_inliers)\n", @@ -475,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -498,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -515,43 +435,18 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'tri_n_inliers_hist' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtri_n_inliers_hist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mNameError\u001b[0m: name 'tri_n_inliers_hist' is not defined" - ] - } - ], + "outputs": [], "source": [ "np.mean(tri_n_inliers_hist)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "square_points = [[-1,1], [1,1], [1,-1], [-1,-1]]\n", "square_points = np.array(square_points)\n", @@ -563,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -613,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -630,42 +525,18 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "96.508" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np.mean(square_n_inliers_hist)" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(points[:,0], points[:,1], \"o\")\n", "plt.fill(vertices[:,0], vertices[:,1], fill=False)\n", @@ -675,47 +546,9 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(array([ 3., 14., 13., 15., 14., 30., 54., 54., 207., 596.]),\n", - " array([ 74. , 76.6, 79.2, 81.8, 84.4, 87. , 89.6, 92.2, 94.8,\n", - " 97.4, 100. ]),\n", - " )" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.hist(tri_n_inliers_hist)\n", "plt.show()\n", @@ -724,21 +557,9 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'square': {'square': 96.169, 'triangle': 61.245},\n", - " 'triangle': {'square': 86.656, 'triangle': 90.023}}" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inliers_hist = {}\n", "inliers_hist[\"square\"] = {\"square\":[], \"triangle\":[]}\n", @@ -792,20 +613,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "points = sample_points(tri_points, 100)\n", "points = rotate_points(points, 90)\n", @@ -833,20 +641,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.53645188, 0.01576866],\n", - " [-0.29888572, -0.47924298],\n", - " [-0.23756616, 0.46347432]])" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "vertices" ] @@ -855,20 +650,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.28867513, 0.5 ],\n", - " [-0.28867513, -0.5 ],\n", - " [ 0.57735027, 0. ]])" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "rotate_points(tri_points, 90)" ] @@ -877,18 +659,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 180. , 0. , -151.04080896])" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "B = np.zeros((3,3))\n", "A = np.zeros((3,3))\n", @@ -915,18 +686,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([4.90462598, 4.45631461, 0. ])" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "s" ] @@ -935,20 +695,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., -1., 0.],\n", - " [ 1., 0., 0.],\n", - " [ 0., 0., 1.]])" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Rotation.from_euler(\"xyz\", [0, 0, 90] , degrees=True).as_matrix()" ] @@ -957,18 +704,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 180. , 0. , -151.04080896])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Rotation.from_matrix(R).as_euler(\"xyz\", degrees=True)" ] @@ -977,18 +713,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([420. , 240. , 88.95919104])" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Rotation.from_matrix(R).as_euler(\"xyz\", degrees=True)+360-120" ] @@ -1009,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1019,34 +744,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "all_points_tr = df_triangle.to_numpy()\n", "all_points_sq = df_square.to_numpy()\n", @@ -1060,7 +760,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1072,22 +772,9 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "points = sample_points_fromlist(all_points_sq[:200], 100)\n", "\n", @@ -1097,20 +784,9 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "68" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "points_scaled = scale_unit(points)\n", "\n", @@ -1128,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1149,7 +825,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1195,33 +871,16 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mCanceled future for execute_request message before replies were done" - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mO Kernel falhou ao executar o código na célula atual ou em uma célula anterior. Examine o código nas células para identificar uma possível causa da falha. Clique aqui para obter mais informações. Consulte o log do Jupyter para obter mais detalhes." - ] - } - ], + "outputs": [], "source": [ "create_features(10, 1e-3, 1e-1)" ] }, { "cell_type": "code", - "execution_count": 106, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1233,7 +892,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1242,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1252,28 +911,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33meltoncn\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import wandb\n", "from wandb.keras import WandbCallback\n", @@ -1283,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1301,7 +941,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1317,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1326,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1341,7 +981,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1352,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1361,94 +1001,16 @@ }, { "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1],\n", - " [ 3],\n", - " [ 4],\n", - " ...,\n", - " [104986],\n", - " [104991],\n", - " [104995]], dtype=int64)" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAABjEElEQVR4nO2ddXgUV/fHP3d3k427hygxNEhwKS20lApUqdBSeau0b0vd3/dXt7cOdXesQluoQAWX4BaSEIe46+r8/tjNkkBCEuLJ/TzPPjs7c2fmTrL7nTPnnnuOUBQFiUQikfR9VN3dAYlEIpF0DVLwJRKJpJ8gBV8ikUj6CVLwJRKJpJ8gBV8ikUj6CZru7kBz+Pj4KOHh4d3dDYlEIulV7Nixo0hRFN+mtvVYwQ8PDycxMbG7uyGRSCS9CiFEZnPbpEtHIpFI+glS8CUSiaSfIAVfIpFI+glS8CUSiaSfIAVfIpFI+glS8CUSiaSfIAVfIpFI+glS8CUSSY9lXXIh+4+Wd3c3+gxS8CUSSY+kuErHLV8kcve3u5B1OzoGKfgSiaRH8vHGdOoMZo4UVrM+pai7u9MnkIIvkUh6HBV1Bj7flMmMQf74uGj5dFNGd3epTyAFXyKR9Di+2JxJpc7IwhnRzBsXyp9JBaQXVXd3t3o9UvAlEkmPolZv4uMN6UyL9WVosDvzxodipxZ8Jq38diMFXyKR9CiWbM+iuFrPHWdGAeDn6sAFw4NYviOHyjpDN/eudyMFXyKR9Bj0RjPvrUtjbIQXY8K9bOuvnxhOlc7I8h053di73k+HCL4Q4lwhxGEhRKoQ4uFTtLtUCKEIIRI64rwSiaRv8cOuo+SW19ms+3riQzwYFerBZ5syMJtliObp0m7BF0KogcXALGAwcJUQYnAT7VyBu4Gt7T2nRCLpe5jMCu/8c4ShwW5MjfY5afv1kyLIKK7h7+SCbuhd36AjLPyxQKqiKGmKouiBb4E5TbR7GngRqOuAc0okkj7G6v25pBdVc8e0KIQQJ22fNTQAfzctn2zM6PrO9RE6QvCDgewGn3Os62wIIUYBIYqi/HKqAwkhbhFCJAohEgsLCzuga5L+gs5oIq9c2hK9FUVRWPzXEQb6OjNzSECTbezUKq4dH8b6lCJSCyq7uId9g04ftBVCqIBXgftaaqsoyvuKoiQoipLg69tkDV6JpEne+yeNWW+s6+5uSE6Tvw4XcCi3ggXTolCpTrbu67lqbCj2GpWciHWadITgHwVCGnweYF1XjyswFPhbCJEBjAdWyoFbSUeyK6uU0hqDzLnSC1EUhUV/phLs4cjsEUGnbOvtomVOfBArdhylvEaGaLaVjhD87UC0ECJCCGEPXAmsrN+oKEq5oig+iqKEK4oSDmwBZiuKktgB55ZIAEjKszziG0xS8HsbW9NL2JlVxm1nRGKnblmSrp8UTq3BxNLE7BbbShrTbsFXFMUI3An8BhwCliqKckAI8ZQQYnZ7jy+RtERZjZ5cq//eaDZ3c28kbWXxX6n4uGi5PCGk5cbAkCB3xkZ48dnmDEwyRLNNdIgPX1GUVYqixCiKMlBRlGet6/6jKMrKJtpOk9a9pCOpt+5BWvi9jT3ZZaxPKeLmKRE42Klbvd+Nk8LJKa1lzaH8Tuxd30POtJX0eg43EHyjSVr4vYm3/07FzUHDvPFhbdpvxiB/gj0c+WRjeif1rG8iBV/SbWQWV1NcpWv3cZLyKmzLRvmI32tIya/ktwP5XD8pAhetpk37atQqrp0Qxpa0Eg7lVrS8gwSQgi/pRh5YvpfZizZSUNm++PlDuQ1dOtLC7y28/fcRnOzV3DAx/LT2v3JMCA52Kj6VE7FajRR8SbdxychgjpbVcuOn26nWGU/rGGazQnJ+JR5OdgAYpQ+/V5BVXMPKPce4emwons72p3UMDyd7Lh45gB92H6WkWt/BPeweymsN/HYgr9OeWqTgS7qNS0cPINzbif1HK7jz652n5X/PLq2hRm9iaJA7IKN0egvvrTuCWghunhrZruPcMCkcndHMN9uyOqhnXYvBZGZ7Rgmv/n6Yi9/eyMinfufWL3bw7j9HOuV8bXOcSSQdiJ1axT1nx3D3t7v563AhT/x4gOcuHtpkHpXmqHfnDA12Z0NqkYzS6QUUVNSxLDGHyxIG4O/m0K5jxfi7MinKmy+3ZHLL1NbF8XcniqKQVlTNhpQi1qcUsSWtmCqdESGgfs7gxIHePDV7aKecXwq+pFu5YHgQi/9KJTm/im+2ZRHi5ciCaVEt72glKa8CIWBwkBsgXTq9gQ/Wp2E0m7lt6sAOOd4NEyO46fNEfjuQxwXDTz1TtzsoqdazMbWIDSlFbEgt4mhZLQChXk7MGRHE2Agvvt91lL8PFzI3YQDPXDQMe03n3Lik4Eu6FbVKcO/Zsdz25Q7sNSpe+vUwwR6OzBkR3PLOQFJuJeHezrg6WL7KBunS6dGUVuv5amsWs+ODCPV26pBjnhnnR6iXE59uzOgRgq8zmtiRWcr6FIvI7z9WjqKAm4OGiQN9WHDmQKZE+RLq7URxlY5bv9hBYmYpD54by+1nDGzTE25bkYIv6XZmDvFn+AB38srrGODpyP3L9uDn6sCEgd4t7ns4v5K4AFfsVBaLSFr4PZtPN2VQozdxexue4lpCrRJcNzGcp38+yL6ccoYNcO+wY7cGRVFIzq9ifUoh61OK2JZeQq3BhEYlGBXqyT0zYpgS7cOwYHc0DVxORwqruOGT7eRX1LH46lGcPzyw0/sqBV/S7QghuO+cWK77eBvzJ4RRUWfkli8SWXH7RGL8XZvdr0ZvJKO4mjkjgtCoLVaRnHjVc6nSGfl0UwbnDPYnNqD5/+vpcHnCAF79/TCfbErn1bkjOvTYTVFQWcfG1CKbFV9QaZlPMtDXmSvGhDA5yofxA72bnV+w+Ugxt325Azu14JtbxjMq1LPT+wxS8CU9hKnRPowN9+KzzZl8c/M4rvpgKzd8sp3vF0zEr5mBveT8KhQF4gLcsLMKvkFOvOqxfL01k/JaAwvO7Djrvh43BzsuGz2Ab7Zl88isQfi6ajv0+HUGE9vSS2xWfH06D08nOyZF+TA12pfJ0T4EeTi2eKzlO3J45Lu9hHk788n1Ywjx6hjXVmuQgi/pEVis/BiueH8LfyYV8Mn1Y5j73mZu+HQ7S2+dgHMTllKSNVZ5UKArZdZUudLC75nUGUx8sD6dyVE+jAjx6JRzzJ8YzmebM/l6axZ3z4hu17HMZoWDuRVsSC1ifUoh2zNK0RvN2KtVJIR78uC5sUyN9mVwoNsp8/efeMxX/0hm0V+pTIry5u15o3F3tGtXP9uKFHxJj2FcpDdTon145+8jXDU2lMXzRnHTZ4nc8fVOPpyf0Mj/CZakaU72akI8najSWcRfhmX2TJbtyKGwUscbV47otHMM9HVhWqwvX27N5PZpA9sc6ZJbXmtz0WxMLaLYOpkrLsCV+ePDmBztw7gIbxztW5/krZ46g4n7l+3h5725XDkmhKcvGtotIaR9UvA3HylmZKhHm7LvSXoG958Ty5zFG/l4QwZ3z4jm6TlDefT7fTz+w36ev2RYowiGpLwKYvxdUamE7ccjJ171PAwmM+/9c4SRoR5MiGx5IL49XD8xnOs/2c6qfblcNPLUkV7VOiNb0ootIp9aRGpBFQC+rlrOiLG4aCZH+TTrUmwtxVU6bv48kZ1ZZTw8K45bp0Z2aiTOqehzgn+ksIqrP9zCdRPC+b/ZQ7q7O5I2Eh/iwTmD/flwfRrXTQzj6nGhHC2rYfFfRwjxcuIOq/9XURSS8iqZNdRS/1Sjqh+0lRZ+T+OnPcfIKa3lydlDOl3opkb7EunrzCcb05kzIqjR+UxmhX1Hy1mfXMj61CJ2ZZViMCk42KkYG+HNFQkhTInxIdbftcP6mVpQxY2fWiJx3pk3ilnDOj8S51T0OcEf6OvCDRMj+HhjOpOifDh7sH93d0nSRu49J4ZZb6znvXVpPHRuHPefE8uxsjpe/u0wQR4OXDxyAPkVOspqDMQFWCZc1Vv4Mnlaz8JsVnj77yPEBbhyVpxfp59PpRJcPzGc//x4gF3ZZfi6aK0WfCEbU4spr7WM9QwNduNfkyOZEu3D6DDPTvEGbEotss0v+faW8YzsokicU9HnBB/goVmxbE0v5oHle1h99xQC3VseOZf0HOIC3LhweBCfbszghknh+Lk68OKlw8krr+PB5Xvxd3VAbxX2+vA+W1imjNLpUfx+MI/UgirevGpkl7gxymsNtlDIS97eZFsf6O7AzCH+TI72ZdJAb7xdOjaK50SWJmbz6Hf7iPBx5uMujsQ5FX1S8LUaNW9dNZIL3trAwm938/XN41G3ciRd0jO45+wYftmXy9t/HeH/Zg/BXqPi3WtHc9k7m7j1yx2cPcjy5BZXL/i2iVfSwu9sFEVBZzRjp1ad8nelKAqL/zpCuLcT53eSK8NgMrMnu4x1KUVsSClkT055o7KHt50xkMtGD2Cgr3OX3HDMZoX//X6Yt/8+wpRoHxbPG4WbQ9dG4pyKPin4AJG+Ljw1Zyj3L9vD4r9SuWt6+8K0JF1LhI8zl40awNdbs7h5aiTBHo64O9rx6Y1juXjxRr7bdRSVsKTIBY7H4UsfPmAR2zqDmWq9kVq9iRq9iZqGywYTtXqjdb3Jtr7WcHxdjXX78f0t+9QaTNRrqpO9GlcHDS5aDa4Odrg6aGyfTWbYd7ScFy8d1mkG13Ufb2PTkWJUAoYP8GDBtIFMjvLhcH4l//nxAIqiEOXn0innPpE6g4n7lu3hl725XDU2lKfmDOlxydz6rOADXDoqmA0phby+JpkJA70ZE+7V3V2StIG7ZkTz/a6jvLU2hRcuHQ5AsIcjH18/hgve2oBZsczedNFqbCGbvSlKx2xWqDU0EFzDiQLbWKwbiXNTgm0wNhBuky37YmtQCXCy1+Bor8bJXo2jneXd2V6Dj4sWZ3s1jvYanKzbHezUGExmquqMVNYZqdQZLO91RnLL66isM1BVZyTW35WLRw7otL9hUl4l5wz25+XL4nG31kT4MymfZ345BNDubJytpcgaibM7u4xHz4vj5indF4lzKvq04AshePqioezKLuPub3ax+u6pti+FpOcT7OHIVWND+HJrFredMZBwH2eARukWFny1k4+uS7BF6fQUC//5VYd4b11ad3fjlGg1KquAa9BqVCAA659PbzKjrzVTZh3krKdewl64dHi3G1Ams0JZjZ7YAFfb7/rX/Xn8+5udxAW48fmNY0+7uEpbSC2o5IZPt1NYqeOdeaM4d2j3RuKcij4t+ACuDna8eeVILn1nEw+t2Ms714zqkXdeSdOMtqZb+PVAHredYUmnm1ZkiZeeEOnNuuRCHv9+P89dMgw4HpapKApGs4LBZMZgVDCYzU0u601mjCYzBpO1bbuXLZ//OJjfPX+wNqAzmtEZzZTWGFpufALPrzrEdwsmdUKvWk9FrQGzAp5Wt95Pe46xcMlu4ge488kNY7tkFutGaySOVqNmyS0TiO+kWcQdhVDa8tzXhSQkJCiJiYkddrz3/jnC86uTePbiocwbF9Zhx5V0Hsn5lcxZtJFag4n4Ae5o7dQYTWZ2ZpWdcj87tehUS18ISxiovVqFRi1OWrZ8FmjUKuwarNOoBGqVdb11spidRqBRHW9n26axtLftqxbH2zRq3/j49hrLQGpDk6b+L9HAgD+J5mWg6b1DvZw7LWd7azlSWMX0V/7h9StGYDIrPLB8DwnhXnx8/Zg2F0U/HZZsz+Kx7/cz0NeFj65PYIBnz4jEEULsUBQloaltfd7Cr+fmKZFsPFLMUz8dJCHMq8Oz9Uk6nhq9iWh/y4CbRq1CQKOcOjOH+ONop+aH3cca7XfTlMgTRNeybBNUtbAK9PHlhgJr39SyxiLqLUWmSLqOUmvqgz8O5rNqfy6TBvrwwfyE00p90Fb+99thFv2V2iMjcU5Fv7HwAQordcx6Yx1ezvb8eMfkLvliSFqHwWS2DU5W64xU60yWd73lvUpnpEZvpEpn4ue9x7BXq/h14VQA9EYz139iida4aXIEj18wuJuvRtIV/H4gj1u+2AHAmbG+vHPN6C5Jp7JkexYPrdjHlWNCeOaioSfleOpupIVvxddVy6tzRzD/420888tBnr14WHd3qdehN5qp1Zuo1httkSPVOks4X7WucTSJRbxPXldjFXHLcSwRJvo2xM872au5tUF5vPoY/QVf7uzy4heS7qOizghYnvTeumpUl7iY9h8t54kfDzAl2odnL+68cNPOol9Z+PXUR1D0hNwWPYG/Dxfw9+FCi0DrTdTomhFog6lNvnG1SqDVqNAZzbbJMEOD3fBx0dqiQ+rfLWF/1mWtGhetBid7Szy3s1aNs1aDs1aDk5261eloJX2boiodv+7P44oxIV0S715eY+DCRRswmMz8/O/JnT5b93SRFv4J3HdOLFvSinloxV6GDXDvMYMtp0JRFA7nV3ZoYieAY2W13PLFDtRC4O5oh5NWbRNhHxd7nOydbLHXTlpL+J7eaKbWYKLOYLHOLcsnr6vSGW156uu5aEQwN02J7LD+S/ovPi5arhnfNQEYZrPCfct2c6ysliW3TuixYt8S/VLw7TUq3rxqJOe/aUm98O0t40/LD/fdzhxe+T2ZocFuJIR5MTrck6FB7i0+Wn6wLg2tnYp548Ja/Ui472g5sxdt5Kk5Q5g/IbzNfW2OV/9IRm8088n1Y9DaqSip1lNcpae4Wk9xlY6Saj2ZJTW25bJaQ5MRHUKAl5M9Xs6Wl4+LFi9ne3LLa1lzqACAeeNCuXpcaIf1XSLpKt5dd4Q1hwr4vwsHMzqs+5OgnS79UvABwrydefbiodz97W7eXJvCvefEtvkY8SEehHg58tuBfH47YIm71mpUxId4kBDmSUK4J6NCPW3T/8FiqW9JK2ZtUgHfbsvmqTlDSGjFBBaV1ap/YXUSZ8b6NZuMqX4ySrFNuHUNRNyyXFSlp6Rab8v/DXDDp9sbHUcIS3yzl7M93s72xAa4Wpe1eLtY3r2c7a3L9ng42Te6eRlMZt7+6wjfbMvC303Li5cOZ1ps52dLlEg6mk1Hivjfb4e5MD6I6yaGd3d32kW/9OE35P5le1ixM4evbxrPhIGnV5zhwLFyPtmYwcrdx5ocfIz2cyEh3JPRYV4khHkS5u3E6v15PPPzQY6V13HJyGAenhV3ykILWcU1TH35LwDGhntx1iA/jpbWWgXcKurVekpr9M1a4B6OdnhbLW9vZ3tW788D4K7p0cT4uzSyzD1PEPC2kFpQxX1Ld7Mnp5zZ8UE8NWdIo5ueRNJbyK+o4/w31+PuaMfKOyc3WWqzp3EqH36/F/xqnZEL39pAtd7I6run4tWOqdiFlTq+3prFF1syKaqyVLEP9nAkxMuRA8cqqLRGFfi4aBkd5sGQIHe2phez6UgxzvYaFs6I5rqJ4U0OQJXXGIh/6nfiAlxtBZRdHTT4uWptVne9kNtEvYEl7ulk18httSe7jDmLN7JwRjQLZ8Sc9jU3xGxW+GRTBi/9moSTvZpnLhrG+cPloLikd2Iwmbnq/S0czK3gxzsmEe3fO+buSMFvgf1Hy7nk7U1MjbFM3GjvoKjOaOLnPbl8vDGdA8cq8HCy44oxIUyI9OZoWS07MkpJzCwlq6TmpH2j/Vx4cvYQJkb5NFpvMisMfHQVd50VxVt/paIo8NoV8aedmOqaD7dyMLeCX++eQkZxDWMj2pcXJae0hvuX7WFLWgnT4/x4/tJh+Ll2TeIqieR0Ka3W88ehfC4fPeCk3/0zPx/kww3pvHnVSGbHB3VTD9vOqQS/Q2KZhBDnCiEOCyFShRAPN7H9XiHEQSHEXiHEWiFEj8ptMDTYnYdnxbHmUAGfb85s9/G0GjWXjh7Az/+ezNJbJzA+wpsP1qXxr88S2ZJWwrUTwlj34Jlse3Q6b88bxY2TIogf4I5GJUgpqOLqD7eyel9uo2OqVQJXBw0VdUaetJZu/K81/Wtb2WCt4XnjpHAWfLWTK9/ffNp55BVFYWliNue+vp59OZZUuB9elyDFXtLjURSFu5fs5sHlezlwrKLRttX7cvlwQzrXTQjrVWLfEu12SAkh1MBi4GwgB9guhFipKMrBBs12AQmKotQIIW4HXgKuaO+5O5IbJoWzIbWIZ1cdYky4F4OD3Np9TCEEYyO8GBvhRXZJDZ9tymDJ9mx+2nOMESEe3Dg5gllDAzjPOhegVm9id3YZB46VN5mEyc3BjopaA77WkLCKOiNLE7O5YkzrI18UReGl35LwdbWUfkvMLGVarO9pRSkVVup45Lu9rDlUwNgIL165PL7HVPaRSFri882ZrEsuBGBnVilDgy2T9tIKq3hg+V5GhHjw2Pl9a9Z2R1j4Y4FURVHSFEXRA98Ccxo2UBTlL0VR6v0XW4DOS5B9mgghePmy4Xg42nHnNzup0Rs79PghXk48fsFgNj86nSdnD6GsRs9d3+xiyot/8fbfqZRW63G0VzNhoDc3TYkkyOPksozujnZU1BlsA8P+blqe+fkQueW1re7Hr/vz2JtTTmGljq3pJQBckRDS5utZvS+Xma+vY11KEY+fP4hvbx4vxV7Sa0gtqOS5VYeYFuuLv5uWHZmlANTojdz+5U7s1IK353XN7N2upCOuJhjIbvA5x7quOf4FrG5qgxDiFiFEohAisbCwsAO61ja8XbS8fsUI0ouqeXLlwZZ3OA1ctBqumxjOn/dN46PrEojyc+GlXw8z4YW1PPLdPlLyK5vd181RQ3mtAZ3BIvivXTECo1nhke/2tcq1YzSZefn3w7bPdmqBl7M90we1vtB7eY2Bhd/u4vavdhLs4cgv/57MTVMi5exXSa9BbzSzcMlunOzVvHTpcEaHebIjsxRFUXj8+/0kF1Ty5lUjmzS6ejtdevsSQlwDJAAvN7VdUZT3FUVJUBQlwdfXtyu7ZmNilA8Lpg1kSaLF9dJZqFSC6YP8+fKmcfy6cAoXjQjmu505nP3aOq79aCt/JRVgPqEgt7ujHRW1RnRGEwDRfq48dG4sfx8uZPmOnBbPuWJnDmmF1QBca52hePHI4FZbMeuSC5n5+jp+2pvLwhnRfLdgYq+JXJBI6nljbTL7j1bw/CXD8XNzYHSYFzmltby2JoXvdh1l4fQYpkR3j/50Nh0h+EeBhj6BAdZ1jRBCzAAeA2YriqLrgPN2GgtnxDAq1INHv9tHdhORNB1NXIAbL1w6nM2PTOeBmbEczrNU0Jnx6j98sTmDap3FveTmYGex8I0WC19rp2L+hHDGhnvx1M8HySuva/YcdQYTD63YB8D1E8MJ83bCYFK4YkzL7pwavZEnftjP/I+34eKg4fsFE1k4I6bH1euUSFpCbzTz/ro0fFy0DLGO09XPnH1zbQrTYn3591lR3dnFTqUjfrHbgWghRIQQwh64EljZsIEQYiTwHhaxL+iAc3YqdmoVb1w5EgT8+5tdGE4zgqWteDnbc8eZUWx46CzeuHIErg4anvjxABOeX8vzqw5RrTdSUXdc8O3VKlQqwYuXDcdgMvPY9827dqa/8g9gmRfwnwsGszQxmxEhHo3KBTbFjswSzntjPV9uzeRfkyP4+d+TGT7Ao0OvWyLpKuw1Kl68dDh1BhOz3ljP0sRsBge64WCnItjDkdfmjujT7sl2C76iKEbgTuA34BCwVFGUA0KIp4QQs63NXgZcgGVCiN1CiJXNHK7HEOLlxAuXDGd3dhmv/pHcpee216iYMyKYH+6YxIrbJzIlxpcPN6Szal8eNXoT/1gjC7RWV0yEjzP3nxPL2qQCfth90sMVn2xM52iZZWD3nwemsSenjOT8KuaeYrBWZzTx4q9JXP7uZgwmhW9uHs8TFwzuknzjEklncsmoAfy6cApDg914cPle7vh6J89dPIwvbxrXJTVwuxM58aoFHvluL99uz+aLG8cxOdqn5R06iaNltZz96j/U6E22da9dEc/5w4Kw16gwmRXmvreZ1IIq/rhnqi1Nw6/7c7nty50ArLh9IqPDPHnku338sOso2x6bjmsTlXoOHqvg3qW7Scqr5IqEEB6/YFCT7SSS3ozZrPDxxnRe+u0wrloNz18yjHOGBHR3t9qNnGnbDmr1Ji5ctIHyWgOr756CTzemRf1h11EWLtndaJ2vq5b548O4elwoZbUGzntjPVNjfHn/2tGsTyli/sfbAEtFoE9uGEuN3sjYZ9cyc0gAr8yNb3Qso8nMe+vSeH1NMu6O9rx46bA2RfBIJF3Jyj3HeOy7fXg42+HrosXXVYuP9d3XVXvSuuaeTpPzK7lnyW4OHKvgstED+O+Fg3u1gSPz4bcDR3s1i64eyexFG7l/2R4+vm5Mt/n43Bwt/64hQW7kltfx6tx4Pt6YwSt/JPPWX6lcNCKI84cF8t2uo/zfygMsTTweuVNf9m/VvjyqdMaTBmvTi6q5b+ludmaVcd6wAJ65aFi78gpJJJ1NSZWOSp2RsRFe1BlNZBTVsD2jlBJrrdsTcXXQ2G4EPg1uCL6uWu6eHs0327JYviOHDSlFvHbFiNNOptiTkYLfCuIC3Hji/EE88eMBPt6Y3m0FPNwdLVZHYaUORzs102L9mBbrR2pBJZ9szGDFzhzqrDH6nzVIEXHV2BAG+lqKgS/dnk2EjzNjwi2RCWazwpdbM3l+VRJ2asEbV45gdnxQhxZZkUg6g0jrd/rmqZGMjzwuzgaTmeIqPYWVOgqr6iiq1FNYpbN8tr4OHatgXaXlhnEieRV1XPXBFgDGhHsywNOpyScGX1ctHo52vWqQVwp+K7lmfBjrU4p48dckxkV4d0vtVDfrY2ZxtZ7QBrNao/xcefbiYTwwM5bnVyWxJPH4PDh7jYq7pkcDlinj2zJKePDcWIQQ5JbX8uDyvaxPKWJqjC8vXTqcAHeZA0fSO4jwcQYgrbC6keDbqVUEuDtYv8un/p3W6k0UVekosN4Iiqp0ZJXU8P66NAC2Z5SyPaMUe2ultxPRqAQ+LlpiA1x588qRuDv1bFeQFPxWIoTgpcuGM+uN9fz7m528d20CMf4uXWoJ11v4JrNii9BpSI3exPqUQtwd7Th7sD/Ld+Rw/cRwAt0tMwaX7chBrRJcNmoA3+3M4b8rD2A0KTx78VCuHhsqrXpJryLYwxGtRkVaYVXLjZvB0V5NiJfTSWlBHj1vEP8kF/LAsj2UVOu566wo5o0Lo6RGb7sx1D8t5FfoWLEzhw/Wp3H/zLYXUupKpOC3AQ8ne964ciTzPtzCzNfX4euqZdJAbyZF+TApyqfTp2K7OR63Hk4U/KIqHdd8tJXKOiPf3DKeN9am4KrVcPsZAwHLgOyKHTkMH+DOf348wK8H8kgI8+SVufGEeTt3ar8lks5ApRJE+DiTXlTdKcc/I8aX3++ZyuM/7Od/vyezNqmAV+eOaPQ0UU+d0cTHG9O5YVJ4j653KwW/jYyN8GLdg2eyPtmSYnhDahE/7LakYIj0cbaJ/4RI7w5/vHOwU9seLRumQyivNTD/o20cK6vli3+NQ2c088fBfO4/J8YWV/xPciEFlZZHV3u1iodnxXHzlMjTrmolkfQEInycbQWBOgMPJ3sWXT2Kc4Yc4/Hv93HeG+t59PxBXDOu8RPxPTOiWb0vl/fWpfHoeYM6rT/tRQr+aRDo7sjcMSHMHROCoigczq9kQ0oRG1OLWLEzhy+2ZKISMCzYnUlRPkyO8mFUmGeHTFpyc7CjqEqHVmM5Vq3exL8+3U5KQSUfzE8gIcyTK9/fgo+LlhsmRQBQUWfgX59ZQlxj/F1486qRxAW0P/2zRNLdRPo68/vBfAwmc6em+pgdH8TYcC8eWL6HJ37Yzx8H83n5suH4W+e7RPm5ctGIYD7blMFNkyNOWa60O5GC306EEMQFuBEX4MZNUyLRG83szi5jY6rlBvDeujTe/vsIWo2KsRFeTBxouQEMDnI7Leva3VFjFXyLpX/rlzvYmVXKW1eNYlqsH/8kF7I1vYQnZw/BWath05Eirv/keIHyn/89pc+lfJX0XyJ9XDCZFbJKamyRaJ1FgLsDn984li+3ZPLsqkOc89o6nrloKBdaC6TcPSOaH/ccY/FfqTw5Z2in9uV0kYLfwdhbhX1shBf3nB1DZZ2BbeklbEwtZmOqJcrnRcDDyY6JA71tN4Awb6dWDZrW+/E1asHCJbtYl1zIS5cO5/zhgZjNCi/9msQAT0cuHhXM/608wKebMmz7rrn3DCn2kj5FhK9l/Cm9sLrTBR8sBt61E8KZFOXDvUv38O9vdvHHwXyemjOEMG9n5iYM4Jtt2dxyxkCCe2B6ZSn4nYyrgx3TB/nbZqwWVNSx6UgxG6xPAKv25QGWiIPJUT5MjLIMAjc3o7c+Uue3A/kAPH7+IOZaJ1H9si+XA8cquH5iOBct3khaYTXXTQhjzaECAtwdiPLr/B+ERNKVRNaHZhZVAV03KzzS14Xlt03gnb+P8MbaFLamF/PyZfHceVY0K3YcZdGfKTx/yfAu609rkYLfxfi5OXDRyGAuGhmMoiikF1Wz0Tr4u2p/ri2GPi7AlclRPkyK9mFsuBfOWsu/yq3BlO+7pkfbJoEZTGZbkrfPNmcQ6ObAVzeNw8FOxWebM7nbGosvkfQlPJzs8XK2t9V56Eo0ahX/nh7NtFg/7lm6m/kfb2P+hDDmjAhiWWIOt50xsMdFwEnB70aEEET6uhDp68K1E8IxmRX2Hy23Wf+fb87kww3paFSCUaGeTIryYWWDoiz3zDgu4ssSc2zhaZeMHMB/Zw/GzcGOB5fvwclezfnDA7v8+iSSriDSx5m0TgrNbAm90YyCwvUTwzmcV8nnmzNxddBgNCu8sTaFV+eO6JZ+NYcU/B6EWiWID/EgPsSDO86Mos5gIjGj1HYDeH3t8TTN/5oc0cjn/9uBPLyd7XnukmHMtGb8q9YZ+XlvLhcMD7Q9IUgkfY1IX2f+TOr4kqiKolBea+BoWS1HS2vZd7Scf5IL2ZtT3mT7/7twMF/fPI4Hlu2lss7ID7uOsmBaVI9ypUoV6ME42KmZHO1jS8tcWq3n8R/288u+XBzsGg++vn7FCDRq0SjL3y97c6nRm1pV1Uoi6a1E+LhQVJVDRZ2hkcuzJQwmM3nldRwrq+VoWa3tfVdWWZti+2P8XTh7sD/XjA9Do1axeuEUnlx5kBU7c/hmWxZPWBMX9gSk4PciPJ3tmRrjwy/7cm1x+A23nciSxGwG+jozKtSzq7ookXQ5kQ0ideJDPACLdV5Ra7QJ+bHyWpulfqyslpzSWgoqW19pdWiwG2fE+DI+0psYf1f8XLXNRtW5Odjxytx4Lk8YQEAPi8eXgt/LqLdgWgqvTC2oZEdmKY+eFydz5Ej6NAOtgv/Sb0nYqVU2Ua9uUCyoNYwM9WBYsDvRfi5E+bkS7e+Ct7P9af9+mkrB0N1Iwe9l1IdlNpU8rSHLEnPQqAQXjxzQFd2SSLqNUC9ngj0c2Zha3GLbADcHov1diPJzIdoq6lG+Ln2+tGE9UvB7GW42wW8+TYPBZGbFzhzOivPD17XnJnKSSE6HjKJq9h8rJyW/itSCKlIKKimorGvUZoCnI9F+LkT7uxLl60KUVeTb4uPvi0jB72X4uzlgpxb4nULI/0wqoKhKLwdrJX2ONQfzufmLRBQFhIAwLyei/FyZPsjfIvB+rkT6OsuotGaQf5Vehq+rlvUPnnVKwV+WmI2fq5YzYny7sGcSSedSWKnjoRV7GRTgxv8ujyfS17lDEhL2J6Tg90JOVZWqoKKOvw4XcsvUSDSdmD1QIulKFEXh4RV7qdQZ+ebKEcT4u3Z3l3olUhH6GBtSizCZFVILqsguqenu7kgkHcI327JZm1TAw+fGSbFvB9LC72PMGhpIcn4Vn2xM56xX/mbeuDDuODNKDt5Kei3pRdU8/fNBAt0dCPN2YkNKEfYaleWlVmGvEdir1cfXWdfbqYUMST4BoShKd/ehSRISEpTExMTu7kavJa+8jjfWprA0MRutRsVNUyK5eUpEo5m4Eklv4IstmTzxw/7T2tdyQ2h4c7C87KzL2hO22zVop21081A1uqHU79dw/bBgd7x6QHinEGKHoigJTW6Tgt+3OVJYxau/J/PLvly8nO2548worhkfesqwTomkJ6EoCmlF1dQZTOiNZvRGMwaTgt5k+ayzrtObzBis7/XtdCYztXoTpTUGymr0lNboKa02UFqjp6aNE7Na4pzB/rw/v0md7VKk4EvYk13GS78lsTG1mGAPR+45O4aLRwbLmraSHkut3sSRwipq9CaqdUaq9UbLu67+8/H1NTpT4+31y3rLTaG1ONurcdJqcNFqcLJX42yvwVlrXWevwUmrtm7T4KJV42Td7qzVMCzYHQ8naeGfFlLwO4cNKZaqW/uOlhPj78IDM+OYMchP+jolPYpqnZHZizZw5BR57u3VKosY21sFWntcoC3vVoG211hFvIFAW7c7azU421sE29FOjaoPGECnEnw5aNvPmBztw8SBk1i9P4///X6Ymz9PJCHMk4dmxTEm3KvTzltUpSMlv4oJA3tefhFJz+OZXw6RVlTNMxcNJdzb2WZF1wu0k71Glus8DaTg90NUKsH5wwM5Z4g/SxOzeWNNCpe/u5npcX48cG4scQFuHX7OBV/tZFt6CfufnImLnAUpOQVrD+XzzbYsbp0ayTXjw7q7O30K+cvrx9ipVcwbF8YlIwfwyaZ03vn7CLPeWM/FI4K55+wYQrycOu5kVs/h7qwyW35/Sd/DZFasA6kmdEYzOkODZaPJ8tl04nozOoPJNvj61dZMBgW6ce85Md19OX0OKfgSHO3VLJgWxdVjQ3nnnyN8ujGDn/YeY964MO48K6rZguptYXS4J9sySkjMLJGC38e4/csdrEsuRGc0YzS3f0wwwM2B168YISPJOgEp+BIbHk72PDJrEDdMjOCNtcl8sSWTZYnZlhj+qZHtcsWMCffkHSAxo7TjOizpEQS6O1KtNzEq1IMzYvzQ2lli2LUateXdzrJcH9tu29agnS3uXa3qEwOnPZUOEXwhxLnAG4Aa+FBRlBdO2K4FPgdGA8XAFYqiZHTEuSUdT4C7A89fMpybpkTyyu+HeWNtCl9syeTR8wZx2ejTy68fP8ADgMTMkg7sqaQn8PCsOHZll3I4r5KXLhtOlJ9MfdBTafcwtxBCDSwGZgGDgauEECcWcfwXUKooShTwGvBie88r6XwG+rrw9rzR/HjHJFwdNLz1Z8ppH6t+BmKdofUx0ZLegb1GxdvzRuFgp+a2L3dSpTN2d5ckzdARcU1jgVRFUdIURdED3wJzTmgzB/jMurwcmC5k4HevIT7EA18Xbbvqc8p/d98m0N2Rt64eSVphFQ8t30tPnd/T3+kIwQ8Gsht8zrGua7KNoihGoByQAdm9iOzSmo6N2pH0OSYO9OHBc+P4ZV8uH21I7+7uSJqgR81cEELcIoRIFEIkFhYWdnd3JFbqDCbyK3SEeHaM4OuMHZvDRNJzuHVqJDOH+PP86iS2prVcY1bStXSE4B8FGtbSG2Bd12QbIYQGcMcyeNsIRVHeVxQlQVGUBF9fWa2pp3CsrBaw1AltD/Upmo+W1ra7T5KeiRCC/10eT5iXE3d+s4uCirqWd5J0GR0h+NuBaCFEhBDCHrgSWHlCm5XAddbly4A/Fenk6zVkWwW6vS6dhDBPAPYdLW93nyQ9F1cHO969djRVdUYWfLUTg6lzB+p1RhOFlTo5btAK2i34Vp/8ncBvwCFgqaIoB4QQTwkhZlubfQR4CyFSgXuBh9t7XknXkVNqqZzVXgs/wZqrR8bi931i/F158bLhJGaW8vyqpE49171L9jDm2TUM+e9vzHpjPbd/uYMl27M69Zy9lQ6Jw1cUZRWw6oR1/2mwXAdc3hHnknQ92SW12KkF/u2I0gEYEeIOwPYMGYvfH5gdH8SurFI+3pjOiFAPZscHdfg5avRG1hzKZ3KUD9H+LmQW17A1vYRNR4q5Ykxoh5+vtyNn2kqaJae0hv/8eIA/kwoI93Zqd+78YA+LSygpr7IjuifpBTx63iD25ZTz8Iq9DApwJbqD69GuSy5CZzRz+7SBJIR7sulIMcXVetIKqjr0PH2FHhWlI+kZKIrC0u3ZnPv6ev5MKgBgQAdE6Mi6uv0PO7WKxfNG4WSv4dYvd1BZZ+jQ43+3MweAzzdnMPrpNdzwyXZS8yu5PCGkhT37J9LClzSioKKOh7/bx59JBYyL8CK9qJqCSh0hXu3z3wOyulY/xd/NgcVXj+TqD7fywLK9vHPNqFNOxKszmCiq0lFUpaeoUkdhlY6iSp1tXaF1Oa+izlamcEdmGRfGB3LO4AAmDPTGwU4mXmsKKfgSwGLV/7Q3lyd+2E+dwcR/LxzMdRPCSXh2DdAxFn5DdEaTzIbYjxgR6sG148P4dFMGV7y/hUtGBtuEu6GIF1bpqKxrOjWDm4MGH1ctPi5aBgW64WCn5mBuBTdOiuDx8wfJpGutQAq+hJJqPU/8sJ9f9uUyIsSDV+bGM9DXxbYN2h+hcyK5ZXWE+zh36DElJ5NVXMPnmzO475xYHO079garM5qOW+E28a5f1tss8xNFfFt6CdvSLQP3jUQ8yI2pLlp8XOzxcdHia13v46rF29n+JKv92V8OklJQyT1nR0uxbyVS8Ps5fxzM55Hv9lJea+DBc2O5ZUokGvXJQzsdlVZhUKAbh3IryCmtlYLfBeRV1PHhhnQq64y8eNnwNu9fWWfg661Z5JbXUVjvXrEKeUVrLPETRNzRXs3d3+4GYN0DZxLqfXrfK0VR+ONgPhMG+uDqYHfSdp3RRE5pLQaTmVh/V5nLyYoU/H5Kea2Bp346yIqdOQwOdOOLf41jUGDj0oamBsUsOsrCTwjz5FBuBUl5FbIQShcwJtyTkaEeLEnMZsJAby4aeWKaq1OTnF/F86stcfSB7g6EeDkxKNAN3+jWW+InMiTIjTmLNrJwyS6+vWXCadWmTS2oIqO4hklRPvy6P5fM4hoyimvIKqkmo6iG3PJa6r++2x6djl87Q4r7ClLw+yHrkgt5aMVeCip13HVWFHeeFd3kj65Gf9yC8+2AqlcAI0I8+GJLJokZpdw0pe37f78rhzHhXh0+ptBXEUJwz4wY5n+8jce+38fwAe5EWt11rWF0mCevzo3n4RX7cLRT8+Klw4lo55NZlJ8rL10Wzx1f7+S5VYf4v9lDmm1bVqMno7iGzOJqMotrrK9qEjMtk/e+2prFV1stk6y8nO0J9XJiTLgnod4DWJ6YjUolOqRiW19BCn4/olpn5PnVh/hySxYDfZ357vaJxId4NNu+Vn88yVlHPRIHW58U6n+wbUFnNHHPkj3cOjWSR84b1CH96Q9MifZhdJgnOzJLuePrXXy/YGKbolguGTWAEC8nbvk8kYvf3sh714xmXGT7kt2ePzyQXVkRfLghnSAPB0aEeNpEPaO4mqySGjKKqk9yGwW4ORDWwA206OqRhHs7E+rthFsD105BRR2L/kzhjjOjpH+/AVLw+wnb0ku4f9kesktruHlKBPedE9vij75G3/FZLQPdLY/WRVU6XlidxGrr4zjAjsdn4H0Ka6x+4C+rpKbD+9WXEUKwcEY01360jUO5FTzzy0GeuWhYm44xJtyLH+6YxI2fbueaj7by/CXDW139zGgyk1ted5KYHym0TI56rkHqBbVKMMDTkVAvJ+aMCCbM24kwb2fCvJ0I9XLCwU5NQUUdY59by31nx3DB8KZn767ccwyzQptdWH0dKfh9nDqDiVd+P8yHG9IJ8XRiyS0TGBvh1ap96wXf7zQmTFXpjGQUWSy2nVmlrN6Xy7HyxpkT3/3nSJuOWVFrmbQjBb/tTI7yISHMk8TMUr7cksX4SO9mxbI5wryd+e72Sdz+1Q7uX7aH9KIq7js79iQLekdmCSt3HyOzxOKCySmtwWA6Ph6k1agI9XIiwseFIUHurNxzDICf/z2Z2ABX7JoIGmjImkOWyYBnD/Fvts13O48SP8DdFm0msSAFvw+zJ7uM+5btIbWgimvGh/LIrEE4t6EQeb41tW1sQOumw3+7LYv//Z5MUZWuVe1vPSOS2fFBDApwa9Vjt83CL65BURQZedEGLFZ+DNd8tBWAh1fsY1iwO2HebfPHuzvZ8dmNY/nPj/tZ/NcR0ouqeXXuCNvTYlZxDfM/2gZAhK8zgwPdmDU0oJGl7u/q0Oj/fe2EMK56fwtvrE3hvWtGt9iHPw7mEeLlSGwzaRoO51VyMLeC/7vwxEqrEin4fRC90cyiP1NY/PcR/Fy1fH7jWKbGtL2+QHK+JedNTBM/rDqDyfbDOnisgoO5Fexoxi9//rBArhobyuAgN7yc7Ql/+BcArhoT2qbQzHrBr9QZKa814OFk39ZL6tdMivJmTLgn2zNKMZrN3PH1TlbcPrHNE+Ds1Cqeu3gYkT4uPLf6EEfLtvDB/NF4O2u5d+luVCrBrwunEuzRusiuMeFePHreIJ76+SDvrjvCgmlRzbat1hnZeKSYa8aFNXvD/37XUdQqwYWdkKyttyMFv4+RlFfBvUv2cDC3gktHDeA/Fw7G3fHkOOXW4GR9GogNcGVdciGHcitsAn+ksMoW9uai1TA40I3rJ4YT7u1EZZ2R3Io6tqWXkFpQxS/7cknMLGFKtG+jG09bY/Eb5mHJKqmRgt9G6q38eR9uJSHMiw2pRTy/KumUUTKnOtbNUyMJ9XZi4be7uXjxJkZZXUavXzGi1WJfzw2TwtmZVcr/fjtM/AAPJkU1HbK7LrkQvdHM2YObdueYzQo/7j7KGTG+pxwP6q9Iwe8jGE1m3l+fxmt/JOPuaMf7147mnCEBbTqGyayQWVxtE/X6QiUPLt9raxPs4cigQFdmDQ1gcJAbgwPdGeDp2KxL5mhZLRtSClmXXMQfB/NZviPHtm3hkl0sunoUo0I9WxWLXXGC4A8f4NGm65PAxIHejA33IqWgkqvHhfLppgzGR3pz7tC2fVfqmTkkgGW3TeCCtzZwtKwWV62GOSPablkLIXjx0uEczqvkrm928dO/JxPUxE3jj4P5eDjZMSbcs8njbEkrJre8jkdlFFeTSMHvA6QVVnHfsj3syirjvGEBPHPRMLycT2391upNJOVVNHLJHM6rtA3UalSCKD8XLhkVzOBAN6u4u7XZqg72cOSKMaFcMSYUk1lhb04ZF7+9CYCiKj1Xvr8FZ3s1EwZ6MzXGlynRvoR7OzX5uN5wer4cuD09hBAsPDuaqz/YSoS3M8MHuPPg8j0MCXI77dnUA31dcNFqqNIZqdQZ+WxTBtdPimjzcZy1Gt69djRzFm1kwVc7WXLr+EbuJqPJzJ+HCzgr1q/J2eAA3+06iotW0+wTQH9HCn4vxmxW+HRTBi/9loRWo+bNq0Zy4fDAk8SyoLKOg8cqOJRb73MvJ72o2uaScXWwuGSuGBPCoECLsEf7u3R4cjO1SjAy1JMXLx3GQyv2Eevvyr3nxLAuuZB1KYW26IsBno5MjfFlarQPEwb62FxSFXVGhAAPRzuypeCfNhMivRkb4cUH69P46qZxXPL2Ju78ZhfLbj29Wa8vrD5Elc7IB/MTWLI9m//76SBpRdX854LBzQpzcwz0deF/lw/nti938szPh3j6oqG2bdszSimrMTQr5rV6E7/uz2PW0ACZLbMZpOD3UrJLanhg+R62pJVwVpwfL1wyDG8XLUcKqxtZ7QePVTSKmgn2cGRwkBsXDA+yWe0DPB27NOKlvnLW4fxKZg4JYKbV9ZRZXG0V/yJW7j7G11uzUKsEI0I8mBLtw7rkQhzt1IR5O0sLvx3Uz7696oMtbEgt4sXLhrPgq528/FsSj53ftsiWf5IL+WxzJjdOiuDswf6W7+LqQ3ywPp3M4hoWXT2yyVw3p+LcoYHcOjWS99alMTLUg0tGWeL9/ziYj71G1WwAwh+H8qnSGbl4lIy9bw4p+L0MRVH4dns2z/x8kGq9ieED3PF303LzFzs4nFdBncFSMNpOLYj2c2VarC+DA91slru70+kN4HYkge5ND+iFeTtz7QRnrp0QjsFkZldWGetTClmXXMgba1Oor1G9O7sMsFTkkikWTo8JA70ZF+HF238fYf2DZ3Lt+DA+WJ/O+Ehvpg9qnTuktFrPA8v2EOPvwoPnxgKWp7jHzh9MhI8LT/y4n8ve2cxH1ye0+f/0wMxY9uSU8ej3+4gLcGNQoCt/HMpj0kDvZkOLv9+ZQ6C7A+Mj2jcLuC8jemql94SEBCUxMbG7u9GjUBSFBV/tZPX+vEbr3Rw0tgHUeqs9ys/ltB7Pu4LyGgPxT/0OQPIzs1rVz9JqPSOf/uOk9ZG+zkyN9mVKtA+To31kjv02sCWtmCvf38J/LhjM1eNCueTtTRwrr2XVXVOaHDBtSP13cc2hfH68YzKDg9xOarMhpYjbv9qBVqPi/fkJjApteqC1OQordVzw1noc7NS8fFk8c9/bzHMXD+PqcSfXqi2q0jHuubXcPCWSh2fFtek8fQ0hxA5FURKa2iYt/F6EolheM4f4Hxf3IDeC3B161SQkN8fjX7tjZbV4u9hTUWekotZAea2BilqD7XNFnYGKWkvcfVOkFVaTVljNp5syuH3aQB46t3//2NvC+EhvJkR6884/R7h6XCiL543igjfXc9c3u/jmlvGnnPG6YudRVu/P4+FZcU2KPcDkaB++XzCRGz9N5Mr3t/DK5fFtio33ddXy9rzRXPn+Zv716XYAZgzya7LtT3uOYTIrXCLdOadEWviSdqEoCrUGExW1RirqGgq2Rajrly3rLW0q6gzsP1rR6nO4ajW4OdpxtKzW8tlBQ2WdkVh/V2YNC8DNwQ5XBw1nxvnJzIhtZGtaMVe8v4XHzx/ETVMi+XH3Ue7+dvcpb57ZJTXMemM9g4Pc+Obm8S2Wriyu0nHrFztIzCzlvrNjuPOsqDYZKJ9tyuC/Kw8wIsSDH+6Y1GSb2Ys2YDIr/HLXaaRg7WNIC1/SKXyxJZOnfjrQKE9Ka3BzOP6183PVcvOUSNwd7XBz1ODmYIebox1uDna4O9rh4qCxCcoZL//FiBAPHjw3jkkv/Mn1k8K5auzJj/eS1jMu0puJA71595805o0LY86IYLakFfPO30cYH+nNGScMkJrMCvcs2Y0AXp0b36o6xd4uWr66eRwPLd/LK38kk15UzfOXDmu1+23+hDCqdEaGBbs3uT21oIq9OeU8fr6MvW8JKfiS02ZUqAfzxll+jFV1RlscdnWDz1W6k6siNUx5OyrUkwB3Bxzs1Gg1KtQqgdGsUGMwYlIUqvVGtBoVDnZqSqr1uDnYEeDmgJ1ayEidDmLhjBjmvreZr7ZmctOUSP5zwRB2ZpZx75LdrLp7ii2qCiwJ7xIzS3l1bnybBmK1GjWvXTGCSF8XXv0jmZzSWt69dnSL80XAElV0x5nNp1v4YddRVAJmy1QKLSJdOpJOxWy2iHa1zkSVzkCl9UZQrTNyx9e7GlXVai1O9mrbBLFwbye0GjVaOxUO1netRmVbp9VYbiSNt6ttNxFLWxVaOzUO1vf6dbbt1neNSvSqsZK2MO/DLRzOq2T9g2fhaK8mtaCSC9/ayPAB7nx10zg0ahX7j5Zz0eKNzBwSwKKrR57232LlnmPcv2wPge4OfHTdGKL8Tj+jpdmsMOWlvxjo58LnN4497eP0JaRLR9JtqFQCVwc7ayx24zJzB570o6BCh85oos5gRmc0oTNa3w1m6qzvOqOZshoDr61JRq0SXD02lA83pAMwfIDH8f0MZqp0Roqrjh+r4XH1RnP7rkXQ4Cahxlmr5qXL4hkd1rbok57IwhkxXP7uZr7cksnNUyOJ8nPlmYuGct+yPby5NoUFZ0axcMluvF3sefbioe268c2ODyLYw5FbPk/kkrc38u41o5nYTO6clkjMLOVoWS33z4w57f70J6TgS7oNBzt1q4tYF1TU8dqaZJ6cPYRrxodRZzTx895c3rxqZKvPZzYr6E1m603k+M3FdlOw3lzqDA1uPEYzRVV61hzM52BuBTV6EzV6E2qVkTBvTzx7wLyGjmBMuBeTo3x4958jzBsfipO9hktHD2BzWjFv/ZXK2qQCUguq+OzGsR2StG50mKetoMr8j7fxzEVDufI0xmO+35WDk73aNnlPcmqk4Et6BfV+f1frgG+olxNlNZbon9ZmA1WpBA4qtXXa/an3yS6pYe2hfNYmFbAlrRiDScHd0Y5psb6cFefHtBi/HjGJrSNZOCOay6xW/i1TBwLw1Jwh7M4u48AxS1TVq38k4+1sz9BmBlDbQoiXEysWTOSOr3by8Hf7SC+q5qFz41pdkrDOYLnpnzskACd7KWWtQf6VJL2C+kyZblZxD7Um+souqcG9A8THZFbYnV3KmkMFrD2UT3K+pfxepK8zN0yK4Kw4PxLCPNucG6Y3kRDuxZRoH977J41rxofhZK/ByV7D4qtHMXvRBnRGM9klNcxetIFrx4dx7zmxp516ux43Bzs+uX4MT/50kPfWpZFeVM3rV45olYD/mVRAZZ1RljFsA1LwJb2C+kyZ9SGdIQ0E/3Stzco6A+tTilhzKJ+/DxdSUq1HoxKMCffi8fNDmD7In4g25OvvCyycEcOl72zii82Z3HqGxcqPDXDlqTlDeGjFPi4dFYzeaOaLLZn8si+Px88fxJwRQe3y6WvUKp6aM4RIX2ee/vkgc9/bzEfXjWkUHdQU3+86ip+rttnc+ZKTkYIv6RXUFz+pT8RVL/htDc3MLqlhzaF81h4qYGv6cVfNmbG+TB/kz9QY33Zbrb2Z0WGeTI3x5b11Fiu/Pm/N3IQQNh0p5qMN6Xx983guGx3C4z/uZ+GS3Xy7PYun5wwlupmSg61BCMENkyII83bi31/vYs6ijXx4XUKzN/PSaj1/Hy7g+onhrZoLILHQd59PJX2KyhN8+G4Odng62bUo+CazwvaMEl5YncTZr/7DlJf+4smfDpJbXsuNkyJYeusEdjw+g9evHMmF8UH9WuzrWTgjmpJqPZ9vzrStE0Lw7MXDCPd25u5vdxHk4cD3t0/kuYuHcSi3kllvrOeF1UnU6E+ed9EWzorzZ9ltExEC5r63mT8O5jfZ7ue9xzCYFOnOaSPSwpc0S35FHTV6E85aNa5aOxzsVN0Wh15R29jCB4sfvynBr6gzsC65kD8PFfDX4QJKawxoVIJxkV5cOTaU6XF+bSqt2N8YFerJGTG+vL/uCPMnHLfyXbQa3rp6JBe/vYl7l+7hk+vHcPW4UGYO8eeF1Um8+88RVu4+yn9nD+Gcwf6n/V0ZHOTGj3dM4qbPE7nli0QenTWIm6ZENDre97uOEuvvyuDApvP4SJpGCr6kSeoMJia98CfGBhOjVMLyo3fRanBx0OBcv2x9OWs1uDax3sXh5DZaTdtuHpV1RlQCnO2PT8cP8XJiv7UMY2ZxNWsOFfBnUj5b00owmhU8new4M9aPswb5MTXGF7c25mXvzyycEc3Fb2/is80ZjYqKDwly54kLBvPED/t5b10at08biLeLlpcvj2fumBCe+GE/t36xg/vOjuHf06NP+/x+bg4suWUC9y7dzbOrDpFWVM1Tc4Zgp1aRUVTNzqwyHp4V12cnwnUW7RJ8IYQXsAQIBzKAuYqilJ7QZgTwDuAGmIBnFUVZ0p7zSjofBzs1X988ni1pxRzKreBQbgUZxTWWLJZ1Rig/eR97tQqD2UxrJm+rVeKEm4UaFwc7XLTq4zcG67uLg4alidloNWrbD9xoMpNZXENGcQ0zXv2H1AJLVE20nws3TYlk+iA/RoV6Sv/uaTIy1JNpsb58sC6N+RPCcWmQg/6acaFsOVLM/34/zJhwTxLCvQBLLP9P/57MvUv38PraFM6I9W1X3WFHezWLrx7Fy78f5p2/j5BdUsPieaP4ftdRhOC0auf2d9qVWkEI8RJQoijKC0KIhwFPRVEeOqFNDKAoipIihAgCdgCDFEUpO9WxZWqFnke1zkhSXqXtBnAot4KkBnVwVQIifJwJ93YmxMuJEC8nQr2ccLJX29Ip1OfXqaqzfK5stN5EVZ3BmobBSLXeeNLN462rRrL2UD5/JxdSVmNx80yO8mH6ID+mx/m3eiKXpGV2Z5dx0eKNPDAz9qRcNhV1Bi54cwMGk5lVd03Bs0FOnPJaAzNfW4ezVs0vd03pkHKDSxOzeez7fYR6OVFnMBPm7cTXN49v93H7IqdKrdBewT8MTFMUJVcIEQj8rShKbAv77AEuUxQl5VTtpOD3DsxmhaySGtsN4GCu5YZQn8oYwMvZnkGBrgwKsFTeGtTKAi1ms8LB3ApW7jnG++vSGh1vWqwvRwqq2JNTzqGnzsXRvusLn2zPKOGZnw+y9LYJfbbwyg2fbGNXdhkbHjqrkZUPsC+nnEve2cjUaF8+vC6hkXtlfUoh1360jX9NjuCJC9pWNrE5Nh8p5rYvd1Bea+Dly4ZzeUJIhxy3r9GZuXT8FUXJtS7nAaesjSaEGAvYA0ea2X4LcAtAaKhMe9sbUKkE4T7OhPs4M2tYoG19ea2BJNuTQCWH8ir4YksmOuPxEowDfV2sNwBX243Aw9GOxMxS/kwqYM2hfNIKqwGI8Xdh+iB/ZgzyY0SIxVXzxeYM9uSUU6UzdovgX/7uZsu11hjwc+ubgr9wRgxzFm/ks00ZJ1n5wwa48+h5g3jyp4N8tCGdm6ZE2rZNifZldnwQH21I58bJEQS3UEGrNUwY6M33Cybyw+5jbSqkIjlOi4IvhFgDNJWo4rGGHxRFUYQQzT4uWJ8AvgCuUxSlySxWiqK8D7wPFgu/pb5Jei7ujnaMi/RmXOTx+qJGk5mM4mrbU8Ch3Ao2HSni+11HbW3sNSr0RjN2asH4SG/mjw9j+iB/W9x9Q+qjRyyhgN1X+MSvhQlCvZn4EA/OivPj/XVpzJ8QdlJB8usnhrMlrZgXVicxOsyTkdYyhkVVOjakFllqLrt23P8m0teFe8+WidJOlxYFX1GUGc1tE0LkCyECG7h0Cppp5wb8AjymKMqW0+6tpFejUauI8nMlys+1Ue7ykmq97QZwrKyOMeGeTInxPcmFcCL10++byrnf2eSV13X5ObuLhTOimb3IYuXfeVbjyBshBC9dGs95b67nzq93sequKbg5anjih/1U1Rl55fL4Pp2OorfR3v/ESuA66/J1wI8nNhBC2APfA58rirK8neeT9EG8nO2ZFOVjKb5x4WBmDQtsUewBW5v6QeOu5J2/UwG4fdrALj93VzN8gAfT4/z4YH26LadRQ9yd7Fh09UjyK+p4cMUeVu45xur9edx7Tky7Zt9KOp72Cv4LwNlCiBRghvUzQogEIcSH1jZzganA9UKI3dbXiHaeVyLBWWvxm3eHhf+ZdRbqDZPCu/zc3cHCGTGU1xr4bGNGk9tHhnry0Llx/HYgn/uX7WFEiAc3N/DpS3oG7RJ8RVGKFUWZrihKtKIoMxRFKbGuT1QU5Sbr8peKotgpijKiwWt3B/Rd0s+p9+FXd7HgN4xs83Ptu/77hgwb4M6MQf58sD6tSSsf4KYpEcwY5IdKCP53eevq3Uq6Fulck/RabIO2uq516RzKrQRA088EbeGMaCrqjHyyIaPJ7UII3r1mNOsePLNdZQslnYcUfEmvpT7NQle7dD5Yb5kTcN85p5xy0ucYGuzO2YP9+WhDGuW1TVv5GrWqxbTGku5DCr6k19I4LLPrqA8jnd0Pp/bfPd1q5W9M7+6uSE4DKfiSXoudWoW9RkVVF7p06gzHz9URk4l6G0OD3TlnsD8fbUhv1sqX9Fyk4Et6Nc726i4dtN2aXgJYQkn7K3fPiKayzsjHG6SV39uQgi/p1ThrNVR3oUvn800ZANx5QpqB/sSQIHdmDvHn443Syu9tSMGX9Gqc7TVdauGvTbJMJj8j1rfLztkTuXt6DJV1Rj6SVn6vQgq+pFfjrFV32Uzb/Irj6RQi+3nFrMFBbpw7JIBPNqRTXiOt/N6CFHxJr8ZZq+mysMx1yYUAhHs7yUpLWH35OiMfbUhrubGkRyAFX9Kr6UqXzpLt2QBcOyG8S87X0xkU6MZ5wwL4eGMGZTX67u6OpBVIwZf0apy1Gqq7ICzTbFZIzLRU7xwf6dXp5+st3DU9miqd9OX3FmQRc0mvxlmr7pIonQPHKmzLcQFunX6+3kJcgBvnDwvkk40Z/GtyBB5Opx+uWmcwkZJfRVJeBYfzKknKqySloJJbpw7kxskRHdjr/osUfEmvxlmr6ZJcOutSLP77ocFuMinYCdw1PZpV+3P5YH0aD8yMa7G92ayQXVpDUl6lVdgttZEziqoxn1D2yM1BQ3yIR+d0vB8iBV/Sq3G2V6M3mdEbzS3WyG0PP1jTKZw/rP+lU2iJ2ABXzhsWyKcbM7hpcmSjgual1XqrsFtEPSmvkuT844XvhYBQLyfiAly5YHgQcQGu7Mgs5ZON6UT4OPPhdWOI6OcRUR2JFHxJr6ZhimR7TefMfq3WGUkpqAJgbIRnp5yjKY6V1fLWnyn898IhONj17Jq5t58xkF/25nL+m+u5MD6IQ1aRz6/Q2dp4OtkRF+DG3IQQ4gJciQt0I8bfxVa5zGAy8+RPB/hySxZnxvryxlUjcTuhpKKkfUjBl/RqnK1iUa03NrIsO5ItacW25WHBHp1yjqb4ZGM632zL5pJRAxgT3jMGihVFIae0lsN5lRzOt9QmPpxXSVqRpdj8sfI6PtyQTlyAK5OifCzCHuBGXIArvq7aZsNZS6r1LPhqB1vSSrj1jEgenBknXWedgBR8Sa/muIXfeX789SlFAIwK9ehUt1FDjCYzP+w+Blgs/e6gvNZgEfa8CqvFXklyXiWVDcJgB3g6EhfgRqCHI+uSCxno68xvC6e2qY5tUl4FN32WSEGljteuiOfikQM643IkSMGX9HLqyxx2ZqTOL/tyAZgc3XXpFDakFlFYaXGH5HZywXS90UxaUZUtMibJarUfa3Bed0c7YgNcuXhUMLFWqz3G3wVXBzsq6wyc/+YGgtwdWHH7xDaJ/W8H8rhnyW5ctBqW3jqBEXKAtlORgi/p1XR2mcPskhqb8I6L6Dq3ync7j+LhZIfRpJDbQRa+oijkltcdF3Zr+OORwioMJkt4jJ1aMNDXhbERXsQGuBEX6EpcgCsBbg5NumMUReHR7/dztKyWJbeMb3VYpqIoLPozlVf+SCZ+gDvvz0+QhVO6ACn4kl6NzYffSYK/IbXItjwy1KNTznEilXUGfjuQx9yEELall3C0rO0WfmWdgeT8eov9ePhjRd3xv1OwhyOxAa6cGedn87VH+jpj1wYLfWliNj/tOcYDM2NJaOU4Q63exP3L9/DL3lwuHhnM85cM6/GD0n0FKfiSXo3NpdNJPvz6/DkjQjxs0SSdzep9eeiMZi4ZFUx2aQ255c1b+EaTmfSi6kYWe1JeJTmlx/dx1WqIDXDlwvgg4gItA6gx/q64O7YvAiY5v5L/rjzApChvbjtjYKv2OVZWy82fJ3Iwt4JHZsVxy9RImZeoC5GCL+nV2Fw6neDDN5rMtnTIXenOWbEzh0hfZ0aEeBDk4cjenPIm25XXGjjj5b8os2ar1KgEkb7OjAz15KqxocQFuBIb4Eqwh2OHi2qdwcSdX+/ERavhtStGtCqiZkdmCbd+sQOdwczH143hzDi/Du2TpGWk4Et6NS5Wwd+SVsyh3Aoqao8Lv0LjaZvKCbM4T/p8QvsavQm90QzA2C4S/OySGraml/DAzFiEEAS5O1BSrafOYDrJ7eGq1TA61JO1SQXMnxDGY+cPQqvpGtfIkz8dJDm/is9uHIufa8u+96Xbs3nsh30Eezjy7S0JRPm5dkEvJSciBV/Sq9FqLHVtV+3Lw9VBc9LAX1N254nGrjihVVPGcEJY1wh+/Yzei0YGAxDobqmbm1ted9KMU5VK8PY1o1jw5U4+35xJtJ9Ll2Ty/HnvMb7ZlsVtZwzkjJhTRy4ZTWaeW5XExxvTmRLtw6KrRuHuJCdTdRdS8CW9GiEEH85PQCUE4yK92jTg2Bqu+XArRVW6LhEpRVH4btdRJkR62wqkB3pYbmC5ZbVNphjQatS8fc0o7vhqJ0/8eAAFmN+Jop9VXMMjK/YxIsSD+86JOWXb8hoDd36zk/UpRdw4KYJHz4trU8impOORgi/p9Uxtwco8XQwmMzuzSrl8dNdMBNqZVUZ6UTULph0fAA2yWvjHThGLr9WoeXveaBZ8tZP//HgARYHrJoZ3eP/0RjP//nYXCHjrqpGnvLmmFlRy02eJHC2r5aVLhzN3TEiH90fSduTtViJphgPHKqjRmxgb4d0l5/tuZw4OdipmDQu0rQtwP27hnwp7jYq3543i7MH+/HflAT7d2PH56f/3+2H2ZJfx4qXDCfFyarbdX0kFXLx4E1U6I9/cPF6KfQ9CCr5E0gzb0i05dMZ0QcI0k1nh5725jAzxxNn++MCrg50aHxf7U1r49dhrVCy+ehTnDPbn/346yCcdKPp/HS7g/XVpzBsXynkNbkgNURSF9/45wo2fbSfU24kf75zc6th8SdcgBV8iaYZt6aVE+Di3KgqlIxgW7M7mtGKu/2R7o9j7QHfHVufTsdeoWHT1KGYO8efJnw7ycQdUosqvqOO+pXuIC3DliQsGN9mmzmDi3qV7eH51EucNC2T5bRNt4xCSnoP04UskTWA2K2zPKOHcIQFdcj61SvD5jWP5YksmL6xO4pxX1/HEBYO5PGEAge4OZBRXN2pvNJnRm8zoDA3fTdRZP18zPoz1KUU89fNBft57jHnjwtCbzJgVhQuGBbV6ENpkVlj47W5q9SYWXT2yyRmx+RV13PLFDvZkl3H/OTHccWaUnEzVQ5GCL5E0QXmtgYo6A9syStiRWcLoLgjLVKkE100MZ1qsLw8u38uDK/by875cHO1UpBRUMeKp320CbzqxNNQp2JlVxs6sMttnD0d7zh/etFvmRN7+K5XNacW8dNnwJmPnd2eXccvniVTpjLx37WhmdtENUnJ6SMGXSJrA09mez24YyyPf7eOydzdz3YRwHpgZa5vZ25mEeTvzzc3j+XJrJs+vSqLWYEKtEsyOD0KrUaHVqLHXqGxzEE78XL+s1ahQCcEDy/eSai3gkhDmyblDWyfK29JLeG1NMnNGBDUZqfT9rhweWrEPP1ct3/1roqz12wsQyonTDXsICQkJSmJiYnd3Q9LPqdYZefm3w3y2OYMgd0eev2RYp4WBNkVWcQ0PrtjDlrQSpkT78MKlw9vsG9cZTcQ+/isA144P4+mLhra4T2m1nvPeXI9Wo+Lnu6bYZjSDxc3z0m9JvPdPGuMivHjnmtF4dVLxGUnbEULsUBQloalt7Rq0FUJ4CSH+EEKkWN+bDWcQQrgJIXKEEIvac06JpCtx1mr4v9lDWHbrBLR2KuZ/vI37l+2hrEbfJecP9Xbi65vG8/ScIezILGXma+v4dlsWbTHUPt6QYVv+Yksm7/1z5JTtFUXhgeV7KarS8dZVoxqJfUWdgZs+2857/6RxzfhQvrxpnBT7XkR7o3QeBtYqihINrLV+bo6ngXXtPJ9E0i0khHux6q4p3HHmQL7fdZQZr65jtbUwSmejUgmunRDObwunMjTYjYe/28f8j7dxtBWRO/uPlvPqH4c5b1gAqc/O4oLhgTy/Ool3TyH6n27KYM2hfB6ZNYhhA9xt69OLqrl48UbWpxTx9EVDeeaiYR0+s1nSubT3vzUH+My6/BlwUVONhBCjAX/g93aeTyLpNhzs1DwwM46Vd07C303L7V/t5PYvd1BQ2bkVqeoJ8bJa+xcNtVn735zC2q8zmFi4ZDeeTvY8e9EwNGoVr18xggvjg3hhdRLv/H2y6O8/Ws7zq5KYHufHDZPCbevXpxQyZ9EGSqr1fPGvcVw7PqyzLlPSibRX8P0VRak3c/KwiHojhBAq4BXg/pYOJoS4RQiRKIRILCwsbGfXJJLOYUiQOz/cMYkHz41lbVIBZ7+6jmWJ2W1ys5wuKpXg2vFh/LZwKsOC3XnEau3nlNac1PaF1UmkFlTxv8vjbQXeNWoVr82NZ3Z8EC/+msTbf6fa2lfpjNz59U68nO15+fJ4hBAoisInG9O5/pPtBLo7svLOyUwY2DUzjyUdT4uCL4RYI4TY38RrTsN2iuXb3tQ3fgGwSlGUnJbOpSjK+4qiJCiKkuDr23UDYxJJW7FTq1gwLYrVd08hxt+FB5bvZf7H28guOVl4O4MQLye+umkcz1w0lJ2ZpZz7+nq+3nrc2v8nuZBPN2Vww6TwkwaZNWoVr86NZ86IIF769TCL/0pFURQe/34fWSU1vHHlCLyc7dEZTTy8Yh9P/nSQ6XF+rFgw8ZQpFSQ9n3ZF6QghDgPTFEXJFUIEAn8rihJ7QpuvgCmAGXAB7IG3FUU5lb9fRulIeg1ms8KXWzN5cXUSCvDgzFjmTwhH1YqiIB1BdkkND63Yy6YjxUyO8uHhWXHc8Ol2PJ3sWHnn5GbLBxpNZu5ftocfdh8j3NuJjOIa7pkRw90zoims1HH7lztIzCzlrrOiWDgjpsuuR9I+ThWl017BfxkoVhTlBSHEw4CXoigPnqL99UCCoih3tnRsKfiS3kZOaQ2Pfr+fdcmFJIR58sKlw4nyc+mScyuKwtfbsnjul0NU6y3lHn+5azJDgtxPuZ/JrHDx2xttVbWOPHceh3IruOXzREpq9Pzv8nguGB7U6f2XdBydFpYJvACcLYRIAWZYPyOESBBCfNjOY0skvYoBnk58dsMYXrk8npSCKs57Yz2L/0rFYDJ3+rmFEMwbF8ZNUyJt655bdahFF5PBZKbOcLwe8PlvrueydzcBsPy2iVLs+xhy4pVE0gkUVur478r9rNqXx+BAN166bDhDg09tbbeXrOIaZr2xjqHB7lwQH8QLqw4B8Mh5g5g3LrTJ/DaP/7CPL7dk8cH8BG7+/PjvbftjM/B11XZqfyWdQ2da+BKJpAl8XbW8PW80714zisIqHXMWb+TFX5MaWdMdidFk5p6lu1GpBK9eMcISyXPPVEaFefL4D/uZ9+HWk6z91fty+XJLFlePC2VZYnajbV9tzeyUfkq6Fyn4Ekkncu7QQNbccwaXjAzmnb+PcN4b69mUWtThIZzv/H2EHZmlPHPRUFvqhQGeTnx+41iev2QYe3PKmfn6Or7YkonZrJBdUsODK/bi7WzPlrRi1hzK578XDubIc+dx2egBvL4mhdf+SO7QPkq6H+nSkUi6iPUphTzy3T5ySmsJ9XLi/OGBnBHjy/AB7jjZn35Stj3ZZVz6zibOGxbIm1eNbLLN0bJaHl6xl/UpRYwN9yKtqJqiKh0Abg4aFs8bxZRoS/im2azw0Iq9LNuRw13To7lnRrRMd9yL6LQonc5ECr6kL1KjN/Lznlx+2nuMTUeKMZkVVAJi/F0ZGerJyBAPRoR6MNDXBXUrwiBr9EYueHMDdQYTq++eeso894qisGR7Ng9/t8+2LtLXmY+uG3NSgXSzWeHh7/ayNDGHu86K4p6zY6To9xJOJfgyPbJE0oU42WuYOyaEuWNCKKvRszOrlN1ZZezKLuOXvcf4ZlsWAC5aDcMHuDMixMPyCvVosvLWs78cIr24mq9uGtdiURMhBEEnZNrUqAQl1XrMikJFrYGKOiMVtQYq64yE+zjj7mjHm3+m4mCvZsG0qI77Q0i6BSn4Ekk34eFkz1lx/pwVZ8lIYjYrpBdXszurjN3Zltf769IwWoudBHs4NroBFFTo+GprFpeMDMbbWcv2jBKbWFfUGU5YNnKksIqkvMpGfUjOr+LSdzY120eNSuDpZEduWdfkC5J0LtKlI5H0YOoMJg4cK2eX9Slgd1ZZq7Jk1qPVqHBztMPNQcORwuNlEq8aG4qbo4ZqnZEvt2TZ1r92RTxDg9xxdbDDzVGDo51aunJ6GdKHL5H0IQordezOLuOpnw+QXXKy+Ad7ODI1xocp0b6MjfDCx8UST68oCjqj+aRUC4qisCwxh6d/PojRrPDwrDiuHR8mUyn0UqTgSyR9GLNZ4UhhleUJwPoUcDi/0lb3doDncVfQyFAPhgS5N5lfJ7e8lodX7OOf5ELGRXjx0mXDCfN2PqmdpGcjBV8i6WfU6I3sP1rB7uxS203gWLnFD2+nFgwKdDs+HhDiQYSPsy0d8rIdOTz9k8Xaf+jcrk0EJ2k/UvAlEgkFFXW2p4BdWaXsyym3JVpzd7Qjvv4pIMQDfzcHXvotib8PFzI2wouXpbXfa5CCL5FITsJkVkgpqGwUFZScX4nVE0SYtxOFlTpq9CYc7dQ8eG4s10lrv8cjBV8ikbSKap2RvTnl1huAxR2UX6Gzbb9geCCLrh7VjT2UtISceCWRSFqFs1bDhIHejcoY5pbX2p4CTpy4JeldSMGXSCSnJNDdkcBhjswaFtjdXZG0E5ktUyKRSPoJUvAlEomknyAFXyKRSPoJUvAlEomknyAFXyKRSPoJUvAlEomknyAFXyKRSPoJUvAlEomkn9BjUysIIQqBTMAHKOrm7nQ18pr7B/Ka+wddfc1hiqL4NrWhxwp+PUKIxObyQvRV5DX3D+Q19w960jVLl45EIpH0E6TgSyQSST+hNwj++93dgW5AXnP/QF5z/6DHXHOP9+FLJBKJpGPoDRa+RCKRSDoAKfgSiUTST+hxgi+E8BJC/CGESLG+ezbTLlQI8bsQ4pAQ4qAQIryLu9phtPaarW3dhBA5QohFXdnHjqY11yyEGCGE2CyEOCCE2CuEuKI7+tpehBDnCiEOCyFShRAPN7FdK4RYYt2+tTd/l6FV13uv9Te7VwixVggR1h397EhauuYG7S4VQihCiG4J0+xxgg88DKxVFCUaWGv93BSfAy8rijIIGAsUdFH/OoPWXjPA08C6LulV59Kaa64B5iuKMgQ4F3hdCOHRdV1sP0IINbAYmAUMBq4SQgw+odm/gFJFUaKA14AXu7aXHUcrr3cXkKAoynBgOfBS1/ayY2nlNSOEcAXuBrZ2bQ+P0xMFfw7wmXX5M+CiExtY/5gaRVH+AFAUpUpRlJou62HH0+I1AwghRgP+wO9d061OpcVrVhQlWVGUFOvyMSw39SZnEPZgxgKpiqKkKYqiB77Fcu0Nafi3WA5MF0KILuxjR9Li9SqK8leD3+sWYEAX97Gjac3/GCzG2otAXVd2riE9UfD9FUXJtS7nYRG4E4kByoQQ3wkhdgkhXrbeZXsrLV6zEEIFvALc35Ud60Ra83+2IYQYC9gDRzq7Yx1MMJDd4HOOdV2TbRRFMQLlgDe9k9Zcb0P+Bazu1B51Pi1esxBiFBCiKMovXdmxE+mWIuZCiDVAQBObHmv4QVEURQjRVNyoBpgCjASygCXA9cBHHdvTjqMDrnkBsEpRlJzeYvx1wDXXHycQ+AK4TlEUc8f2UtJdCCGuARKAM7q7L52J1Vh7FYtGdSvdIviKosxobpsQIl8IEagoSq71h96Ubz4H2K0oSpp1nx+A8fRgwe+Aa54ATBFCLABcAHshRJWiKKfy93crHXDNCCHcgF+AxxRF2dJJXe1MjgIhDT4PsK5rqk2OEEIDuAPFXdO9Dqc114sQYgaWG/8ZiqLouqhvnUVL1+wKDAX+thprAcBKIcRsRVESu6yX9EyXzkrgOuvydcCPTbTZDngIIer9uWcBB7ugb51Fi9esKMo8RVFCFUUJx+LW+bwni30raPGahRD2wPdYrnV5F/atI9kORAshIqzXcyWWa29Iw7/FZcCfSu+dEdni9QohRgLvAbMVRenNwRb1nPKaFUUpVxTFR1GUcOvvdwuWa+9Ssa/vTI96YfFdrgVSgDWAl3V9AvBhg3ZnA3uBfcCngH13972zr7lB++uBRd3d786+ZuAawADsbvAa0d19P41rPQ9IxjL+8Jh13VNYfvQADsAyIBXYBkR2d587+XrXAPkN/qcru7vPnX3NJ7T9G0uUUpf3U6ZWkEgkkn5CT3TpSCQSiaQTkIIvkUgk/QQp+BKJRNJPkIIvkUgk/QQp+BKJRNJPkIIvkUgk/QQp+BKJRNJP+H9zTOcre3c4PwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "indexes = np.argwhere(train_targets == 1)\n", "\n", @@ -1461,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1504,673 +1066,9 @@ }, { "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "wandb version 0.13.4 is available! To upgrade, please run:\n", - " $ pip install wandb --upgrade" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Tracking run with wandb version 0.12.21" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Run data is saved locally in d:\\Github\\JogosTeP\\Dev\\wandb\\run-20221016_211010-35qxb58i" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Syncing run gentle-terrain-41 to Weights & Biases (docs)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/1000\n", - " 1/21 [>.............................] - ETA: 11s - loss: 6.1439 - sparse_categorical_crossentropy: 1.1469 - accuracy: 0.3350WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0032s vs `on_train_batch_end` time: 0.0058s). Check your callbacks.\n", - "21/21 [==============================] - 1s 20ms/step - loss: 4.7695 - sparse_categorical_crossentropy: 1.1104 - accuracy: 0.3373 - val_loss: 3.5052 - val_sparse_categorical_crossentropy: 1.0901 - val_accuracy: 0.3459\n", - "Epoch 2/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 2.7607 - sparse_categorical_crossentropy: 1.0875 - accuracy: 0.3824 - val_loss: 2.1131 - val_sparse_categorical_crossentropy: 1.0798 - val_accuracy: 0.5075\n", - "Epoch 3/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 1.7666 - sparse_categorical_crossentropy: 1.0716 - accuracy: 0.4726 - val_loss: 1.4751 - val_sparse_categorical_crossentropy: 1.0584 - val_accuracy: 0.5967\n", - "Epoch 4/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 1.3360 - sparse_categorical_crossentropy: 1.0533 - accuracy: 0.5206 - val_loss: 1.2244 - val_sparse_categorical_crossentropy: 1.0472 - val_accuracy: 0.5968\n", - "Epoch 5/1000\n", - "21/21 [==============================] - 0s 11ms/step - loss: 1.1755 - sparse_categorical_crossentropy: 1.0467 - accuracy: 0.5166 - val_loss: 1.1394 - val_sparse_categorical_crossentropy: 1.0476 - val_accuracy: 0.5614\n", - "Epoch 6/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 1.1217 - sparse_categorical_crossentropy: 1.0443 - accuracy: 0.5127 - val_loss: 1.1137 - val_sparse_categorical_crossentropy: 1.0465 - val_accuracy: 0.5153\n", - "Epoch 7/1000\n", - "21/21 [==============================] - 0s 11ms/step - loss: 1.1021 - sparse_categorical_crossentropy: 1.0359 - accuracy: 0.5146 - val_loss: 1.1038 - val_sparse_categorical_crossentropy: 1.0369 - val_accuracy: 0.5398\n", - "Epoch 8/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 1.0899 - sparse_categorical_crossentropy: 1.0186 - accuracy: 0.5324 - val_loss: 1.0968 - val_sparse_categorical_crossentropy: 1.0193 - val_accuracy: 0.5384\n", - "Epoch 9/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 1.0781 - sparse_categorical_crossentropy: 0.9929 - accuracy: 0.5424 - val_loss: 1.0882 - val_sparse_categorical_crossentropy: 0.9942 - val_accuracy: 0.5436\n", - "Epoch 10/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 1.0652 - sparse_categorical_crossentropy: 0.9618 - accuracy: 0.5503 - val_loss: 1.0776 - val_sparse_categorical_crossentropy: 0.9650 - val_accuracy: 0.5667\n", - "Epoch 11/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 1.0529 - sparse_categorical_crossentropy: 0.9317 - accuracy: 0.5597 - val_loss: 1.0674 - val_sparse_categorical_crossentropy: 0.9390 - val_accuracy: 0.5779\n", - "Epoch 12/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 1.0392 - sparse_categorical_crossentropy: 0.9038 - accuracy: 0.5749 - val_loss: 1.0565 - val_sparse_categorical_crossentropy: 0.9151 - val_accuracy: 0.5916\n", - "Epoch 13/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 1.0276 - sparse_categorical_crossentropy: 0.8808 - accuracy: 0.5825 - val_loss: 1.0448 - val_sparse_categorical_crossentropy: 0.8940 - val_accuracy: 0.6021\n", - "Epoch 14/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 1.0176 - sparse_categorical_crossentropy: 0.8634 - accuracy: 0.5880 - val_loss: 1.0359 - val_sparse_categorical_crossentropy: 0.8802 - val_accuracy: 0.5994\n", - "Epoch 15/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 1.0087 - sparse_categorical_crossentropy: 0.8503 - accuracy: 0.5908 - val_loss: 1.0297 - val_sparse_categorical_crossentropy: 0.8714 - val_accuracy: 0.6019\n", - "Epoch 16/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.9996 - sparse_categorical_crossentropy: 0.8377 - accuracy: 0.5979 - val_loss: 1.0198 - val_sparse_categorical_crossentropy: 0.8569 - val_accuracy: 0.6170\n", - "Epoch 17/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.9898 - sparse_categorical_crossentropy: 0.8262 - accuracy: 0.6057 - val_loss: 1.0103 - val_sparse_categorical_crossentropy: 0.8478 - val_accuracy: 0.6288\n", - "Epoch 18/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.9828 - sparse_categorical_crossentropy: 0.8181 - accuracy: 0.6084 - val_loss: 1.0047 - val_sparse_categorical_crossentropy: 0.8389 - val_accuracy: 0.6153\n", - "Epoch 19/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.9758 - sparse_categorical_crossentropy: 0.8104 - accuracy: 0.6093 - val_loss: 1.0024 - val_sparse_categorical_crossentropy: 0.8355 - val_accuracy: 0.6182\n", - "Epoch 20/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.9689 - sparse_categorical_crossentropy: 0.8030 - accuracy: 0.6150 - val_loss: 0.9932 - val_sparse_categorical_crossentropy: 0.8271 - val_accuracy: 0.6231\n", - "Epoch 21/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9640 - sparse_categorical_crossentropy: 0.7981 - accuracy: 0.6155 - val_loss: 0.9890 - val_sparse_categorical_crossentropy: 0.8244 - val_accuracy: 0.6307\n", - "Epoch 22/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.9596 - sparse_categorical_crossentropy: 0.7938 - accuracy: 0.6181 - val_loss: 0.9837 - val_sparse_categorical_crossentropy: 0.8180 - val_accuracy: 0.6284\n", - "Epoch 23/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.9523 - sparse_categorical_crossentropy: 0.7864 - accuracy: 0.6215 - val_loss: 0.9785 - val_sparse_categorical_crossentropy: 0.8126 - val_accuracy: 0.6307\n", - "Epoch 24/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.9474 - sparse_categorical_crossentropy: 0.7817 - accuracy: 0.6245 - val_loss: 0.9737 - val_sparse_categorical_crossentropy: 0.8092 - val_accuracy: 0.6336\n", - "Epoch 25/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9415 - sparse_categorical_crossentropy: 0.7759 - accuracy: 0.6246 - val_loss: 0.9696 - val_sparse_categorical_crossentropy: 0.8036 - val_accuracy: 0.6346\n", - "Epoch 26/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9390 - sparse_categorical_crossentropy: 0.7735 - accuracy: 0.6277 - val_loss: 0.9654 - val_sparse_categorical_crossentropy: 0.8011 - val_accuracy: 0.6410\n", - "Epoch 27/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9347 - sparse_categorical_crossentropy: 0.7701 - accuracy: 0.6287 - val_loss: 0.9637 - val_sparse_categorical_crossentropy: 0.7989 - val_accuracy: 0.6325\n", - "Epoch 28/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9291 - sparse_categorical_crossentropy: 0.7643 - accuracy: 0.6304 - val_loss: 0.9596 - val_sparse_categorical_crossentropy: 0.7940 - val_accuracy: 0.6339\n", - "Epoch 29/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9248 - sparse_categorical_crossentropy: 0.7599 - accuracy: 0.6307 - val_loss: 0.9573 - val_sparse_categorical_crossentropy: 0.7949 - val_accuracy: 0.6423\n", - "Epoch 30/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9224 - sparse_categorical_crossentropy: 0.7584 - accuracy: 0.6335 - val_loss: 0.9553 - val_sparse_categorical_crossentropy: 0.7909 - val_accuracy: 0.6366\n", - "Epoch 31/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9173 - sparse_categorical_crossentropy: 0.7534 - accuracy: 0.6344 - val_loss: 0.9519 - val_sparse_categorical_crossentropy: 0.7877 - val_accuracy: 0.6308\n", - "Epoch 32/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9177 - sparse_categorical_crossentropy: 0.7542 - accuracy: 0.6369 - val_loss: 0.9509 - val_sparse_categorical_crossentropy: 0.7869 - val_accuracy: 0.6298\n", - "Epoch 33/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9144 - sparse_categorical_crossentropy: 0.7510 - accuracy: 0.6370 - val_loss: 0.9460 - val_sparse_categorical_crossentropy: 0.7835 - val_accuracy: 0.6416\n", - "Epoch 34/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9081 - sparse_categorical_crossentropy: 0.7449 - accuracy: 0.6399 - val_loss: 0.9445 - val_sparse_categorical_crossentropy: 0.7826 - val_accuracy: 0.6452\n", - "Epoch 35/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9047 - sparse_categorical_crossentropy: 0.7416 - accuracy: 0.6422 - val_loss: 0.9391 - val_sparse_categorical_crossentropy: 0.7775 - val_accuracy: 0.6512\n", - "Epoch 36/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.9029 - sparse_categorical_crossentropy: 0.7400 - accuracy: 0.6409 - val_loss: 0.9396 - val_sparse_categorical_crossentropy: 0.7772 - val_accuracy: 0.6459\n", - "Epoch 37/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8996 - sparse_categorical_crossentropy: 0.7373 - accuracy: 0.6430 - val_loss: 0.9370 - val_sparse_categorical_crossentropy: 0.7756 - val_accuracy: 0.6450\n", - "Epoch 38/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8963 - sparse_categorical_crossentropy: 0.7341 - accuracy: 0.6442 - val_loss: 0.9377 - val_sparse_categorical_crossentropy: 0.7745 - val_accuracy: 0.6381\n", - "Epoch 39/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8942 - sparse_categorical_crossentropy: 0.7318 - accuracy: 0.6437 - val_loss: 0.9317 - val_sparse_categorical_crossentropy: 0.7706 - val_accuracy: 0.6492\n", - "Epoch 40/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 0.8946 - sparse_categorical_crossentropy: 0.7328 - accuracy: 0.6437 - val_loss: 0.9309 - val_sparse_categorical_crossentropy: 0.7706 - val_accuracy: 0.6497\n", - "Epoch 41/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8921 - sparse_categorical_crossentropy: 0.7309 - accuracy: 0.6471 - val_loss: 0.9298 - val_sparse_categorical_crossentropy: 0.7696 - val_accuracy: 0.6515\n", - "Epoch 42/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8891 - sparse_categorical_crossentropy: 0.7283 - accuracy: 0.6468 - val_loss: 0.9275 - val_sparse_categorical_crossentropy: 0.7666 - val_accuracy: 0.6475\n", - "Epoch 43/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8848 - sparse_categorical_crossentropy: 0.7241 - accuracy: 0.6493 - val_loss: 0.9281 - val_sparse_categorical_crossentropy: 0.7672 - val_accuracy: 0.6434\n", - "Epoch 44/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8809 - sparse_categorical_crossentropy: 0.7207 - accuracy: 0.6503 - val_loss: 0.9274 - val_sparse_categorical_crossentropy: 0.7658 - val_accuracy: 0.6411\n", - "Epoch 45/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 0.8792 - sparse_categorical_crossentropy: 0.7188 - accuracy: 0.6515 - val_loss: 0.9266 - val_sparse_categorical_crossentropy: 0.7660 - val_accuracy: 0.6440\n", - "Epoch 46/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8789 - sparse_categorical_crossentropy: 0.7186 - accuracy: 0.6514 - val_loss: 0.9196 - val_sparse_categorical_crossentropy: 0.7606 - val_accuracy: 0.6517\n", - "Epoch 47/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8758 - sparse_categorical_crossentropy: 0.7160 - accuracy: 0.6526 - val_loss: 0.9164 - val_sparse_categorical_crossentropy: 0.7576 - val_accuracy: 0.6527\n", - "Epoch 48/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8743 - sparse_categorical_crossentropy: 0.7149 - accuracy: 0.6513 - val_loss: 0.9276 - val_sparse_categorical_crossentropy: 0.7659 - val_accuracy: 0.6341\n", - "Epoch 49/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8731 - sparse_categorical_crossentropy: 0.7129 - accuracy: 0.6556 - val_loss: 0.9212 - val_sparse_categorical_crossentropy: 0.7598 - val_accuracy: 0.6410\n", - "Epoch 50/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8714 - sparse_categorical_crossentropy: 0.7121 - accuracy: 0.6555 - val_loss: 0.9228 - val_sparse_categorical_crossentropy: 0.7629 - val_accuracy: 0.6369\n", - "Epoch 51/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8700 - sparse_categorical_crossentropy: 0.7111 - accuracy: 0.6561 - val_loss: 0.9236 - val_sparse_categorical_crossentropy: 0.7630 - val_accuracy: 0.6327\n", - "Epoch 52/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8659 - sparse_categorical_crossentropy: 0.7067 - accuracy: 0.6571 - val_loss: 0.9113 - val_sparse_categorical_crossentropy: 0.7529 - val_accuracy: 0.6513\n", - "Epoch 53/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8659 - sparse_categorical_crossentropy: 0.7070 - accuracy: 0.6567 - val_loss: 0.9086 - val_sparse_categorical_crossentropy: 0.7509 - val_accuracy: 0.6559\n", - "Epoch 54/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8637 - sparse_categorical_crossentropy: 0.7050 - accuracy: 0.6589 - val_loss: 0.9075 - val_sparse_categorical_crossentropy: 0.7500 - val_accuracy: 0.6562\n", - "Epoch 55/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8626 - sparse_categorical_crossentropy: 0.7040 - accuracy: 0.6593 - val_loss: 0.9059 - val_sparse_categorical_crossentropy: 0.7484 - val_accuracy: 0.6534\n", - "Epoch 56/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8572 - sparse_categorical_crossentropy: 0.6993 - accuracy: 0.6604 - val_loss: 0.9028 - val_sparse_categorical_crossentropy: 0.7460 - val_accuracy: 0.6580\n", - "Epoch 57/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8581 - sparse_categorical_crossentropy: 0.6997 - accuracy: 0.6593 - val_loss: 0.9031 - val_sparse_categorical_crossentropy: 0.7463 - val_accuracy: 0.6574\n", - "Epoch 58/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8526 - sparse_categorical_crossentropy: 0.6947 - accuracy: 0.6633 - val_loss: 0.9012 - val_sparse_categorical_crossentropy: 0.7442 - val_accuracy: 0.6566\n", - "Epoch 59/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8562 - sparse_categorical_crossentropy: 0.6984 - accuracy: 0.6582 - val_loss: 0.9028 - val_sparse_categorical_crossentropy: 0.7470 - val_accuracy: 0.6573\n", - "Epoch 60/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8528 - sparse_categorical_crossentropy: 0.6955 - accuracy: 0.6608 - val_loss: 0.8990 - val_sparse_categorical_crossentropy: 0.7419 - val_accuracy: 0.6539\n", - "Epoch 61/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8496 - sparse_categorical_crossentropy: 0.6920 - accuracy: 0.6639 - val_loss: 0.8964 - val_sparse_categorical_crossentropy: 0.7401 - val_accuracy: 0.6584\n", - "Epoch 62/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8472 - sparse_categorical_crossentropy: 0.6899 - accuracy: 0.6652 - val_loss: 0.8984 - val_sparse_categorical_crossentropy: 0.7421 - val_accuracy: 0.6571\n", - "Epoch 63/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8490 - sparse_categorical_crossentropy: 0.6924 - accuracy: 0.6622 - val_loss: 0.8978 - val_sparse_categorical_crossentropy: 0.7419 - val_accuracy: 0.6542\n", - "Epoch 64/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8472 - sparse_categorical_crossentropy: 0.6906 - accuracy: 0.6633 - val_loss: 0.8960 - val_sparse_categorical_crossentropy: 0.7403 - val_accuracy: 0.6586\n", - "Epoch 65/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8458 - sparse_categorical_crossentropy: 0.6894 - accuracy: 0.6662 - val_loss: 0.8986 - val_sparse_categorical_crossentropy: 0.7418 - val_accuracy: 0.6493\n", - "Epoch 66/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8449 - sparse_categorical_crossentropy: 0.6886 - accuracy: 0.6654 - val_loss: 0.9017 - val_sparse_categorical_crossentropy: 0.7450 - val_accuracy: 0.6451\n", - "Epoch 67/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8417 - sparse_categorical_crossentropy: 0.6858 - accuracy: 0.6671 - val_loss: 0.9065 - val_sparse_categorical_crossentropy: 0.7491 - val_accuracy: 0.6356\n", - "Epoch 68/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8402 - sparse_categorical_crossentropy: 0.6843 - accuracy: 0.6680 - val_loss: 0.8915 - val_sparse_categorical_crossentropy: 0.7361 - val_accuracy: 0.6525\n", - "Epoch 69/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8420 - sparse_categorical_crossentropy: 0.6866 - accuracy: 0.6667 - val_loss: 0.8939 - val_sparse_categorical_crossentropy: 0.7391 - val_accuracy: 0.6555\n", - "Epoch 70/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8403 - sparse_categorical_crossentropy: 0.6854 - accuracy: 0.6659 - val_loss: 0.8955 - val_sparse_categorical_crossentropy: 0.7403 - val_accuracy: 0.6462\n", - "Epoch 71/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8376 - sparse_categorical_crossentropy: 0.6829 - accuracy: 0.6679 - val_loss: 0.8911 - val_sparse_categorical_crossentropy: 0.7373 - val_accuracy: 0.6572\n", - "Epoch 72/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8364 - sparse_categorical_crossentropy: 0.6818 - accuracy: 0.6695 - val_loss: 0.8907 - val_sparse_categorical_crossentropy: 0.7362 - val_accuracy: 0.6528\n", - "Epoch 73/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8341 - sparse_categorical_crossentropy: 0.6793 - accuracy: 0.6709 - val_loss: 0.8936 - val_sparse_categorical_crossentropy: 0.7387 - val_accuracy: 0.6509\n", - "Epoch 74/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8335 - sparse_categorical_crossentropy: 0.6790 - accuracy: 0.6696 - val_loss: 0.8920 - val_sparse_categorical_crossentropy: 0.7369 - val_accuracy: 0.6483\n", - "Epoch 75/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8336 - sparse_categorical_crossentropy: 0.6790 - accuracy: 0.6665 - val_loss: 0.8930 - val_sparse_categorical_crossentropy: 0.7391 - val_accuracy: 0.6500\n", - "Epoch 76/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8303 - sparse_categorical_crossentropy: 0.6761 - accuracy: 0.6710 - val_loss: 0.8897 - val_sparse_categorical_crossentropy: 0.7354 - val_accuracy: 0.6527\n", - "Epoch 77/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8308 - sparse_categorical_crossentropy: 0.6766 - accuracy: 0.6708 - val_loss: 0.8881 - val_sparse_categorical_crossentropy: 0.7335 - val_accuracy: 0.6529\n", - "Epoch 78/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8293 - sparse_categorical_crossentropy: 0.6752 - accuracy: 0.6710 - val_loss: 0.8883 - val_sparse_categorical_crossentropy: 0.7345 - val_accuracy: 0.6488\n", - "Epoch 79/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8257 - sparse_categorical_crossentropy: 0.6718 - accuracy: 0.6743 - val_loss: 0.8836 - val_sparse_categorical_crossentropy: 0.7300 - val_accuracy: 0.6574\n", - "Epoch 80/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8280 - sparse_categorical_crossentropy: 0.6741 - accuracy: 0.6730 - val_loss: 0.8923 - val_sparse_categorical_crossentropy: 0.7376 - val_accuracy: 0.6404\n", - "Epoch 81/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8263 - sparse_categorical_crossentropy: 0.6728 - accuracy: 0.6720 - val_loss: 0.8847 - val_sparse_categorical_crossentropy: 0.7319 - val_accuracy: 0.6546\n", - "Epoch 82/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8255 - sparse_categorical_crossentropy: 0.6724 - accuracy: 0.6726 - val_loss: 0.8874 - val_sparse_categorical_crossentropy: 0.7338 - val_accuracy: 0.6494\n", - "Epoch 83/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8221 - sparse_categorical_crossentropy: 0.6687 - accuracy: 0.6757 - val_loss: 0.8987 - val_sparse_categorical_crossentropy: 0.7440 - val_accuracy: 0.6333\n", - "Epoch 84/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8224 - sparse_categorical_crossentropy: 0.6692 - accuracy: 0.6731 - val_loss: 0.8797 - val_sparse_categorical_crossentropy: 0.7281 - val_accuracy: 0.6623\n", - "Epoch 85/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8242 - sparse_categorical_crossentropy: 0.6715 - accuracy: 0.6728 - val_loss: 0.8933 - val_sparse_categorical_crossentropy: 0.7394 - val_accuracy: 0.6401\n", - "Epoch 86/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8248 - sparse_categorical_crossentropy: 0.6720 - accuracy: 0.6707 - val_loss: 0.8919 - val_sparse_categorical_crossentropy: 0.7386 - val_accuracy: 0.6441\n", - "Epoch 87/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8205 - sparse_categorical_crossentropy: 0.6682 - accuracy: 0.6738 - val_loss: 0.8858 - val_sparse_categorical_crossentropy: 0.7333 - val_accuracy: 0.6485\n", - "Epoch 88/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8199 - sparse_categorical_crossentropy: 0.6675 - accuracy: 0.6740 - val_loss: 0.8827 - val_sparse_categorical_crossentropy: 0.7304 - val_accuracy: 0.6505\n", - "Epoch 89/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8204 - sparse_categorical_crossentropy: 0.6685 - accuracy: 0.6757 - val_loss: 0.8776 - val_sparse_categorical_crossentropy: 0.7270 - val_accuracy: 0.6619\n", - "Epoch 90/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8152 - sparse_categorical_crossentropy: 0.6633 - accuracy: 0.6761 - val_loss: 0.8834 - val_sparse_categorical_crossentropy: 0.7306 - val_accuracy: 0.6502\n", - "Epoch 91/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8140 - sparse_categorical_crossentropy: 0.6615 - accuracy: 0.6807 - val_loss: 0.8791 - val_sparse_categorical_crossentropy: 0.7279 - val_accuracy: 0.6591\n", - "Epoch 92/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8166 - sparse_categorical_crossentropy: 0.6650 - accuracy: 0.6760 - val_loss: 0.8761 - val_sparse_categorical_crossentropy: 0.7264 - val_accuracy: 0.6635\n", - "Epoch 93/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8159 - sparse_categorical_crossentropy: 0.6645 - accuracy: 0.6750 - val_loss: 0.8808 - val_sparse_categorical_crossentropy: 0.7290 - val_accuracy: 0.6512\n", - "Epoch 94/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8157 - sparse_categorical_crossentropy: 0.6642 - accuracy: 0.6761 - val_loss: 0.8780 - val_sparse_categorical_crossentropy: 0.7274 - val_accuracy: 0.6589\n", - "Epoch 95/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8141 - sparse_categorical_crossentropy: 0.6628 - accuracy: 0.6764 - val_loss: 0.8817 - val_sparse_categorical_crossentropy: 0.7302 - val_accuracy: 0.6474\n", - "Epoch 96/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8137 - sparse_categorical_crossentropy: 0.6627 - accuracy: 0.6769 - val_loss: 0.8862 - val_sparse_categorical_crossentropy: 0.7343 - val_accuracy: 0.6446\n", - "Epoch 97/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8107 - sparse_categorical_crossentropy: 0.6595 - accuracy: 0.6814 - val_loss: 0.8810 - val_sparse_categorical_crossentropy: 0.7296 - val_accuracy: 0.6499\n", - "Epoch 98/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8096 - sparse_categorical_crossentropy: 0.6586 - accuracy: 0.6786 - val_loss: 0.8772 - val_sparse_categorical_crossentropy: 0.7281 - val_accuracy: 0.6601\n", - "Epoch 99/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8111 - sparse_categorical_crossentropy: 0.6602 - accuracy: 0.6783 - val_loss: 0.8793 - val_sparse_categorical_crossentropy: 0.7281 - val_accuracy: 0.6494\n", - "Epoch 100/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8065 - sparse_categorical_crossentropy: 0.6558 - accuracy: 0.6816 - val_loss: 0.8770 - val_sparse_categorical_crossentropy: 0.7265 - val_accuracy: 0.6525\n", - "Epoch 101/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8066 - sparse_categorical_crossentropy: 0.6560 - accuracy: 0.6812 - val_loss: 0.8774 - val_sparse_categorical_crossentropy: 0.7269 - val_accuracy: 0.6525\n", - "Epoch 102/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8070 - sparse_categorical_crossentropy: 0.6567 - accuracy: 0.6805 - val_loss: 0.8800 - val_sparse_categorical_crossentropy: 0.7295 - val_accuracy: 0.6465\n", - "Epoch 103/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.8085 - sparse_categorical_crossentropy: 0.6583 - accuracy: 0.6787 - val_loss: 0.8754 - val_sparse_categorical_crossentropy: 0.7261 - val_accuracy: 0.6577\n", - "Epoch 104/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8072 - sparse_categorical_crossentropy: 0.6572 - accuracy: 0.6790 - val_loss: 0.8789 - val_sparse_categorical_crossentropy: 0.7290 - val_accuracy: 0.6496\n", - "Epoch 105/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8053 - sparse_categorical_crossentropy: 0.6554 - accuracy: 0.6802 - val_loss: 0.8734 - val_sparse_categorical_crossentropy: 0.7240 - val_accuracy: 0.6551\n", - "Epoch 106/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8061 - sparse_categorical_crossentropy: 0.6562 - accuracy: 0.6804 - val_loss: 0.8767 - val_sparse_categorical_crossentropy: 0.7270 - val_accuracy: 0.6510\n", - "Epoch 107/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 0.8044 - sparse_categorical_crossentropy: 0.6550 - accuracy: 0.6797 - val_loss: 0.8806 - val_sparse_categorical_crossentropy: 0.7308 - val_accuracy: 0.6434\n", - "Epoch 108/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8029 - sparse_categorical_crossentropy: 0.6537 - accuracy: 0.6810 - val_loss: 0.8814 - val_sparse_categorical_crossentropy: 0.7316 - val_accuracy: 0.6436\n", - "Epoch 109/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8012 - sparse_categorical_crossentropy: 0.6513 - accuracy: 0.6827 - val_loss: 0.8760 - val_sparse_categorical_crossentropy: 0.7261 - val_accuracy: 0.6519\n", - "Epoch 110/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 0.7999 - sparse_categorical_crossentropy: 0.6499 - accuracy: 0.6828 - val_loss: 0.8735 - val_sparse_categorical_crossentropy: 0.7239 - val_accuracy: 0.6550\n", - "Epoch 111/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 0.7974 - sparse_categorical_crossentropy: 0.6481 - accuracy: 0.6852 - val_loss: 0.8749 - val_sparse_categorical_crossentropy: 0.7254 - val_accuracy: 0.6517\n", - "Epoch 112/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.8003 - sparse_categorical_crossentropy: 0.6507 - accuracy: 0.6840 - val_loss: 0.8690 - val_sparse_categorical_crossentropy: 0.7213 - val_accuracy: 0.6646\n", - "Epoch 113/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7987 - sparse_categorical_crossentropy: 0.6496 - accuracy: 0.6839 - val_loss: 0.8727 - val_sparse_categorical_crossentropy: 0.7236 - val_accuracy: 0.6509\n", - "Epoch 114/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 0.7976 - sparse_categorical_crossentropy: 0.6486 - accuracy: 0.6842 - val_loss: 0.8698 - val_sparse_categorical_crossentropy: 0.7209 - val_accuracy: 0.6558\n", - "Epoch 115/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7968 - sparse_categorical_crossentropy: 0.6477 - accuracy: 0.6830 - val_loss: 0.8671 - val_sparse_categorical_crossentropy: 0.7193 - val_accuracy: 0.6598\n", - "Epoch 116/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7976 - sparse_categorical_crossentropy: 0.6490 - accuracy: 0.6827 - val_loss: 0.8692 - val_sparse_categorical_crossentropy: 0.7207 - val_accuracy: 0.6548\n", - "Epoch 117/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7979 - sparse_categorical_crossentropy: 0.6495 - accuracy: 0.6840 - val_loss: 0.8731 - val_sparse_categorical_crossentropy: 0.7248 - val_accuracy: 0.6502\n", - "Epoch 118/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7962 - sparse_categorical_crossentropy: 0.6480 - accuracy: 0.6864 - val_loss: 0.8701 - val_sparse_categorical_crossentropy: 0.7219 - val_accuracy: 0.6497\n", - "Epoch 119/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7943 - sparse_categorical_crossentropy: 0.6459 - accuracy: 0.6877 - val_loss: 0.8686 - val_sparse_categorical_crossentropy: 0.7209 - val_accuracy: 0.6554\n", - "Epoch 120/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7962 - sparse_categorical_crossentropy: 0.6480 - accuracy: 0.6862 - val_loss: 0.8771 - val_sparse_categorical_crossentropy: 0.7280 - val_accuracy: 0.6412\n", - "Epoch 121/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7938 - sparse_categorical_crossentropy: 0.6453 - accuracy: 0.6863 - val_loss: 0.8689 - val_sparse_categorical_crossentropy: 0.7207 - val_accuracy: 0.6529\n", - "Epoch 122/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7925 - sparse_categorical_crossentropy: 0.6442 - accuracy: 0.6865 - val_loss: 0.8720 - val_sparse_categorical_crossentropy: 0.7242 - val_accuracy: 0.6528\n", - "Epoch 123/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7909 - sparse_categorical_crossentropy: 0.6430 - accuracy: 0.6870 - val_loss: 0.8876 - val_sparse_categorical_crossentropy: 0.7383 - val_accuracy: 0.6332\n", - "Epoch 124/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7917 - sparse_categorical_crossentropy: 0.6434 - accuracy: 0.6855 - val_loss: 0.8679 - val_sparse_categorical_crossentropy: 0.7204 - val_accuracy: 0.6604\n", - "Epoch 125/1000\n", - "21/21 [==============================] - 0s 11ms/step - loss: 0.7901 - sparse_categorical_crossentropy: 0.6418 - accuracy: 0.6877 - val_loss: 0.8702 - val_sparse_categorical_crossentropy: 0.7221 - val_accuracy: 0.6537\n", - "Epoch 126/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7907 - sparse_categorical_crossentropy: 0.6426 - accuracy: 0.6895 - val_loss: 0.8710 - val_sparse_categorical_crossentropy: 0.7231 - val_accuracy: 0.6489\n", - "Epoch 127/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7904 - sparse_categorical_crossentropy: 0.6424 - accuracy: 0.6874 - val_loss: 0.8700 - val_sparse_categorical_crossentropy: 0.7221 - val_accuracy: 0.6522\n", - "Epoch 128/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7878 - sparse_categorical_crossentropy: 0.6402 - accuracy: 0.6878 - val_loss: 0.8729 - val_sparse_categorical_crossentropy: 0.7247 - val_accuracy: 0.6483\n", - "Epoch 129/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7886 - sparse_categorical_crossentropy: 0.6410 - accuracy: 0.6861 - val_loss: 0.8718 - val_sparse_categorical_crossentropy: 0.7238 - val_accuracy: 0.6469\n", - "Epoch 130/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7865 - sparse_categorical_crossentropy: 0.6388 - accuracy: 0.6890 - val_loss: 0.8649 - val_sparse_categorical_crossentropy: 0.7176 - val_accuracy: 0.6547\n", - "Epoch 131/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7877 - sparse_categorical_crossentropy: 0.6405 - accuracy: 0.6860 - val_loss: 0.8662 - val_sparse_categorical_crossentropy: 0.7196 - val_accuracy: 0.6574\n", - "Epoch 132/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7874 - sparse_categorical_crossentropy: 0.6404 - accuracy: 0.6867 - val_loss: 0.8684 - val_sparse_categorical_crossentropy: 0.7215 - val_accuracy: 0.6524\n", - "Epoch 133/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7867 - sparse_categorical_crossentropy: 0.6396 - accuracy: 0.6879 - val_loss: 0.8636 - val_sparse_categorical_crossentropy: 0.7175 - val_accuracy: 0.6596\n", - "Epoch 134/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7864 - sparse_categorical_crossentropy: 0.6397 - accuracy: 0.6879 - val_loss: 0.8640 - val_sparse_categorical_crossentropy: 0.7177 - val_accuracy: 0.6572\n", - "Epoch 135/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7841 - sparse_categorical_crossentropy: 0.6374 - accuracy: 0.6877 - val_loss: 0.8673 - val_sparse_categorical_crossentropy: 0.7208 - val_accuracy: 0.6545\n", - "Epoch 136/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7819 - sparse_categorical_crossentropy: 0.6353 - accuracy: 0.6895 - val_loss: 0.8643 - val_sparse_categorical_crossentropy: 0.7182 - val_accuracy: 0.6607\n", - "Epoch 137/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7884 - sparse_categorical_crossentropy: 0.6417 - accuracy: 0.6879 - val_loss: 0.8651 - val_sparse_categorical_crossentropy: 0.7191 - val_accuracy: 0.6560\n", - "Epoch 138/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7844 - sparse_categorical_crossentropy: 0.6382 - accuracy: 0.6888 - val_loss: 0.8668 - val_sparse_categorical_crossentropy: 0.7218 - val_accuracy: 0.6594\n", - "Epoch 139/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7837 - sparse_categorical_crossentropy: 0.6379 - accuracy: 0.6903 - val_loss: 0.8635 - val_sparse_categorical_crossentropy: 0.7177 - val_accuracy: 0.6573\n", - "Epoch 140/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7824 - sparse_categorical_crossentropy: 0.6361 - accuracy: 0.6909 - val_loss: 0.8666 - val_sparse_categorical_crossentropy: 0.7207 - val_accuracy: 0.6581\n", - "Epoch 141/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7808 - sparse_categorical_crossentropy: 0.6345 - accuracy: 0.6906 - val_loss: 0.8663 - val_sparse_categorical_crossentropy: 0.7200 - val_accuracy: 0.6517\n", - "Epoch 142/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7786 - sparse_categorical_crossentropy: 0.6325 - accuracy: 0.6911 - val_loss: 0.8626 - val_sparse_categorical_crossentropy: 0.7171 - val_accuracy: 0.6563\n", - "Epoch 143/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7815 - sparse_categorical_crossentropy: 0.6356 - accuracy: 0.6905 - val_loss: 0.8641 - val_sparse_categorical_crossentropy: 0.7188 - val_accuracy: 0.6558\n", - "Epoch 144/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7799 - sparse_categorical_crossentropy: 0.6339 - accuracy: 0.6922 - val_loss: 0.8684 - val_sparse_categorical_crossentropy: 0.7222 - val_accuracy: 0.6497\n", - "Epoch 145/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7788 - sparse_categorical_crossentropy: 0.6329 - accuracy: 0.6920 - val_loss: 0.8674 - val_sparse_categorical_crossentropy: 0.7218 - val_accuracy: 0.6510\n", - "Epoch 146/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7802 - sparse_categorical_crossentropy: 0.6347 - accuracy: 0.6903 - val_loss: 0.8707 - val_sparse_categorical_crossentropy: 0.7245 - val_accuracy: 0.6458\n", - "Epoch 147/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7786 - sparse_categorical_crossentropy: 0.6328 - accuracy: 0.6908 - val_loss: 0.8594 - val_sparse_categorical_crossentropy: 0.7144 - val_accuracy: 0.6563\n", - "Epoch 148/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7796 - sparse_categorical_crossentropy: 0.6340 - accuracy: 0.6908 - val_loss: 0.8700 - val_sparse_categorical_crossentropy: 0.7236 - val_accuracy: 0.6442\n", - "Epoch 149/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7777 - sparse_categorical_crossentropy: 0.6322 - accuracy: 0.6929 - val_loss: 0.8642 - val_sparse_categorical_crossentropy: 0.7182 - val_accuracy: 0.6486\n", - "Epoch 150/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7778 - sparse_categorical_crossentropy: 0.6321 - accuracy: 0.6918 - val_loss: 0.8599 - val_sparse_categorical_crossentropy: 0.7154 - val_accuracy: 0.6594\n", - "Epoch 151/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7784 - sparse_categorical_crossentropy: 0.6328 - accuracy: 0.6923 - val_loss: 0.8633 - val_sparse_categorical_crossentropy: 0.7186 - val_accuracy: 0.6582\n", - "Epoch 152/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7787 - sparse_categorical_crossentropy: 0.6333 - accuracy: 0.6921 - val_loss: 0.8700 - val_sparse_categorical_crossentropy: 0.7245 - val_accuracy: 0.6449\n", - "Epoch 153/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7775 - sparse_categorical_crossentropy: 0.6326 - accuracy: 0.6914 - val_loss: 0.8740 - val_sparse_categorical_crossentropy: 0.7286 - val_accuracy: 0.6413\n", - "Epoch 154/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7748 - sparse_categorical_crossentropy: 0.6293 - accuracy: 0.6920 - val_loss: 0.8633 - val_sparse_categorical_crossentropy: 0.7183 - val_accuracy: 0.6558\n", - "Epoch 155/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7741 - sparse_categorical_crossentropy: 0.6286 - accuracy: 0.6935 - val_loss: 0.8656 - val_sparse_categorical_crossentropy: 0.7203 - val_accuracy: 0.6485\n", - "Epoch 156/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7749 - sparse_categorical_crossentropy: 0.6298 - accuracy: 0.6924 - val_loss: 0.8591 - val_sparse_categorical_crossentropy: 0.7152 - val_accuracy: 0.6558\n", - "Epoch 157/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7748 - sparse_categorical_crossentropy: 0.6297 - accuracy: 0.6940 - val_loss: 0.8792 - val_sparse_categorical_crossentropy: 0.7326 - val_accuracy: 0.6375\n", - "Epoch 158/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7753 - sparse_categorical_crossentropy: 0.6298 - accuracy: 0.6907 - val_loss: 0.8626 - val_sparse_categorical_crossentropy: 0.7191 - val_accuracy: 0.6648\n", - "Epoch 159/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7750 - sparse_categorical_crossentropy: 0.6299 - accuracy: 0.6939 - val_loss: 0.8681 - val_sparse_categorical_crossentropy: 0.7229 - val_accuracy: 0.6492\n", - "Epoch 160/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7732 - sparse_categorical_crossentropy: 0.6288 - accuracy: 0.6933 - val_loss: 0.8651 - val_sparse_categorical_crossentropy: 0.7215 - val_accuracy: 0.6540\n", - "Epoch 161/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7710 - sparse_categorical_crossentropy: 0.6265 - accuracy: 0.6957 - val_loss: 0.8679 - val_sparse_categorical_crossentropy: 0.7227 - val_accuracy: 0.6467\n", - "Epoch 162/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7735 - sparse_categorical_crossentropy: 0.6288 - accuracy: 0.6945 - val_loss: 0.8619 - val_sparse_categorical_crossentropy: 0.7183 - val_accuracy: 0.6537\n", - "Epoch 163/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7729 - sparse_categorical_crossentropy: 0.6289 - accuracy: 0.6941 - val_loss: 0.8606 - val_sparse_categorical_crossentropy: 0.7173 - val_accuracy: 0.6574\n", - "Epoch 164/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7712 - sparse_categorical_crossentropy: 0.6272 - accuracy: 0.6949 - val_loss: 0.8720 - val_sparse_categorical_crossentropy: 0.7265 - val_accuracy: 0.6397\n", - "Epoch 165/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7705 - sparse_categorical_crossentropy: 0.6257 - accuracy: 0.6944 - val_loss: 0.8593 - val_sparse_categorical_crossentropy: 0.7164 - val_accuracy: 0.6609\n", - "Epoch 166/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7705 - sparse_categorical_crossentropy: 0.6265 - accuracy: 0.6944 - val_loss: 0.8645 - val_sparse_categorical_crossentropy: 0.7205 - val_accuracy: 0.6491\n", - "Epoch 167/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7701 - sparse_categorical_crossentropy: 0.6259 - accuracy: 0.6945 - val_loss: 0.8737 - val_sparse_categorical_crossentropy: 0.7284 - val_accuracy: 0.6406\n", - "Epoch 168/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7677 - sparse_categorical_crossentropy: 0.6231 - accuracy: 0.6967 - val_loss: 0.8597 - val_sparse_categorical_crossentropy: 0.7160 - val_accuracy: 0.6579\n", - "Epoch 169/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7703 - sparse_categorical_crossentropy: 0.6260 - accuracy: 0.6950 - val_loss: 0.8690 - val_sparse_categorical_crossentropy: 0.7252 - val_accuracy: 0.6466\n", - "Epoch 170/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7689 - sparse_categorical_crossentropy: 0.6254 - accuracy: 0.6969 - val_loss: 0.8640 - val_sparse_categorical_crossentropy: 0.7198 - val_accuracy: 0.6491\n", - "Epoch 171/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7697 - sparse_categorical_crossentropy: 0.6256 - accuracy: 0.6953 - val_loss: 0.8584 - val_sparse_categorical_crossentropy: 0.7152 - val_accuracy: 0.6582\n", - "Epoch 172/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7660 - sparse_categorical_crossentropy: 0.6220 - accuracy: 0.6992 - val_loss: 0.8612 - val_sparse_categorical_crossentropy: 0.7170 - val_accuracy: 0.6526\n", - "Epoch 173/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7657 - sparse_categorical_crossentropy: 0.6217 - accuracy: 0.6985 - val_loss: 0.8601 - val_sparse_categorical_crossentropy: 0.7167 - val_accuracy: 0.6544\n", - "Epoch 174/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7643 - sparse_categorical_crossentropy: 0.6208 - accuracy: 0.6966 - val_loss: 0.8620 - val_sparse_categorical_crossentropy: 0.7184 - val_accuracy: 0.6526\n", - "Epoch 175/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7649 - sparse_categorical_crossentropy: 0.6212 - accuracy: 0.6968 - val_loss: 0.8578 - val_sparse_categorical_crossentropy: 0.7147 - val_accuracy: 0.6570\n", - "Epoch 176/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7673 - sparse_categorical_crossentropy: 0.6237 - accuracy: 0.6951 - val_loss: 0.8579 - val_sparse_categorical_crossentropy: 0.7144 - val_accuracy: 0.6507\n", - "Epoch 177/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7648 - sparse_categorical_crossentropy: 0.6211 - accuracy: 0.6981 - val_loss: 0.8622 - val_sparse_categorical_crossentropy: 0.7183 - val_accuracy: 0.6483\n", - "Epoch 178/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7626 - sparse_categorical_crossentropy: 0.6192 - accuracy: 0.6971 - val_loss: 0.8646 - val_sparse_categorical_crossentropy: 0.7212 - val_accuracy: 0.6473\n", - "Epoch 179/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7638 - sparse_categorical_crossentropy: 0.6204 - accuracy: 0.6990 - val_loss: 0.8727 - val_sparse_categorical_crossentropy: 0.7284 - val_accuracy: 0.6382\n", - "Epoch 180/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7631 - sparse_categorical_crossentropy: 0.6196 - accuracy: 0.6978 - val_loss: 0.8564 - val_sparse_categorical_crossentropy: 0.7140 - val_accuracy: 0.6613\n", - "Epoch 181/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7624 - sparse_categorical_crossentropy: 0.6193 - accuracy: 0.6983 - val_loss: 0.8617 - val_sparse_categorical_crossentropy: 0.7185 - val_accuracy: 0.6496\n", - "Epoch 182/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7635 - sparse_categorical_crossentropy: 0.6205 - accuracy: 0.6974 - val_loss: 0.8572 - val_sparse_categorical_crossentropy: 0.7147 - val_accuracy: 0.6581\n", - "Epoch 183/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7644 - sparse_categorical_crossentropy: 0.6217 - accuracy: 0.6970 - val_loss: 0.8654 - val_sparse_categorical_crossentropy: 0.7224 - val_accuracy: 0.6487\n", - "Epoch 184/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7641 - sparse_categorical_crossentropy: 0.6212 - accuracy: 0.6971 - val_loss: 0.8577 - val_sparse_categorical_crossentropy: 0.7160 - val_accuracy: 0.6596\n", - "Epoch 185/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7631 - sparse_categorical_crossentropy: 0.6208 - accuracy: 0.6976 - val_loss: 0.8690 - val_sparse_categorical_crossentropy: 0.7258 - val_accuracy: 0.6423\n", - "Epoch 186/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7603 - sparse_categorical_crossentropy: 0.6173 - accuracy: 0.6986 - val_loss: 0.8560 - val_sparse_categorical_crossentropy: 0.7136 - val_accuracy: 0.6568\n", - "Epoch 187/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7618 - sparse_categorical_crossentropy: 0.6189 - accuracy: 0.6988 - val_loss: 0.8687 - val_sparse_categorical_crossentropy: 0.7246 - val_accuracy: 0.6435\n", - "Epoch 188/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7646 - sparse_categorical_crossentropy: 0.6214 - accuracy: 0.6965 - val_loss: 0.8532 - val_sparse_categorical_crossentropy: 0.7113 - val_accuracy: 0.6587\n", - "Epoch 189/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7616 - sparse_categorical_crossentropy: 0.6186 - accuracy: 0.6959 - val_loss: 0.8633 - val_sparse_categorical_crossentropy: 0.7199 - val_accuracy: 0.6478\n", - "Epoch 190/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7624 - sparse_categorical_crossentropy: 0.6197 - accuracy: 0.6976 - val_loss: 0.8558 - val_sparse_categorical_crossentropy: 0.7132 - val_accuracy: 0.6534\n", - "Epoch 191/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7628 - sparse_categorical_crossentropy: 0.6202 - accuracy: 0.6980 - val_loss: 0.8536 - val_sparse_categorical_crossentropy: 0.7123 - val_accuracy: 0.6598\n", - "Epoch 192/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7607 - sparse_categorical_crossentropy: 0.6185 - accuracy: 0.6996 - val_loss: 0.8698 - val_sparse_categorical_crossentropy: 0.7265 - val_accuracy: 0.6373\n", - "Epoch 193/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7601 - sparse_categorical_crossentropy: 0.6176 - accuracy: 0.6990 - val_loss: 0.8609 - val_sparse_categorical_crossentropy: 0.7181 - val_accuracy: 0.6453\n", - "Epoch 194/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7613 - sparse_categorical_crossentropy: 0.6194 - accuracy: 0.6967 - val_loss: 0.8639 - val_sparse_categorical_crossentropy: 0.7221 - val_accuracy: 0.6441\n", - "Epoch 195/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7592 - sparse_categorical_crossentropy: 0.6171 - accuracy: 0.6994 - val_loss: 0.8582 - val_sparse_categorical_crossentropy: 0.7156 - val_accuracy: 0.6525\n", - "Epoch 196/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7581 - sparse_categorical_crossentropy: 0.6157 - accuracy: 0.6998 - val_loss: 0.8544 - val_sparse_categorical_crossentropy: 0.7128 - val_accuracy: 0.6575\n", - "Epoch 197/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7543 - sparse_categorical_crossentropy: 0.6120 - accuracy: 0.7019 - val_loss: 0.8586 - val_sparse_categorical_crossentropy: 0.7162 - val_accuracy: 0.6514\n", - "Epoch 198/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7559 - sparse_categorical_crossentropy: 0.6138 - accuracy: 0.7011 - val_loss: 0.8510 - val_sparse_categorical_crossentropy: 0.7098 - val_accuracy: 0.6633\n", - "Epoch 199/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7559 - sparse_categorical_crossentropy: 0.6140 - accuracy: 0.7011 - val_loss: 0.8522 - val_sparse_categorical_crossentropy: 0.7110 - val_accuracy: 0.6604\n", - "Epoch 200/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7562 - sparse_categorical_crossentropy: 0.6145 - accuracy: 0.7001 - val_loss: 0.8618 - val_sparse_categorical_crossentropy: 0.7194 - val_accuracy: 0.6516\n", - "Epoch 201/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7559 - sparse_categorical_crossentropy: 0.6140 - accuracy: 0.7017 - val_loss: 0.8539 - val_sparse_categorical_crossentropy: 0.7127 - val_accuracy: 0.6585\n", - "Epoch 202/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7561 - sparse_categorical_crossentropy: 0.6145 - accuracy: 0.7001 - val_loss: 0.8499 - val_sparse_categorical_crossentropy: 0.7093 - val_accuracy: 0.6624\n", - "Epoch 203/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7542 - sparse_categorical_crossentropy: 0.6125 - accuracy: 0.7025 - val_loss: 0.8562 - val_sparse_categorical_crossentropy: 0.7143 - val_accuracy: 0.6562\n", - "Epoch 204/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7550 - sparse_categorical_crossentropy: 0.6131 - accuracy: 0.7004 - val_loss: 0.8557 - val_sparse_categorical_crossentropy: 0.7144 - val_accuracy: 0.6555\n", - "Epoch 205/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7535 - sparse_categorical_crossentropy: 0.6123 - accuracy: 0.7002 - val_loss: 0.8598 - val_sparse_categorical_crossentropy: 0.7185 - val_accuracy: 0.6509\n", - "Epoch 206/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7553 - sparse_categorical_crossentropy: 0.6141 - accuracy: 0.6998 - val_loss: 0.8523 - val_sparse_categorical_crossentropy: 0.7113 - val_accuracy: 0.6572\n", - "Epoch 207/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7544 - sparse_categorical_crossentropy: 0.6134 - accuracy: 0.7021 - val_loss: 0.8568 - val_sparse_categorical_crossentropy: 0.7157 - val_accuracy: 0.6512\n", - "Epoch 208/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7533 - sparse_categorical_crossentropy: 0.6123 - accuracy: 0.6999 - val_loss: 0.8530 - val_sparse_categorical_crossentropy: 0.7126 - val_accuracy: 0.6603\n", - "Epoch 209/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7528 - sparse_categorical_crossentropy: 0.6121 - accuracy: 0.7033 - val_loss: 0.8495 - val_sparse_categorical_crossentropy: 0.7091 - val_accuracy: 0.6585\n", - "Epoch 210/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7539 - sparse_categorical_crossentropy: 0.6129 - accuracy: 0.7000 - val_loss: 0.8518 - val_sparse_categorical_crossentropy: 0.7116 - val_accuracy: 0.6619\n", - "Epoch 211/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7528 - sparse_categorical_crossentropy: 0.6121 - accuracy: 0.7014 - val_loss: 0.8679 - val_sparse_categorical_crossentropy: 0.7261 - val_accuracy: 0.6418\n", - "Epoch 212/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7520 - sparse_categorical_crossentropy: 0.6109 - accuracy: 0.7006 - val_loss: 0.8589 - val_sparse_categorical_crossentropy: 0.7182 - val_accuracy: 0.6538\n", - "Epoch 213/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7528 - sparse_categorical_crossentropy: 0.6121 - accuracy: 0.7004 - val_loss: 0.8497 - val_sparse_categorical_crossentropy: 0.7099 - val_accuracy: 0.6624\n", - "Epoch 214/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7507 - sparse_categorical_crossentropy: 0.6100 - accuracy: 0.7047 - val_loss: 0.8582 - val_sparse_categorical_crossentropy: 0.7172 - val_accuracy: 0.6511\n", - "Epoch 215/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7523 - sparse_categorical_crossentropy: 0.6116 - accuracy: 0.7006 - val_loss: 0.8479 - val_sparse_categorical_crossentropy: 0.7084 - val_accuracy: 0.6644\n", - "Epoch 216/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7495 - sparse_categorical_crossentropy: 0.6090 - accuracy: 0.7041 - val_loss: 0.8541 - val_sparse_categorical_crossentropy: 0.7135 - val_accuracy: 0.6567\n", - "Epoch 217/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7494 - sparse_categorical_crossentropy: 0.6088 - accuracy: 0.7045 - val_loss: 0.8519 - val_sparse_categorical_crossentropy: 0.7119 - val_accuracy: 0.6626\n", - "Epoch 218/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7527 - sparse_categorical_crossentropy: 0.6119 - accuracy: 0.7025 - val_loss: 0.8598 - val_sparse_categorical_crossentropy: 0.7193 - val_accuracy: 0.6505\n", - "Epoch 219/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7514 - sparse_categorical_crossentropy: 0.6111 - accuracy: 0.7025 - val_loss: 0.8522 - val_sparse_categorical_crossentropy: 0.7130 - val_accuracy: 0.6642\n", - "Epoch 220/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7505 - sparse_categorical_crossentropy: 0.6101 - accuracy: 0.7037 - val_loss: 0.8662 - val_sparse_categorical_crossentropy: 0.7246 - val_accuracy: 0.6434\n", - "Epoch 221/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7496 - sparse_categorical_crossentropy: 0.6088 - accuracy: 0.7028 - val_loss: 0.8525 - val_sparse_categorical_crossentropy: 0.7127 - val_accuracy: 0.6603\n", - "Epoch 222/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7503 - sparse_categorical_crossentropy: 0.6098 - accuracy: 0.7030 - val_loss: 0.8520 - val_sparse_categorical_crossentropy: 0.7125 - val_accuracy: 0.6585\n", - "Epoch 223/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7475 - sparse_categorical_crossentropy: 0.6073 - accuracy: 0.7047 - val_loss: 0.8554 - val_sparse_categorical_crossentropy: 0.7151 - val_accuracy: 0.6522\n", - "Epoch 224/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7462 - sparse_categorical_crossentropy: 0.6059 - accuracy: 0.7056 - val_loss: 0.8637 - val_sparse_categorical_crossentropy: 0.7225 - val_accuracy: 0.6447\n", - "Epoch 225/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7464 - sparse_categorical_crossentropy: 0.6057 - accuracy: 0.7045 - val_loss: 0.8564 - val_sparse_categorical_crossentropy: 0.7166 - val_accuracy: 0.6546\n", - "Epoch 226/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7540 - sparse_categorical_crossentropy: 0.6143 - accuracy: 0.7019 - val_loss: 0.8674 - val_sparse_categorical_crossentropy: 0.7269 - val_accuracy: 0.6393\n", - "Epoch 227/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7530 - sparse_categorical_crossentropy: 0.6126 - accuracy: 0.7026 - val_loss: 0.8528 - val_sparse_categorical_crossentropy: 0.7127 - val_accuracy: 0.6556\n", - "Epoch 228/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7479 - sparse_categorical_crossentropy: 0.6075 - accuracy: 0.7054 - val_loss: 0.8571 - val_sparse_categorical_crossentropy: 0.7168 - val_accuracy: 0.6533\n", - "Epoch 229/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7463 - sparse_categorical_crossentropy: 0.6061 - accuracy: 0.7046 - val_loss: 0.8514 - val_sparse_categorical_crossentropy: 0.7117 - val_accuracy: 0.6577\n", - "Epoch 230/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7477 - sparse_categorical_crossentropy: 0.6076 - accuracy: 0.7031 - val_loss: 0.8546 - val_sparse_categorical_crossentropy: 0.7144 - val_accuracy: 0.6545\n", - "Epoch 231/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7495 - sparse_categorical_crossentropy: 0.6097 - accuracy: 0.7022 - val_loss: 0.8526 - val_sparse_categorical_crossentropy: 0.7136 - val_accuracy: 0.6533\n", - "Epoch 232/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7434 - sparse_categorical_crossentropy: 0.6035 - accuracy: 0.7053 - val_loss: 0.8619 - val_sparse_categorical_crossentropy: 0.7214 - val_accuracy: 0.6485\n", - "Epoch 233/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7444 - sparse_categorical_crossentropy: 0.6042 - accuracy: 0.7057 - val_loss: 0.8528 - val_sparse_categorical_crossentropy: 0.7132 - val_accuracy: 0.6554\n", - "Epoch 234/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7450 - sparse_categorical_crossentropy: 0.6052 - accuracy: 0.7067 - val_loss: 0.8549 - val_sparse_categorical_crossentropy: 0.7156 - val_accuracy: 0.6534\n", - "Epoch 235/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7465 - sparse_categorical_crossentropy: 0.6071 - accuracy: 0.7044 - val_loss: 0.8527 - val_sparse_categorical_crossentropy: 0.7140 - val_accuracy: 0.6594\n", - "Epoch 236/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7487 - sparse_categorical_crossentropy: 0.6089 - accuracy: 0.7036 - val_loss: 0.8816 - val_sparse_categorical_crossentropy: 0.7409 - val_accuracy: 0.6316\n", - "Epoch 237/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7477 - sparse_categorical_crossentropy: 0.6078 - accuracy: 0.7043 - val_loss: 0.8522 - val_sparse_categorical_crossentropy: 0.7133 - val_accuracy: 0.6596\n", - "Epoch 238/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7458 - sparse_categorical_crossentropy: 0.6065 - accuracy: 0.7040 - val_loss: 0.8654 - val_sparse_categorical_crossentropy: 0.7256 - val_accuracy: 0.6443\n", - "Epoch 239/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7439 - sparse_categorical_crossentropy: 0.6044 - accuracy: 0.7054 - val_loss: 0.8559 - val_sparse_categorical_crossentropy: 0.7164 - val_accuracy: 0.6537\n", - "Epoch 240/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7444 - sparse_categorical_crossentropy: 0.6050 - accuracy: 0.7066 - val_loss: 0.8488 - val_sparse_categorical_crossentropy: 0.7098 - val_accuracy: 0.6592\n", - "Epoch 241/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7448 - sparse_categorical_crossentropy: 0.6055 - accuracy: 0.7043 - val_loss: 0.8506 - val_sparse_categorical_crossentropy: 0.7118 - val_accuracy: 0.6565\n", - "Epoch 242/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 0.7441 - sparse_categorical_crossentropy: 0.6048 - accuracy: 0.7052 - val_loss: 0.8531 - val_sparse_categorical_crossentropy: 0.7142 - val_accuracy: 0.6561\n", - "Epoch 243/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7428 - sparse_categorical_crossentropy: 0.6039 - accuracy: 0.7061 - val_loss: 0.8580 - val_sparse_categorical_crossentropy: 0.7190 - val_accuracy: 0.6528\n", - "Epoch 244/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7442 - sparse_categorical_crossentropy: 0.6053 - accuracy: 0.7051 - val_loss: 0.8561 - val_sparse_categorical_crossentropy: 0.7179 - val_accuracy: 0.6538\n", - "Epoch 245/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7417 - sparse_categorical_crossentropy: 0.6028 - accuracy: 0.7083 - val_loss: 0.8608 - val_sparse_categorical_crossentropy: 0.7220 - val_accuracy: 0.6493\n", - "Epoch 246/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7457 - sparse_categorical_crossentropy: 0.6068 - accuracy: 0.7044 - val_loss: 0.8516 - val_sparse_categorical_crossentropy: 0.7135 - val_accuracy: 0.6596\n", - "Epoch 247/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7443 - sparse_categorical_crossentropy: 0.6055 - accuracy: 0.7070 - val_loss: 0.8616 - val_sparse_categorical_crossentropy: 0.7221 - val_accuracy: 0.6417\n", - "Epoch 248/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7423 - sparse_categorical_crossentropy: 0.6034 - accuracy: 0.7056 - val_loss: 0.8481 - val_sparse_categorical_crossentropy: 0.7093 - val_accuracy: 0.6546\n", - "Epoch 249/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7420 - sparse_categorical_crossentropy: 0.6029 - accuracy: 0.7074 - val_loss: 0.8858 - val_sparse_categorical_crossentropy: 0.7455 - val_accuracy: 0.6253\n", - "Epoch 250/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7429 - sparse_categorical_crossentropy: 0.6040 - accuracy: 0.7074 - val_loss: 0.8706 - val_sparse_categorical_crossentropy: 0.7310 - val_accuracy: 0.6393\n", - "Epoch 251/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7430 - sparse_categorical_crossentropy: 0.6041 - accuracy: 0.7064 - val_loss: 0.8489 - val_sparse_categorical_crossentropy: 0.7111 - val_accuracy: 0.6616\n", - "Epoch 252/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7419 - sparse_categorical_crossentropy: 0.6031 - accuracy: 0.7063 - val_loss: 0.8626 - val_sparse_categorical_crossentropy: 0.7233 - val_accuracy: 0.6432\n", - "Epoch 253/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7429 - sparse_categorical_crossentropy: 0.6041 - accuracy: 0.7059 - val_loss: 0.8632 - val_sparse_categorical_crossentropy: 0.7245 - val_accuracy: 0.6419\n", - "Epoch 254/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7418 - sparse_categorical_crossentropy: 0.6032 - accuracy: 0.7064 - val_loss: 0.8482 - val_sparse_categorical_crossentropy: 0.7110 - val_accuracy: 0.6650\n", - "Epoch 255/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7423 - sparse_categorical_crossentropy: 0.6033 - accuracy: 0.7052 - val_loss: 0.8707 - val_sparse_categorical_crossentropy: 0.7312 - val_accuracy: 0.6339\n", - "Epoch 256/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7431 - sparse_categorical_crossentropy: 0.6044 - accuracy: 0.7078 - val_loss: 0.8497 - val_sparse_categorical_crossentropy: 0.7113 - val_accuracy: 0.6594\n", - "Epoch 257/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7401 - sparse_categorical_crossentropy: 0.6009 - accuracy: 0.7070 - val_loss: 0.8709 - val_sparse_categorical_crossentropy: 0.7307 - val_accuracy: 0.6364\n", - "Epoch 258/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7438 - sparse_categorical_crossentropy: 0.6047 - accuracy: 0.7053 - val_loss: 0.8511 - val_sparse_categorical_crossentropy: 0.7133 - val_accuracy: 0.6638\n", - "Epoch 259/1000\n", - "21/21 [==============================] - 0s 10ms/step - loss: 0.7420 - sparse_categorical_crossentropy: 0.6032 - accuracy: 0.7059 - val_loss: 0.8664 - val_sparse_categorical_crossentropy: 0.7276 - val_accuracy: 0.6417\n", - "Epoch 260/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7425 - sparse_categorical_crossentropy: 0.6038 - accuracy: 0.7056 - val_loss: 0.8473 - val_sparse_categorical_crossentropy: 0.7096 - val_accuracy: 0.6594\n", - "Epoch 261/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7419 - sparse_categorical_crossentropy: 0.6029 - accuracy: 0.7071 - val_loss: 0.8632 - val_sparse_categorical_crossentropy: 0.7236 - val_accuracy: 0.6433\n", - "Epoch 262/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7391 - sparse_categorical_crossentropy: 0.6001 - accuracy: 0.7076 - val_loss: 0.8508 - val_sparse_categorical_crossentropy: 0.7129 - val_accuracy: 0.6565\n", - "Epoch 263/1000\n", - "21/21 [==============================] - 0s 8ms/step - loss: 0.7388 - sparse_categorical_crossentropy: 0.6004 - accuracy: 0.7086 - val_loss: 0.8476 - val_sparse_categorical_crossentropy: 0.7098 - val_accuracy: 0.6572\n", - "Epoch 264/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7392 - sparse_categorical_crossentropy: 0.6007 - accuracy: 0.7059 - val_loss: 0.8604 - val_sparse_categorical_crossentropy: 0.7220 - val_accuracy: 0.6455\n", - "Epoch 265/1000\n", - "21/21 [==============================] - 0s 9ms/step - loss: 0.7370 - sparse_categorical_crossentropy: 0.5985 - accuracy: 0.7083 - val_loss: 0.8549 - val_sparse_categorical_crossentropy: 0.7163 - val_accuracy: 0.6491\n" - ] - }, - { - "data": { - "text/html": [ - "Waiting for W&B process to finish... (success)." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "898ef05caba7440ca58a1ce0e485a684", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='2.804 MB of 2.804 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "

Run history:


accuracy▁▁▄▅▅▅▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇▇▇▇████████████████
epoch▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇███
loss█▅▄▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
sparse_categorical_crossentropy█▇▅▄▄▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
val_accuracy▄▁▅▆▆▇▇▆██▇▇█▇▇▇▇██▇█▇▇█▇█▇██████▇▇▆███▇
val_loss█▆▄▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁
val_sparse_categorical_crossentropy█▇▄▃▃▂▂▂▂▂▂▂▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁

Run summary:


GFLOPS0.00024
accuracy0.70833
best_epoch214
best_val_sparse_categorical_crossentropy0.70844
epoch264
loss0.73695
sparse_categorical_crossentropy0.59847
val_accuracy0.6491
val_loss0.85487
val_sparse_categorical_crossentropy0.71627

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Synced gentle-terrain-41: https://wandb.ai/eltoncn/SygilDetector/runs/35qxb58i
Synced 5 W&B file(s), 1 media file(s), 0 artifact file(s) and 1 other file(s)" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Find logs at: .\\wandb\\run-20221016_211010-35qxb58i\\logs" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "units = [400, 400]\n", "regularizer = [0.0075, 0.0075]\n", @@ -2190,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2208,27 +1106,16 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 1], dtype=int64)" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np.argmax(result.numpy(), axis=1)" ] }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2248,7 +1135,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2258,24 +1145,16 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: models\\assets\n" - ] - } - ], + "outputs": [], "source": [ "model.save(\"models\")" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2291,7 +1170,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2302,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2313,27 +1192,16 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model(np.array([points.flatten()]))" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2344,64 +1212,18 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[9.5850545e-01, 3.9079445e-08, 4.1494541e-02]], dtype=float32)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model(np.array([points.flatten()])).numpy()" ] }, { "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([[7.3972428e-01, 2.5870226e-04, 2.6001698e-01]], dtype=float32)" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df = pd.read_csv(\"input.csv\", sep=\";\", decimal=\",\", names=[\"x\", \"y\"])\n", "points = df.to_numpy()[100:]\n", @@ -2421,42 +1243,18 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.00111005, 0.99008113, 0.00880879]], dtype=float32)" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model(np.array([points.flatten()/2])).numpy()" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYlElEQVR4nO3de4xc5XnH8d/jZU3XqZI1YBE84Ji0riNcB1bZAJHVkHCpaauYrWMCSUkdNcRqWlQ1VFaXgqhBtHZiVU2koDYW/YMEWiCELE6guMGmUopCiskaXBMcmyiAh5tDWFrhTVivn/4xM3g8Pmdu58yc2/cjWZ6Zc3bO+9qzz3nneW/m7gIA5N+cpAsAAOgPAj4AFAQBHwAKgoAPAAVBwAeAgjgh6QKEOeWUU3zx4sVJFwMAMuWJJ574ubsvCDqW2oC/ePFi7dy5M+liAECmmNlzYcdI6QBAQRDwAaAgCPgAUBAEfAAoCAI+ABREakfpdGtisqzN2/bqxalpDc8blLv0xvSMFg4P6aPvW6BHnjmoF6emtXB4SOtXLtXYSOm4n2s8Fvb+zc5rVbbaz0pq67WxkVKkawOApXW1zNHRUe90WObEZFnX3bdb0zOzbZ0/NDigjauXS9JxP1c7Vh9Qg94/6Lx2yzY4YJJLM0eO/h8MzjHJpJnZo68NDQ7o4x8o6VtPlNu+NjcHIFsmJsu66Tt79PqhGUnS8NCgNqxa1vHvrZk94e6jgcfyFPBXbNqh8tR0Rz9TGh6SpMCfKw0P6dHxC1u+f+N5cZWt3oCZZgP+r4KuHeXGFLd2bjyd3Jy4kSFpYZ/BZp/NVp/bGyZ2647Hnj/uWoNzTJsvP7ujz3izgJ+rlM6LXQTUZj/TeCzs3Hau203Z6gUF+7D33bxt73HfcqZnZrV5296+BsfGG095alrX3bdbko75RWh1Tifv16wsRb1R5KHuaalD2Gdw53O/OOYbeP1nU1LTz21YsJcq3/7j/L3NVaftwmprvdOfCfu5xtfbPS+ustUbMGv7faPcmOLU7MbTyTndnFuv9ktanpqW6+gv3MRkucMaZU8e6p6mOoR9Bv/thy+EfjbDfmbD1j1aduNDocG+Js7f21wF/PUrl2pocKDt84cGB7R+5dLAn6sda/X+Qee1W7bBAavk7Otfm2OV3H7DNT553hltXzvKjSlO7dx4Ork5dXsj6/ZGkQd5qHua6hD2WWv2DTzsZ6amZ/TmW637G+P8vc1VwB8bKWnj6uUqDQ/JJM2fN6jhoUGZKrnuq85f9Pax0vDQ2zntxp+rP9bs/cPOa6dspeEhbV5ztjZffvaxr11+tjavOfu4a9wytrzta0e5McWpnRtPJzenbm9kafnGk4Q81D1NdQj7rDX7Bh4lYA/OsVh/b2PJ4ZvZpZK+ImlA0m3uvqnh+LWSrpZ0WNJBSX/i7qEL/ERRC+C9+rlu37/Zz3byWrtllIKHdvbT+pVLAzuP6z/A7ZzTzbn1Fg4PBXaY9/sbTxLyUPc01SHsMxg2iq722exk9GDNvME5+vvV74/19zZywDezAUm3SrpE0gFJj5vZVnd/uu60SUmj7n7IzD4v6UuSroh6bYSLcmOKswxS8xtPJzenbm9k3d4o8iAPdU9THZp9Bkffc1LTz2b9sUNvHX57+GWQq85fpFvGlsde/sjDMs3sQ5I2uPvK6vPrJMndN4acPyLpq+6+otn7djMsEwiTllEeSchD3fNQh3oTk2Wtv/fJY+bb1EQN9j0dh29mayRd6u5XV59/WtJ57n5NyPlflfSyu98ScGydpHWStGjRog8891xPsj4AkLi4Jlo1Ss04fDO7StKopAuCjrv7FklbpEoLv49FA4C+SiLtGkfAL0s6o+756dXXjmFmF0u6XtIF7v6rGK4LAKmT5vRTHAH/cUlLzOxMVQL9lZI+VX9CNW//NVVSP6/GcE0ASJ0bJnbrzseeVy090cls8H6IPA7f3Q9LukbSNkk/lnSPu+8xs5vNbFX1tM2Sfl3SN81sl5ltjXpdAEiTicnyMcG+Jk0T3WLJ4bv7g5IebHjtxrrHF8dxnU6k+WsVgPzZvG3vccG+Ji0T3XK1eFpNlEW2AKAbzYJ6Wia65WpphZo0rb0BoBjCgrpJqZnolsuAH3anLU9NZ2qVQADZEbSGlUn6o/MXpSazkMuUTtjaG5JI7QDoWDt9gmlZw6qZXO14VdNqq8N2dqgCACldO8i1o9lM21ymdGpLEYdJS485gPTLU59gLgO+VAn6pZBOlDlm5PIBtCVN6/FHlduAL4XvgDXrnrlt3gAkIy07yMUh1wG/ltoJ2o0mq1/JAPRXWnaQi0OuA75UCfpHmuw3CQDNRNnaNG1yOSyzUdgwTZe0YtOO1A2dApAuadhBLg65b+FL4bl86eiyC+TzAeRdIQJ+/VeyIOTzARRBIQK+VAn6j45fqOO7byvI5wPIu8IE/Jo8DbECgE4ULuCH5fPf/NVh8vgAcq0Qo3Tq1Xra63eLl6Sp6RkWVgMyjo2PmitcC1+qBPR5c4+/19F5C2RXbZGz8tS0XIzAC1LIgC/la30MAPla5KxXChvw6bwF8oVGXGuFDfh5Wh8DAI24dhQ24OdpfQwANOLaUbhROvXysj4GgGxsMZi0Qgd8APlCI665wqZ0AKBoaOGHYAIHgLwh4Ado3KW+NoFDYhYu0G80vuJDSicAEziAdGD2bLwI+AGYwAGkA42veBHwAzCBA0gHGl/xIuAHYAIHkA40vuJFwA/ALFwgHWh8xYtROiGYwAEkj9mz8SLgA0g1Gl/xIaUDAAVBCx9A3zGZKhmxtPDN7FIz22tm+81sPOD4h83sR2Z22MzWxHFNANnEZKrkRA74ZjYg6VZJvyfpLEmfNLOzGk57XtJnJP1r1OsByDYmUyUnjpTOuZL2u/tPJcnM7pJ0maSnaye4+8+qx47EcD0AGcZkquTEEfBLkl6oe35A0nndvJGZrZO0TpIWLVoUvWQ9RA4S6M7C4SGVA4I7k6l6L1WjdNx9i7uPuvvoggULki5OKHKQQPeYTJWcOAJ+WdIZdc9Pr76WW+Qgge4xkz05caR0Hpe0xMzOVCXQXynpUzG8b2qRgwSiYTJVMiK38N39sKRrJG2T9GNJ97j7HjO72cxWSZKZfdDMDki6XNLXzGxP1OsmiQWdgGNNTJa1YtMOnTn+gFZs2kF6M6VimXjl7g9KerDhtRvrHj+uSqonF9avXHrMjlgSOUgUFzvEZUeqOm2zghwkcBR9WtnB0gpdIgcJVNCnlR208AFEQp9WdhDwAUTCuPrsIOAD6EjjiBxJ9GllBDl8AG0LG5GzcfVyPTp+YcKlQyu08AG0jRE52UbAB9A2RuRkGwEfQNsYkZNtBPw+YNo58oIROdlGp22PMe0ceVL7zLIXRDYR8HusWScXvyTIImaZZxcBv8fo5ELasXtbcZDD7zE6uZBm7N5WLAT8HqOTC2nGuPpiIaXTY3RyIc1IORYLAb8P6ORCGgTl6hcOD6kcENxJOeYTKR2gAMJy9R993wJSjgVCwAcKICxX/8gzB1npskBI6QAF0CxXT8qxOGjhAwXA8GBIBHygEBgeDImAD+QSu1IhCDl8IGfYlQphaOEDOcPsWYShhZ8iLGKFODB7FmFo4acEi1ghLozIQRgCfkrwNRxxYUQOwpDSSQm+hiMuLNiHMAT8lGARK8SJ2bMIQkonJfgaDqDXaOGnBF/DAfQaAT9F+BoOoJcI+EAGMEcDcSDgAykXtlSCJII+OkKnLZByzNFAXAj4QMoxRwNxiSXgm9mlZrbXzPab2XjA8RPN7O7q8R+a2eI4rgsUAUslIC6Rc/hmNiDpVkmXSDog6XEz2+ruT9ed9llJr7v7b5rZlZK+KOmKqNcG8qixg/aj71ugbz1RPiatwxwNdCOOFv65kva7+0/d/S1Jd0m6rOGcyyTdXn18r6SLzMxiuDaQK0GL6H3ribI+/oESm5cgsjhG6ZQkvVD3/ICk88LOcffDZvaGpJMl/bz+JDNbJ2mdJC1atCiGogHZEtZB+8gzB9m8BJGlqtPW3be4+6i7jy5YsCDp4gB9RwcteimOFn5Z0hl1z0+vvhZ0zgEzO0HSuyS9FsO1C4uJOPnEInropTha+I9LWmJmZ5rZXElXStracM5WSWurj9dI2uHuHsO1C4nNUvKLRfTQS5EDvrsflnSNpG2SfizpHnffY2Y3m9mq6mn/IulkM9sv6VpJxw3dRPuYiJNfYyMlbVy9nA5a9EQsSyu4+4OSHmx47ca6x7+UdHkc1wJ53rwIS8uxiB56JVWdtmgPE3Gyj7QckkDAzyDyvNlHWg5JIOBnUGOed/68QZ14whx94e5dWrFpB63EDCAthyQQ8DNqbKSkR8cv1D9ecY5+OXNEU9MzpAYyhLQckkDAzzhSA9lEWg5JYAOUjCM1kE3sYYwkEPAzLmxm5hwzTUyWCSApxvBL9BspnYwLSg1I0qw7uXwAxyDgZ1xtxM5AwGrT0zOz+qt7niToA5BEwM+FsZGSjoQsTURLH0ANAT8nmg3nY9QOAImAnxthufwaRu0AIODnRLNcvsSEHgAE/FwZGynpHz5xdmBL/9Bbh8njAwVHwM+ZWkt/eGjwmNdfPzRD5y1QcAT8HBobKekdJx4/p47OW6DYCPg5FdZJW56appUPFBQBP6eaddKS2gGKiYCfU82GaU7PzGrD1j19LhGApBHwc6rWeRtmanqGVj5QMAT8HBsbKanUJLVDBy5QLAT8nGu2oQazb4FiIeDn3NhISfPnDQYeY/ZtdBOTZa3YtENnjj/AfsJIPQJ+Afztx5axnV4PTEyWdd19u1WemmY/YWQCAb8Aah24peEhmaTS8JA2rl7ObksRsZ8wsoYtDguC7fTix37CyBpa+ECXwvpA6BtBWhHwgS4FTW6jbwRpRkqn4CYmy9q8ba9enJrWwuEhrV+5lNRPm2r/Tvz7ISsI+AVWG2VS63isjTKRRNBqE30jyBJSOgXGKBOgWGjhF1irJZRpuR5F6gt5QAu/wFhCuT1MsEJeEPALrNUSyqR2Kkh9IS8I+AXWaglldseqYIIV8oKAX3CtllAmdcEEK+RHpIBvZieZ2ffMbF/17/kh5z1kZlNm9t0o10NvkNppjglWyIuoLfxxSdvdfYmk7dXnQTZL+nTEa6FHWqV2ip66YPE55EXUYZmXSfpI9fHtkv5T0l83nuTu283sI42vIz3GRkravG2vygHBfY5Z4YdpMsEKeRC1hX+qu79UffyypFMjvh8SFJbamXUnlw/kQMuAb2YPm9n/BPy5rP48d3dJHqUwZrbOzHaa2c6DBw9GeSt0oZa6GDA77hi5fCD7WgZ8d7/Y3X874M/9kl4xs9Mkqfr3q1EK4+5b3H3U3UcXLFgQ5a3QpbGRko548H276Ll8IOuipnS2SlpbfbxW0v0R3w8pwDBEIJ+iBvxNki4xs32SLq4+l5mNmtlttZPM7PuSvinpIjM7YGYrI14XPcQwRCCfIo3ScffXJF0U8PpOSVfXPf+dKNdBf7HOO5BPrJaJQI3DECcmy1qxaQc3ACDDCPhoaWKyrPX3PqmZ2UpnbnlqWuvvfVISG6UAWcJaOmjppu/seTvY18zMum76zp6ESgSgGwR8tPT6oZnQ15mMBWQHAR+RMAMXyA4CPloaHhoMPcYMXCA7CPhoacOqZRqcc/xyCzVBC64BSB9G6aCl2kicv7x7V+DxoLV3AKQPLXy0pdnwy1l3rdi0g1w+kHIEfLSt2VaI5alpfeHuXbphYncfSwSgEwR8tK3ZVohSZW3sOx97npY+kFIEfLStfqu/MC4xagdIKQI+OjI2UtKj4xe2TO8ASB8CPrrSaqnkJNM6tYXezhx/gM5koA4BH11ptWhaUjNwJybLuu6+3SpPTctV+bbBbGCggoCPrjVL60zPzGrD1v4vrrZ5215Nz8weVxb6FQACPiJoNWpnarr/i6uF7bvLfrwAAR8R1EbtNJtp+zf3PdXHErEfL9AMAR+RjI2U9A+fODv0+KGZI31t5bMfLxCOgI/IWnXg9nOjlPq5AqZKP8PG1cvZmQsQi6chJsNDg5qaDt8opZ8a9+MFUEELH7HYsGpZ0+MMiwSSR8BHLFq1qBkWCSSPgI/YNNsZi+UWgOQR8BGbVmkdAMki4CM2rdI659z0H+TygQQR8NE3U9MzuvbuXQR9ICEEfMRqxW+c1PT4ESmRNXYAEPARszs/96GWQT9svH4zLHkMREfAR+zu/NyHmq6k2SmWPAbiQcBHT8S5dg1LHgPxIOCjJ8ZGSnrH3PClkztpnbPkMRAPAj565u/+cHnosU4WVGPJYyAeBHz0TLNx+Z0sqMaSx0A8WC0TqVe7cWzetlcvTk1r4fCQ1q9cyoqYQIcI+OgpM8k9+PVOsOQxEB0BHz0VFOybvT4xWaYlD/RIpBy+mZ1kZt8zs33Vv+cHnHOOmf3AzPaY2VNmdkWUayJb5s8LXkEz6HXG2wO9FbXTdlzSdndfIml79XmjQ5L+2N2XSbpU0pfNbDjidZERnbTwGW8P9FbUgH+ZpNurj2+XNNZ4grv/xN33VR+/KOlVSQsiXhcZ8UbIMgpBrzPeHuitqAH/VHd/qfr4ZUmnNjvZzM6VNFfSsyHH15nZTjPbefDgwYhFQxp0Moae8fZAb7XstDWzhyW9O+DQ9fVP3N3NLOQLvGRmp0n6hqS17n4k6Bx33yJpiySNjo6GvheyY/3Kpbruvt3HpWqmDr31dm6+1kn7rqFBDQ6YZmaP/tcz3h6IT8uA7+4Xhx0zs1fM7DR3f6ka0F8NOe+dkh6QdL27P9Z1aZE5YyMl7XzuF7rjseePef3Nt2Z17T27NDDnaICfmp7R4BzT/HmDmjo0wygdIGZRh2VulbRW0qbq3/c3nmBmcyV9W9LX3f3eiNdDBj3yTHB67ohLR2aP/SI3c8Q1b+4Jmrzxd/tRNKBQoubwN0m6xMz2Sbq4+lxmNmpmt1XP+YSkD0v6jJntqv45J+J1kSGddrrSSQv0RqQWvru/JumigNd3Srq6+vgOSXdEuQ6ybeHwkModBHE6aYHeYPE09Fwnna500gK9Q8BHz7Xb6VoaHtLG1cvppAV6hLV0kAoDZnp0/MKkiwHkGi189MXwUPCaOjWzYWswAIgNAR99sWHVsqbHBzpdLxlAxwj46IuxkZKuOn9R6PFPnndGH0sDFBMBH31zy9hyffmKczQ0ePRjN8ekq85fpFvGwve/BRAPOm3RV+xcBSSHFj4AFAQBHwAKgoAPAAVBwAeAgiDgA0BBmKd0hqOZHZT0XIS3OEXSz2MqTlZQ5/wrWn0l6typ97h74L7hqQ34UZnZTncfTboc/USd869o9ZWoc5xI6QBAQRDwAaAg8hzwtyRdgARQ5/wrWn0l6hyb3ObwAQDHynMLHwBQh4APAAWRm4BvZieZ2ffMbF/17/kB55xjZj8wsz1m9pSZXZFEWePSTp2r5z1kZlNm9t1+lzEOZnapme01s/1mNh5w/EQzu7t6/IdmtjiBYsaqjTp/2Mx+ZGaHzWxNEmWMWxt1vtbMnq7+7m43s/ckUc44tVHnPzWz3Wa2y8z+y8zOinRBd8/FH0lfkjRefTwu6YsB5/yWpCXVxwslvSRpOOmy97LO1WMXSfqYpO8mXeYu6jgg6VlJ75U0V9KTks5qOOfPJP1z9fGVku5Outx9qPNiSe+X9HVJa5Iuc5/q/FFJ86qPP1+Q/+d31j1eJemhKNfMTQtf0mWSbq8+vl3SWOMJ7v4Td99XffyipFclBc5Iy4iWdZYkd98u6f/6VKa4nStpv7v/1N3fknSXKvWuV//vcK+ki8wyvWdiyzq7+8/c/SlJR5IoYA+0U+dH3P1Q9eljkk7vcxnj1k6d/7fu6TskRRplk6eAf6q7v1R9/LKkU5udbGbnqnJXfbbXBeuhjuqcUSVJL9Q9P1B9LfAcdz8s6Q1JJ/eldL3RTp3zptM6f1bSv/e0RL3XVp3N7M/N7FlVvtH/RZQLZmrHKzN7WNK7Aw5dX//E3d3MQu+EZnaapG9IWuvuqW4hxVVnIC/M7CpJo5IuSLos/eDut0q61cw+JekGSWu7fa9MBXx3vzjsmJm9YmanuftL1YD+ash575T0gKTr3f2xHhU1NnHUOePKkup3OD+9+lrQOQfM7ARJ75L0Wn+K1xPt1Dlv2qqzmV2sSmPnAnf/VZ/K1iud/j/fJemfolwwTymdrTp651sr6f7GE8xsrqRvS/q6u9/bx7L1Sss658DjkpaY2ZnV/78rVal3vfp/hzWSdni1lyuj2qlz3rSss5mNSPqapFXunofGTTt1XlL39A8k7Yt0xaR7qmPs8T5Z0vbqP8jDkk6qvj4q6bbq46skzUjaVffnnKTL3ss6V59/X9JBSdOq5AlXJl32Duv5+5J+okp/y/XV125W5Rdfkn5N0jcl7Zf035Lem3SZ+1DnD1b/L99U5dvMnqTL3Ic6Pyzplbrf3a1Jl7kPdf6KpD3V+j4iaVmU67G0AgAURJ5SOgCAJgj4AFAQBHwAKAgCPgAUBAEfAAqCgA8ABUHAB4CC+H9uo8di8YyH8QAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(points[:,0]/2, points[:,1]/2, \"o\")\n", "plt.show()" @@ -2464,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2478,20 +1276,9 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.00282414, 0.9776854 , 0.01949057]], dtype=float32)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "points = sample_points_fromlist_sequential(all_points_tr, 100)\n", "points = center_points(points)\n", @@ -2509,7 +1296,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2542,20 +1329,9 @@ }, { "cell_type": "code", - "execution_count": 315, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 315, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import importlib\n", "importlib.reload(ep)" @@ -2563,7 +1339,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2603,942 +1379,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "wandb version 0.13.4 is available! To upgrade, please run:\n", - " $ pip install wandb --upgrade" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Tracking run with wandb version 0.12.21" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Run data is saved locally in d:\\Github\\JogosTeP\\Dev\\wandb\\run-20221016_202310-233p0mdm" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Syncing run spring-darkness-40 to Weights & Biases (docs)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The save_model argument by default saves the model in the HDF5 format that cannot save custom objects like subclassed models and custom layers. This behavior will be deprecated in a future release in favor of the SavedModel format. Meanwhile, the HDF5 model is saved as W&B files and the SavedModel as W&B Artifacts.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From c:\\Python38\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3777: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n", - "Epoch 1/400\n", - "3/3 [==============================] - 8s 3s/step - loss: 78.6250 - sparse_categorical_crossentropy: 79.9023 - accuracy: 0.3450 - val_loss: 81.3198 - val_sparse_categorical_crossentropy: 81.3198 - val_accuracy: 0.3320\n", - "Epoch 2/400\n", - "3/3 [==============================] - 5s 1s/step - loss: 78.8996 - sparse_categorical_crossentropy: 79.9023 - accuracy: 0.3450 - val_loss: 81.3198 - val_sparse_categorical_crossentropy: 81.3198 - val_accuracy: 0.3320\n", - "Epoch 3/400\n", - "3/3 [==============================] - 5s 1s/step - loss: 81.8312 - sparse_categorical_crossentropy: 79.9023 - accuracy: 0.3450 - val_loss: 81.3198 - val_sparse_categorical_crossentropy: 81.3198 - val_accuracy: 0.3320\n", - "Epoch 4/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 82.0725 - sparse_categorical_crossentropy: 79.9023 - accuracy: 0.3450 - val_loss: 81.3198 - val_sparse_categorical_crossentropy: 81.3198 - val_accuracy: 0.3320\n", - "Epoch 5/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 77.7772 - sparse_categorical_crossentropy: 79.6259 - accuracy: 0.3453 - val_loss: 83.3643 - val_sparse_categorical_crossentropy: 83.3643 - val_accuracy: 0.3415\n", - "Epoch 6/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 78.7980 - sparse_categorical_crossentropy: 79.6295 - accuracy: 0.3472 - val_loss: 83.0815 - val_sparse_categorical_crossentropy: 83.0815 - val_accuracy: 0.3468\n", - "Epoch 7/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 81.0465 - sparse_categorical_crossentropy: 81.1259 - accuracy: 0.3409 - val_loss: 82.1942 - val_sparse_categorical_crossentropy: 82.1942 - val_accuracy: 0.3297\n", - "Epoch 8/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 78.6992 - sparse_categorical_crossentropy: 79.4977 - accuracy: 0.3433 - val_loss: 81.5319 - val_sparse_categorical_crossentropy: 81.5319 - val_accuracy: 0.3408\n", - "Epoch 9/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 77.2100 - sparse_categorical_crossentropy: 78.8257 - accuracy: 0.3421 - val_loss: 79.6080 - val_sparse_categorical_crossentropy: 79.6080 - val_accuracy: 0.3393\n", - "Epoch 10/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 76.3440 - sparse_categorical_crossentropy: 78.4588 - accuracy: 0.3371 - val_loss: 78.6012 - val_sparse_categorical_crossentropy: 78.6012 - val_accuracy: 0.3378\n", - "Epoch 11/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 76.5983 - sparse_categorical_crossentropy: 78.3684 - accuracy: 0.3404 - val_loss: 79.1356 - val_sparse_categorical_crossentropy: 79.1356 - val_accuracy: 0.3433\n", - "Epoch 12/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 73.4164 - sparse_categorical_crossentropy: 76.0352 - accuracy: 0.3382 - val_loss: 76.5126 - val_sparse_categorical_crossentropy: 76.5126 - val_accuracy: 0.3407\n", - "Epoch 13/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 69.8323 - sparse_categorical_crossentropy: 73.2014 - accuracy: 0.3480 - val_loss: 73.7187 - val_sparse_categorical_crossentropy: 73.7187 - val_accuracy: 0.3340\n", - "Epoch 14/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 69.6252 - sparse_categorical_crossentropy: 71.8943 - accuracy: 0.3460 - val_loss: 71.9556 - val_sparse_categorical_crossentropy: 71.9556 - val_accuracy: 0.3350\n", - "Epoch 15/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 69.0580 - sparse_categorical_crossentropy: 69.8641 - accuracy: 0.3392 - val_loss: 73.6221 - val_sparse_categorical_crossentropy: 73.6221 - val_accuracy: 0.3282\n", - "Epoch 16/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 67.3056 - sparse_categorical_crossentropy: 69.8584 - accuracy: 0.3394 - val_loss: 72.5147 - val_sparse_categorical_crossentropy: 72.5147 - val_accuracy: 0.3295\n", - "Epoch 17/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 62.1024 - sparse_categorical_crossentropy: 68.9684 - accuracy: 0.3390 - val_loss: 69.9754 - val_sparse_categorical_crossentropy: 69.9754 - val_accuracy: 0.3442\n", - "Epoch 18/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 67.7088 - sparse_categorical_crossentropy: 69.0567 - accuracy: 0.3456 - val_loss: 69.3902 - val_sparse_categorical_crossentropy: 69.3902 - val_accuracy: 0.3377\n", - "Epoch 19/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 64.1017 - sparse_categorical_crossentropy: 67.4050 - accuracy: 0.3470 - val_loss: 71.5089 - val_sparse_categorical_crossentropy: 71.5089 - val_accuracy: 0.3355\n", - "Epoch 20/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 62.4892 - sparse_categorical_crossentropy: 65.7488 - accuracy: 0.3405 - val_loss: 70.3746 - val_sparse_categorical_crossentropy: 70.3746 - val_accuracy: 0.3387\n", - "Epoch 21/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 63.9254 - sparse_categorical_crossentropy: 65.4433 - accuracy: 0.3500 - val_loss: 66.7580 - val_sparse_categorical_crossentropy: 66.7580 - val_accuracy: 0.3350\n", - "Epoch 22/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 61.7056 - sparse_categorical_crossentropy: 65.0026 - accuracy: 0.3384 - val_loss: 67.7991 - val_sparse_categorical_crossentropy: 67.7991 - val_accuracy: 0.3322\n", - "Epoch 23/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 60.7776 - sparse_categorical_crossentropy: 63.4901 - accuracy: 0.3431 - val_loss: 66.3631 - val_sparse_categorical_crossentropy: 66.3631 - val_accuracy: 0.3513\n", - "Epoch 24/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 59.7072 - sparse_categorical_crossentropy: 62.9959 - accuracy: 0.3486 - val_loss: 66.5251 - val_sparse_categorical_crossentropy: 66.5251 - val_accuracy: 0.3407\n", - "Epoch 25/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 60.3862 - sparse_categorical_crossentropy: 61.8934 - accuracy: 0.3451 - val_loss: 63.7316 - val_sparse_categorical_crossentropy: 63.7316 - val_accuracy: 0.3322\n", - "Epoch 26/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.0261 - sparse_categorical_crossentropy: 60.8048 - accuracy: 0.3501 - val_loss: 63.7248 - val_sparse_categorical_crossentropy: 63.7248 - val_accuracy: 0.3468\n", - "Epoch 27/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.5935 - sparse_categorical_crossentropy: 60.2618 - accuracy: 0.3446 - val_loss: 61.9301 - val_sparse_categorical_crossentropy: 61.9301 - val_accuracy: 0.3335\n", - "Epoch 28/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.8074 - sparse_categorical_crossentropy: 59.4428 - accuracy: 0.3495 - val_loss: 62.1162 - val_sparse_categorical_crossentropy: 62.1162 - val_accuracy: 0.3443\n", - "Epoch 29/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.5989 - sparse_categorical_crossentropy: 59.4487 - accuracy: 0.3496 - val_loss: 59.3608 - val_sparse_categorical_crossentropy: 59.3608 - val_accuracy: 0.3468\n", - "Epoch 30/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.5988 - sparse_categorical_crossentropy: 58.4016 - accuracy: 0.3503 - val_loss: 59.9371 - val_sparse_categorical_crossentropy: 59.9371 - val_accuracy: 0.3357\n", - "Epoch 31/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.9833 - sparse_categorical_crossentropy: 57.9318 - accuracy: 0.3581 - val_loss: 62.0237 - val_sparse_categorical_crossentropy: 62.0237 - val_accuracy: 0.3287\n", - "Epoch 32/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.8622 - sparse_categorical_crossentropy: 57.9071 - accuracy: 0.3490 - val_loss: 58.1778 - val_sparse_categorical_crossentropy: 58.1778 - val_accuracy: 0.3443\n", - "Epoch 33/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.8087 - sparse_categorical_crossentropy: 57.6046 - accuracy: 0.3533 - val_loss: 59.1959 - val_sparse_categorical_crossentropy: 59.1959 - val_accuracy: 0.3410\n", - "Epoch 34/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.1601 - sparse_categorical_crossentropy: 57.3644 - accuracy: 0.3500 - val_loss: 59.6244 - val_sparse_categorical_crossentropy: 59.6244 - val_accuracy: 0.3463\n", - "Epoch 35/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.6254 - sparse_categorical_crossentropy: 56.9010 - accuracy: 0.3519 - val_loss: 57.2709 - val_sparse_categorical_crossentropy: 57.2709 - val_accuracy: 0.3433\n", - "Epoch 36/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.6546 - sparse_categorical_crossentropy: 56.4533 - accuracy: 0.3527 - val_loss: 59.4641 - val_sparse_categorical_crossentropy: 59.4641 - val_accuracy: 0.3325\n", - "Epoch 37/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.0552 - sparse_categorical_crossentropy: 56.2977 - accuracy: 0.3502 - val_loss: 58.5124 - val_sparse_categorical_crossentropy: 58.5124 - val_accuracy: 0.3435\n", - "Epoch 38/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.0532 - sparse_categorical_crossentropy: 56.0680 - accuracy: 0.3517 - val_loss: 57.5131 - val_sparse_categorical_crossentropy: 57.5131 - val_accuracy: 0.3435\n", - "Epoch 39/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.4819 - sparse_categorical_crossentropy: 55.6748 - accuracy: 0.3506 - val_loss: 57.5881 - val_sparse_categorical_crossentropy: 57.5881 - val_accuracy: 0.3345\n", - "Epoch 40/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.4827 - sparse_categorical_crossentropy: 55.3395 - accuracy: 0.3547 - val_loss: 58.4317 - val_sparse_categorical_crossentropy: 58.4317 - val_accuracy: 0.3420\n", - "Epoch 41/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2346 - sparse_categorical_crossentropy: 55.0295 - accuracy: 0.3532 - val_loss: 56.7493 - val_sparse_categorical_crossentropy: 56.7493 - val_accuracy: 0.3408\n", - "Epoch 42/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.4081 - sparse_categorical_crossentropy: 54.9585 - accuracy: 0.3521 - val_loss: 58.2193 - val_sparse_categorical_crossentropy: 58.2193 - val_accuracy: 0.3388\n", - "Epoch 43/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.5770 - sparse_categorical_crossentropy: 54.8012 - accuracy: 0.3536 - val_loss: 57.3529 - val_sparse_categorical_crossentropy: 57.3529 - val_accuracy: 0.3345\n", - "Epoch 44/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.2193 - sparse_categorical_crossentropy: 54.5562 - accuracy: 0.3549 - val_loss: 56.8460 - val_sparse_categorical_crossentropy: 56.8460 - val_accuracy: 0.3347\n", - "Epoch 45/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.1417 - sparse_categorical_crossentropy: 54.4004 - accuracy: 0.3549 - val_loss: 56.9884 - val_sparse_categorical_crossentropy: 56.9884 - val_accuracy: 0.3352\n", - "Epoch 46/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.5445 - sparse_categorical_crossentropy: 54.3989 - accuracy: 0.3531 - val_loss: 56.9541 - val_sparse_categorical_crossentropy: 56.9541 - val_accuracy: 0.3390\n", - "Epoch 47/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8589 - sparse_categorical_crossentropy: 54.2839 - accuracy: 0.3537 - val_loss: 56.7298 - val_sparse_categorical_crossentropy: 56.7298 - val_accuracy: 0.3360\n", - "Epoch 48/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.6992 - sparse_categorical_crossentropy: 54.2399 - accuracy: 0.3555 - val_loss: 56.9344 - val_sparse_categorical_crossentropy: 56.9344 - val_accuracy: 0.3385\n", - "Epoch 49/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.3529 - sparse_categorical_crossentropy: 54.2101 - accuracy: 0.3539 - val_loss: 56.8088 - val_sparse_categorical_crossentropy: 56.8088 - val_accuracy: 0.3348\n", - "Epoch 50/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.3048 - sparse_categorical_crossentropy: 54.2005 - accuracy: 0.3548 - val_loss: 56.7384 - val_sparse_categorical_crossentropy: 56.7384 - val_accuracy: 0.3323\n", - "Epoch 51/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2895 - sparse_categorical_crossentropy: 54.1590 - accuracy: 0.3539 - val_loss: 56.6828 - val_sparse_categorical_crossentropy: 56.6828 - val_accuracy: 0.3347\n", - "Epoch 52/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.4970 - sparse_categorical_crossentropy: 54.1907 - accuracy: 0.3550 - val_loss: 56.6910 - val_sparse_categorical_crossentropy: 56.6910 - val_accuracy: 0.3370\n", - "Epoch 53/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.8006 - sparse_categorical_crossentropy: 54.1637 - accuracy: 0.3542 - val_loss: 56.8580 - val_sparse_categorical_crossentropy: 56.8580 - val_accuracy: 0.3348\n", - "Epoch 54/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.3394 - sparse_categorical_crossentropy: 54.1644 - accuracy: 0.3535 - val_loss: 56.7250 - val_sparse_categorical_crossentropy: 56.7250 - val_accuracy: 0.3373\n", - "Epoch 55/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.9797 - sparse_categorical_crossentropy: 54.1492 - accuracy: 0.3545 - val_loss: 56.7789 - val_sparse_categorical_crossentropy: 56.7789 - val_accuracy: 0.3382\n", - "Epoch 56/400\n", - "3/3 [==============================] - 5s 1s/step - loss: 56.1487 - sparse_categorical_crossentropy: 54.1536 - accuracy: 0.3545 - val_loss: 56.6706 - val_sparse_categorical_crossentropy: 56.6706 - val_accuracy: 0.3353\n", - "Epoch 57/400\n", - "3/3 [==============================] - 5s 2s/step - loss: 51.3778 - sparse_categorical_crossentropy: 54.1466 - accuracy: 0.3546 - val_loss: 56.7487 - val_sparse_categorical_crossentropy: 56.7487 - val_accuracy: 0.3360\n", - "Epoch 58/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.0764 - sparse_categorical_crossentropy: 54.1434 - accuracy: 0.3536 - val_loss: 56.7394 - val_sparse_categorical_crossentropy: 56.7394 - val_accuracy: 0.3377\n", - "Epoch 59/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.6160 - sparse_categorical_crossentropy: 54.1390 - accuracy: 0.3539 - val_loss: 56.7446 - val_sparse_categorical_crossentropy: 56.7446 - val_accuracy: 0.3363\n", - "Epoch 60/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.0036 - sparse_categorical_crossentropy: 54.1379 - accuracy: 0.3543 - val_loss: 56.7405 - val_sparse_categorical_crossentropy: 56.7405 - val_accuracy: 0.3365\n", - "Epoch 61/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.4327 - sparse_categorical_crossentropy: 54.1400 - accuracy: 0.3542 - val_loss: 56.7309 - val_sparse_categorical_crossentropy: 56.7309 - val_accuracy: 0.3367\n", - "Epoch 62/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.9221 - sparse_categorical_crossentropy: 54.1396 - accuracy: 0.3544 - val_loss: 56.7309 - val_sparse_categorical_crossentropy: 56.7309 - val_accuracy: 0.3367\n", - "Epoch 63/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.4229 - sparse_categorical_crossentropy: 54.1399 - accuracy: 0.3544 - val_loss: 56.7282 - val_sparse_categorical_crossentropy: 56.7282 - val_accuracy: 0.3365\n", - "Epoch 64/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.8988 - sparse_categorical_crossentropy: 54.1397 - accuracy: 0.3544 - val_loss: 56.7293 - val_sparse_categorical_crossentropy: 56.7293 - val_accuracy: 0.3363\n", - "Epoch 65/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.7618 - sparse_categorical_crossentropy: 54.1400 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 66/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.5588 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 67/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.2501 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 68/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.0972 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 69/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.5517 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 70/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2963 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 71/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.2727 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 72/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.5483 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 73/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7500 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 74/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.1833 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 75/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.5122 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 76/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.1625 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 77/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.3668 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 78/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.5516 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 79/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.1500 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 80/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7270 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 81/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.0461 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 82/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.6029 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 83/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.4261 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 84/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.8811 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 85/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6681 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 86/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.8186 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 87/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.3402 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 88/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.2908 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 89/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2247 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 90/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.6941 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 91/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.0575 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 92/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.9118 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 93/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.9996 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 94/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6031 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 95/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.2347 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 96/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.0126 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 97/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.3601 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 98/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.3686 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 99/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.2782 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 100/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.0300 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 101/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.0654 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 102/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.8239 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 103/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.8599 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 104/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.3026 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 105/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.1670 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 106/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.6048 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 107/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8059 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 108/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.9939 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 109/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.9574 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 110/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.8622 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 111/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.1545 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 112/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.5640 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 113/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.5533 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 114/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.1553 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 115/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.7909 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 116/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.9416 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 117/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.3513 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 118/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.0671 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 119/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.9484 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 120/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.9507 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 121/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.9558 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 122/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.1958 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 123/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.7670 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 124/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.0342 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 125/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.4446 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 126/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.5656 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 127/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.3201 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 128/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.3356 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 129/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.2951 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 130/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.9054 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 131/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.7264 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 132/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 48.7790 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 133/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.0646 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 134/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.3353 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 135/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.9380 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 136/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.8842 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 137/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.4683 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 138/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.0643 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 139/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7821 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 140/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 59.5333 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 141/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8365 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 142/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.4950 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 143/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.4445 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 144/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.1036 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 145/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6288 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 146/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.9936 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 147/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.4799 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 148/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.8525 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 149/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6510 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 150/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.7819 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 151/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.4142 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 152/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.8158 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 153/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.0512 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 154/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.0982 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 155/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2488 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 156/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.0751 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 157/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7779 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 158/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.7381 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 159/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6959 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 160/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.4269 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 161/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.7036 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 162/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8644 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 163/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.7301 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 164/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.4779 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 165/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.8991 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 166/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.8562 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 167/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6398 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 168/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 59.5000 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 169/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.2000 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 170/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.3807 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 171/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.0336 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 172/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.9291 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 173/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.0659 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 174/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.9859 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 175/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.7337 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 176/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.4263 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 177/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.4412 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 178/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.6800 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 179/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.2451 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 180/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.5898 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 181/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.4832 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 182/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6175 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 183/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.2830 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 184/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.0435 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 185/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.0840 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 186/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.5773 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 187/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.1309 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 188/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.3595 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 189/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2444 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 190/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6580 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 191/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.5008 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 192/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2376 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 193/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.4827 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 194/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.2802 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 195/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.1943 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 196/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.9119 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 197/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.2339 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 198/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.7420 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 199/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.0868 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 200/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.7376 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 201/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.3442 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 202/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.6317 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 203/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.3737 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 204/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.2403 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 205/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.9978 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 206/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.1561 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 207/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.7728 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 208/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.6468 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 209/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 59.2723 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 210/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.1388 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 211/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.4628 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 212/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.4769 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 213/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.5827 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 214/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.8288 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 215/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.2581 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 216/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.5591 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 217/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 59.2802 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 218/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.0927 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 219/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.1544 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 220/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.6287 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 221/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.0146 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 222/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.5640 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 223/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.9511 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 224/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.0015 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 225/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.7697 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 226/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.7516 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 227/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.5903 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 228/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7728 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 229/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.0518 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 230/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.5506 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 231/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7092 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 232/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.9994 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 233/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.4503 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 234/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.0363 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 235/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.8375 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 236/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.3722 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 237/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.9749 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 238/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.1519 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 239/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.1105 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 240/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.0749 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 241/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.1139 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 242/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7764 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 243/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.5914 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 244/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6892 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 245/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.7691 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 246/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.1180 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 247/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7110 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 248/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.2786 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 249/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.7198 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 250/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.7252 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 251/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.9146 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 252/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6255 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 253/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.4659 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 254/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.1507 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 255/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.5198 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 256/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.1481 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 257/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.9728 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 258/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.1445 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 259/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7456 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 260/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.4193 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 261/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.1427 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 262/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.6258 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 263/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.2779 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 264/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.2045 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 265/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.8555 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 266/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.1414 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 267/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.6368 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 268/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.5150 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 269/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.7237 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 270/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.5279 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 271/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.3936 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 272/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.4103 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 273/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.6628 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 274/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.8820 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 275/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.4965 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 276/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.5496 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 277/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8403 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 278/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.3307 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 279/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8417 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 280/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.5277 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 281/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2380 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 282/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.5062 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 283/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.2106 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 284/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.4202 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 285/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.2257 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 286/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.2278 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 287/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.7671 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 288/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.7434 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 289/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.0221 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 290/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6641 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 291/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.0442 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 292/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.3872 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 293/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.4116 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 294/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.8186 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 295/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.7724 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 296/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6339 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 297/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.8913 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 298/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.0078 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 299/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.0342 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 300/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2222 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 301/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7939 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 302/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.9120 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 303/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.2769 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 304/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.5865 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 305/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.1880 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 306/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.7888 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 307/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.7696 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 308/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.5030 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 309/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8342 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 310/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.3884 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 311/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.8049 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 312/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.8981 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 313/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.2742 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 314/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 48.8831 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 315/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.7687 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 316/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.2177 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 317/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6164 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 318/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.2866 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 319/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.9668 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 320/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8079 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 321/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.5982 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 322/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.0325 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 323/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.7127 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 324/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.8239 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 325/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.5755 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 326/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 49.5102 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 327/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8962 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 328/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.9985 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 329/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2286 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 330/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.5851 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 331/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.7358 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 332/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.3682 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 333/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.4469 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 334/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7033 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 335/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.5986 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 336/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.6334 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 337/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.4417 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 338/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8001 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 339/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.9066 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 340/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.0911 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 341/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.5948 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 342/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.4524 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 343/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.4655 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 344/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.1376 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 345/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.1262 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 346/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.0932 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 347/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.2357 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 348/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.3045 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 349/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.0661 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 350/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.0846 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 351/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.1283 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 352/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8281 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 353/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.9364 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 354/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.4833 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 355/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.4077 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 356/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.7617 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 357/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.4158 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 358/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.6691 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 359/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.9257 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 360/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.3471 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 361/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.9821 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 362/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.0115 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 363/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.2039 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 364/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7258 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 365/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.3722 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 366/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 56.2266 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 367/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.9051 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 368/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.9096 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 369/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.6489 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 370/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.8830 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 371/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.3749 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 372/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.0751 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 373/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.2828 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 374/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.2439 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 375/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.2283 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 376/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.9785 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 377/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.9632 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 378/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.4359 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 379/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.4829 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 380/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.5245 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 381/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.8970 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 382/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.8448 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 383/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.0527 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 384/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.3904 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 385/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.8183 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 386/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.6872 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 387/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.0579 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 388/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 58.2854 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 389/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.5670 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 390/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.1021 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 391/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.9098 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 392/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 54.3154 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 393/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 52.7683 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 394/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.0394 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 395/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.1572 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 396/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 57.8127 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 397/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 50.9481 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 398/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 53.6293 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 399/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 55.4761 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n", - "Epoch 400/400\n", - "3/3 [==============================] - 4s 1s/step - loss: 51.5747 - sparse_categorical_crossentropy: 54.1402 - accuracy: 0.3544 - val_loss: 56.7288 - val_sparse_categorical_crossentropy: 56.7288 - val_accuracy: 0.3365\n" - ] - }, - { - "data": { - "text/html": [ - "Waiting for W&B process to finish... (success)." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a559ed7924374c22aee6ffc22f7be69b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='0.940 MB of 0.940 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "

Run history:


accuracy▄▁▅▅████████████████████████████████████
epoch▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
loss█▆▃▃▁▂▂▂▂▂▁▂▁▃▂▁▂▂▂▂▁▂▂▂▂▁▂▁▂▂▃▁▁▂▂▂▂▃▃▁
sparse_categorical_crossentropy█▇▃▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
val_accuracy▁▆▆█▃▄▃▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄
val_loss█▇▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
val_sparse_categorical_crossentropy█▇▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁

Run summary:


GFLOPS0.00024
accuracy0.35438
best_epoch55
best_val_sparse_categorical_crossentropy56.67062
epoch399
loss51.57471
sparse_categorical_crossentropy54.14021
val_accuracy0.3365
val_loss56.72884
val_sparse_categorical_crossentropy56.72884

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Synced spring-darkness-40: https://wandb.ai/eltoncn/SygilDetector/runs/233p0mdm
Synced 5 W&B file(s), 1 media file(s), 0 artifact file(s) and 1 other file(s)" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Find logs at: .\\wandb\\run-20221016_202310-233p0mdm\\logs" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from evolvepy.integrations.tf_keras import EvolutionaryModel\n", "\n", @@ -3581,7 +1424,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.5 (tags/v3.8.5:580fbb0, Jul 20 2020, 15:57:54) [MSC v.1924 64 bit (AMD64)]" }, "orig_nbformat": 4, "vscode": { diff --git a/Dev/SygilDetector2.ipynb b/Dev/SygilDetector2.ipynb index 8257a19..7be3d27 100644 --- a/Dev/SygilDetector2.ipynb +++ b/Dev/SygilDetector2.ipynb @@ -1,8 +1,18 @@ { "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sigil Detection\n", + "\n", + "This notebook generates synthetic data and trains models for sigil detection (detection of the shape the player draws on the screen)" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -26,37 +36,19 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33meltoncn\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "wandb.login()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# Data generation" + "## Data generation" ] }, { @@ -68,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -235,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -265,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -302,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -357,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -428,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -438,15 +430,16 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Separation" + "### Separation" ] }, { "cell_type": "code", - "execution_count": 146, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -458,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -478,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -494,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -503,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -518,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -529,34 +522,24 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "n_class" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# NN" + "## Model" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -599,2132 +582,9 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "wandb version 0.13.4 is available! To upgrade, please run:\n", - " $ pip install wandb --upgrade" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Tracking run with wandb version 0.12.21" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Run data is saved locally in d:\\Github\\JogosTeP\\Dev\\wandb\\run-20221017_184647-2u9kpjot" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Syncing run electric-oath-48 to Weights & Biases (docs)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/1000\n", - "4/4 [==============================] - 1s 68ms/step - loss: 6.4737 - sparse_categorical_crossentropy: 1.6531 - accuracy: 0.2018 - val_loss: 6.0493 - val_sparse_categorical_crossentropy: 1.5995 - val_accuracy: 0.2282\n", - "Epoch 2/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 5.8897 - sparse_categorical_crossentropy: 1.6107 - accuracy: 0.2198 - val_loss: 5.5021 - val_sparse_categorical_crossentropy: 1.5750 - val_accuracy: 0.2444\n", - "Epoch 3/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 5.3565 - sparse_categorical_crossentropy: 1.5877 - accuracy: 0.2489 - val_loss: 5.0048 - val_sparse_categorical_crossentropy: 1.5607 - val_accuracy: 0.4625\n", - "Epoch 4/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 4.8761 - sparse_categorical_crossentropy: 1.5768 - accuracy: 0.2877 - val_loss: 4.5534 - val_sparse_categorical_crossentropy: 1.5494 - val_accuracy: 0.4679\n", - "Epoch 5/1000\n", - "4/4 [==============================] - 0s 26ms/step - loss: 4.4361 - sparse_categorical_crossentropy: 1.5627 - accuracy: 0.3201 - val_loss: 4.1448 - val_sparse_categorical_crossentropy: 1.5366 - val_accuracy: 0.4789\n", - "Epoch 6/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 4.0393 - sparse_categorical_crossentropy: 1.5473 - accuracy: 0.3423 - val_loss: 3.7772 - val_sparse_categorical_crossentropy: 1.5205 - val_accuracy: 0.4539\n", - "Epoch 7/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 3.6848 - sparse_categorical_crossentropy: 1.5307 - accuracy: 0.3746 - val_loss: 3.4450 - val_sparse_categorical_crossentropy: 1.4972 - val_accuracy: 0.4839\n", - "Epoch 8/1000\n", - "4/4 [==============================] - 0s 21ms/step - loss: 3.3680 - sparse_categorical_crossentropy: 1.5093 - accuracy: 0.3925 - val_loss: 3.1471 - val_sparse_categorical_crossentropy: 1.4671 - val_accuracy: 0.5183\n", - "Epoch 9/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 3.0806 - sparse_categorical_crossentropy: 1.4769 - accuracy: 0.4129 - val_loss: 2.8775 - val_sparse_categorical_crossentropy: 1.4263 - val_accuracy: 0.5311\n", - "Epoch 10/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 2.8239 - sparse_categorical_crossentropy: 1.4373 - accuracy: 0.4205 - val_loss: 2.6311 - val_sparse_categorical_crossentropy: 1.3728 - val_accuracy: 0.5369\n", - "Epoch 11/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 2.5894 - sparse_categorical_crossentropy: 1.3850 - accuracy: 0.4283 - val_loss: 2.4103 - val_sparse_categorical_crossentropy: 1.3125 - val_accuracy: 0.5239\n", - "Epoch 12/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 2.3797 - sparse_categorical_crossentropy: 1.3265 - accuracy: 0.4373 - val_loss: 2.2151 - val_sparse_categorical_crossentropy: 1.2501 - val_accuracy: 0.5315\n", - "Epoch 13/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 2.1972 - sparse_categorical_crossentropy: 1.2693 - accuracy: 0.4461 - val_loss: 2.0533 - val_sparse_categorical_crossentropy: 1.1993 - val_accuracy: 0.5483\n", - "Epoch 14/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 2.0442 - sparse_categorical_crossentropy: 1.2218 - accuracy: 0.4611 - val_loss: 1.9174 - val_sparse_categorical_crossentropy: 1.1588 - val_accuracy: 0.5471\n", - "Epoch 15/1000\n", - "4/4 [==============================] - 0s 22ms/step - loss: 1.9157 - sparse_categorical_crossentropy: 1.1849 - accuracy: 0.4756 - val_loss: 1.8026 - val_sparse_categorical_crossentropy: 1.1285 - val_accuracy: 0.5817\n", - "Epoch 16/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 1.8058 - sparse_categorical_crossentropy: 1.1568 - accuracy: 0.4920 - val_loss: 1.7056 - val_sparse_categorical_crossentropy: 1.1076 - val_accuracy: 0.5955\n", - "Epoch 17/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 1.7150 - sparse_categorical_crossentropy: 1.1393 - accuracy: 0.5035 - val_loss: 1.6205 - val_sparse_categorical_crossentropy: 1.0899 - val_accuracy: 0.5849\n", - "Epoch 18/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 1.6326 - sparse_categorical_crossentropy: 1.1218 - accuracy: 0.5127 - val_loss: 1.5475 - val_sparse_categorical_crossentropy: 1.0764 - val_accuracy: 0.5965\n", - "Epoch 19/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 1.5618 - sparse_categorical_crossentropy: 1.1077 - accuracy: 0.5260 - val_loss: 1.4863 - val_sparse_categorical_crossentropy: 1.0661 - val_accuracy: 0.5801\n", - "Epoch 20/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 1.5035 - sparse_categorical_crossentropy: 1.0975 - accuracy: 0.5238 - val_loss: 1.4325 - val_sparse_categorical_crossentropy: 1.0546 - val_accuracy: 0.5941\n", - "Epoch 21/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 1.4516 - sparse_categorical_crossentropy: 1.0854 - accuracy: 0.5417 - val_loss: 1.3866 - val_sparse_categorical_crossentropy: 1.0434 - val_accuracy: 0.6261\n", - "Epoch 22/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 1.4085 - sparse_categorical_crossentropy: 1.0745 - accuracy: 0.5437 - val_loss: 1.3476 - val_sparse_categorical_crossentropy: 1.0319 - val_accuracy: 0.6161\n", - "Epoch 23/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 1.3696 - sparse_categorical_crossentropy: 1.0613 - accuracy: 0.5577 - val_loss: 1.3145 - val_sparse_categorical_crossentropy: 1.0206 - val_accuracy: 0.6211\n", - "Epoch 24/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 1.3398 - sparse_categorical_crossentropy: 1.0517 - accuracy: 0.5605 - val_loss: 1.2851 - val_sparse_categorical_crossentropy: 1.0081 - val_accuracy: 0.6363\n", - "Epoch 25/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 1.3120 - sparse_categorical_crossentropy: 1.0394 - accuracy: 0.5593 - val_loss: 1.2602 - val_sparse_categorical_crossentropy: 0.9964 - val_accuracy: 0.6399\n", - "Epoch 26/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.2865 - sparse_categorical_crossentropy: 1.0262 - accuracy: 0.5709 - val_loss: 1.2362 - val_sparse_categorical_crossentropy: 0.9828 - val_accuracy: 0.6581\n", - "Epoch 27/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.2607 - sparse_categorical_crossentropy: 1.0099 - accuracy: 0.5906 - val_loss: 1.2150 - val_sparse_categorical_crossentropy: 0.9691 - val_accuracy: 0.6691\n", - "Epoch 28/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.2418 - sparse_categorical_crossentropy: 0.9980 - accuracy: 0.5895 - val_loss: 1.1999 - val_sparse_categorical_crossentropy: 0.9602 - val_accuracy: 0.6589\n", - "Epoch 29/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.2258 - sparse_categorical_crossentropy: 0.9877 - accuracy: 0.5970 - val_loss: 1.1756 - val_sparse_categorical_crossentropy: 0.9407 - val_accuracy: 0.6811\n", - "Epoch 30/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 1.2044 - sparse_categorical_crossentropy: 0.9705 - accuracy: 0.6026 - val_loss: 1.1637 - val_sparse_categorical_crossentropy: 0.9321 - val_accuracy: 0.6567\n", - "Epoch 31/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 1.1915 - sparse_categorical_crossentropy: 0.9606 - accuracy: 0.6101 - val_loss: 1.1510 - val_sparse_categorical_crossentropy: 0.9210 - val_accuracy: 0.6789\n", - "Epoch 32/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 1.1790 - sparse_categorical_crossentropy: 0.9495 - accuracy: 0.6181 - val_loss: 1.1345 - val_sparse_categorical_crossentropy: 0.9057 - val_accuracy: 0.6909\n", - "Epoch 33/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.1640 - sparse_categorical_crossentropy: 0.9353 - accuracy: 0.6232 - val_loss: 1.1195 - val_sparse_categorical_crossentropy: 0.8907 - val_accuracy: 0.6907\n", - "Epoch 34/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.1516 - sparse_categorical_crossentropy: 0.9227 - accuracy: 0.6314 - val_loss: 1.1036 - val_sparse_categorical_crossentropy: 0.8745 - val_accuracy: 0.6939\n", - "Epoch 35/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.1382 - sparse_categorical_crossentropy: 0.9085 - accuracy: 0.6383 - val_loss: 1.0909 - val_sparse_categorical_crossentropy: 0.8600 - val_accuracy: 0.7129\n", - "Epoch 36/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 1.1251 - sparse_categorical_crossentropy: 0.8936 - accuracy: 0.6483 - val_loss: 1.0893 - val_sparse_categorical_crossentropy: 0.8559 - val_accuracy: 0.6901\n", - "Epoch 37/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.1165 - sparse_categorical_crossentropy: 0.8825 - accuracy: 0.6518 - val_loss: 1.0666 - val_sparse_categorical_crossentropy: 0.8309 - val_accuracy: 0.7071\n", - "Epoch 38/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.1014 - sparse_categorical_crossentropy: 0.8646 - accuracy: 0.6602 - val_loss: 1.0523 - val_sparse_categorical_crossentropy: 0.8134 - val_accuracy: 0.7035\n", - "Epoch 39/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.0838 - sparse_categorical_crossentropy: 0.8438 - accuracy: 0.6695 - val_loss: 1.0407 - val_sparse_categorical_crossentropy: 0.7982 - val_accuracy: 0.7065\n", - "Epoch 40/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.0737 - sparse_categorical_crossentropy: 0.8298 - accuracy: 0.6743 - val_loss: 1.0256 - val_sparse_categorical_crossentropy: 0.7786 - val_accuracy: 0.7225\n", - "Epoch 41/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.0596 - sparse_categorical_crossentropy: 0.8113 - accuracy: 0.6830 - val_loss: 1.0115 - val_sparse_categorical_crossentropy: 0.7600 - val_accuracy: 0.7245\n", - "Epoch 42/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.0495 - sparse_categorical_crossentropy: 0.7965 - accuracy: 0.6874 - val_loss: 1.0095 - val_sparse_categorical_crossentropy: 0.7537 - val_accuracy: 0.7181\n", - "Epoch 43/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 1.0378 - sparse_categorical_crossentropy: 0.7806 - accuracy: 0.6942 - val_loss: 0.9928 - val_sparse_categorical_crossentropy: 0.7334 - val_accuracy: 0.7217\n", - "Epoch 44/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 1.0244 - sparse_categorical_crossentropy: 0.7638 - accuracy: 0.6955 - val_loss: 0.9720 - val_sparse_categorical_crossentropy: 0.7091 - val_accuracy: 0.7459\n", - "Epoch 45/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 1.0128 - sparse_categorical_crossentropy: 0.7488 - accuracy: 0.7026 - val_loss: 0.9588 - val_sparse_categorical_crossentropy: 0.6927 - val_accuracy: 0.7520\n", - "Epoch 46/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.9996 - sparse_categorical_crossentropy: 0.7324 - accuracy: 0.7029 - val_loss: 0.9511 - val_sparse_categorical_crossentropy: 0.6818 - val_accuracy: 0.7620\n", - "Epoch 47/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.9895 - sparse_categorical_crossentropy: 0.7194 - accuracy: 0.7145 - val_loss: 0.9436 - val_sparse_categorical_crossentropy: 0.6717 - val_accuracy: 0.7485\n", - "Epoch 48/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.9854 - sparse_categorical_crossentropy: 0.7126 - accuracy: 0.7123 - val_loss: 0.9305 - val_sparse_categorical_crossentropy: 0.6564 - val_accuracy: 0.7441\n", - "Epoch 49/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.9692 - sparse_categorical_crossentropy: 0.6943 - accuracy: 0.7220 - val_loss: 0.9273 - val_sparse_categorical_crossentropy: 0.6513 - val_accuracy: 0.7417\n", - "Epoch 50/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.9626 - sparse_categorical_crossentropy: 0.6860 - accuracy: 0.7227 - val_loss: 0.9131 - val_sparse_categorical_crossentropy: 0.6351 - val_accuracy: 0.7620\n", - "Epoch 51/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.9549 - sparse_categorical_crossentropy: 0.6766 - accuracy: 0.7239 - val_loss: 0.9068 - val_sparse_categorical_crossentropy: 0.6281 - val_accuracy: 0.7626\n", - "Epoch 52/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.9523 - sparse_categorical_crossentropy: 0.6731 - accuracy: 0.7206 - val_loss: 0.8985 - val_sparse_categorical_crossentropy: 0.6186 - val_accuracy: 0.7578\n", - "Epoch 53/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.9394 - sparse_categorical_crossentropy: 0.6594 - accuracy: 0.7300 - val_loss: 0.8919 - val_sparse_categorical_crossentropy: 0.6122 - val_accuracy: 0.7449\n", - "Epoch 54/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.9345 - sparse_categorical_crossentropy: 0.6544 - accuracy: 0.7297 - val_loss: 0.8835 - val_sparse_categorical_crossentropy: 0.6029 - val_accuracy: 0.7624\n", - "Epoch 55/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.9295 - sparse_categorical_crossentropy: 0.6491 - accuracy: 0.7308 - val_loss: 0.8815 - val_sparse_categorical_crossentropy: 0.6010 - val_accuracy: 0.7700\n", - "Epoch 56/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.9224 - sparse_categorical_crossentropy: 0.6421 - accuracy: 0.7327 - val_loss: 0.8701 - val_sparse_categorical_crossentropy: 0.5900 - val_accuracy: 0.7722\n", - "Epoch 57/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.9194 - sparse_categorical_crossentropy: 0.6393 - accuracy: 0.7319 - val_loss: 0.8662 - val_sparse_categorical_crossentropy: 0.5862 - val_accuracy: 0.7674\n", - "Epoch 58/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.9121 - sparse_categorical_crossentropy: 0.6322 - accuracy: 0.7370 - val_loss: 0.8585 - val_sparse_categorical_crossentropy: 0.5786 - val_accuracy: 0.7670\n", - "Epoch 59/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.9061 - sparse_categorical_crossentropy: 0.6263 - accuracy: 0.7366 - val_loss: 0.8599 - val_sparse_categorical_crossentropy: 0.5805 - val_accuracy: 0.7678\n", - "Epoch 60/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.9051 - sparse_categorical_crossentropy: 0.6258 - accuracy: 0.7383 - val_loss: 0.8601 - val_sparse_categorical_crossentropy: 0.5810 - val_accuracy: 0.7688\n", - "Epoch 61/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.9031 - sparse_categorical_crossentropy: 0.6243 - accuracy: 0.7377 - val_loss: 0.8563 - val_sparse_categorical_crossentropy: 0.5776 - val_accuracy: 0.7752\n", - "Epoch 62/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.9003 - sparse_categorical_crossentropy: 0.6221 - accuracy: 0.7352 - val_loss: 0.8444 - val_sparse_categorical_crossentropy: 0.5669 - val_accuracy: 0.7668\n", - "Epoch 63/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8905 - sparse_categorical_crossentropy: 0.6129 - accuracy: 0.7393 - val_loss: 0.8397 - val_sparse_categorical_crossentropy: 0.5620 - val_accuracy: 0.7718\n", - "Epoch 64/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8825 - sparse_categorical_crossentropy: 0.6048 - accuracy: 0.7450 - val_loss: 0.8375 - val_sparse_categorical_crossentropy: 0.5597 - val_accuracy: 0.7828\n", - "Epoch 65/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8838 - sparse_categorical_crossentropy: 0.6063 - accuracy: 0.7393 - val_loss: 0.8353 - val_sparse_categorical_crossentropy: 0.5577 - val_accuracy: 0.7814\n", - "Epoch 66/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8816 - sparse_categorical_crossentropy: 0.6047 - accuracy: 0.7430 - val_loss: 0.8280 - val_sparse_categorical_crossentropy: 0.5517 - val_accuracy: 0.7830\n", - "Epoch 67/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.8735 - sparse_categorical_crossentropy: 0.5975 - accuracy: 0.7459 - val_loss: 0.8226 - val_sparse_categorical_crossentropy: 0.5467 - val_accuracy: 0.7874\n", - "Epoch 68/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8684 - sparse_categorical_crossentropy: 0.5928 - accuracy: 0.7482 - val_loss: 0.8184 - val_sparse_categorical_crossentropy: 0.5438 - val_accuracy: 0.7844\n", - "Epoch 69/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8661 - sparse_categorical_crossentropy: 0.5916 - accuracy: 0.7476 - val_loss: 0.8165 - val_sparse_categorical_crossentropy: 0.5419 - val_accuracy: 0.7784\n", - "Epoch 70/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8614 - sparse_categorical_crossentropy: 0.5870 - accuracy: 0.7494 - val_loss: 0.8140 - val_sparse_categorical_crossentropy: 0.5407 - val_accuracy: 0.7724\n", - "Epoch 71/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8604 - sparse_categorical_crossentropy: 0.5870 - accuracy: 0.7528 - val_loss: 0.8149 - val_sparse_categorical_crossentropy: 0.5416 - val_accuracy: 0.7658\n", - "Epoch 72/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8556 - sparse_categorical_crossentropy: 0.5827 - accuracy: 0.7515 - val_loss: 0.8117 - val_sparse_categorical_crossentropy: 0.5395 - val_accuracy: 0.7800\n", - "Epoch 73/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8540 - sparse_categorical_crossentropy: 0.5822 - accuracy: 0.7497 - val_loss: 0.8114 - val_sparse_categorical_crossentropy: 0.5402 - val_accuracy: 0.7876\n", - "Epoch 74/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8515 - sparse_categorical_crossentropy: 0.5806 - accuracy: 0.7474 - val_loss: 0.8093 - val_sparse_categorical_crossentropy: 0.5386 - val_accuracy: 0.7910\n", - "Epoch 75/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8527 - sparse_categorical_crossentropy: 0.5823 - accuracy: 0.7517 - val_loss: 0.8031 - val_sparse_categorical_crossentropy: 0.5328 - val_accuracy: 0.7950\n", - "Epoch 76/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8467 - sparse_categorical_crossentropy: 0.5767 - accuracy: 0.7503 - val_loss: 0.7954 - val_sparse_categorical_crossentropy: 0.5257 - val_accuracy: 0.7980\n", - "Epoch 77/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.8440 - sparse_categorical_crossentropy: 0.5747 - accuracy: 0.7530 - val_loss: 0.7967 - val_sparse_categorical_crossentropy: 0.5277 - val_accuracy: 0.7944\n", - "Epoch 78/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.8428 - sparse_categorical_crossentropy: 0.5741 - accuracy: 0.7550 - val_loss: 0.7941 - val_sparse_categorical_crossentropy: 0.5260 - val_accuracy: 0.7918\n", - "Epoch 79/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8364 - sparse_categorical_crossentropy: 0.5687 - accuracy: 0.7547 - val_loss: 0.7956 - val_sparse_categorical_crossentropy: 0.5286 - val_accuracy: 0.7894\n", - "Epoch 80/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.8370 - sparse_categorical_crossentropy: 0.5704 - accuracy: 0.7530 - val_loss: 0.7895 - val_sparse_categorical_crossentropy: 0.5238 - val_accuracy: 0.7888\n", - "Epoch 81/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.8311 - sparse_categorical_crossentropy: 0.5655 - accuracy: 0.7545 - val_loss: 0.7908 - val_sparse_categorical_crossentropy: 0.5251 - val_accuracy: 0.7966\n", - "Epoch 82/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.8330 - sparse_categorical_crossentropy: 0.5678 - accuracy: 0.7545 - val_loss: 0.7950 - val_sparse_categorical_crossentropy: 0.5305 - val_accuracy: 0.7942\n", - "Epoch 83/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8345 - sparse_categorical_crossentropy: 0.5699 - accuracy: 0.7518 - val_loss: 0.7851 - val_sparse_categorical_crossentropy: 0.5213 - val_accuracy: 0.7924\n", - "Epoch 84/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.8285 - sparse_categorical_crossentropy: 0.5648 - accuracy: 0.7571 - val_loss: 0.7821 - val_sparse_categorical_crossentropy: 0.5187 - val_accuracy: 0.7840\n", - "Epoch 85/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.8284 - sparse_categorical_crossentropy: 0.5654 - accuracy: 0.7546 - val_loss: 0.7779 - val_sparse_categorical_crossentropy: 0.5158 - val_accuracy: 0.7792\n", - "Epoch 86/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8195 - sparse_categorical_crossentropy: 0.5573 - accuracy: 0.7616 - val_loss: 0.7722 - val_sparse_categorical_crossentropy: 0.5107 - val_accuracy: 0.7958\n", - "Epoch 87/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.8192 - sparse_categorical_crossentropy: 0.5580 - accuracy: 0.7612 - val_loss: 0.7745 - val_sparse_categorical_crossentropy: 0.5130 - val_accuracy: 0.8018\n", - "Epoch 88/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8172 - sparse_categorical_crossentropy: 0.5560 - accuracy: 0.7621 - val_loss: 0.7687 - val_sparse_categorical_crossentropy: 0.5083 - val_accuracy: 0.8060\n", - "Epoch 89/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8151 - sparse_categorical_crossentropy: 0.5544 - accuracy: 0.7613 - val_loss: 0.7648 - val_sparse_categorical_crossentropy: 0.5037 - val_accuracy: 0.7986\n", - "Epoch 90/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8101 - sparse_categorical_crossentropy: 0.5494 - accuracy: 0.7637 - val_loss: 0.7673 - val_sparse_categorical_crossentropy: 0.5073 - val_accuracy: 0.8012\n", - "Epoch 91/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8117 - sparse_categorical_crossentropy: 0.5518 - accuracy: 0.7627 - val_loss: 0.7708 - val_sparse_categorical_crossentropy: 0.5111 - val_accuracy: 0.7800\n", - "Epoch 92/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8109 - sparse_categorical_crossentropy: 0.5514 - accuracy: 0.7610 - val_loss: 0.7729 - val_sparse_categorical_crossentropy: 0.5139 - val_accuracy: 0.7814\n", - "Epoch 93/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8096 - sparse_categorical_crossentropy: 0.5509 - accuracy: 0.7667 - val_loss: 0.7642 - val_sparse_categorical_crossentropy: 0.5067 - val_accuracy: 0.7922\n", - "Epoch 94/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8075 - sparse_categorical_crossentropy: 0.5502 - accuracy: 0.7689 - val_loss: 0.7652 - val_sparse_categorical_crossentropy: 0.5081 - val_accuracy: 0.7970\n", - "Epoch 95/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8020 - sparse_categorical_crossentropy: 0.5455 - accuracy: 0.7660 - val_loss: 0.7601 - val_sparse_categorical_crossentropy: 0.5039 - val_accuracy: 0.8064\n", - "Epoch 96/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.8090 - sparse_categorical_crossentropy: 0.5531 - accuracy: 0.7627 - val_loss: 0.7580 - val_sparse_categorical_crossentropy: 0.5030 - val_accuracy: 0.7982\n", - "Epoch 97/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7989 - sparse_categorical_crossentropy: 0.5440 - accuracy: 0.7655 - val_loss: 0.7524 - val_sparse_categorical_crossentropy: 0.4981 - val_accuracy: 0.8078\n", - "Epoch 98/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.8003 - sparse_categorical_crossentropy: 0.5462 - accuracy: 0.7663 - val_loss: 0.7622 - val_sparse_categorical_crossentropy: 0.5083 - val_accuracy: 0.7926\n", - "Epoch 99/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.8011 - sparse_categorical_crossentropy: 0.5474 - accuracy: 0.7669 - val_loss: 0.7580 - val_sparse_categorical_crossentropy: 0.5050 - val_accuracy: 0.8014\n", - "Epoch 100/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7941 - sparse_categorical_crossentropy: 0.5413 - accuracy: 0.7655 - val_loss: 0.7559 - val_sparse_categorical_crossentropy: 0.5031 - val_accuracy: 0.7958\n", - "Epoch 101/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.7980 - sparse_categorical_crossentropy: 0.5457 - accuracy: 0.7673 - val_loss: 0.7454 - val_sparse_categorical_crossentropy: 0.4933 - val_accuracy: 0.8150\n", - "Epoch 102/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7870 - sparse_categorical_crossentropy: 0.5349 - accuracy: 0.7740 - val_loss: 0.7440 - val_sparse_categorical_crossentropy: 0.4923 - val_accuracy: 0.8160\n", - "Epoch 103/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7881 - sparse_categorical_crossentropy: 0.5365 - accuracy: 0.7726 - val_loss: 0.7408 - val_sparse_categorical_crossentropy: 0.4891 - val_accuracy: 0.8128\n", - "Epoch 104/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7862 - sparse_categorical_crossentropy: 0.5348 - accuracy: 0.7690 - val_loss: 0.7402 - val_sparse_categorical_crossentropy: 0.4893 - val_accuracy: 0.8148\n", - "Epoch 105/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7813 - sparse_categorical_crossentropy: 0.5304 - accuracy: 0.7717 - val_loss: 0.7422 - val_sparse_categorical_crossentropy: 0.4915 - val_accuracy: 0.8134\n", - "Epoch 106/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7810 - sparse_categorical_crossentropy: 0.5307 - accuracy: 0.7735 - val_loss: 0.7389 - val_sparse_categorical_crossentropy: 0.4893 - val_accuracy: 0.8128\n", - "Epoch 107/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7846 - sparse_categorical_crossentropy: 0.5351 - accuracy: 0.7714 - val_loss: 0.7362 - val_sparse_categorical_crossentropy: 0.4878 - val_accuracy: 0.8140\n", - "Epoch 108/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.7773 - sparse_categorical_crossentropy: 0.5291 - accuracy: 0.7750 - val_loss: 0.7367 - val_sparse_categorical_crossentropy: 0.4890 - val_accuracy: 0.8090\n", - "Epoch 109/1000\n", - "4/4 [==============================] - 0s 21ms/step - loss: 0.7765 - sparse_categorical_crossentropy: 0.5290 - accuracy: 0.7739 - val_loss: 0.7357 - val_sparse_categorical_crossentropy: 0.4884 - val_accuracy: 0.8030\n", - "Epoch 110/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7766 - sparse_categorical_crossentropy: 0.5294 - accuracy: 0.7763 - val_loss: 0.7300 - val_sparse_categorical_crossentropy: 0.4835 - val_accuracy: 0.8156\n", - "Epoch 111/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.7777 - sparse_categorical_crossentropy: 0.5313 - accuracy: 0.7762 - val_loss: 0.7281 - val_sparse_categorical_crossentropy: 0.4816 - val_accuracy: 0.8258\n", - "Epoch 112/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7742 - sparse_categorical_crossentropy: 0.5280 - accuracy: 0.7806 - val_loss: 0.7306 - val_sparse_categorical_crossentropy: 0.4848 - val_accuracy: 0.8166\n", - "Epoch 113/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.7730 - sparse_categorical_crossentropy: 0.5274 - accuracy: 0.7778 - val_loss: 0.7293 - val_sparse_categorical_crossentropy: 0.4844 - val_accuracy: 0.8124\n", - "Epoch 114/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7706 - sparse_categorical_crossentropy: 0.5258 - accuracy: 0.7764 - val_loss: 0.7295 - val_sparse_categorical_crossentropy: 0.4851 - val_accuracy: 0.8100\n", - "Epoch 115/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7700 - sparse_categorical_crossentropy: 0.5260 - accuracy: 0.7774 - val_loss: 0.7309 - val_sparse_categorical_crossentropy: 0.4871 - val_accuracy: 0.8044\n", - "Epoch 116/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7730 - sparse_categorical_crossentropy: 0.5293 - accuracy: 0.7699 - val_loss: 0.7292 - val_sparse_categorical_crossentropy: 0.4854 - val_accuracy: 0.8080\n", - "Epoch 117/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.7696 - sparse_categorical_crossentropy: 0.5258 - accuracy: 0.7773 - val_loss: 0.7237 - val_sparse_categorical_crossentropy: 0.4804 - val_accuracy: 0.8248\n", - "Epoch 118/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7649 - sparse_categorical_crossentropy: 0.5220 - accuracy: 0.7781 - val_loss: 0.7220 - val_sparse_categorical_crossentropy: 0.4800 - val_accuracy: 0.8190\n", - "Epoch 119/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7656 - sparse_categorical_crossentropy: 0.5239 - accuracy: 0.7764 - val_loss: 0.7239 - val_sparse_categorical_crossentropy: 0.4822 - val_accuracy: 0.8194\n", - "Epoch 120/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7634 - sparse_categorical_crossentropy: 0.5224 - accuracy: 0.7803 - val_loss: 0.7228 - val_sparse_categorical_crossentropy: 0.4824 - val_accuracy: 0.8282\n", - "Epoch 121/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7665 - sparse_categorical_crossentropy: 0.5260 - accuracy: 0.7777 - val_loss: 0.7206 - val_sparse_categorical_crossentropy: 0.4804 - val_accuracy: 0.8138\n", - "Epoch 122/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7648 - sparse_categorical_crossentropy: 0.5245 - accuracy: 0.7763 - val_loss: 0.7270 - val_sparse_categorical_crossentropy: 0.4865 - val_accuracy: 0.8060\n", - "Epoch 123/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.7699 - sparse_categorical_crossentropy: 0.5296 - accuracy: 0.7746 - val_loss: 0.7139 - val_sparse_categorical_crossentropy: 0.4744 - val_accuracy: 0.8280\n", - "Epoch 124/1000\n", - "4/4 [==============================] - 0s 23ms/step - loss: 0.7611 - sparse_categorical_crossentropy: 0.5217 - accuracy: 0.7802 - val_loss: 0.7152 - val_sparse_categorical_crossentropy: 0.4762 - val_accuracy: 0.8212\n", - "Epoch 125/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.7615 - sparse_categorical_crossentropy: 0.5226 - accuracy: 0.7830 - val_loss: 0.7252 - val_sparse_categorical_crossentropy: 0.4864 - val_accuracy: 0.7982\n", - "Epoch 126/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7628 - sparse_categorical_crossentropy: 0.5243 - accuracy: 0.7786 - val_loss: 0.7227 - val_sparse_categorical_crossentropy: 0.4843 - val_accuracy: 0.8062\n", - "Epoch 127/1000\n", - "4/4 [==============================] - 0s 24ms/step - loss: 0.7624 - sparse_categorical_crossentropy: 0.5238 - accuracy: 0.7799 - val_loss: 0.7183 - val_sparse_categorical_crossentropy: 0.4799 - val_accuracy: 0.8116\n", - "Epoch 128/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.7598 - sparse_categorical_crossentropy: 0.5213 - accuracy: 0.7827 - val_loss: 0.7162 - val_sparse_categorical_crossentropy: 0.4782 - val_accuracy: 0.8226\n", - "Epoch 129/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7536 - sparse_categorical_crossentropy: 0.5153 - accuracy: 0.7859 - val_loss: 0.7102 - val_sparse_categorical_crossentropy: 0.4721 - val_accuracy: 0.8250\n", - "Epoch 130/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.7533 - sparse_categorical_crossentropy: 0.5155 - accuracy: 0.7799 - val_loss: 0.7104 - val_sparse_categorical_crossentropy: 0.4728 - val_accuracy: 0.8170\n", - "Epoch 131/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7498 - sparse_categorical_crossentropy: 0.5123 - accuracy: 0.7819 - val_loss: 0.7135 - val_sparse_categorical_crossentropy: 0.4763 - val_accuracy: 0.8222\n", - "Epoch 132/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7523 - sparse_categorical_crossentropy: 0.5153 - accuracy: 0.7792 - val_loss: 0.7063 - val_sparse_categorical_crossentropy: 0.4689 - val_accuracy: 0.8224\n", - "Epoch 133/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.7511 - sparse_categorical_crossentropy: 0.5141 - accuracy: 0.7840 - val_loss: 0.7061 - val_sparse_categorical_crossentropy: 0.4697 - val_accuracy: 0.8324\n", - "Epoch 134/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7488 - sparse_categorical_crossentropy: 0.5122 - accuracy: 0.7822 - val_loss: 0.7086 - val_sparse_categorical_crossentropy: 0.4733 - val_accuracy: 0.8198\n", - "Epoch 135/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.7490 - sparse_categorical_crossentropy: 0.5139 - accuracy: 0.7875 - val_loss: 0.7085 - val_sparse_categorical_crossentropy: 0.4728 - val_accuracy: 0.8310\n", - "Epoch 136/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7457 - sparse_categorical_crossentropy: 0.5108 - accuracy: 0.7855 - val_loss: 0.7078 - val_sparse_categorical_crossentropy: 0.4735 - val_accuracy: 0.8244\n", - "Epoch 137/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7431 - sparse_categorical_crossentropy: 0.5085 - accuracy: 0.7875 - val_loss: 0.7044 - val_sparse_categorical_crossentropy: 0.4695 - val_accuracy: 0.8330\n", - "Epoch 138/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.7387 - sparse_categorical_crossentropy: 0.5041 - accuracy: 0.7925 - val_loss: 0.6977 - val_sparse_categorical_crossentropy: 0.4630 - val_accuracy: 0.8382\n", - "Epoch 139/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.7437 - sparse_categorical_crossentropy: 0.5091 - accuracy: 0.7943 - val_loss: 0.6958 - val_sparse_categorical_crossentropy: 0.4619 - val_accuracy: 0.8370\n", - "Epoch 140/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7373 - sparse_categorical_crossentropy: 0.5033 - accuracy: 0.7898 - val_loss: 0.6983 - val_sparse_categorical_crossentropy: 0.4651 - val_accuracy: 0.8244\n", - "Epoch 141/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7416 - sparse_categorical_crossentropy: 0.5085 - accuracy: 0.7867 - val_loss: 0.6964 - val_sparse_categorical_crossentropy: 0.4635 - val_accuracy: 0.8270\n", - "Epoch 142/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7411 - sparse_categorical_crossentropy: 0.5085 - accuracy: 0.7896 - val_loss: 0.6975 - val_sparse_categorical_crossentropy: 0.4658 - val_accuracy: 0.8302\n", - "Epoch 143/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7372 - sparse_categorical_crossentropy: 0.5051 - accuracy: 0.7915 - val_loss: 0.6931 - val_sparse_categorical_crossentropy: 0.4607 - val_accuracy: 0.8304\n", - "Epoch 144/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7353 - sparse_categorical_crossentropy: 0.5030 - accuracy: 0.7886 - val_loss: 0.6936 - val_sparse_categorical_crossentropy: 0.4619 - val_accuracy: 0.8362\n", - "Epoch 145/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7325 - sparse_categorical_crossentropy: 0.5008 - accuracy: 0.7941 - val_loss: 0.6926 - val_sparse_categorical_crossentropy: 0.4611 - val_accuracy: 0.8332\n", - "Epoch 146/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7327 - sparse_categorical_crossentropy: 0.5014 - accuracy: 0.7954 - val_loss: 0.6980 - val_sparse_categorical_crossentropy: 0.4671 - val_accuracy: 0.8244\n", - "Epoch 147/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7351 - sparse_categorical_crossentropy: 0.5040 - accuracy: 0.7896 - val_loss: 0.6981 - val_sparse_categorical_crossentropy: 0.4671 - val_accuracy: 0.8198\n", - "Epoch 148/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7344 - sparse_categorical_crossentropy: 0.5036 - accuracy: 0.7887 - val_loss: 0.6931 - val_sparse_categorical_crossentropy: 0.4620 - val_accuracy: 0.8316\n", - "Epoch 149/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7308 - sparse_categorical_crossentropy: 0.4999 - accuracy: 0.7935 - val_loss: 0.6905 - val_sparse_categorical_crossentropy: 0.4597 - val_accuracy: 0.8400\n", - "Epoch 150/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.7282 - sparse_categorical_crossentropy: 0.4972 - accuracy: 0.7954 - val_loss: 0.6946 - val_sparse_categorical_crossentropy: 0.4646 - val_accuracy: 0.8352\n", - "Epoch 151/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7302 - sparse_categorical_crossentropy: 0.5004 - accuracy: 0.7934 - val_loss: 0.6954 - val_sparse_categorical_crossentropy: 0.4657 - val_accuracy: 0.8286\n", - "Epoch 152/1000\n", - "4/4 [==============================] - 0s 23ms/step - loss: 0.7313 - sparse_categorical_crossentropy: 0.5020 - accuracy: 0.7955 - val_loss: 0.6929 - val_sparse_categorical_crossentropy: 0.4640 - val_accuracy: 0.8160\n", - "Epoch 153/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.7293 - sparse_categorical_crossentropy: 0.5004 - accuracy: 0.7926 - val_loss: 0.6881 - val_sparse_categorical_crossentropy: 0.4602 - val_accuracy: 0.8378\n", - "Epoch 154/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7282 - sparse_categorical_crossentropy: 0.5002 - accuracy: 0.7999 - val_loss: 0.6888 - val_sparse_categorical_crossentropy: 0.4607 - val_accuracy: 0.8342\n", - "Epoch 155/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7288 - sparse_categorical_crossentropy: 0.5013 - accuracy: 0.7934 - val_loss: 0.6881 - val_sparse_categorical_crossentropy: 0.4611 - val_accuracy: 0.8398\n", - "Epoch 156/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7269 - sparse_categorical_crossentropy: 0.5000 - accuracy: 0.7985 - val_loss: 0.6811 - val_sparse_categorical_crossentropy: 0.4542 - val_accuracy: 0.8464\n", - "Epoch 157/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7214 - sparse_categorical_crossentropy: 0.4946 - accuracy: 0.8009 - val_loss: 0.6895 - val_sparse_categorical_crossentropy: 0.4627 - val_accuracy: 0.8124\n", - "Epoch 158/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7300 - sparse_categorical_crossentropy: 0.5029 - accuracy: 0.7906 - val_loss: 0.6905 - val_sparse_categorical_crossentropy: 0.4633 - val_accuracy: 0.8242\n", - "Epoch 159/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7260 - sparse_categorical_crossentropy: 0.4985 - accuracy: 0.7969 - val_loss: 0.6939 - val_sparse_categorical_crossentropy: 0.4671 - val_accuracy: 0.8136\n", - "Epoch 160/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.7248 - sparse_categorical_crossentropy: 0.4977 - accuracy: 0.7929 - val_loss: 0.6896 - val_sparse_categorical_crossentropy: 0.4625 - val_accuracy: 0.8222\n", - "Epoch 161/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7235 - sparse_categorical_crossentropy: 0.4964 - accuracy: 0.7927 - val_loss: 0.6914 - val_sparse_categorical_crossentropy: 0.4648 - val_accuracy: 0.8202\n", - "Epoch 162/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.7261 - sparse_categorical_crossentropy: 0.4997 - accuracy: 0.7935 - val_loss: 0.6919 - val_sparse_categorical_crossentropy: 0.4662 - val_accuracy: 0.8128\n", - "Epoch 163/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7214 - sparse_categorical_crossentropy: 0.4959 - accuracy: 0.7995 - val_loss: 0.6891 - val_sparse_categorical_crossentropy: 0.4639 - val_accuracy: 0.8226\n", - "Epoch 164/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7213 - sparse_categorical_crossentropy: 0.4962 - accuracy: 0.7987 - val_loss: 0.6855 - val_sparse_categorical_crossentropy: 0.4611 - val_accuracy: 0.8386\n", - "Epoch 165/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7195 - sparse_categorical_crossentropy: 0.4951 - accuracy: 0.8021 - val_loss: 0.6790 - val_sparse_categorical_crossentropy: 0.4542 - val_accuracy: 0.8410\n", - "Epoch 166/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7153 - sparse_categorical_crossentropy: 0.4903 - accuracy: 0.7982 - val_loss: 0.6731 - val_sparse_categorical_crossentropy: 0.4484 - val_accuracy: 0.8444\n", - "Epoch 167/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7169 - sparse_categorical_crossentropy: 0.4917 - accuracy: 0.7987 - val_loss: 0.6725 - val_sparse_categorical_crossentropy: 0.4476 - val_accuracy: 0.8496\n", - "Epoch 168/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7113 - sparse_categorical_crossentropy: 0.4864 - accuracy: 0.8011 - val_loss: 0.6781 - val_sparse_categorical_crossentropy: 0.4535 - val_accuracy: 0.8338\n", - "Epoch 169/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7109 - sparse_categorical_crossentropy: 0.4863 - accuracy: 0.8014 - val_loss: 0.6806 - val_sparse_categorical_crossentropy: 0.4560 - val_accuracy: 0.8288\n", - "Epoch 170/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.7175 - sparse_categorical_crossentropy: 0.4931 - accuracy: 0.7986 - val_loss: 0.6784 - val_sparse_categorical_crossentropy: 0.4543 - val_accuracy: 0.8352\n", - "Epoch 171/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7160 - sparse_categorical_crossentropy: 0.4921 - accuracy: 0.8005 - val_loss: 0.6733 - val_sparse_categorical_crossentropy: 0.4502 - val_accuracy: 0.8426\n", - "Epoch 172/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7127 - sparse_categorical_crossentropy: 0.4895 - accuracy: 0.8055 - val_loss: 0.6720 - val_sparse_categorical_crossentropy: 0.4494 - val_accuracy: 0.8430\n", - "Epoch 173/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7129 - sparse_categorical_crossentropy: 0.4904 - accuracy: 0.8007 - val_loss: 0.6682 - val_sparse_categorical_crossentropy: 0.4456 - val_accuracy: 0.8522\n", - "Epoch 174/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7062 - sparse_categorical_crossentropy: 0.4835 - accuracy: 0.8100 - val_loss: 0.6681 - val_sparse_categorical_crossentropy: 0.4455 - val_accuracy: 0.8438\n", - "Epoch 175/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7057 - sparse_categorical_crossentropy: 0.4832 - accuracy: 0.8074 - val_loss: 0.6755 - val_sparse_categorical_crossentropy: 0.4530 - val_accuracy: 0.8330\n", - "Epoch 176/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7105 - sparse_categorical_crossentropy: 0.4879 - accuracy: 0.8035 - val_loss: 0.6646 - val_sparse_categorical_crossentropy: 0.4425 - val_accuracy: 0.8540\n", - "Epoch 177/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7082 - sparse_categorical_crossentropy: 0.4862 - accuracy: 0.8023 - val_loss: 0.6642 - val_sparse_categorical_crossentropy: 0.4424 - val_accuracy: 0.8510\n", - "Epoch 178/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7045 - sparse_categorical_crossentropy: 0.4828 - accuracy: 0.8092 - val_loss: 0.6635 - val_sparse_categorical_crossentropy: 0.4415 - val_accuracy: 0.8492\n", - "Epoch 179/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7037 - sparse_categorical_crossentropy: 0.4816 - accuracy: 0.8044 - val_loss: 0.6642 - val_sparse_categorical_crossentropy: 0.4425 - val_accuracy: 0.8456\n", - "Epoch 180/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7023 - sparse_categorical_crossentropy: 0.4807 - accuracy: 0.8089 - val_loss: 0.6637 - val_sparse_categorical_crossentropy: 0.4418 - val_accuracy: 0.8452\n", - "Epoch 181/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.7012 - sparse_categorical_crossentropy: 0.4798 - accuracy: 0.8053 - val_loss: 0.6619 - val_sparse_categorical_crossentropy: 0.4406 - val_accuracy: 0.8378\n", - "Epoch 182/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.7000 - sparse_categorical_crossentropy: 0.4787 - accuracy: 0.8089 - val_loss: 0.6622 - val_sparse_categorical_crossentropy: 0.4411 - val_accuracy: 0.8562\n", - "Epoch 183/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.7004 - sparse_categorical_crossentropy: 0.4795 - accuracy: 0.8105 - val_loss: 0.6615 - val_sparse_categorical_crossentropy: 0.4405 - val_accuracy: 0.8510\n", - "Epoch 184/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7033 - sparse_categorical_crossentropy: 0.4826 - accuracy: 0.8063 - val_loss: 0.6660 - val_sparse_categorical_crossentropy: 0.4456 - val_accuracy: 0.8362\n", - "Epoch 185/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7029 - sparse_categorical_crossentropy: 0.4826 - accuracy: 0.8053 - val_loss: 0.6619 - val_sparse_categorical_crossentropy: 0.4429 - val_accuracy: 0.8496\n", - "Epoch 186/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6987 - sparse_categorical_crossentropy: 0.4795 - accuracy: 0.8126 - val_loss: 0.6595 - val_sparse_categorical_crossentropy: 0.4397 - val_accuracy: 0.8542\n", - "Epoch 187/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6973 - sparse_categorical_crossentropy: 0.4780 - accuracy: 0.8117 - val_loss: 0.6632 - val_sparse_categorical_crossentropy: 0.4435 - val_accuracy: 0.8466\n", - "Epoch 188/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.7022 - sparse_categorical_crossentropy: 0.4823 - accuracy: 0.8075 - val_loss: 0.6560 - val_sparse_categorical_crossentropy: 0.4367 - val_accuracy: 0.8550\n", - "Epoch 189/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.7001 - sparse_categorical_crossentropy: 0.4806 - accuracy: 0.8102 - val_loss: 0.6598 - val_sparse_categorical_crossentropy: 0.4398 - val_accuracy: 0.8428\n", - "Epoch 190/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.7003 - sparse_categorical_crossentropy: 0.4806 - accuracy: 0.8071 - val_loss: 0.6592 - val_sparse_categorical_crossentropy: 0.4395 - val_accuracy: 0.8408\n", - "Epoch 191/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6973 - sparse_categorical_crossentropy: 0.4775 - accuracy: 0.8117 - val_loss: 0.6545 - val_sparse_categorical_crossentropy: 0.4359 - val_accuracy: 0.8594\n", - "Epoch 192/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6917 - sparse_categorical_crossentropy: 0.4730 - accuracy: 0.8118 - val_loss: 0.6529 - val_sparse_categorical_crossentropy: 0.4338 - val_accuracy: 0.8542\n", - "Epoch 193/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6896 - sparse_categorical_crossentropy: 0.4711 - accuracy: 0.8146 - val_loss: 0.6561 - val_sparse_categorical_crossentropy: 0.4379 - val_accuracy: 0.8482\n", - "Epoch 194/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6930 - sparse_categorical_crossentropy: 0.4746 - accuracy: 0.8101 - val_loss: 0.6513 - val_sparse_categorical_crossentropy: 0.4336 - val_accuracy: 0.8560\n", - "Epoch 195/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6954 - sparse_categorical_crossentropy: 0.4776 - accuracy: 0.8085 - val_loss: 0.6553 - val_sparse_categorical_crossentropy: 0.4371 - val_accuracy: 0.8454\n", - "Epoch 196/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6924 - sparse_categorical_crossentropy: 0.4747 - accuracy: 0.8066 - val_loss: 0.6612 - val_sparse_categorical_crossentropy: 0.4437 - val_accuracy: 0.8280\n", - "Epoch 197/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6969 - sparse_categorical_crossentropy: 0.4793 - accuracy: 0.8089 - val_loss: 0.6561 - val_sparse_categorical_crossentropy: 0.4386 - val_accuracy: 0.8408\n", - "Epoch 198/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6943 - sparse_categorical_crossentropy: 0.4768 - accuracy: 0.8070 - val_loss: 0.6510 - val_sparse_categorical_crossentropy: 0.4336 - val_accuracy: 0.8516\n", - "Epoch 199/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6891 - sparse_categorical_crossentropy: 0.4716 - accuracy: 0.8143 - val_loss: 0.6528 - val_sparse_categorical_crossentropy: 0.4360 - val_accuracy: 0.8358\n", - "Epoch 200/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6910 - sparse_categorical_crossentropy: 0.4740 - accuracy: 0.8131 - val_loss: 0.6480 - val_sparse_categorical_crossentropy: 0.4312 - val_accuracy: 0.8452\n", - "Epoch 201/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.6891 - sparse_categorical_crossentropy: 0.4723 - accuracy: 0.8137 - val_loss: 0.6509 - val_sparse_categorical_crossentropy: 0.4343 - val_accuracy: 0.8510\n", - "Epoch 202/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6895 - sparse_categorical_crossentropy: 0.4727 - accuracy: 0.8114 - val_loss: 0.6515 - val_sparse_categorical_crossentropy: 0.4352 - val_accuracy: 0.8396\n", - "Epoch 203/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6903 - sparse_categorical_crossentropy: 0.4735 - accuracy: 0.8110 - val_loss: 0.6501 - val_sparse_categorical_crossentropy: 0.4326 - val_accuracy: 0.8602\n", - "Epoch 204/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6897 - sparse_categorical_crossentropy: 0.4727 - accuracy: 0.8104 - val_loss: 0.6480 - val_sparse_categorical_crossentropy: 0.4310 - val_accuracy: 0.8574\n", - "Epoch 205/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6956 - sparse_categorical_crossentropy: 0.4786 - accuracy: 0.8070 - val_loss: 0.6469 - val_sparse_categorical_crossentropy: 0.4300 - val_accuracy: 0.8492\n", - "Epoch 206/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6841 - sparse_categorical_crossentropy: 0.4672 - accuracy: 0.8177 - val_loss: 0.6473 - val_sparse_categorical_crossentropy: 0.4306 - val_accuracy: 0.8518\n", - "Epoch 207/1000\n", - "4/4 [==============================] - 0s 21ms/step - loss: 0.6824 - sparse_categorical_crossentropy: 0.4657 - accuracy: 0.8207 - val_loss: 0.6480 - val_sparse_categorical_crossentropy: 0.4310 - val_accuracy: 0.8478\n", - "Epoch 208/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6829 - sparse_categorical_crossentropy: 0.4660 - accuracy: 0.8164 - val_loss: 0.6442 - val_sparse_categorical_crossentropy: 0.4277 - val_accuracy: 0.8636\n", - "Epoch 209/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6884 - sparse_categorical_crossentropy: 0.4719 - accuracy: 0.8148 - val_loss: 0.6657 - val_sparse_categorical_crossentropy: 0.4502 - val_accuracy: 0.8302\n", - "Epoch 210/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6954 - sparse_categorical_crossentropy: 0.4805 - accuracy: 0.8060 - val_loss: 0.6558 - val_sparse_categorical_crossentropy: 0.4412 - val_accuracy: 0.8344\n", - "Epoch 211/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6900 - sparse_categorical_crossentropy: 0.4754 - accuracy: 0.8091 - val_loss: 0.6522 - val_sparse_categorical_crossentropy: 0.4381 - val_accuracy: 0.8202\n", - "Epoch 212/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6945 - sparse_categorical_crossentropy: 0.4797 - accuracy: 0.8030 - val_loss: 0.6589 - val_sparse_categorical_crossentropy: 0.4444 - val_accuracy: 0.8234\n", - "Epoch 213/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6914 - sparse_categorical_crossentropy: 0.4766 - accuracy: 0.8102 - val_loss: 0.6572 - val_sparse_categorical_crossentropy: 0.4418 - val_accuracy: 0.8246\n", - "Epoch 214/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6914 - sparse_categorical_crossentropy: 0.4758 - accuracy: 0.8081 - val_loss: 0.6519 - val_sparse_categorical_crossentropy: 0.4368 - val_accuracy: 0.8322\n", - "Epoch 215/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6823 - sparse_categorical_crossentropy: 0.4672 - accuracy: 0.8167 - val_loss: 0.6506 - val_sparse_categorical_crossentropy: 0.4362 - val_accuracy: 0.8382\n", - "Epoch 216/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6852 - sparse_categorical_crossentropy: 0.4708 - accuracy: 0.8122 - val_loss: 0.6511 - val_sparse_categorical_crossentropy: 0.4365 - val_accuracy: 0.8400\n", - "Epoch 217/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6802 - sparse_categorical_crossentropy: 0.4654 - accuracy: 0.8133 - val_loss: 0.6467 - val_sparse_categorical_crossentropy: 0.4326 - val_accuracy: 0.8420\n", - "Epoch 218/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6842 - sparse_categorical_crossentropy: 0.4695 - accuracy: 0.8173 - val_loss: 0.6410 - val_sparse_categorical_crossentropy: 0.4262 - val_accuracy: 0.8460\n", - "Epoch 219/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6759 - sparse_categorical_crossentropy: 0.4609 - accuracy: 0.8177 - val_loss: 0.6406 - val_sparse_categorical_crossentropy: 0.4256 - val_accuracy: 0.8558\n", - "Epoch 220/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6734 - sparse_categorical_crossentropy: 0.4581 - accuracy: 0.8255 - val_loss: 0.6412 - val_sparse_categorical_crossentropy: 0.4253 - val_accuracy: 0.8558\n", - "Epoch 221/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6774 - sparse_categorical_crossentropy: 0.4621 - accuracy: 0.8186 - val_loss: 0.6417 - val_sparse_categorical_crossentropy: 0.4268 - val_accuracy: 0.8560\n", - "Epoch 222/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.6764 - sparse_categorical_crossentropy: 0.4616 - accuracy: 0.8180 - val_loss: 0.6329 - val_sparse_categorical_crossentropy: 0.4185 - val_accuracy: 0.8664\n", - "Epoch 223/1000\n", - "4/4 [==============================] - 0s 22ms/step - loss: 0.6747 - sparse_categorical_crossentropy: 0.4604 - accuracy: 0.8194 - val_loss: 0.6388 - val_sparse_categorical_crossentropy: 0.4239 - val_accuracy: 0.8416\n", - "Epoch 224/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6749 - sparse_categorical_crossentropy: 0.4606 - accuracy: 0.8192 - val_loss: 0.6378 - val_sparse_categorical_crossentropy: 0.4237 - val_accuracy: 0.8522\n", - "Epoch 225/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.6759 - sparse_categorical_crossentropy: 0.4621 - accuracy: 0.8181 - val_loss: 0.6302 - val_sparse_categorical_crossentropy: 0.4163 - val_accuracy: 0.8640\n", - "Epoch 226/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6695 - sparse_categorical_crossentropy: 0.4555 - accuracy: 0.8235 - val_loss: 0.6359 - val_sparse_categorical_crossentropy: 0.4214 - val_accuracy: 0.8562\n", - "Epoch 227/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6709 - sparse_categorical_crossentropy: 0.4566 - accuracy: 0.8228 - val_loss: 0.6366 - val_sparse_categorical_crossentropy: 0.4237 - val_accuracy: 0.8412\n", - "Epoch 228/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.6779 - sparse_categorical_crossentropy: 0.4644 - accuracy: 0.8163 - val_loss: 0.6323 - val_sparse_categorical_crossentropy: 0.4189 - val_accuracy: 0.8520\n", - "Epoch 229/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6712 - sparse_categorical_crossentropy: 0.4577 - accuracy: 0.8235 - val_loss: 0.6379 - val_sparse_categorical_crossentropy: 0.4247 - val_accuracy: 0.8492\n", - "Epoch 230/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6661 - sparse_categorical_crossentropy: 0.4528 - accuracy: 0.8275 - val_loss: 0.6287 - val_sparse_categorical_crossentropy: 0.4154 - val_accuracy: 0.8654\n", - "Epoch 231/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6668 - sparse_categorical_crossentropy: 0.4535 - accuracy: 0.8241 - val_loss: 0.6274 - val_sparse_categorical_crossentropy: 0.4147 - val_accuracy: 0.8658\n", - "Epoch 232/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6705 - sparse_categorical_crossentropy: 0.4574 - accuracy: 0.8168 - val_loss: 0.6344 - val_sparse_categorical_crossentropy: 0.4212 - val_accuracy: 0.8480\n", - "Epoch 233/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6649 - sparse_categorical_crossentropy: 0.4520 - accuracy: 0.8249 - val_loss: 0.6368 - val_sparse_categorical_crossentropy: 0.4246 - val_accuracy: 0.8372\n", - "Epoch 234/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6764 - sparse_categorical_crossentropy: 0.4642 - accuracy: 0.8145 - val_loss: 0.6276 - val_sparse_categorical_crossentropy: 0.4161 - val_accuracy: 0.8558\n", - "Epoch 235/1000\n", - "4/4 [==============================] - 0s 21ms/step - loss: 0.6727 - sparse_categorical_crossentropy: 0.4612 - accuracy: 0.8195 - val_loss: 0.6238 - val_sparse_categorical_crossentropy: 0.4121 - val_accuracy: 0.8708\n", - "Epoch 236/1000\n", - "4/4 [==============================] - 0s 23ms/step - loss: 0.6694 - sparse_categorical_crossentropy: 0.4578 - accuracy: 0.8191 - val_loss: 0.6284 - val_sparse_categorical_crossentropy: 0.4166 - val_accuracy: 0.8622\n", - "Epoch 237/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6656 - sparse_categorical_crossentropy: 0.4544 - accuracy: 0.8230 - val_loss: 0.6341 - val_sparse_categorical_crossentropy: 0.4237 - val_accuracy: 0.8402\n", - "Epoch 238/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6633 - sparse_categorical_crossentropy: 0.4528 - accuracy: 0.8239 - val_loss: 0.6325 - val_sparse_categorical_crossentropy: 0.4218 - val_accuracy: 0.8530\n", - "Epoch 239/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6620 - sparse_categorical_crossentropy: 0.4511 - accuracy: 0.8255 - val_loss: 0.6319 - val_sparse_categorical_crossentropy: 0.4219 - val_accuracy: 0.8460\n", - "Epoch 240/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6654 - sparse_categorical_crossentropy: 0.4549 - accuracy: 0.8222 - val_loss: 0.6315 - val_sparse_categorical_crossentropy: 0.4206 - val_accuracy: 0.8528\n", - "Epoch 241/1000\n", - "4/4 [==============================] - 0s 22ms/step - loss: 0.6645 - sparse_categorical_crossentropy: 0.4533 - accuracy: 0.8233 - val_loss: 0.6218 - val_sparse_categorical_crossentropy: 0.4103 - val_accuracy: 0.8654\n", - "Epoch 242/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6610 - sparse_categorical_crossentropy: 0.4492 - accuracy: 0.8281 - val_loss: 0.6220 - val_sparse_categorical_crossentropy: 0.4102 - val_accuracy: 0.8626\n", - "Epoch 243/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.6626 - sparse_categorical_crossentropy: 0.4507 - accuracy: 0.8231 - val_loss: 0.6226 - val_sparse_categorical_crossentropy: 0.4103 - val_accuracy: 0.8708\n", - "Epoch 244/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.6618 - sparse_categorical_crossentropy: 0.4500 - accuracy: 0.8257 - val_loss: 0.6178 - val_sparse_categorical_crossentropy: 0.4063 - val_accuracy: 0.8708\n", - "Epoch 245/1000\n", - "4/4 [==============================] - 0s 21ms/step - loss: 0.6562 - sparse_categorical_crossentropy: 0.4447 - accuracy: 0.8304 - val_loss: 0.6181 - val_sparse_categorical_crossentropy: 0.4076 - val_accuracy: 0.8718\n", - "Epoch 246/1000\n", - "4/4 [==============================] - 0s 21ms/step - loss: 0.6611 - sparse_categorical_crossentropy: 0.4507 - accuracy: 0.8287 - val_loss: 0.6258 - val_sparse_categorical_crossentropy: 0.4147 - val_accuracy: 0.8480\n", - "Epoch 247/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.6579 - sparse_categorical_crossentropy: 0.4475 - accuracy: 0.8266 - val_loss: 0.6315 - val_sparse_categorical_crossentropy: 0.4211 - val_accuracy: 0.8360\n", - "Epoch 248/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6674 - sparse_categorical_crossentropy: 0.4564 - accuracy: 0.8209 - val_loss: 0.6175 - val_sparse_categorical_crossentropy: 0.4074 - val_accuracy: 0.8640\n", - "Epoch 249/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6624 - sparse_categorical_crossentropy: 0.4520 - accuracy: 0.8251 - val_loss: 0.6218 - val_sparse_categorical_crossentropy: 0.4111 - val_accuracy: 0.8652\n", - "Epoch 250/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6588 - sparse_categorical_crossentropy: 0.4489 - accuracy: 0.8275 - val_loss: 0.6188 - val_sparse_categorical_crossentropy: 0.4084 - val_accuracy: 0.8706\n", - "Epoch 251/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6579 - sparse_categorical_crossentropy: 0.4476 - accuracy: 0.8301 - val_loss: 0.6184 - val_sparse_categorical_crossentropy: 0.4092 - val_accuracy: 0.8702\n", - "Epoch 252/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6603 - sparse_categorical_crossentropy: 0.4506 - accuracy: 0.8245 - val_loss: 0.6149 - val_sparse_categorical_crossentropy: 0.4055 - val_accuracy: 0.8638\n", - "Epoch 253/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6575 - sparse_categorical_crossentropy: 0.4482 - accuracy: 0.8273 - val_loss: 0.6221 - val_sparse_categorical_crossentropy: 0.4127 - val_accuracy: 0.8436\n", - "Epoch 254/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6594 - sparse_categorical_crossentropy: 0.4502 - accuracy: 0.8252 - val_loss: 0.6204 - val_sparse_categorical_crossentropy: 0.4113 - val_accuracy: 0.8490\n", - "Epoch 255/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6583 - sparse_categorical_crossentropy: 0.4488 - accuracy: 0.8298 - val_loss: 0.6179 - val_sparse_categorical_crossentropy: 0.4088 - val_accuracy: 0.8594\n", - "Epoch 256/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6535 - sparse_categorical_crossentropy: 0.4441 - accuracy: 0.8300 - val_loss: 0.6260 - val_sparse_categorical_crossentropy: 0.4162 - val_accuracy: 0.8422\n", - "Epoch 257/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6552 - sparse_categorical_crossentropy: 0.4456 - accuracy: 0.8255 - val_loss: 0.6102 - val_sparse_categorical_crossentropy: 0.4013 - val_accuracy: 0.8728\n", - "Epoch 258/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6579 - sparse_categorical_crossentropy: 0.4487 - accuracy: 0.8269 - val_loss: 0.6126 - val_sparse_categorical_crossentropy: 0.4027 - val_accuracy: 0.8772\n", - "Epoch 259/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6575 - sparse_categorical_crossentropy: 0.4480 - accuracy: 0.8266 - val_loss: 0.6217 - val_sparse_categorical_crossentropy: 0.4123 - val_accuracy: 0.8550\n", - "Epoch 260/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6499 - sparse_categorical_crossentropy: 0.4408 - accuracy: 0.8325 - val_loss: 0.6133 - val_sparse_categorical_crossentropy: 0.4048 - val_accuracy: 0.8626\n", - "Epoch 261/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6552 - sparse_categorical_crossentropy: 0.4464 - accuracy: 0.8273 - val_loss: 0.6134 - val_sparse_categorical_crossentropy: 0.4044 - val_accuracy: 0.8664\n", - "Epoch 262/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6498 - sparse_categorical_crossentropy: 0.4410 - accuracy: 0.8357 - val_loss: 0.6240 - val_sparse_categorical_crossentropy: 0.4157 - val_accuracy: 0.8412\n", - "Epoch 263/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6567 - sparse_categorical_crossentropy: 0.4481 - accuracy: 0.8269 - val_loss: 0.6136 - val_sparse_categorical_crossentropy: 0.4050 - val_accuracy: 0.8618\n", - "Epoch 264/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6464 - sparse_categorical_crossentropy: 0.4379 - accuracy: 0.8367 - val_loss: 0.6116 - val_sparse_categorical_crossentropy: 0.4037 - val_accuracy: 0.8678\n", - "Epoch 265/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.6513 - sparse_categorical_crossentropy: 0.4433 - accuracy: 0.8333 - val_loss: 0.6175 - val_sparse_categorical_crossentropy: 0.4091 - val_accuracy: 0.8584\n", - "Epoch 266/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6503 - sparse_categorical_crossentropy: 0.4420 - accuracy: 0.8303 - val_loss: 0.6160 - val_sparse_categorical_crossentropy: 0.4074 - val_accuracy: 0.8550\n", - "Epoch 267/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6505 - sparse_categorical_crossentropy: 0.4420 - accuracy: 0.8347 - val_loss: 0.6129 - val_sparse_categorical_crossentropy: 0.4045 - val_accuracy: 0.8600\n", - "Epoch 268/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6518 - sparse_categorical_crossentropy: 0.4434 - accuracy: 0.8319 - val_loss: 0.6122 - val_sparse_categorical_crossentropy: 0.4037 - val_accuracy: 0.8714\n", - "Epoch 269/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6528 - sparse_categorical_crossentropy: 0.4448 - accuracy: 0.8296 - val_loss: 0.6188 - val_sparse_categorical_crossentropy: 0.4107 - val_accuracy: 0.8486\n", - "Epoch 270/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6530 - sparse_categorical_crossentropy: 0.4453 - accuracy: 0.8285 - val_loss: 0.6115 - val_sparse_categorical_crossentropy: 0.4044 - val_accuracy: 0.8612\n", - "Epoch 271/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6452 - sparse_categorical_crossentropy: 0.4377 - accuracy: 0.8313 - val_loss: 0.6171 - val_sparse_categorical_crossentropy: 0.4095 - val_accuracy: 0.8580\n", - "Epoch 272/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6433 - sparse_categorical_crossentropy: 0.4360 - accuracy: 0.8332 - val_loss: 0.6150 - val_sparse_categorical_crossentropy: 0.4081 - val_accuracy: 0.8512\n", - "Epoch 273/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6455 - sparse_categorical_crossentropy: 0.4383 - accuracy: 0.8346 - val_loss: 0.6150 - val_sparse_categorical_crossentropy: 0.4083 - val_accuracy: 0.8496\n", - "Epoch 274/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6466 - sparse_categorical_crossentropy: 0.4394 - accuracy: 0.8329 - val_loss: 0.6094 - val_sparse_categorical_crossentropy: 0.4015 - val_accuracy: 0.8714\n", - "Epoch 275/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.6475 - sparse_categorical_crossentropy: 0.4401 - accuracy: 0.8313 - val_loss: 0.6060 - val_sparse_categorical_crossentropy: 0.3986 - val_accuracy: 0.8738\n", - "Epoch 276/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6447 - sparse_categorical_crossentropy: 0.4373 - accuracy: 0.8333 - val_loss: 0.6104 - val_sparse_categorical_crossentropy: 0.4033 - val_accuracy: 0.8654\n", - "Epoch 277/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6435 - sparse_categorical_crossentropy: 0.4367 - accuracy: 0.8345 - val_loss: 0.6101 - val_sparse_categorical_crossentropy: 0.4029 - val_accuracy: 0.8682\n", - "Epoch 278/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6447 - sparse_categorical_crossentropy: 0.4381 - accuracy: 0.8327 - val_loss: 0.6018 - val_sparse_categorical_crossentropy: 0.3954 - val_accuracy: 0.8778\n", - "Epoch 279/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6485 - sparse_categorical_crossentropy: 0.4420 - accuracy: 0.8293 - val_loss: 0.6062 - val_sparse_categorical_crossentropy: 0.4000 - val_accuracy: 0.8592\n", - "Epoch 280/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6399 - sparse_categorical_crossentropy: 0.4334 - accuracy: 0.8353 - val_loss: 0.6032 - val_sparse_categorical_crossentropy: 0.3963 - val_accuracy: 0.8714\n", - "Epoch 281/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6397 - sparse_categorical_crossentropy: 0.4328 - accuracy: 0.8396 - val_loss: 0.6031 - val_sparse_categorical_crossentropy: 0.3960 - val_accuracy: 0.8672\n", - "Epoch 282/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6386 - sparse_categorical_crossentropy: 0.4317 - accuracy: 0.8405 - val_loss: 0.5992 - val_sparse_categorical_crossentropy: 0.3925 - val_accuracy: 0.8652\n", - "Epoch 283/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6386 - sparse_categorical_crossentropy: 0.4317 - accuracy: 0.8398 - val_loss: 0.5999 - val_sparse_categorical_crossentropy: 0.3927 - val_accuracy: 0.8726\n", - "Epoch 284/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6400 - sparse_categorical_crossentropy: 0.4328 - accuracy: 0.8366 - val_loss: 0.5961 - val_sparse_categorical_crossentropy: 0.3890 - val_accuracy: 0.8784\n", - "Epoch 285/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6363 - sparse_categorical_crossentropy: 0.4295 - accuracy: 0.8433 - val_loss: 0.6037 - val_sparse_categorical_crossentropy: 0.3965 - val_accuracy: 0.8728\n", - "Epoch 286/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.6378 - sparse_categorical_crossentropy: 0.4309 - accuracy: 0.8386 - val_loss: 0.5977 - val_sparse_categorical_crossentropy: 0.3906 - val_accuracy: 0.8790\n", - "Epoch 287/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.6391 - sparse_categorical_crossentropy: 0.4324 - accuracy: 0.8361 - val_loss: 0.6104 - val_sparse_categorical_crossentropy: 0.4043 - val_accuracy: 0.8478\n", - "Epoch 288/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.6393 - sparse_categorical_crossentropy: 0.4330 - accuracy: 0.8325 - val_loss: 0.6047 - val_sparse_categorical_crossentropy: 0.3988 - val_accuracy: 0.8560\n", - "Epoch 289/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6386 - sparse_categorical_crossentropy: 0.4325 - accuracy: 0.8388 - val_loss: 0.5933 - val_sparse_categorical_crossentropy: 0.3876 - val_accuracy: 0.8760\n", - "Epoch 290/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6350 - sparse_categorical_crossentropy: 0.4291 - accuracy: 0.8404 - val_loss: 0.5961 - val_sparse_categorical_crossentropy: 0.3896 - val_accuracy: 0.8754\n", - "Epoch 291/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6351 - sparse_categorical_crossentropy: 0.4288 - accuracy: 0.8397 - val_loss: 0.5950 - val_sparse_categorical_crossentropy: 0.3892 - val_accuracy: 0.8790\n", - "Epoch 292/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6357 - sparse_categorical_crossentropy: 0.4299 - accuracy: 0.8381 - val_loss: 0.5973 - val_sparse_categorical_crossentropy: 0.3917 - val_accuracy: 0.8796\n", - "Epoch 293/1000\n", - "4/4 [==============================] - 0s 21ms/step - loss: 0.6329 - sparse_categorical_crossentropy: 0.4275 - accuracy: 0.8428 - val_loss: 0.5976 - val_sparse_categorical_crossentropy: 0.3915 - val_accuracy: 0.8782\n", - "Epoch 294/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6332 - sparse_categorical_crossentropy: 0.4270 - accuracy: 0.8418 - val_loss: 0.6028 - val_sparse_categorical_crossentropy: 0.3973 - val_accuracy: 0.8564\n", - "Epoch 295/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6423 - sparse_categorical_crossentropy: 0.4362 - accuracy: 0.8344 - val_loss: 0.5925 - val_sparse_categorical_crossentropy: 0.3863 - val_accuracy: 0.8758\n", - "Epoch 296/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6313 - sparse_categorical_crossentropy: 0.4252 - accuracy: 0.8424 - val_loss: 0.5909 - val_sparse_categorical_crossentropy: 0.3844 - val_accuracy: 0.8828\n", - "Epoch 297/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6287 - sparse_categorical_crossentropy: 0.4227 - accuracy: 0.8471 - val_loss: 0.5938 - val_sparse_categorical_crossentropy: 0.3887 - val_accuracy: 0.8800\n", - "Epoch 298/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6336 - sparse_categorical_crossentropy: 0.4285 - accuracy: 0.8416 - val_loss: 0.5987 - val_sparse_categorical_crossentropy: 0.3938 - val_accuracy: 0.8628\n", - "Epoch 299/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6335 - sparse_categorical_crossentropy: 0.4283 - accuracy: 0.8366 - val_loss: 0.5941 - val_sparse_categorical_crossentropy: 0.3880 - val_accuracy: 0.8758\n", - "Epoch 300/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.6349 - sparse_categorical_crossentropy: 0.4292 - accuracy: 0.8391 - val_loss: 0.5894 - val_sparse_categorical_crossentropy: 0.3841 - val_accuracy: 0.8760\n", - "Epoch 301/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6329 - sparse_categorical_crossentropy: 0.4275 - accuracy: 0.8380 - val_loss: 0.5975 - val_sparse_categorical_crossentropy: 0.3922 - val_accuracy: 0.8582\n", - "Epoch 302/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6331 - sparse_categorical_crossentropy: 0.4274 - accuracy: 0.8381 - val_loss: 0.6031 - val_sparse_categorical_crossentropy: 0.3975 - val_accuracy: 0.8596\n", - "Epoch 303/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6357 - sparse_categorical_crossentropy: 0.4302 - accuracy: 0.8370 - val_loss: 0.5956 - val_sparse_categorical_crossentropy: 0.3906 - val_accuracy: 0.8672\n", - "Epoch 304/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6318 - sparse_categorical_crossentropy: 0.4267 - accuracy: 0.8412 - val_loss: 0.5903 - val_sparse_categorical_crossentropy: 0.3853 - val_accuracy: 0.8762\n", - "Epoch 305/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6280 - sparse_categorical_crossentropy: 0.4230 - accuracy: 0.8434 - val_loss: 0.5882 - val_sparse_categorical_crossentropy: 0.3834 - val_accuracy: 0.8818\n", - "Epoch 306/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6298 - sparse_categorical_crossentropy: 0.4253 - accuracy: 0.8380 - val_loss: 0.5876 - val_sparse_categorical_crossentropy: 0.3837 - val_accuracy: 0.8756\n", - "Epoch 307/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6289 - sparse_categorical_crossentropy: 0.4251 - accuracy: 0.8408 - val_loss: 0.5898 - val_sparse_categorical_crossentropy: 0.3864 - val_accuracy: 0.8686\n", - "Epoch 308/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6303 - sparse_categorical_crossentropy: 0.4266 - accuracy: 0.8389 - val_loss: 0.5917 - val_sparse_categorical_crossentropy: 0.3884 - val_accuracy: 0.8698\n", - "Epoch 309/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6276 - sparse_categorical_crossentropy: 0.4241 - accuracy: 0.8409 - val_loss: 0.5938 - val_sparse_categorical_crossentropy: 0.3896 - val_accuracy: 0.8726\n", - "Epoch 310/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6298 - sparse_categorical_crossentropy: 0.4254 - accuracy: 0.8430 - val_loss: 0.5859 - val_sparse_categorical_crossentropy: 0.3814 - val_accuracy: 0.8826\n", - "Epoch 311/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6235 - sparse_categorical_crossentropy: 0.4187 - accuracy: 0.8477 - val_loss: 0.5823 - val_sparse_categorical_crossentropy: 0.3773 - val_accuracy: 0.8894\n", - "Epoch 312/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6208 - sparse_categorical_crossentropy: 0.4158 - accuracy: 0.8470 - val_loss: 0.5860 - val_sparse_categorical_crossentropy: 0.3811 - val_accuracy: 0.8794\n", - "Epoch 313/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6232 - sparse_categorical_crossentropy: 0.4182 - accuracy: 0.8471 - val_loss: 0.5868 - val_sparse_categorical_crossentropy: 0.3821 - val_accuracy: 0.8756\n", - "Epoch 314/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6222 - sparse_categorical_crossentropy: 0.4178 - accuracy: 0.8479 - val_loss: 0.5879 - val_sparse_categorical_crossentropy: 0.3835 - val_accuracy: 0.8776\n", - "Epoch 315/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6262 - sparse_categorical_crossentropy: 0.4218 - accuracy: 0.8449 - val_loss: 0.5823 - val_sparse_categorical_crossentropy: 0.3782 - val_accuracy: 0.8870\n", - "Epoch 316/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6180 - sparse_categorical_crossentropy: 0.4137 - accuracy: 0.8477 - val_loss: 0.5815 - val_sparse_categorical_crossentropy: 0.3765 - val_accuracy: 0.8838\n", - "Epoch 317/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.6182 - sparse_categorical_crossentropy: 0.4133 - accuracy: 0.8485 - val_loss: 0.5822 - val_sparse_categorical_crossentropy: 0.3779 - val_accuracy: 0.8800\n", - "Epoch 318/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6200 - sparse_categorical_crossentropy: 0.4156 - accuracy: 0.8470 - val_loss: 0.5802 - val_sparse_categorical_crossentropy: 0.3756 - val_accuracy: 0.8870\n", - "Epoch 319/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6254 - sparse_categorical_crossentropy: 0.4212 - accuracy: 0.8427 - val_loss: 0.5816 - val_sparse_categorical_crossentropy: 0.3777 - val_accuracy: 0.8862\n", - "Epoch 320/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6180 - sparse_categorical_crossentropy: 0.4140 - accuracy: 0.8488 - val_loss: 0.5856 - val_sparse_categorical_crossentropy: 0.3817 - val_accuracy: 0.8790\n", - "Epoch 321/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6211 - sparse_categorical_crossentropy: 0.4173 - accuracy: 0.8510 - val_loss: 0.5827 - val_sparse_categorical_crossentropy: 0.3790 - val_accuracy: 0.8866\n", - "Epoch 322/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6279 - sparse_categorical_crossentropy: 0.4243 - accuracy: 0.8406 - val_loss: 0.5951 - val_sparse_categorical_crossentropy: 0.3914 - val_accuracy: 0.8654\n", - "Epoch 323/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6300 - sparse_categorical_crossentropy: 0.4265 - accuracy: 0.8374 - val_loss: 0.5931 - val_sparse_categorical_crossentropy: 0.3903 - val_accuracy: 0.8762\n", - "Epoch 324/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6243 - sparse_categorical_crossentropy: 0.4215 - accuracy: 0.8434 - val_loss: 0.5972 - val_sparse_categorical_crossentropy: 0.3938 - val_accuracy: 0.8662\n", - "Epoch 325/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.6335 - sparse_categorical_crossentropy: 0.4303 - accuracy: 0.8371 - val_loss: 0.5781 - val_sparse_categorical_crossentropy: 0.3744 - val_accuracy: 0.8822\n", - "Epoch 326/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6212 - sparse_categorical_crossentropy: 0.4174 - accuracy: 0.8450 - val_loss: 0.5918 - val_sparse_categorical_crossentropy: 0.3886 - val_accuracy: 0.8608\n", - "Epoch 327/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6242 - sparse_categorical_crossentropy: 0.4210 - accuracy: 0.8382 - val_loss: 0.5808 - val_sparse_categorical_crossentropy: 0.3778 - val_accuracy: 0.8776\n", - "Epoch 328/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6191 - sparse_categorical_crossentropy: 0.4161 - accuracy: 0.8477 - val_loss: 0.5871 - val_sparse_categorical_crossentropy: 0.3840 - val_accuracy: 0.8692\n", - "Epoch 329/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6153 - sparse_categorical_crossentropy: 0.4126 - accuracy: 0.8483 - val_loss: 0.5903 - val_sparse_categorical_crossentropy: 0.3878 - val_accuracy: 0.8654\n", - "Epoch 330/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.6256 - sparse_categorical_crossentropy: 0.4229 - accuracy: 0.8398 - val_loss: 0.5841 - val_sparse_categorical_crossentropy: 0.3820 - val_accuracy: 0.8750\n", - "Epoch 331/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6274 - sparse_categorical_crossentropy: 0.4253 - accuracy: 0.8372 - val_loss: 0.5958 - val_sparse_categorical_crossentropy: 0.3941 - val_accuracy: 0.8512\n", - "Epoch 332/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.6295 - sparse_categorical_crossentropy: 0.4275 - accuracy: 0.8332 - val_loss: 0.5854 - val_sparse_categorical_crossentropy: 0.3832 - val_accuracy: 0.8674\n", - "Epoch 333/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6197 - sparse_categorical_crossentropy: 0.4173 - accuracy: 0.8446 - val_loss: 0.5895 - val_sparse_categorical_crossentropy: 0.3873 - val_accuracy: 0.8624\n", - "Epoch 334/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6168 - sparse_categorical_crossentropy: 0.4143 - accuracy: 0.8453 - val_loss: 0.5775 - val_sparse_categorical_crossentropy: 0.3754 - val_accuracy: 0.8812\n", - "Epoch 335/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.6190 - sparse_categorical_crossentropy: 0.4166 - accuracy: 0.8445 - val_loss: 0.5783 - val_sparse_categorical_crossentropy: 0.3757 - val_accuracy: 0.8826\n", - "Epoch 336/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6218 - sparse_categorical_crossentropy: 0.4195 - accuracy: 0.8413 - val_loss: 0.5826 - val_sparse_categorical_crossentropy: 0.3807 - val_accuracy: 0.8676\n", - "Epoch 337/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6279 - sparse_categorical_crossentropy: 0.4259 - accuracy: 0.8391 - val_loss: 0.5885 - val_sparse_categorical_crossentropy: 0.3862 - val_accuracy: 0.8640\n", - "Epoch 338/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6286 - sparse_categorical_crossentropy: 0.4266 - accuracy: 0.8393 - val_loss: 0.5823 - val_sparse_categorical_crossentropy: 0.3806 - val_accuracy: 0.8752\n", - "Epoch 339/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.6196 - sparse_categorical_crossentropy: 0.4179 - accuracy: 0.8474 - val_loss: 0.5804 - val_sparse_categorical_crossentropy: 0.3787 - val_accuracy: 0.8842\n", - "Epoch 340/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6209 - sparse_categorical_crossentropy: 0.4192 - accuracy: 0.8436 - val_loss: 0.5871 - val_sparse_categorical_crossentropy: 0.3851 - val_accuracy: 0.8738\n", - "Epoch 341/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6182 - sparse_categorical_crossentropy: 0.4161 - accuracy: 0.8468 - val_loss: 0.5839 - val_sparse_categorical_crossentropy: 0.3819 - val_accuracy: 0.8734\n", - "Epoch 342/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6177 - sparse_categorical_crossentropy: 0.4153 - accuracy: 0.8466 - val_loss: 0.5923 - val_sparse_categorical_crossentropy: 0.3897 - val_accuracy: 0.8662\n", - "Epoch 343/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6226 - sparse_categorical_crossentropy: 0.4198 - accuracy: 0.8402 - val_loss: 0.5800 - val_sparse_categorical_crossentropy: 0.3768 - val_accuracy: 0.8846\n", - "Epoch 344/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6096 - sparse_categorical_crossentropy: 0.4064 - accuracy: 0.8543 - val_loss: 0.5750 - val_sparse_categorical_crossentropy: 0.3720 - val_accuracy: 0.8826\n", - "Epoch 345/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6086 - sparse_categorical_crossentropy: 0.4058 - accuracy: 0.8525 - val_loss: 0.5727 - val_sparse_categorical_crossentropy: 0.3697 - val_accuracy: 0.8840\n", - "Epoch 346/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6073 - sparse_categorical_crossentropy: 0.4043 - accuracy: 0.8538 - val_loss: 0.5708 - val_sparse_categorical_crossentropy: 0.3682 - val_accuracy: 0.8888\n", - "Epoch 347/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6080 - sparse_categorical_crossentropy: 0.4052 - accuracy: 0.8542 - val_loss: 0.5733 - val_sparse_categorical_crossentropy: 0.3702 - val_accuracy: 0.8788\n", - "Epoch 348/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6074 - sparse_categorical_crossentropy: 0.4042 - accuracy: 0.8534 - val_loss: 0.5809 - val_sparse_categorical_crossentropy: 0.3777 - val_accuracy: 0.8696\n", - "Epoch 349/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6110 - sparse_categorical_crossentropy: 0.4079 - accuracy: 0.8480 - val_loss: 0.5706 - val_sparse_categorical_crossentropy: 0.3672 - val_accuracy: 0.8826\n", - "Epoch 350/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6094 - sparse_categorical_crossentropy: 0.4061 - accuracy: 0.8493 - val_loss: 0.5724 - val_sparse_categorical_crossentropy: 0.3693 - val_accuracy: 0.8780\n", - "Epoch 351/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6055 - sparse_categorical_crossentropy: 0.4025 - accuracy: 0.8554 - val_loss: 0.5695 - val_sparse_categorical_crossentropy: 0.3672 - val_accuracy: 0.8844\n", - "Epoch 352/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.6054 - sparse_categorical_crossentropy: 0.4027 - accuracy: 0.8532 - val_loss: 0.5707 - val_sparse_categorical_crossentropy: 0.3683 - val_accuracy: 0.8806\n", - "Epoch 353/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6105 - sparse_categorical_crossentropy: 0.4080 - accuracy: 0.8502 - val_loss: 0.5785 - val_sparse_categorical_crossentropy: 0.3760 - val_accuracy: 0.8746\n", - "Epoch 354/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6147 - sparse_categorical_crossentropy: 0.4126 - accuracy: 0.8477 - val_loss: 0.5661 - val_sparse_categorical_crossentropy: 0.3648 - val_accuracy: 0.8870\n", - "Epoch 355/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6076 - sparse_categorical_crossentropy: 0.4057 - accuracy: 0.8522 - val_loss: 0.5715 - val_sparse_categorical_crossentropy: 0.3697 - val_accuracy: 0.8786\n", - "Epoch 356/1000\n", - "4/4 [==============================] - 0s 35ms/step - loss: 0.6055 - sparse_categorical_crossentropy: 0.4037 - accuracy: 0.8503 - val_loss: 0.5683 - val_sparse_categorical_crossentropy: 0.3658 - val_accuracy: 0.8804\n", - "Epoch 357/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6077 - sparse_categorical_crossentropy: 0.4058 - accuracy: 0.8538 - val_loss: 0.5782 - val_sparse_categorical_crossentropy: 0.3767 - val_accuracy: 0.8716\n", - "Epoch 358/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6084 - sparse_categorical_crossentropy: 0.4070 - accuracy: 0.8537 - val_loss: 0.5660 - val_sparse_categorical_crossentropy: 0.3651 - val_accuracy: 0.8948\n", - "Epoch 359/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6019 - sparse_categorical_crossentropy: 0.4006 - accuracy: 0.8518 - val_loss: 0.5856 - val_sparse_categorical_crossentropy: 0.3833 - val_accuracy: 0.8698\n", - "Epoch 360/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6148 - sparse_categorical_crossentropy: 0.4128 - accuracy: 0.8450 - val_loss: 0.5739 - val_sparse_categorical_crossentropy: 0.3719 - val_accuracy: 0.8750\n", - "Epoch 361/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6108 - sparse_categorical_crossentropy: 0.4086 - accuracy: 0.8490 - val_loss: 0.5722 - val_sparse_categorical_crossentropy: 0.3705 - val_accuracy: 0.8722\n", - "Epoch 362/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6112 - sparse_categorical_crossentropy: 0.4092 - accuracy: 0.8481 - val_loss: 0.5702 - val_sparse_categorical_crossentropy: 0.3683 - val_accuracy: 0.8786\n", - "Epoch 363/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.6052 - sparse_categorical_crossentropy: 0.4031 - accuracy: 0.8533 - val_loss: 0.5629 - val_sparse_categorical_crossentropy: 0.3600 - val_accuracy: 0.8918\n", - "Epoch 364/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6012 - sparse_categorical_crossentropy: 0.3988 - accuracy: 0.8572 - val_loss: 0.5594 - val_sparse_categorical_crossentropy: 0.3571 - val_accuracy: 0.8946\n", - "Epoch 365/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.6046 - sparse_categorical_crossentropy: 0.4023 - accuracy: 0.8542 - val_loss: 0.5658 - val_sparse_categorical_crossentropy: 0.3640 - val_accuracy: 0.8860\n", - "Epoch 366/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6018 - sparse_categorical_crossentropy: 0.3999 - accuracy: 0.8550 - val_loss: 0.5647 - val_sparse_categorical_crossentropy: 0.3628 - val_accuracy: 0.8836\n", - "Epoch 367/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5994 - sparse_categorical_crossentropy: 0.3975 - accuracy: 0.8579 - val_loss: 0.5648 - val_sparse_categorical_crossentropy: 0.3624 - val_accuracy: 0.8834\n", - "Epoch 368/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6036 - sparse_categorical_crossentropy: 0.4014 - accuracy: 0.8546 - val_loss: 0.5569 - val_sparse_categorical_crossentropy: 0.3548 - val_accuracy: 0.8958\n", - "Epoch 369/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6065 - sparse_categorical_crossentropy: 0.4041 - accuracy: 0.8535 - val_loss: 0.5749 - val_sparse_categorical_crossentropy: 0.3728 - val_accuracy: 0.8756\n", - "Epoch 370/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6079 - sparse_categorical_crossentropy: 0.4056 - accuracy: 0.8498 - val_loss: 0.5573 - val_sparse_categorical_crossentropy: 0.3551 - val_accuracy: 0.8930\n", - "Epoch 371/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.6020 - sparse_categorical_crossentropy: 0.4001 - accuracy: 0.8545 - val_loss: 0.5702 - val_sparse_categorical_crossentropy: 0.3688 - val_accuracy: 0.8758\n", - "Epoch 372/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6056 - sparse_categorical_crossentropy: 0.4043 - accuracy: 0.8522 - val_loss: 0.5766 - val_sparse_categorical_crossentropy: 0.3755 - val_accuracy: 0.8712\n", - "Epoch 373/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.6027 - sparse_categorical_crossentropy: 0.4014 - accuracy: 0.8539 - val_loss: 0.5563 - val_sparse_categorical_crossentropy: 0.3547 - val_accuracy: 0.8942\n", - "Epoch 374/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5957 - sparse_categorical_crossentropy: 0.3941 - accuracy: 0.8573 - val_loss: 0.5586 - val_sparse_categorical_crossentropy: 0.3575 - val_accuracy: 0.8900\n", - "Epoch 375/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5952 - sparse_categorical_crossentropy: 0.3939 - accuracy: 0.8590 - val_loss: 0.5609 - val_sparse_categorical_crossentropy: 0.3593 - val_accuracy: 0.8870\n", - "Epoch 376/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5953 - sparse_categorical_crossentropy: 0.3937 - accuracy: 0.8587 - val_loss: 0.5550 - val_sparse_categorical_crossentropy: 0.3533 - val_accuracy: 0.8992\n", - "Epoch 377/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5928 - sparse_categorical_crossentropy: 0.3910 - accuracy: 0.8624 - val_loss: 0.5556 - val_sparse_categorical_crossentropy: 0.3532 - val_accuracy: 0.8938\n", - "Epoch 378/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5924 - sparse_categorical_crossentropy: 0.3897 - accuracy: 0.8658 - val_loss: 0.5562 - val_sparse_categorical_crossentropy: 0.3536 - val_accuracy: 0.8890\n", - "Epoch 379/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5900 - sparse_categorical_crossentropy: 0.3875 - accuracy: 0.8632 - val_loss: 0.5589 - val_sparse_categorical_crossentropy: 0.3562 - val_accuracy: 0.8910\n", - "Epoch 380/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5917 - sparse_categorical_crossentropy: 0.3891 - accuracy: 0.8623 - val_loss: 0.5561 - val_sparse_categorical_crossentropy: 0.3540 - val_accuracy: 0.8978\n", - "Epoch 381/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5987 - sparse_categorical_crossentropy: 0.3965 - accuracy: 0.8591 - val_loss: 0.5583 - val_sparse_categorical_crossentropy: 0.3563 - val_accuracy: 0.8906\n", - "Epoch 382/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5921 - sparse_categorical_crossentropy: 0.3901 - accuracy: 0.8650 - val_loss: 0.5601 - val_sparse_categorical_crossentropy: 0.3579 - val_accuracy: 0.8948\n", - "Epoch 383/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5990 - sparse_categorical_crossentropy: 0.3968 - accuracy: 0.8549 - val_loss: 0.5616 - val_sparse_categorical_crossentropy: 0.3596 - val_accuracy: 0.8834\n", - "Epoch 384/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5970 - sparse_categorical_crossentropy: 0.3946 - accuracy: 0.8587 - val_loss: 0.5585 - val_sparse_categorical_crossentropy: 0.3560 - val_accuracy: 0.8920\n", - "Epoch 385/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5953 - sparse_categorical_crossentropy: 0.3928 - accuracy: 0.8611 - val_loss: 0.5610 - val_sparse_categorical_crossentropy: 0.3584 - val_accuracy: 0.8874\n", - "Epoch 386/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.5953 - sparse_categorical_crossentropy: 0.3929 - accuracy: 0.8546 - val_loss: 0.5646 - val_sparse_categorical_crossentropy: 0.3613 - val_accuracy: 0.8780\n", - "Epoch 387/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5881 - sparse_categorical_crossentropy: 0.3850 - accuracy: 0.8661 - val_loss: 0.5569 - val_sparse_categorical_crossentropy: 0.3540 - val_accuracy: 0.8912\n", - "Epoch 388/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5937 - sparse_categorical_crossentropy: 0.3906 - accuracy: 0.8605 - val_loss: 0.5499 - val_sparse_categorical_crossentropy: 0.3476 - val_accuracy: 0.8992\n", - "Epoch 389/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5908 - sparse_categorical_crossentropy: 0.3885 - accuracy: 0.8603 - val_loss: 0.5512 - val_sparse_categorical_crossentropy: 0.3490 - val_accuracy: 0.8936\n", - "Epoch 390/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5894 - sparse_categorical_crossentropy: 0.3877 - accuracy: 0.8635 - val_loss: 0.5498 - val_sparse_categorical_crossentropy: 0.3471 - val_accuracy: 0.8996\n", - "Epoch 391/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5910 - sparse_categorical_crossentropy: 0.3886 - accuracy: 0.8601 - val_loss: 0.5514 - val_sparse_categorical_crossentropy: 0.3499 - val_accuracy: 0.8996\n", - "Epoch 392/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5929 - sparse_categorical_crossentropy: 0.3912 - accuracy: 0.8645 - val_loss: 0.5581 - val_sparse_categorical_crossentropy: 0.3567 - val_accuracy: 0.8872\n", - "Epoch 393/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5880 - sparse_categorical_crossentropy: 0.3861 - accuracy: 0.8643 - val_loss: 0.5511 - val_sparse_categorical_crossentropy: 0.3491 - val_accuracy: 0.8958\n", - "Epoch 394/1000\n", - "4/4 [==============================] - 0s 25ms/step - loss: 0.5915 - sparse_categorical_crossentropy: 0.3896 - accuracy: 0.8594 - val_loss: 0.5518 - val_sparse_categorical_crossentropy: 0.3494 - val_accuracy: 0.8988\n", - "Epoch 395/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5882 - sparse_categorical_crossentropy: 0.3858 - accuracy: 0.8626 - val_loss: 0.5491 - val_sparse_categorical_crossentropy: 0.3461 - val_accuracy: 0.8996\n", - "Epoch 396/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5940 - sparse_categorical_crossentropy: 0.3910 - accuracy: 0.8611 - val_loss: 0.5604 - val_sparse_categorical_crossentropy: 0.3581 - val_accuracy: 0.8806\n", - "Epoch 397/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5898 - sparse_categorical_crossentropy: 0.3877 - accuracy: 0.8605 - val_loss: 0.5532 - val_sparse_categorical_crossentropy: 0.3514 - val_accuracy: 0.8812\n", - "Epoch 398/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5893 - sparse_categorical_crossentropy: 0.3877 - accuracy: 0.8605 - val_loss: 0.5462 - val_sparse_categorical_crossentropy: 0.3452 - val_accuracy: 0.8980\n", - "Epoch 399/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5895 - sparse_categorical_crossentropy: 0.3882 - accuracy: 0.8618 - val_loss: 0.5529 - val_sparse_categorical_crossentropy: 0.3513 - val_accuracy: 0.8946\n", - "Epoch 400/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5886 - sparse_categorical_crossentropy: 0.3871 - accuracy: 0.8615 - val_loss: 0.5493 - val_sparse_categorical_crossentropy: 0.3479 - val_accuracy: 0.8942\n", - "Epoch 401/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5928 - sparse_categorical_crossentropy: 0.3913 - accuracy: 0.8596 - val_loss: 0.5756 - val_sparse_categorical_crossentropy: 0.3739 - val_accuracy: 0.8612\n", - "Epoch 402/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.6047 - sparse_categorical_crossentropy: 0.4033 - accuracy: 0.8478 - val_loss: 0.5660 - val_sparse_categorical_crossentropy: 0.3650 - val_accuracy: 0.8774\n", - "Epoch 403/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.6090 - sparse_categorical_crossentropy: 0.4081 - accuracy: 0.8470 - val_loss: 0.5799 - val_sparse_categorical_crossentropy: 0.3796 - val_accuracy: 0.8638\n", - "Epoch 404/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.6082 - sparse_categorical_crossentropy: 0.4074 - accuracy: 0.8471 - val_loss: 0.5707 - val_sparse_categorical_crossentropy: 0.3701 - val_accuracy: 0.8698\n", - "Epoch 405/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5986 - sparse_categorical_crossentropy: 0.3978 - accuracy: 0.8533 - val_loss: 0.5625 - val_sparse_categorical_crossentropy: 0.3615 - val_accuracy: 0.8812\n", - "Epoch 406/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5923 - sparse_categorical_crossentropy: 0.3913 - accuracy: 0.8608 - val_loss: 0.5531 - val_sparse_categorical_crossentropy: 0.3531 - val_accuracy: 0.8910\n", - "Epoch 407/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5831 - sparse_categorical_crossentropy: 0.3826 - accuracy: 0.8689 - val_loss: 0.5450 - val_sparse_categorical_crossentropy: 0.3444 - val_accuracy: 0.8946\n", - "Epoch 408/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5784 - sparse_categorical_crossentropy: 0.3775 - accuracy: 0.8686 - val_loss: 0.5417 - val_sparse_categorical_crossentropy: 0.3400 - val_accuracy: 0.9036\n", - "Epoch 409/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5820 - sparse_categorical_crossentropy: 0.3803 - accuracy: 0.8689 - val_loss: 0.5436 - val_sparse_categorical_crossentropy: 0.3416 - val_accuracy: 0.9022\n", - "Epoch 410/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.5825 - sparse_categorical_crossentropy: 0.3803 - accuracy: 0.8676 - val_loss: 0.5466 - val_sparse_categorical_crossentropy: 0.3435 - val_accuracy: 0.8920\n", - "Epoch 411/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5902 - sparse_categorical_crossentropy: 0.3876 - accuracy: 0.8616 - val_loss: 0.5507 - val_sparse_categorical_crossentropy: 0.3470 - val_accuracy: 0.8916\n", - "Epoch 412/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5869 - sparse_categorical_crossentropy: 0.3835 - accuracy: 0.8625 - val_loss: 0.5509 - val_sparse_categorical_crossentropy: 0.3482 - val_accuracy: 0.8894\n", - "Epoch 413/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5909 - sparse_categorical_crossentropy: 0.3881 - accuracy: 0.8606 - val_loss: 0.5624 - val_sparse_categorical_crossentropy: 0.3606 - val_accuracy: 0.8842\n", - "Epoch 414/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5817 - sparse_categorical_crossentropy: 0.3796 - accuracy: 0.8683 - val_loss: 0.5459 - val_sparse_categorical_crossentropy: 0.3431 - val_accuracy: 0.8978\n", - "Epoch 415/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5797 - sparse_categorical_crossentropy: 0.3769 - accuracy: 0.8697 - val_loss: 0.5370 - val_sparse_categorical_crossentropy: 0.3341 - val_accuracy: 0.9036\n", - "Epoch 416/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5772 - sparse_categorical_crossentropy: 0.3742 - accuracy: 0.8719 - val_loss: 0.5415 - val_sparse_categorical_crossentropy: 0.3387 - val_accuracy: 0.9022\n", - "Epoch 417/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5782 - sparse_categorical_crossentropy: 0.3755 - accuracy: 0.8714 - val_loss: 0.5517 - val_sparse_categorical_crossentropy: 0.3479 - val_accuracy: 0.8830\n", - "Epoch 418/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5824 - sparse_categorical_crossentropy: 0.3791 - accuracy: 0.8667 - val_loss: 0.5368 - val_sparse_categorical_crossentropy: 0.3341 - val_accuracy: 0.9010\n", - "Epoch 419/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5795 - sparse_categorical_crossentropy: 0.3770 - accuracy: 0.8701 - val_loss: 0.5473 - val_sparse_categorical_crossentropy: 0.3450 - val_accuracy: 0.8984\n", - "Epoch 420/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5727 - sparse_categorical_crossentropy: 0.3705 - accuracy: 0.8719 - val_loss: 0.5338 - val_sparse_categorical_crossentropy: 0.3315 - val_accuracy: 0.9110\n", - "Epoch 421/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5719 - sparse_categorical_crossentropy: 0.3695 - accuracy: 0.8738 - val_loss: 0.5320 - val_sparse_categorical_crossentropy: 0.3294 - val_accuracy: 0.9022\n", - "Epoch 422/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5720 - sparse_categorical_crossentropy: 0.3690 - accuracy: 0.8754 - val_loss: 0.5408 - val_sparse_categorical_crossentropy: 0.3380 - val_accuracy: 0.9038\n", - "Epoch 423/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5768 - sparse_categorical_crossentropy: 0.3740 - accuracy: 0.8692 - val_loss: 0.5340 - val_sparse_categorical_crossentropy: 0.3309 - val_accuracy: 0.9070\n", - "Epoch 424/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5741 - sparse_categorical_crossentropy: 0.3710 - accuracy: 0.8723 - val_loss: 0.5289 - val_sparse_categorical_crossentropy: 0.3257 - val_accuracy: 0.9088\n", - "Epoch 425/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5695 - sparse_categorical_crossentropy: 0.3661 - accuracy: 0.8734 - val_loss: 0.5272 - val_sparse_categorical_crossentropy: 0.3237 - val_accuracy: 0.9080\n", - "Epoch 426/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5676 - sparse_categorical_crossentropy: 0.3642 - accuracy: 0.8747 - val_loss: 0.5304 - val_sparse_categorical_crossentropy: 0.3271 - val_accuracy: 0.9050\n", - "Epoch 427/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5732 - sparse_categorical_crossentropy: 0.3700 - accuracy: 0.8718 - val_loss: 0.5280 - val_sparse_categorical_crossentropy: 0.3250 - val_accuracy: 0.9130\n", - "Epoch 428/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5707 - sparse_categorical_crossentropy: 0.3676 - accuracy: 0.8731 - val_loss: 0.5370 - val_sparse_categorical_crossentropy: 0.3333 - val_accuracy: 0.9046\n", - "Epoch 429/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5724 - sparse_categorical_crossentropy: 0.3690 - accuracy: 0.8726 - val_loss: 0.5333 - val_sparse_categorical_crossentropy: 0.3302 - val_accuracy: 0.9088\n", - "Epoch 430/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5688 - sparse_categorical_crossentropy: 0.3659 - accuracy: 0.8745 - val_loss: 0.5436 - val_sparse_categorical_crossentropy: 0.3417 - val_accuracy: 0.8842\n", - "Epoch 431/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5750 - sparse_categorical_crossentropy: 0.3728 - accuracy: 0.8698 - val_loss: 0.5258 - val_sparse_categorical_crossentropy: 0.3235 - val_accuracy: 0.9128\n", - "Epoch 432/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5727 - sparse_categorical_crossentropy: 0.3705 - accuracy: 0.8717 - val_loss: 0.5381 - val_sparse_categorical_crossentropy: 0.3355 - val_accuracy: 0.8932\n", - "Epoch 433/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5783 - sparse_categorical_crossentropy: 0.3759 - accuracy: 0.8676 - val_loss: 0.5280 - val_sparse_categorical_crossentropy: 0.3255 - val_accuracy: 0.9036\n", - "Epoch 434/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5718 - sparse_categorical_crossentropy: 0.3695 - accuracy: 0.8686 - val_loss: 0.5364 - val_sparse_categorical_crossentropy: 0.3344 - val_accuracy: 0.8986\n", - "Epoch 435/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5705 - sparse_categorical_crossentropy: 0.3686 - accuracy: 0.8742 - val_loss: 0.5278 - val_sparse_categorical_crossentropy: 0.3260 - val_accuracy: 0.9084\n", - "Epoch 436/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5672 - sparse_categorical_crossentropy: 0.3650 - accuracy: 0.8736 - val_loss: 0.5328 - val_sparse_categorical_crossentropy: 0.3304 - val_accuracy: 0.9044\n", - "Epoch 437/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.5651 - sparse_categorical_crossentropy: 0.3626 - accuracy: 0.8774 - val_loss: 0.5303 - val_sparse_categorical_crossentropy: 0.3280 - val_accuracy: 0.9058\n", - "Epoch 438/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5657 - sparse_categorical_crossentropy: 0.3631 - accuracy: 0.8790 - val_loss: 0.5300 - val_sparse_categorical_crossentropy: 0.3275 - val_accuracy: 0.9094\n", - "Epoch 439/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5759 - sparse_categorical_crossentropy: 0.3737 - accuracy: 0.8685 - val_loss: 0.5398 - val_sparse_categorical_crossentropy: 0.3374 - val_accuracy: 0.8900\n", - "Epoch 440/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5716 - sparse_categorical_crossentropy: 0.3689 - accuracy: 0.8731 - val_loss: 0.5242 - val_sparse_categorical_crossentropy: 0.3217 - val_accuracy: 0.9110\n", - "Epoch 441/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5677 - sparse_categorical_crossentropy: 0.3649 - accuracy: 0.8743 - val_loss: 0.5250 - val_sparse_categorical_crossentropy: 0.3225 - val_accuracy: 0.9116\n", - "Epoch 442/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5697 - sparse_categorical_crossentropy: 0.3674 - accuracy: 0.8750 - val_loss: 0.5274 - val_sparse_categorical_crossentropy: 0.3239 - val_accuracy: 0.9044\n", - "Epoch 443/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5700 - sparse_categorical_crossentropy: 0.3670 - accuracy: 0.8757 - val_loss: 0.5293 - val_sparse_categorical_crossentropy: 0.3266 - val_accuracy: 0.9006\n", - "Epoch 444/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5659 - sparse_categorical_crossentropy: 0.3633 - accuracy: 0.8774 - val_loss: 0.5191 - val_sparse_categorical_crossentropy: 0.3166 - val_accuracy: 0.9134\n", - "Epoch 445/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5609 - sparse_categorical_crossentropy: 0.3584 - accuracy: 0.8779 - val_loss: 0.5208 - val_sparse_categorical_crossentropy: 0.3184 - val_accuracy: 0.9104\n", - "Epoch 446/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.5672 - sparse_categorical_crossentropy: 0.3645 - accuracy: 0.8792 - val_loss: 0.5231 - val_sparse_categorical_crossentropy: 0.3198 - val_accuracy: 0.9118\n", - "Epoch 447/1000\n", - "4/4 [==============================] - 0s 22ms/step - loss: 0.5672 - sparse_categorical_crossentropy: 0.3642 - accuracy: 0.8727 - val_loss: 0.5326 - val_sparse_categorical_crossentropy: 0.3290 - val_accuracy: 0.8998\n", - "Epoch 448/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5711 - sparse_categorical_crossentropy: 0.3674 - accuracy: 0.8743 - val_loss: 0.5319 - val_sparse_categorical_crossentropy: 0.3286 - val_accuracy: 0.8980\n", - "Epoch 449/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5665 - sparse_categorical_crossentropy: 0.3630 - accuracy: 0.8766 - val_loss: 0.5267 - val_sparse_categorical_crossentropy: 0.3237 - val_accuracy: 0.9086\n", - "Epoch 450/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5641 - sparse_categorical_crossentropy: 0.3612 - accuracy: 0.8775 - val_loss: 0.5227 - val_sparse_categorical_crossentropy: 0.3197 - val_accuracy: 0.9120\n", - "Epoch 451/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5606 - sparse_categorical_crossentropy: 0.3577 - accuracy: 0.8834 - val_loss: 0.5236 - val_sparse_categorical_crossentropy: 0.3208 - val_accuracy: 0.9068\n", - "Epoch 452/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5625 - sparse_categorical_crossentropy: 0.3599 - accuracy: 0.8793 - val_loss: 0.5305 - val_sparse_categorical_crossentropy: 0.3286 - val_accuracy: 0.9026\n", - "Epoch 453/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5658 - sparse_categorical_crossentropy: 0.3636 - accuracy: 0.8768 - val_loss: 0.5315 - val_sparse_categorical_crossentropy: 0.3294 - val_accuracy: 0.8994\n", - "Epoch 454/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.5635 - sparse_categorical_crossentropy: 0.3612 - accuracy: 0.8794 - val_loss: 0.5255 - val_sparse_categorical_crossentropy: 0.3230 - val_accuracy: 0.9138\n", - "Epoch 455/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5614 - sparse_categorical_crossentropy: 0.3588 - accuracy: 0.8805 - val_loss: 0.5268 - val_sparse_categorical_crossentropy: 0.3234 - val_accuracy: 0.9118\n", - "Epoch 456/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5605 - sparse_categorical_crossentropy: 0.3568 - accuracy: 0.8805 - val_loss: 0.5236 - val_sparse_categorical_crossentropy: 0.3201 - val_accuracy: 0.9082\n", - "Epoch 457/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5562 - sparse_categorical_crossentropy: 0.3526 - accuracy: 0.8846 - val_loss: 0.5190 - val_sparse_categorical_crossentropy: 0.3148 - val_accuracy: 0.9176\n", - "Epoch 458/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5603 - sparse_categorical_crossentropy: 0.3564 - accuracy: 0.8817 - val_loss: 0.5188 - val_sparse_categorical_crossentropy: 0.3149 - val_accuracy: 0.9142\n", - "Epoch 459/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5585 - sparse_categorical_crossentropy: 0.3546 - accuracy: 0.8811 - val_loss: 0.5148 - val_sparse_categorical_crossentropy: 0.3115 - val_accuracy: 0.9168\n", - "Epoch 460/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5531 - sparse_categorical_crossentropy: 0.3497 - accuracy: 0.8873 - val_loss: 0.5134 - val_sparse_categorical_crossentropy: 0.3103 - val_accuracy: 0.9170\n", - "Epoch 461/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5539 - sparse_categorical_crossentropy: 0.3509 - accuracy: 0.8812 - val_loss: 0.5183 - val_sparse_categorical_crossentropy: 0.3156 - val_accuracy: 0.9084\n", - "Epoch 462/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.5602 - sparse_categorical_crossentropy: 0.3574 - accuracy: 0.8777 - val_loss: 0.5157 - val_sparse_categorical_crossentropy: 0.3130 - val_accuracy: 0.9084\n", - "Epoch 463/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5585 - sparse_categorical_crossentropy: 0.3558 - accuracy: 0.8792 - val_loss: 0.5308 - val_sparse_categorical_crossentropy: 0.3281 - val_accuracy: 0.9010\n", - "Epoch 464/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5602 - sparse_categorical_crossentropy: 0.3575 - accuracy: 0.8789 - val_loss: 0.5147 - val_sparse_categorical_crossentropy: 0.3123 - val_accuracy: 0.9132\n", - "Epoch 465/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5600 - sparse_categorical_crossentropy: 0.3573 - accuracy: 0.8798 - val_loss: 0.5237 - val_sparse_categorical_crossentropy: 0.3209 - val_accuracy: 0.9082\n", - "Epoch 466/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5614 - sparse_categorical_crossentropy: 0.3584 - accuracy: 0.8789 - val_loss: 0.5257 - val_sparse_categorical_crossentropy: 0.3229 - val_accuracy: 0.9034\n", - "Epoch 467/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5654 - sparse_categorical_crossentropy: 0.3625 - accuracy: 0.8735 - val_loss: 0.5219 - val_sparse_categorical_crossentropy: 0.3186 - val_accuracy: 0.9082\n", - "Epoch 468/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5596 - sparse_categorical_crossentropy: 0.3565 - accuracy: 0.8805 - val_loss: 0.5130 - val_sparse_categorical_crossentropy: 0.3100 - val_accuracy: 0.9172\n", - "Epoch 469/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5525 - sparse_categorical_crossentropy: 0.3498 - accuracy: 0.8873 - val_loss: 0.5091 - val_sparse_categorical_crossentropy: 0.3064 - val_accuracy: 0.9204\n", - "Epoch 470/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.5496 - sparse_categorical_crossentropy: 0.3469 - accuracy: 0.8879 - val_loss: 0.5155 - val_sparse_categorical_crossentropy: 0.3127 - val_accuracy: 0.9170\n", - "Epoch 471/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5555 - sparse_categorical_crossentropy: 0.3526 - accuracy: 0.8805 - val_loss: 0.5267 - val_sparse_categorical_crossentropy: 0.3240 - val_accuracy: 0.9036\n", - "Epoch 472/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5624 - sparse_categorical_crossentropy: 0.3595 - accuracy: 0.8754 - val_loss: 0.5099 - val_sparse_categorical_crossentropy: 0.3073 - val_accuracy: 0.9204\n", - "Epoch 473/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5562 - sparse_categorical_crossentropy: 0.3536 - accuracy: 0.8816 - val_loss: 0.5194 - val_sparse_categorical_crossentropy: 0.3162 - val_accuracy: 0.9124\n", - "Epoch 474/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5528 - sparse_categorical_crossentropy: 0.3502 - accuracy: 0.8825 - val_loss: 0.5140 - val_sparse_categorical_crossentropy: 0.3115 - val_accuracy: 0.9114\n", - "Epoch 475/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5580 - sparse_categorical_crossentropy: 0.3555 - accuracy: 0.8796 - val_loss: 0.5169 - val_sparse_categorical_crossentropy: 0.3144 - val_accuracy: 0.9060\n", - "Epoch 476/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5596 - sparse_categorical_crossentropy: 0.3568 - accuracy: 0.8802 - val_loss: 0.5259 - val_sparse_categorical_crossentropy: 0.3234 - val_accuracy: 0.8992\n", - "Epoch 477/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5575 - sparse_categorical_crossentropy: 0.3550 - accuracy: 0.8805 - val_loss: 0.5108 - val_sparse_categorical_crossentropy: 0.3080 - val_accuracy: 0.9184\n", - "Epoch 478/1000\n", - "4/4 [==============================] - 0s 33ms/step - loss: 0.5591 - sparse_categorical_crossentropy: 0.3566 - accuracy: 0.8839 - val_loss: 0.5110 - val_sparse_categorical_crossentropy: 0.3086 - val_accuracy: 0.9162\n", - "Epoch 479/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5525 - sparse_categorical_crossentropy: 0.3500 - accuracy: 0.8843 - val_loss: 0.5205 - val_sparse_categorical_crossentropy: 0.3177 - val_accuracy: 0.9114\n", - "Epoch 480/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5533 - sparse_categorical_crossentropy: 0.3505 - accuracy: 0.8842 - val_loss: 0.5340 - val_sparse_categorical_crossentropy: 0.3307 - val_accuracy: 0.8998\n", - "Epoch 481/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5737 - sparse_categorical_crossentropy: 0.3708 - accuracy: 0.8706 - val_loss: 0.5359 - val_sparse_categorical_crossentropy: 0.3329 - val_accuracy: 0.8958\n", - "Epoch 482/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5655 - sparse_categorical_crossentropy: 0.3623 - accuracy: 0.8773 - val_loss: 0.5255 - val_sparse_categorical_crossentropy: 0.3227 - val_accuracy: 0.8978\n", - "Epoch 483/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5606 - sparse_categorical_crossentropy: 0.3578 - accuracy: 0.8806 - val_loss: 0.5141 - val_sparse_categorical_crossentropy: 0.3118 - val_accuracy: 0.9062\n", - "Epoch 484/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5600 - sparse_categorical_crossentropy: 0.3575 - accuracy: 0.8765 - val_loss: 0.5160 - val_sparse_categorical_crossentropy: 0.3132 - val_accuracy: 0.9042\n", - "Epoch 485/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5537 - sparse_categorical_crossentropy: 0.3512 - accuracy: 0.8821 - val_loss: 0.5070 - val_sparse_categorical_crossentropy: 0.3050 - val_accuracy: 0.9200\n", - "Epoch 486/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5503 - sparse_categorical_crossentropy: 0.3480 - accuracy: 0.8850 - val_loss: 0.5109 - val_sparse_categorical_crossentropy: 0.3089 - val_accuracy: 0.9130\n", - "Epoch 487/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.5490 - sparse_categorical_crossentropy: 0.3471 - accuracy: 0.8854 - val_loss: 0.5142 - val_sparse_categorical_crossentropy: 0.3110 - val_accuracy: 0.9184\n", - "Epoch 488/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5489 - sparse_categorical_crossentropy: 0.3461 - accuracy: 0.8853 - val_loss: 0.5052 - val_sparse_categorical_crossentropy: 0.3028 - val_accuracy: 0.9206\n", - "Epoch 489/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5452 - sparse_categorical_crossentropy: 0.3424 - accuracy: 0.8888 - val_loss: 0.5139 - val_sparse_categorical_crossentropy: 0.3110 - val_accuracy: 0.9082\n", - "Epoch 490/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5599 - sparse_categorical_crossentropy: 0.3570 - accuracy: 0.8790 - val_loss: 0.5088 - val_sparse_categorical_crossentropy: 0.3053 - val_accuracy: 0.9154\n", - "Epoch 491/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5548 - sparse_categorical_crossentropy: 0.3516 - accuracy: 0.8778 - val_loss: 0.5240 - val_sparse_categorical_crossentropy: 0.3207 - val_accuracy: 0.8948\n", - "Epoch 492/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5544 - sparse_categorical_crossentropy: 0.3512 - accuracy: 0.8800 - val_loss: 0.5285 - val_sparse_categorical_crossentropy: 0.3254 - val_accuracy: 0.8948\n", - "Epoch 493/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5592 - sparse_categorical_crossentropy: 0.3559 - accuracy: 0.8769 - val_loss: 0.5288 - val_sparse_categorical_crossentropy: 0.3260 - val_accuracy: 0.8980\n", - "Epoch 494/1000\n", - "4/4 [==============================] - 0s 22ms/step - loss: 0.5609 - sparse_categorical_crossentropy: 0.3583 - accuracy: 0.8751 - val_loss: 0.5294 - val_sparse_categorical_crossentropy: 0.3274 - val_accuracy: 0.8916\n", - "Epoch 495/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5550 - sparse_categorical_crossentropy: 0.3527 - accuracy: 0.8783 - val_loss: 0.5070 - val_sparse_categorical_crossentropy: 0.3053 - val_accuracy: 0.9170\n", - "Epoch 496/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5528 - sparse_categorical_crossentropy: 0.3509 - accuracy: 0.8782 - val_loss: 0.5082 - val_sparse_categorical_crossentropy: 0.3066 - val_accuracy: 0.9102\n", - "Epoch 497/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5496 - sparse_categorical_crossentropy: 0.3479 - accuracy: 0.8829 - val_loss: 0.5034 - val_sparse_categorical_crossentropy: 0.3008 - val_accuracy: 0.9186\n", - "Epoch 498/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5479 - sparse_categorical_crossentropy: 0.3457 - accuracy: 0.8850 - val_loss: 0.5065 - val_sparse_categorical_crossentropy: 0.3044 - val_accuracy: 0.9104\n", - "Epoch 499/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5489 - sparse_categorical_crossentropy: 0.3467 - accuracy: 0.8845 - val_loss: 0.5081 - val_sparse_categorical_crossentropy: 0.3053 - val_accuracy: 0.9176\n", - "Epoch 500/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5484 - sparse_categorical_crossentropy: 0.3460 - accuracy: 0.8830 - val_loss: 0.5093 - val_sparse_categorical_crossentropy: 0.3068 - val_accuracy: 0.9094\n", - "Epoch 501/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5490 - sparse_categorical_crossentropy: 0.3466 - accuracy: 0.8831 - val_loss: 0.5038 - val_sparse_categorical_crossentropy: 0.3018 - val_accuracy: 0.9158\n", - "Epoch 502/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5415 - sparse_categorical_crossentropy: 0.3395 - accuracy: 0.8870 - val_loss: 0.4979 - val_sparse_categorical_crossentropy: 0.2963 - val_accuracy: 0.9256\n", - "Epoch 503/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5381 - sparse_categorical_crossentropy: 0.3365 - accuracy: 0.8902 - val_loss: 0.4986 - val_sparse_categorical_crossentropy: 0.2965 - val_accuracy: 0.9198\n", - "Epoch 504/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5385 - sparse_categorical_crossentropy: 0.3364 - accuracy: 0.8896 - val_loss: 0.4963 - val_sparse_categorical_crossentropy: 0.2936 - val_accuracy: 0.9256\n", - "Epoch 505/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5376 - sparse_categorical_crossentropy: 0.3349 - accuracy: 0.8903 - val_loss: 0.4924 - val_sparse_categorical_crossentropy: 0.2899 - val_accuracy: 0.9272\n", - "Epoch 506/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5336 - sparse_categorical_crossentropy: 0.3311 - accuracy: 0.8919 - val_loss: 0.4960 - val_sparse_categorical_crossentropy: 0.2929 - val_accuracy: 0.9210\n", - "Epoch 507/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5341 - sparse_categorical_crossentropy: 0.3309 - accuracy: 0.8937 - val_loss: 0.4910 - val_sparse_categorical_crossentropy: 0.2877 - val_accuracy: 0.9260\n", - "Epoch 508/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5346 - sparse_categorical_crossentropy: 0.3314 - accuracy: 0.8934 - val_loss: 0.4981 - val_sparse_categorical_crossentropy: 0.2945 - val_accuracy: 0.9220\n", - "Epoch 509/1000\n", - "4/4 [==============================] - 0s 26ms/step - loss: 0.5393 - sparse_categorical_crossentropy: 0.3361 - accuracy: 0.8886 - val_loss: 0.5094 - val_sparse_categorical_crossentropy: 0.3063 - val_accuracy: 0.9108\n", - "Epoch 510/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5420 - sparse_categorical_crossentropy: 0.3391 - accuracy: 0.8858 - val_loss: 0.5037 - val_sparse_categorical_crossentropy: 0.3010 - val_accuracy: 0.9084\n", - "Epoch 511/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5473 - sparse_categorical_crossentropy: 0.3449 - accuracy: 0.8830 - val_loss: 0.5125 - val_sparse_categorical_crossentropy: 0.3110 - val_accuracy: 0.9072\n", - "Epoch 512/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5514 - sparse_categorical_crossentropy: 0.3495 - accuracy: 0.8821 - val_loss: 0.5118 - val_sparse_categorical_crossentropy: 0.3102 - val_accuracy: 0.9078\n", - "Epoch 513/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5452 - sparse_categorical_crossentropy: 0.3438 - accuracy: 0.8847 - val_loss: 0.5034 - val_sparse_categorical_crossentropy: 0.3019 - val_accuracy: 0.9162\n", - "Epoch 514/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5405 - sparse_categorical_crossentropy: 0.3397 - accuracy: 0.8864 - val_loss: 0.4964 - val_sparse_categorical_crossentropy: 0.2956 - val_accuracy: 0.9158\n", - "Epoch 515/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5416 - sparse_categorical_crossentropy: 0.3410 - accuracy: 0.8871 - val_loss: 0.4926 - val_sparse_categorical_crossentropy: 0.2924 - val_accuracy: 0.9242\n", - "Epoch 516/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5386 - sparse_categorical_crossentropy: 0.3382 - accuracy: 0.8898 - val_loss: 0.4924 - val_sparse_categorical_crossentropy: 0.2917 - val_accuracy: 0.9256\n", - "Epoch 517/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5348 - sparse_categorical_crossentropy: 0.3338 - accuracy: 0.8896 - val_loss: 0.4935 - val_sparse_categorical_crossentropy: 0.2920 - val_accuracy: 0.9256\n", - "Epoch 518/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5315 - sparse_categorical_crossentropy: 0.3298 - accuracy: 0.8935 - val_loss: 0.4922 - val_sparse_categorical_crossentropy: 0.2905 - val_accuracy: 0.9222\n", - "Epoch 519/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5315 - sparse_categorical_crossentropy: 0.3295 - accuracy: 0.8954 - val_loss: 0.5031 - val_sparse_categorical_crossentropy: 0.3017 - val_accuracy: 0.9090\n", - "Epoch 520/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5403 - sparse_categorical_crossentropy: 0.3390 - accuracy: 0.8865 - val_loss: 0.5055 - val_sparse_categorical_crossentropy: 0.3039 - val_accuracy: 0.9124\n", - "Epoch 521/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5472 - sparse_categorical_crossentropy: 0.3461 - accuracy: 0.8845 - val_loss: 0.5070 - val_sparse_categorical_crossentropy: 0.3057 - val_accuracy: 0.9092\n", - "Epoch 522/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5474 - sparse_categorical_crossentropy: 0.3464 - accuracy: 0.8823 - val_loss: 0.5106 - val_sparse_categorical_crossentropy: 0.3104 - val_accuracy: 0.9092\n", - "Epoch 523/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5449 - sparse_categorical_crossentropy: 0.3445 - accuracy: 0.8866 - val_loss: 0.4988 - val_sparse_categorical_crossentropy: 0.2983 - val_accuracy: 0.9204\n", - "Epoch 524/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5317 - sparse_categorical_crossentropy: 0.3312 - accuracy: 0.8932 - val_loss: 0.4993 - val_sparse_categorical_crossentropy: 0.2983 - val_accuracy: 0.9146\n", - "Epoch 525/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5300 - sparse_categorical_crossentropy: 0.3289 - accuracy: 0.8963 - val_loss: 0.4921 - val_sparse_categorical_crossentropy: 0.2909 - val_accuracy: 0.9198\n", - "Epoch 526/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5384 - sparse_categorical_crossentropy: 0.3368 - accuracy: 0.8887 - val_loss: 0.5040 - val_sparse_categorical_crossentropy: 0.3018 - val_accuracy: 0.9106\n", - "Epoch 527/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5444 - sparse_categorical_crossentropy: 0.3426 - accuracy: 0.8844 - val_loss: 0.4969 - val_sparse_categorical_crossentropy: 0.2950 - val_accuracy: 0.9168\n", - "Epoch 528/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5358 - sparse_categorical_crossentropy: 0.3342 - accuracy: 0.8899 - val_loss: 0.4991 - val_sparse_categorical_crossentropy: 0.2978 - val_accuracy: 0.9142\n", - "Epoch 529/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5416 - sparse_categorical_crossentropy: 0.3404 - accuracy: 0.8845 - val_loss: 0.5009 - val_sparse_categorical_crossentropy: 0.3001 - val_accuracy: 0.9132\n", - "Epoch 530/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5414 - sparse_categorical_crossentropy: 0.3408 - accuracy: 0.8854 - val_loss: 0.5166 - val_sparse_categorical_crossentropy: 0.3159 - val_accuracy: 0.8976\n", - "Epoch 531/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5384 - sparse_categorical_crossentropy: 0.3380 - accuracy: 0.8870 - val_loss: 0.5037 - val_sparse_categorical_crossentropy: 0.3039 - val_accuracy: 0.9096\n", - "Epoch 532/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5463 - sparse_categorical_crossentropy: 0.3464 - accuracy: 0.8820 - val_loss: 0.4951 - val_sparse_categorical_crossentropy: 0.2956 - val_accuracy: 0.9200\n", - "Epoch 533/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5330 - sparse_categorical_crossentropy: 0.3335 - accuracy: 0.8894 - val_loss: 0.5043 - val_sparse_categorical_crossentropy: 0.3047 - val_accuracy: 0.9154\n", - "Epoch 534/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5366 - sparse_categorical_crossentropy: 0.3374 - accuracy: 0.8869 - val_loss: 0.4927 - val_sparse_categorical_crossentropy: 0.2928 - val_accuracy: 0.9186\n", - "Epoch 535/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5351 - sparse_categorical_crossentropy: 0.3356 - accuracy: 0.8915 - val_loss: 0.4908 - val_sparse_categorical_crossentropy: 0.2921 - val_accuracy: 0.9200\n", - "Epoch 536/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5259 - sparse_categorical_crossentropy: 0.3268 - accuracy: 0.8962 - val_loss: 0.4911 - val_sparse_categorical_crossentropy: 0.2921 - val_accuracy: 0.9240\n", - "Epoch 537/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5284 - sparse_categorical_crossentropy: 0.3292 - accuracy: 0.8943 - val_loss: 0.5023 - val_sparse_categorical_crossentropy: 0.3022 - val_accuracy: 0.9142\n", - "Epoch 538/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5366 - sparse_categorical_crossentropy: 0.3368 - accuracy: 0.8907 - val_loss: 0.4907 - val_sparse_categorical_crossentropy: 0.2903 - val_accuracy: 0.9252\n", - "Epoch 539/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5294 - sparse_categorical_crossentropy: 0.3289 - accuracy: 0.8926 - val_loss: 0.4892 - val_sparse_categorical_crossentropy: 0.2886 - val_accuracy: 0.9236\n", - "Epoch 540/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5272 - sparse_categorical_crossentropy: 0.3260 - accuracy: 0.8949 - val_loss: 0.4866 - val_sparse_categorical_crossentropy: 0.2855 - val_accuracy: 0.9274\n", - "Epoch 541/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5238 - sparse_categorical_crossentropy: 0.3225 - accuracy: 0.8978 - val_loss: 0.4864 - val_sparse_categorical_crossentropy: 0.2846 - val_accuracy: 0.9258\n", - "Epoch 542/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5224 - sparse_categorical_crossentropy: 0.3209 - accuracy: 0.8962 - val_loss: 0.4930 - val_sparse_categorical_crossentropy: 0.2916 - val_accuracy: 0.9154\n", - "Epoch 543/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5302 - sparse_categorical_crossentropy: 0.3285 - accuracy: 0.8914 - val_loss: 0.4864 - val_sparse_categorical_crossentropy: 0.2847 - val_accuracy: 0.9296\n", - "Epoch 544/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5367 - sparse_categorical_crossentropy: 0.3353 - accuracy: 0.8859 - val_loss: 0.4967 - val_sparse_categorical_crossentropy: 0.2957 - val_accuracy: 0.9148\n", - "Epoch 545/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5376 - sparse_categorical_crossentropy: 0.3368 - accuracy: 0.8863 - val_loss: 0.4889 - val_sparse_categorical_crossentropy: 0.2879 - val_accuracy: 0.9206\n", - "Epoch 546/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5282 - sparse_categorical_crossentropy: 0.3274 - accuracy: 0.8952 - val_loss: 0.4890 - val_sparse_categorical_crossentropy: 0.2891 - val_accuracy: 0.9208\n", - "Epoch 547/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5352 - sparse_categorical_crossentropy: 0.3354 - accuracy: 0.8879 - val_loss: 0.4983 - val_sparse_categorical_crossentropy: 0.2985 - val_accuracy: 0.9132\n", - "Epoch 548/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5364 - sparse_categorical_crossentropy: 0.3369 - accuracy: 0.8858 - val_loss: 0.4947 - val_sparse_categorical_crossentropy: 0.2948 - val_accuracy: 0.9172\n", - "Epoch 549/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5444 - sparse_categorical_crossentropy: 0.3451 - accuracy: 0.8827 - val_loss: 0.4962 - val_sparse_categorical_crossentropy: 0.2970 - val_accuracy: 0.9150\n", - "Epoch 550/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5398 - sparse_categorical_crossentropy: 0.3407 - accuracy: 0.8847 - val_loss: 0.4963 - val_sparse_categorical_crossentropy: 0.2979 - val_accuracy: 0.9142\n", - "Epoch 551/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5344 - sparse_categorical_crossentropy: 0.3355 - accuracy: 0.8905 - val_loss: 0.4875 - val_sparse_categorical_crossentropy: 0.2882 - val_accuracy: 0.9168\n", - "Epoch 552/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5378 - sparse_categorical_crossentropy: 0.3390 - accuracy: 0.8859 - val_loss: 0.4909 - val_sparse_categorical_crossentropy: 0.2921 - val_accuracy: 0.9230\n", - "Epoch 553/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5349 - sparse_categorical_crossentropy: 0.3363 - accuracy: 0.8910 - val_loss: 0.5012 - val_sparse_categorical_crossentropy: 0.3031 - val_accuracy: 0.9084\n", - "Epoch 554/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5320 - sparse_categorical_crossentropy: 0.3333 - accuracy: 0.8892 - val_loss: 0.4967 - val_sparse_categorical_crossentropy: 0.2985 - val_accuracy: 0.9142\n", - "Epoch 555/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5239 - sparse_categorical_crossentropy: 0.3251 - accuracy: 0.8947 - val_loss: 0.4917 - val_sparse_categorical_crossentropy: 0.2919 - val_accuracy: 0.9130\n", - "Epoch 556/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5240 - sparse_categorical_crossentropy: 0.3241 - accuracy: 0.8943 - val_loss: 0.4851 - val_sparse_categorical_crossentropy: 0.2845 - val_accuracy: 0.9258\n", - "Epoch 557/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5273 - sparse_categorical_crossentropy: 0.3265 - accuracy: 0.8945 - val_loss: 0.4851 - val_sparse_categorical_crossentropy: 0.2847 - val_accuracy: 0.9240\n", - "Epoch 558/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5299 - sparse_categorical_crossentropy: 0.3294 - accuracy: 0.8886 - val_loss: 0.4881 - val_sparse_categorical_crossentropy: 0.2881 - val_accuracy: 0.9186\n", - "Epoch 559/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5286 - sparse_categorical_crossentropy: 0.3285 - accuracy: 0.8910 - val_loss: 0.4975 - val_sparse_categorical_crossentropy: 0.2977 - val_accuracy: 0.9100\n", - "Epoch 560/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5291 - sparse_categorical_crossentropy: 0.3296 - accuracy: 0.8881 - val_loss: 0.4926 - val_sparse_categorical_crossentropy: 0.2929 - val_accuracy: 0.9142\n", - "Epoch 561/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5291 - sparse_categorical_crossentropy: 0.3298 - accuracy: 0.8884 - val_loss: 0.4820 - val_sparse_categorical_crossentropy: 0.2832 - val_accuracy: 0.9222\n", - "Epoch 562/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5242 - sparse_categorical_crossentropy: 0.3252 - accuracy: 0.8972 - val_loss: 0.4932 - val_sparse_categorical_crossentropy: 0.2945 - val_accuracy: 0.9180\n", - "Epoch 563/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5317 - sparse_categorical_crossentropy: 0.3332 - accuracy: 0.8901 - val_loss: 0.4920 - val_sparse_categorical_crossentropy: 0.2933 - val_accuracy: 0.9130\n", - "Epoch 564/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5315 - sparse_categorical_crossentropy: 0.3329 - accuracy: 0.8886 - val_loss: 0.4851 - val_sparse_categorical_crossentropy: 0.2865 - val_accuracy: 0.9222\n", - "Epoch 565/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5384 - sparse_categorical_crossentropy: 0.3397 - accuracy: 0.8870 - val_loss: 0.5098 - val_sparse_categorical_crossentropy: 0.3108 - val_accuracy: 0.8916\n", - "Epoch 566/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5425 - sparse_categorical_crossentropy: 0.3440 - accuracy: 0.8800 - val_loss: 0.4774 - val_sparse_categorical_crossentropy: 0.2791 - val_accuracy: 0.9280\n", - "Epoch 567/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5293 - sparse_categorical_crossentropy: 0.3308 - accuracy: 0.8915 - val_loss: 0.4850 - val_sparse_categorical_crossentropy: 0.2866 - val_accuracy: 0.9186\n", - "Epoch 568/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5331 - sparse_categorical_crossentropy: 0.3347 - accuracy: 0.8910 - val_loss: 0.4839 - val_sparse_categorical_crossentropy: 0.2853 - val_accuracy: 0.9258\n", - "Epoch 569/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5242 - sparse_categorical_crossentropy: 0.3255 - accuracy: 0.8954 - val_loss: 0.5008 - val_sparse_categorical_crossentropy: 0.3013 - val_accuracy: 0.9188\n", - "Epoch 570/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5258 - sparse_categorical_crossentropy: 0.3261 - accuracy: 0.8949 - val_loss: 0.4971 - val_sparse_categorical_crossentropy: 0.2980 - val_accuracy: 0.9160\n", - "Epoch 571/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5241 - sparse_categorical_crossentropy: 0.3246 - accuracy: 0.8913 - val_loss: 0.4912 - val_sparse_categorical_crossentropy: 0.2916 - val_accuracy: 0.9178\n", - "Epoch 572/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5251 - sparse_categorical_crossentropy: 0.3254 - accuracy: 0.8910 - val_loss: 0.4875 - val_sparse_categorical_crossentropy: 0.2876 - val_accuracy: 0.9180\n", - "Epoch 573/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5221 - sparse_categorical_crossentropy: 0.3220 - accuracy: 0.8977 - val_loss: 0.4855 - val_sparse_categorical_crossentropy: 0.2855 - val_accuracy: 0.9214\n", - "Epoch 574/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5177 - sparse_categorical_crossentropy: 0.3178 - accuracy: 0.8967 - val_loss: 0.4740 - val_sparse_categorical_crossentropy: 0.2740 - val_accuracy: 0.9294\n", - "Epoch 575/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5176 - sparse_categorical_crossentropy: 0.3176 - accuracy: 0.8985 - val_loss: 0.4781 - val_sparse_categorical_crossentropy: 0.2782 - val_accuracy: 0.9236\n", - "Epoch 576/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5164 - sparse_categorical_crossentropy: 0.3165 - accuracy: 0.8995 - val_loss: 0.4745 - val_sparse_categorical_crossentropy: 0.2747 - val_accuracy: 0.9296\n", - "Epoch 577/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5165 - sparse_categorical_crossentropy: 0.3167 - accuracy: 0.8972 - val_loss: 0.4761 - val_sparse_categorical_crossentropy: 0.2765 - val_accuracy: 0.9266\n", - "Epoch 578/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5128 - sparse_categorical_crossentropy: 0.3132 - accuracy: 0.9008 - val_loss: 0.4916 - val_sparse_categorical_crossentropy: 0.2924 - val_accuracy: 0.9148\n", - "Epoch 579/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5236 - sparse_categorical_crossentropy: 0.3246 - accuracy: 0.8925 - val_loss: 0.4887 - val_sparse_categorical_crossentropy: 0.2902 - val_accuracy: 0.9134\n", - "Epoch 580/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5215 - sparse_categorical_crossentropy: 0.3227 - accuracy: 0.8948 - val_loss: 0.4713 - val_sparse_categorical_crossentropy: 0.2724 - val_accuracy: 0.9266\n", - "Epoch 581/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5096 - sparse_categorical_crossentropy: 0.3106 - accuracy: 0.9017 - val_loss: 0.4751 - val_sparse_categorical_crossentropy: 0.2761 - val_accuracy: 0.9278\n", - "Epoch 582/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5173 - sparse_categorical_crossentropy: 0.3185 - accuracy: 0.9001 - val_loss: 0.4767 - val_sparse_categorical_crossentropy: 0.2781 - val_accuracy: 0.9246\n", - "Epoch 583/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5214 - sparse_categorical_crossentropy: 0.3227 - accuracy: 0.8937 - val_loss: 0.4799 - val_sparse_categorical_crossentropy: 0.2813 - val_accuracy: 0.9184\n", - "Epoch 584/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5229 - sparse_categorical_crossentropy: 0.3245 - accuracy: 0.8934 - val_loss: 0.4780 - val_sparse_categorical_crossentropy: 0.2802 - val_accuracy: 0.9210\n", - "Epoch 585/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5161 - sparse_categorical_crossentropy: 0.3183 - accuracy: 0.8969 - val_loss: 0.4752 - val_sparse_categorical_crossentropy: 0.2771 - val_accuracy: 0.9272\n", - "Epoch 586/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5244 - sparse_categorical_crossentropy: 0.3266 - accuracy: 0.8918 - val_loss: 0.5011 - val_sparse_categorical_crossentropy: 0.3040 - val_accuracy: 0.9006\n", - "Epoch 587/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5349 - sparse_categorical_crossentropy: 0.3374 - accuracy: 0.8817 - val_loss: 0.5018 - val_sparse_categorical_crossentropy: 0.3049 - val_accuracy: 0.8990\n", - "Epoch 588/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5323 - sparse_categorical_crossentropy: 0.3352 - accuracy: 0.8863 - val_loss: 0.5082 - val_sparse_categorical_crossentropy: 0.3111 - val_accuracy: 0.8936\n", - "Epoch 589/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5402 - sparse_categorical_crossentropy: 0.3430 - accuracy: 0.8800 - val_loss: 0.5142 - val_sparse_categorical_crossentropy: 0.3179 - val_accuracy: 0.8924\n", - "Epoch 590/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5399 - sparse_categorical_crossentropy: 0.3432 - accuracy: 0.8795 - val_loss: 0.5056 - val_sparse_categorical_crossentropy: 0.3093 - val_accuracy: 0.8956\n", - "Epoch 591/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5347 - sparse_categorical_crossentropy: 0.3383 - accuracy: 0.8855 - val_loss: 0.4970 - val_sparse_categorical_crossentropy: 0.3008 - val_accuracy: 0.9134\n", - "Epoch 592/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5253 - sparse_categorical_crossentropy: 0.3289 - accuracy: 0.8911 - val_loss: 0.4821 - val_sparse_categorical_crossentropy: 0.2860 - val_accuracy: 0.9220\n", - "Epoch 593/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5179 - sparse_categorical_crossentropy: 0.3212 - accuracy: 0.8951 - val_loss: 0.4833 - val_sparse_categorical_crossentropy: 0.2866 - val_accuracy: 0.9120\n", - "Epoch 594/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5186 - sparse_categorical_crossentropy: 0.3216 - accuracy: 0.8916 - val_loss: 0.4829 - val_sparse_categorical_crossentropy: 0.2855 - val_accuracy: 0.9230\n", - "Epoch 595/1000\n", - "4/4 [==============================] - 0s 27ms/step - loss: 0.5200 - sparse_categorical_crossentropy: 0.3228 - accuracy: 0.8960 - val_loss: 0.4842 - val_sparse_categorical_crossentropy: 0.2866 - val_accuracy: 0.9148\n", - "Epoch 596/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5151 - sparse_categorical_crossentropy: 0.3174 - accuracy: 0.8979 - val_loss: 0.4827 - val_sparse_categorical_crossentropy: 0.2858 - val_accuracy: 0.9114\n", - "Epoch 597/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5157 - sparse_categorical_crossentropy: 0.3181 - accuracy: 0.8972 - val_loss: 0.4794 - val_sparse_categorical_crossentropy: 0.2815 - val_accuracy: 0.9250\n", - "Epoch 598/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5077 - sparse_categorical_crossentropy: 0.3100 - accuracy: 0.9018 - val_loss: 0.4778 - val_sparse_categorical_crossentropy: 0.2795 - val_accuracy: 0.9218\n", - "Epoch 599/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5101 - sparse_categorical_crossentropy: 0.3119 - accuracy: 0.8997 - val_loss: 0.4719 - val_sparse_categorical_crossentropy: 0.2740 - val_accuracy: 0.9256\n", - "Epoch 600/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5086 - sparse_categorical_crossentropy: 0.3104 - accuracy: 0.9018 - val_loss: 0.4660 - val_sparse_categorical_crossentropy: 0.2677 - val_accuracy: 0.9316\n", - "Epoch 601/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5031 - sparse_categorical_crossentropy: 0.3051 - accuracy: 0.9023 - val_loss: 0.4697 - val_sparse_categorical_crossentropy: 0.2717 - val_accuracy: 0.9266\n", - "Epoch 602/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5126 - sparse_categorical_crossentropy: 0.3148 - accuracy: 0.8987 - val_loss: 0.4748 - val_sparse_categorical_crossentropy: 0.2776 - val_accuracy: 0.9222\n", - "Epoch 603/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5085 - sparse_categorical_crossentropy: 0.3113 - accuracy: 0.9022 - val_loss: 0.4734 - val_sparse_categorical_crossentropy: 0.2770 - val_accuracy: 0.9208\n", - "Epoch 604/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5145 - sparse_categorical_crossentropy: 0.3180 - accuracy: 0.8970 - val_loss: 0.4859 - val_sparse_categorical_crossentropy: 0.2898 - val_accuracy: 0.9080\n", - "Epoch 605/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5166 - sparse_categorical_crossentropy: 0.3205 - accuracy: 0.8953 - val_loss: 0.4812 - val_sparse_categorical_crossentropy: 0.2857 - val_accuracy: 0.9166\n", - "Epoch 606/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5113 - sparse_categorical_crossentropy: 0.3155 - accuracy: 0.8969 - val_loss: 0.4654 - val_sparse_categorical_crossentropy: 0.2696 - val_accuracy: 0.9316\n", - "Epoch 607/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5099 - sparse_categorical_crossentropy: 0.3140 - accuracy: 0.8981 - val_loss: 0.4675 - val_sparse_categorical_crossentropy: 0.2715 - val_accuracy: 0.9258\n", - "Epoch 608/1000\n", - "4/4 [==============================] - ETA: 0s - loss: 0.5043 - sparse_categorical_crossentropy: 0.3083 - accuracy: 0.89 - 0s 17ms/step - loss: 0.5082 - sparse_categorical_crossentropy: 0.3122 - accuracy: 0.8991 - val_loss: 0.4698 - val_sparse_categorical_crossentropy: 0.2740 - val_accuracy: 0.9256\n", - "Epoch 609/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5080 - sparse_categorical_crossentropy: 0.3120 - accuracy: 0.9014 - val_loss: 0.4746 - val_sparse_categorical_crossentropy: 0.2785 - val_accuracy: 0.9222\n", - "Epoch 610/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5107 - sparse_categorical_crossentropy: 0.3144 - accuracy: 0.8981 - val_loss: 0.4734 - val_sparse_categorical_crossentropy: 0.2774 - val_accuracy: 0.9246\n", - "Epoch 611/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5132 - sparse_categorical_crossentropy: 0.3170 - accuracy: 0.8962 - val_loss: 0.4700 - val_sparse_categorical_crossentropy: 0.2736 - val_accuracy: 0.9248\n", - "Epoch 612/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5127 - sparse_categorical_crossentropy: 0.3166 - accuracy: 0.8955 - val_loss: 0.4837 - val_sparse_categorical_crossentropy: 0.2869 - val_accuracy: 0.9170\n", - "Epoch 613/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5143 - sparse_categorical_crossentropy: 0.3178 - accuracy: 0.8953 - val_loss: 0.4744 - val_sparse_categorical_crossentropy: 0.2782 - val_accuracy: 0.9214\n", - "Epoch 614/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.5177 - sparse_categorical_crossentropy: 0.3214 - accuracy: 0.8942 - val_loss: 0.4669 - val_sparse_categorical_crossentropy: 0.2713 - val_accuracy: 0.9222\n", - "Epoch 615/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.5103 - sparse_categorical_crossentropy: 0.3151 - accuracy: 0.8968 - val_loss: 0.4758 - val_sparse_categorical_crossentropy: 0.2807 - val_accuracy: 0.9150\n", - "Epoch 616/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5150 - sparse_categorical_crossentropy: 0.3200 - accuracy: 0.8934 - val_loss: 0.4731 - val_sparse_categorical_crossentropy: 0.2783 - val_accuracy: 0.9208\n", - "Epoch 617/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5171 - sparse_categorical_crossentropy: 0.3223 - accuracy: 0.8922 - val_loss: 0.4839 - val_sparse_categorical_crossentropy: 0.2888 - val_accuracy: 0.9100\n", - "Epoch 618/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5137 - sparse_categorical_crossentropy: 0.3189 - accuracy: 0.8977 - val_loss: 0.4812 - val_sparse_categorical_crossentropy: 0.2862 - val_accuracy: 0.9178\n", - "Epoch 619/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5105 - sparse_categorical_crossentropy: 0.3153 - accuracy: 0.8949 - val_loss: 0.4716 - val_sparse_categorical_crossentropy: 0.2765 - val_accuracy: 0.9250\n", - "Epoch 620/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.5177 - sparse_categorical_crossentropy: 0.3223 - accuracy: 0.8922 - val_loss: 0.4759 - val_sparse_categorical_crossentropy: 0.2806 - val_accuracy: 0.9240\n", - "Epoch 621/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5102 - sparse_categorical_crossentropy: 0.3152 - accuracy: 0.8958 - val_loss: 0.4673 - val_sparse_categorical_crossentropy: 0.2724 - val_accuracy: 0.9266\n", - "Epoch 622/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5095 - sparse_categorical_crossentropy: 0.3148 - accuracy: 0.8964 - val_loss: 0.4633 - val_sparse_categorical_crossentropy: 0.2690 - val_accuracy: 0.9276\n", - "Epoch 623/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5056 - sparse_categorical_crossentropy: 0.3110 - accuracy: 0.9008 - val_loss: 0.4642 - val_sparse_categorical_crossentropy: 0.2690 - val_accuracy: 0.9328\n", - "Epoch 624/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5002 - sparse_categorical_crossentropy: 0.3050 - accuracy: 0.9020 - val_loss: 0.4662 - val_sparse_categorical_crossentropy: 0.2710 - val_accuracy: 0.9246\n", - "Epoch 625/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5023 - sparse_categorical_crossentropy: 0.3071 - accuracy: 0.9026 - val_loss: 0.4646 - val_sparse_categorical_crossentropy: 0.2687 - val_accuracy: 0.9276\n", - "Epoch 626/1000\n", - "4/4 [==============================] - 0s 22ms/step - loss: 0.5001 - sparse_categorical_crossentropy: 0.3043 - accuracy: 0.9053 - val_loss: 0.4577 - val_sparse_categorical_crossentropy: 0.2620 - val_accuracy: 0.9284\n", - "Epoch 627/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4984 - sparse_categorical_crossentropy: 0.3028 - accuracy: 0.9042 - val_loss: 0.4593 - val_sparse_categorical_crossentropy: 0.2635 - val_accuracy: 0.9288\n", - "Epoch 628/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4987 - sparse_categorical_crossentropy: 0.3028 - accuracy: 0.9033 - val_loss: 0.4620 - val_sparse_categorical_crossentropy: 0.2663 - val_accuracy: 0.9266\n", - "Epoch 629/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5024 - sparse_categorical_crossentropy: 0.3069 - accuracy: 0.9029 - val_loss: 0.4668 - val_sparse_categorical_crossentropy: 0.2712 - val_accuracy: 0.9224\n", - "Epoch 630/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5076 - sparse_categorical_crossentropy: 0.3122 - accuracy: 0.9002 - val_loss: 0.4628 - val_sparse_categorical_crossentropy: 0.2682 - val_accuracy: 0.9248\n", - "Epoch 631/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4996 - sparse_categorical_crossentropy: 0.3049 - accuracy: 0.9017 - val_loss: 0.4620 - val_sparse_categorical_crossentropy: 0.2672 - val_accuracy: 0.9294\n", - "Epoch 632/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5041 - sparse_categorical_crossentropy: 0.3094 - accuracy: 0.9007 - val_loss: 0.4752 - val_sparse_categorical_crossentropy: 0.2805 - val_accuracy: 0.9186\n", - "Epoch 633/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5056 - sparse_categorical_crossentropy: 0.3113 - accuracy: 0.8996 - val_loss: 0.4818 - val_sparse_categorical_crossentropy: 0.2870 - val_accuracy: 0.9110\n", - "Epoch 634/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5083 - sparse_categorical_crossentropy: 0.3136 - accuracy: 0.8981 - val_loss: 0.4694 - val_sparse_categorical_crossentropy: 0.2754 - val_accuracy: 0.9242\n", - "Epoch 635/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5091 - sparse_categorical_crossentropy: 0.3146 - accuracy: 0.9009 - val_loss: 0.4784 - val_sparse_categorical_crossentropy: 0.2844 - val_accuracy: 0.9114\n", - "Epoch 636/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5069 - sparse_categorical_crossentropy: 0.3127 - accuracy: 0.8961 - val_loss: 0.4673 - val_sparse_categorical_crossentropy: 0.2726 - val_accuracy: 0.9262\n", - "Epoch 637/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5080 - sparse_categorical_crossentropy: 0.3132 - accuracy: 0.8981 - val_loss: 0.4702 - val_sparse_categorical_crossentropy: 0.2755 - val_accuracy: 0.9214\n", - "Epoch 638/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.5064 - sparse_categorical_crossentropy: 0.3116 - accuracy: 0.8962 - val_loss: 0.4598 - val_sparse_categorical_crossentropy: 0.2650 - val_accuracy: 0.9304\n", - "Epoch 639/1000\n", - "4/4 [==============================] - 0s 22ms/step - loss: 0.5018 - sparse_categorical_crossentropy: 0.3071 - accuracy: 0.9003 - val_loss: 0.4697 - val_sparse_categorical_crossentropy: 0.2751 - val_accuracy: 0.9140\n", - "Epoch 640/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5037 - sparse_categorical_crossentropy: 0.3093 - accuracy: 0.8974 - val_loss: 0.4708 - val_sparse_categorical_crossentropy: 0.2765 - val_accuracy: 0.9178\n", - "Epoch 641/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5085 - sparse_categorical_crossentropy: 0.3144 - accuracy: 0.8973 - val_loss: 0.4577 - val_sparse_categorical_crossentropy: 0.2634 - val_accuracy: 0.9270\n", - "Epoch 642/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4981 - sparse_categorical_crossentropy: 0.3040 - accuracy: 0.9023 - val_loss: 0.4592 - val_sparse_categorical_crossentropy: 0.2651 - val_accuracy: 0.9310\n", - "Epoch 643/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4988 - sparse_categorical_crossentropy: 0.3047 - accuracy: 0.9044 - val_loss: 0.4547 - val_sparse_categorical_crossentropy: 0.2604 - val_accuracy: 0.9338\n", - "Epoch 644/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4961 - sparse_categorical_crossentropy: 0.3018 - accuracy: 0.9042 - val_loss: 0.4633 - val_sparse_categorical_crossentropy: 0.2691 - val_accuracy: 0.9270\n", - "Epoch 645/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.5036 - sparse_categorical_crossentropy: 0.3095 - accuracy: 0.9014 - val_loss: 0.4595 - val_sparse_categorical_crossentropy: 0.2656 - val_accuracy: 0.9236\n", - "Epoch 646/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5018 - sparse_categorical_crossentropy: 0.3079 - accuracy: 0.8995 - val_loss: 0.4554 - val_sparse_categorical_crossentropy: 0.2616 - val_accuracy: 0.9332\n", - "Epoch 647/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4925 - sparse_categorical_crossentropy: 0.2988 - accuracy: 0.9069 - val_loss: 0.4640 - val_sparse_categorical_crossentropy: 0.2709 - val_accuracy: 0.9174\n", - "Epoch 648/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5023 - sparse_categorical_crossentropy: 0.3092 - accuracy: 0.8986 - val_loss: 0.4502 - val_sparse_categorical_crossentropy: 0.2572 - val_accuracy: 0.9336\n", - "Epoch 649/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4968 - sparse_categorical_crossentropy: 0.3038 - accuracy: 0.9034 - val_loss: 0.4760 - val_sparse_categorical_crossentropy: 0.2835 - val_accuracy: 0.9144\n", - "Epoch 650/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5115 - sparse_categorical_crossentropy: 0.3189 - accuracy: 0.8931 - val_loss: 0.4611 - val_sparse_categorical_crossentropy: 0.2688 - val_accuracy: 0.9242\n", - "Epoch 651/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5014 - sparse_categorical_crossentropy: 0.3091 - accuracy: 0.8971 - val_loss: 0.4584 - val_sparse_categorical_crossentropy: 0.2652 - val_accuracy: 0.9306\n", - "Epoch 652/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4971 - sparse_categorical_crossentropy: 0.3042 - accuracy: 0.9048 - val_loss: 0.4540 - val_sparse_categorical_crossentropy: 0.2613 - val_accuracy: 0.9286\n", - "Epoch 653/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4956 - sparse_categorical_crossentropy: 0.3026 - accuracy: 0.9011 - val_loss: 0.4607 - val_sparse_categorical_crossentropy: 0.2676 - val_accuracy: 0.9266\n", - "Epoch 654/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4993 - sparse_categorical_crossentropy: 0.3063 - accuracy: 0.9007 - val_loss: 0.4604 - val_sparse_categorical_crossentropy: 0.2671 - val_accuracy: 0.9298\n", - "Epoch 655/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4972 - sparse_categorical_crossentropy: 0.3040 - accuracy: 0.9014 - val_loss: 0.4578 - val_sparse_categorical_crossentropy: 0.2648 - val_accuracy: 0.9294\n", - "Epoch 656/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4970 - sparse_categorical_crossentropy: 0.3041 - accuracy: 0.9036 - val_loss: 0.4650 - val_sparse_categorical_crossentropy: 0.2723 - val_accuracy: 0.9234\n", - "Epoch 657/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4999 - sparse_categorical_crossentropy: 0.3073 - accuracy: 0.9014 - val_loss: 0.4567 - val_sparse_categorical_crossentropy: 0.2644 - val_accuracy: 0.9306\n", - "Epoch 658/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4941 - sparse_categorical_crossentropy: 0.3016 - accuracy: 0.9029 - val_loss: 0.4620 - val_sparse_categorical_crossentropy: 0.2696 - val_accuracy: 0.9258\n", - "Epoch 659/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5018 - sparse_categorical_crossentropy: 0.3091 - accuracy: 0.9011 - val_loss: 0.4697 - val_sparse_categorical_crossentropy: 0.2768 - val_accuracy: 0.9186\n", - "Epoch 660/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.5020 - sparse_categorical_crossentropy: 0.3088 - accuracy: 0.9021 - val_loss: 0.4625 - val_sparse_categorical_crossentropy: 0.2697 - val_accuracy: 0.9258\n", - "Epoch 661/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5014 - sparse_categorical_crossentropy: 0.3085 - accuracy: 0.8978 - val_loss: 0.4709 - val_sparse_categorical_crossentropy: 0.2782 - val_accuracy: 0.9182\n", - "Epoch 662/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5031 - sparse_categorical_crossentropy: 0.3106 - accuracy: 0.8982 - val_loss: 0.4626 - val_sparse_categorical_crossentropy: 0.2696 - val_accuracy: 0.9262\n", - "Epoch 663/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5010 - sparse_categorical_crossentropy: 0.3082 - accuracy: 0.8982 - val_loss: 0.4650 - val_sparse_categorical_crossentropy: 0.2723 - val_accuracy: 0.9178\n", - "Epoch 664/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.5011 - sparse_categorical_crossentropy: 0.3084 - accuracy: 0.8989 - val_loss: 0.4545 - val_sparse_categorical_crossentropy: 0.2620 - val_accuracy: 0.9290\n", - "Epoch 665/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.4918 - sparse_categorical_crossentropy: 0.2994 - accuracy: 0.9049 - val_loss: 0.4526 - val_sparse_categorical_crossentropy: 0.2599 - val_accuracy: 0.9306\n", - "Epoch 666/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4912 - sparse_categorical_crossentropy: 0.2986 - accuracy: 0.9066 - val_loss: 0.4495 - val_sparse_categorical_crossentropy: 0.2569 - val_accuracy: 0.9322\n", - "Epoch 667/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4925 - sparse_categorical_crossentropy: 0.3002 - accuracy: 0.9019 - val_loss: 0.4513 - val_sparse_categorical_crossentropy: 0.2594 - val_accuracy: 0.9310\n", - "Epoch 668/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4919 - sparse_categorical_crossentropy: 0.3002 - accuracy: 0.9066 - val_loss: 0.4610 - val_sparse_categorical_crossentropy: 0.2696 - val_accuracy: 0.9214\n", - "Epoch 669/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4987 - sparse_categorical_crossentropy: 0.3078 - accuracy: 0.8982 - val_loss: 0.4512 - val_sparse_categorical_crossentropy: 0.2601 - val_accuracy: 0.9312\n", - "Epoch 670/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4953 - sparse_categorical_crossentropy: 0.3041 - accuracy: 0.9035 - val_loss: 0.4679 - val_sparse_categorical_crossentropy: 0.2763 - val_accuracy: 0.9148\n", - "Epoch 671/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5103 - sparse_categorical_crossentropy: 0.3188 - accuracy: 0.8928 - val_loss: 0.4596 - val_sparse_categorical_crossentropy: 0.2676 - val_accuracy: 0.9274\n", - "Epoch 672/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5024 - sparse_categorical_crossentropy: 0.3106 - accuracy: 0.8974 - val_loss: 0.4604 - val_sparse_categorical_crossentropy: 0.2687 - val_accuracy: 0.9230\n", - "Epoch 673/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4997 - sparse_categorical_crossentropy: 0.3077 - accuracy: 0.9003 - val_loss: 0.4692 - val_sparse_categorical_crossentropy: 0.2778 - val_accuracy: 0.9200\n", - "Epoch 674/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5014 - sparse_categorical_crossentropy: 0.3097 - accuracy: 0.8973 - val_loss: 0.4637 - val_sparse_categorical_crossentropy: 0.2722 - val_accuracy: 0.9196\n", - "Epoch 675/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4970 - sparse_categorical_crossentropy: 0.3056 - accuracy: 0.8986 - val_loss: 0.4625 - val_sparse_categorical_crossentropy: 0.2705 - val_accuracy: 0.9208\n", - "Epoch 676/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.5024 - sparse_categorical_crossentropy: 0.3104 - accuracy: 0.9008 - val_loss: 0.4678 - val_sparse_categorical_crossentropy: 0.2766 - val_accuracy: 0.9164\n", - "Epoch 677/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.5056 - sparse_categorical_crossentropy: 0.3141 - accuracy: 0.8956 - val_loss: 0.4543 - val_sparse_categorical_crossentropy: 0.2634 - val_accuracy: 0.9296\n", - "Epoch 678/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4931 - sparse_categorical_crossentropy: 0.3023 - accuracy: 0.9038 - val_loss: 0.4631 - val_sparse_categorical_crossentropy: 0.2724 - val_accuracy: 0.9216\n", - "Epoch 679/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5034 - sparse_categorical_crossentropy: 0.3129 - accuracy: 0.8954 - val_loss: 0.4592 - val_sparse_categorical_crossentropy: 0.2679 - val_accuracy: 0.9238\n", - "Epoch 680/1000\n", - "4/4 [==============================] - 0s 27ms/step - loss: 0.4992 - sparse_categorical_crossentropy: 0.3081 - accuracy: 0.8989 - val_loss: 0.4597 - val_sparse_categorical_crossentropy: 0.2684 - val_accuracy: 0.9262\n", - "Epoch 681/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4939 - sparse_categorical_crossentropy: 0.3024 - accuracy: 0.9038 - val_loss: 0.4574 - val_sparse_categorical_crossentropy: 0.2660 - val_accuracy: 0.9272\n", - "Epoch 682/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4911 - sparse_categorical_crossentropy: 0.2996 - accuracy: 0.9052 - val_loss: 0.4687 - val_sparse_categorical_crossentropy: 0.2768 - val_accuracy: 0.9142\n", - "Epoch 683/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4963 - sparse_categorical_crossentropy: 0.3045 - accuracy: 0.9005 - val_loss: 0.4593 - val_sparse_categorical_crossentropy: 0.2683 - val_accuracy: 0.9234\n", - "Epoch 684/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.5013 - sparse_categorical_crossentropy: 0.3101 - accuracy: 0.8997 - val_loss: 0.4596 - val_sparse_categorical_crossentropy: 0.2684 - val_accuracy: 0.9300\n", - "Epoch 685/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4997 - sparse_categorical_crossentropy: 0.3086 - accuracy: 0.8977 - val_loss: 0.4515 - val_sparse_categorical_crossentropy: 0.2609 - val_accuracy: 0.9324\n", - "Epoch 686/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4919 - sparse_categorical_crossentropy: 0.3012 - accuracy: 0.9054 - val_loss: 0.4518 - val_sparse_categorical_crossentropy: 0.2607 - val_accuracy: 0.9310\n", - "Epoch 687/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4877 - sparse_categorical_crossentropy: 0.2967 - accuracy: 0.9069 - val_loss: 0.4644 - val_sparse_categorical_crossentropy: 0.2738 - val_accuracy: 0.9160\n", - "Epoch 688/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4915 - sparse_categorical_crossentropy: 0.3007 - accuracy: 0.9027 - val_loss: 0.4569 - val_sparse_categorical_crossentropy: 0.2664 - val_accuracy: 0.9218\n", - "Epoch 689/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4942 - sparse_categorical_crossentropy: 0.3035 - accuracy: 0.9019 - val_loss: 0.4492 - val_sparse_categorical_crossentropy: 0.2586 - val_accuracy: 0.9294\n", - "Epoch 690/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4864 - sparse_categorical_crossentropy: 0.2959 - accuracy: 0.9071 - val_loss: 0.4514 - val_sparse_categorical_crossentropy: 0.2609 - val_accuracy: 0.9322\n", - "Epoch 691/1000\n", - "4/4 [==============================] - 0s 25ms/step - loss: 0.4906 - sparse_categorical_crossentropy: 0.3002 - accuracy: 0.9035 - val_loss: 0.4482 - val_sparse_categorical_crossentropy: 0.2582 - val_accuracy: 0.9312\n", - "Epoch 692/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4925 - sparse_categorical_crossentropy: 0.3022 - accuracy: 0.9039 - val_loss: 0.4501 - val_sparse_categorical_crossentropy: 0.2598 - val_accuracy: 0.9294\n", - "Epoch 693/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4890 - sparse_categorical_crossentropy: 0.2984 - accuracy: 0.9034 - val_loss: 0.4554 - val_sparse_categorical_crossentropy: 0.2644 - val_accuracy: 0.9248\n", - "Epoch 694/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4849 - sparse_categorical_crossentropy: 0.2940 - accuracy: 0.9065 - val_loss: 0.4460 - val_sparse_categorical_crossentropy: 0.2553 - val_accuracy: 0.9300\n", - "Epoch 695/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4849 - sparse_categorical_crossentropy: 0.2941 - accuracy: 0.9068 - val_loss: 0.4531 - val_sparse_categorical_crossentropy: 0.2625 - val_accuracy: 0.9294\n", - "Epoch 696/1000\n", - "4/4 [==============================] - 0s 24ms/step - loss: 0.4903 - sparse_categorical_crossentropy: 0.2996 - accuracy: 0.9053 - val_loss: 0.4517 - val_sparse_categorical_crossentropy: 0.2607 - val_accuracy: 0.9264\n", - "Epoch 697/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4943 - sparse_categorical_crossentropy: 0.3035 - accuracy: 0.9000 - val_loss: 0.4628 - val_sparse_categorical_crossentropy: 0.2723 - val_accuracy: 0.9232\n", - "Epoch 698/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4981 - sparse_categorical_crossentropy: 0.3077 - accuracy: 0.8987 - val_loss: 0.4505 - val_sparse_categorical_crossentropy: 0.2600 - val_accuracy: 0.9260\n", - "Epoch 699/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4968 - sparse_categorical_crossentropy: 0.3066 - accuracy: 0.8987 - val_loss: 0.4524 - val_sparse_categorical_crossentropy: 0.2625 - val_accuracy: 0.9270\n", - "Epoch 700/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4862 - sparse_categorical_crossentropy: 0.2965 - accuracy: 0.9051 - val_loss: 0.4475 - val_sparse_categorical_crossentropy: 0.2577 - val_accuracy: 0.9288\n", - "Epoch 701/1000\n", - "4/4 [==============================] - 0s 13ms/step - loss: 0.4860 - sparse_categorical_crossentropy: 0.2964 - accuracy: 0.9054 - val_loss: 0.4460 - val_sparse_categorical_crossentropy: 0.2563 - val_accuracy: 0.9270\n", - "Epoch 702/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4891 - sparse_categorical_crossentropy: 0.2994 - accuracy: 0.9042 - val_loss: 0.4569 - val_sparse_categorical_crossentropy: 0.2679 - val_accuracy: 0.9190\n", - "Epoch 703/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4981 - sparse_categorical_crossentropy: 0.3089 - accuracy: 0.8965 - val_loss: 0.4537 - val_sparse_categorical_crossentropy: 0.2646 - val_accuracy: 0.9270\n", - "Epoch 704/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4860 - sparse_categorical_crossentropy: 0.2969 - accuracy: 0.9061 - val_loss: 0.4546 - val_sparse_categorical_crossentropy: 0.2651 - val_accuracy: 0.9274\n", - "Epoch 705/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4856 - sparse_categorical_crossentropy: 0.2964 - accuracy: 0.9064 - val_loss: 0.4458 - val_sparse_categorical_crossentropy: 0.2568 - val_accuracy: 0.9288\n", - "Epoch 706/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4827 - sparse_categorical_crossentropy: 0.2933 - accuracy: 0.9060 - val_loss: 0.4453 - val_sparse_categorical_crossentropy: 0.2563 - val_accuracy: 0.9348\n", - "Epoch 707/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4868 - sparse_categorical_crossentropy: 0.2973 - accuracy: 0.9047 - val_loss: 0.4430 - val_sparse_categorical_crossentropy: 0.2532 - val_accuracy: 0.9326\n", - "Epoch 708/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4813 - sparse_categorical_crossentropy: 0.2916 - accuracy: 0.9084 - val_loss: 0.4502 - val_sparse_categorical_crossentropy: 0.2602 - val_accuracy: 0.9270\n", - "Epoch 709/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4820 - sparse_categorical_crossentropy: 0.2920 - accuracy: 0.9067 - val_loss: 0.4612 - val_sparse_categorical_crossentropy: 0.2711 - val_accuracy: 0.9146\n", - "Epoch 710/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4922 - sparse_categorical_crossentropy: 0.3022 - accuracy: 0.9009 - val_loss: 0.4488 - val_sparse_categorical_crossentropy: 0.2591 - val_accuracy: 0.9266\n", - "Epoch 711/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4898 - sparse_categorical_crossentropy: 0.3000 - accuracy: 0.9025 - val_loss: 0.4544 - val_sparse_categorical_crossentropy: 0.2649 - val_accuracy: 0.9248\n", - "Epoch 712/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4840 - sparse_categorical_crossentropy: 0.2945 - accuracy: 0.9062 - val_loss: 0.4429 - val_sparse_categorical_crossentropy: 0.2533 - val_accuracy: 0.9312\n", - "Epoch 713/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4821 - sparse_categorical_crossentropy: 0.2924 - accuracy: 0.9082 - val_loss: 0.4460 - val_sparse_categorical_crossentropy: 0.2569 - val_accuracy: 0.9280\n", - "Epoch 714/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4890 - sparse_categorical_crossentropy: 0.2995 - accuracy: 0.9026 - val_loss: 0.4635 - val_sparse_categorical_crossentropy: 0.2736 - val_accuracy: 0.9130\n", - "Epoch 715/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4893 - sparse_categorical_crossentropy: 0.2996 - accuracy: 0.9040 - val_loss: 0.4483 - val_sparse_categorical_crossentropy: 0.2587 - val_accuracy: 0.9272\n", - "Epoch 716/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4870 - sparse_categorical_crossentropy: 0.2974 - accuracy: 0.9053 - val_loss: 0.4495 - val_sparse_categorical_crossentropy: 0.2604 - val_accuracy: 0.9256\n", - "Epoch 717/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4888 - sparse_categorical_crossentropy: 0.2997 - accuracy: 0.9043 - val_loss: 0.4475 - val_sparse_categorical_crossentropy: 0.2586 - val_accuracy: 0.9280\n", - "Epoch 718/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4869 - sparse_categorical_crossentropy: 0.2981 - accuracy: 0.9045 - val_loss: 0.4414 - val_sparse_categorical_crossentropy: 0.2529 - val_accuracy: 0.9310\n", - "Epoch 719/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4872 - sparse_categorical_crossentropy: 0.2989 - accuracy: 0.9051 - val_loss: 0.4416 - val_sparse_categorical_crossentropy: 0.2531 - val_accuracy: 0.9290\n", - "Epoch 720/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4859 - sparse_categorical_crossentropy: 0.2978 - accuracy: 0.9063 - val_loss: 0.4420 - val_sparse_categorical_crossentropy: 0.2537 - val_accuracy: 0.9324\n", - "Epoch 721/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4827 - sparse_categorical_crossentropy: 0.2945 - accuracy: 0.9070 - val_loss: 0.4426 - val_sparse_categorical_crossentropy: 0.2544 - val_accuracy: 0.9310\n", - "Epoch 722/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4856 - sparse_categorical_crossentropy: 0.2972 - accuracy: 0.9034 - val_loss: 0.4529 - val_sparse_categorical_crossentropy: 0.2640 - val_accuracy: 0.9232\n", - "Epoch 723/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4845 - sparse_categorical_crossentropy: 0.2958 - accuracy: 0.9045 - val_loss: 0.4527 - val_sparse_categorical_crossentropy: 0.2637 - val_accuracy: 0.9274\n", - "Epoch 724/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4863 - sparse_categorical_crossentropy: 0.2972 - accuracy: 0.9034 - val_loss: 0.4615 - val_sparse_categorical_crossentropy: 0.2723 - val_accuracy: 0.9194\n", - "Epoch 725/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4842 - sparse_categorical_crossentropy: 0.2948 - accuracy: 0.9050 - val_loss: 0.4511 - val_sparse_categorical_crossentropy: 0.2624 - val_accuracy: 0.9278\n", - "Epoch 726/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4830 - sparse_categorical_crossentropy: 0.2941 - accuracy: 0.9076 - val_loss: 0.4472 - val_sparse_categorical_crossentropy: 0.2582 - val_accuracy: 0.9294\n", - "Epoch 727/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4804 - sparse_categorical_crossentropy: 0.2917 - accuracy: 0.9067 - val_loss: 0.4473 - val_sparse_categorical_crossentropy: 0.2590 - val_accuracy: 0.9224\n", - "Epoch 728/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4840 - sparse_categorical_crossentropy: 0.2958 - accuracy: 0.9071 - val_loss: 0.4391 - val_sparse_categorical_crossentropy: 0.2513 - val_accuracy: 0.9324\n", - "Epoch 729/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4806 - sparse_categorical_crossentropy: 0.2927 - accuracy: 0.9085 - val_loss: 0.4455 - val_sparse_categorical_crossentropy: 0.2571 - val_accuracy: 0.9270\n", - "Epoch 730/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4791 - sparse_categorical_crossentropy: 0.2909 - accuracy: 0.9088 - val_loss: 0.4408 - val_sparse_categorical_crossentropy: 0.2523 - val_accuracy: 0.9312\n", - "Epoch 731/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4800 - sparse_categorical_crossentropy: 0.2916 - accuracy: 0.9081 - val_loss: 0.4429 - val_sparse_categorical_crossentropy: 0.2545 - val_accuracy: 0.9294\n", - "Epoch 732/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4844 - sparse_categorical_crossentropy: 0.2959 - accuracy: 0.9038 - val_loss: 0.4635 - val_sparse_categorical_crossentropy: 0.2757 - val_accuracy: 0.9154\n", - "Epoch 733/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4888 - sparse_categorical_crossentropy: 0.3009 - accuracy: 0.9029 - val_loss: 0.4538 - val_sparse_categorical_crossentropy: 0.2659 - val_accuracy: 0.9212\n", - "Epoch 734/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4889 - sparse_categorical_crossentropy: 0.3011 - accuracy: 0.9007 - val_loss: 0.4340 - val_sparse_categorical_crossentropy: 0.2465 - val_accuracy: 0.9324\n", - "Epoch 735/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4830 - sparse_categorical_crossentropy: 0.2952 - accuracy: 0.9049 - val_loss: 0.4462 - val_sparse_categorical_crossentropy: 0.2578 - val_accuracy: 0.9292\n", - "Epoch 736/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4809 - sparse_categorical_crossentropy: 0.2926 - accuracy: 0.9054 - val_loss: 0.4411 - val_sparse_categorical_crossentropy: 0.2528 - val_accuracy: 0.9304\n", - "Epoch 737/1000\n", - "4/4 [==============================] - 0s 22ms/step - loss: 0.4766 - sparse_categorical_crossentropy: 0.2885 - accuracy: 0.9102 - val_loss: 0.4427 - val_sparse_categorical_crossentropy: 0.2547 - val_accuracy: 0.9272\n", - "Epoch 738/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4842 - sparse_categorical_crossentropy: 0.2963 - accuracy: 0.9030 - val_loss: 0.4550 - val_sparse_categorical_crossentropy: 0.2670 - val_accuracy: 0.9240\n", - "Epoch 739/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4851 - sparse_categorical_crossentropy: 0.2973 - accuracy: 0.9046 - val_loss: 0.4488 - val_sparse_categorical_crossentropy: 0.2610 - val_accuracy: 0.9292\n", - "Epoch 740/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4861 - sparse_categorical_crossentropy: 0.2982 - accuracy: 0.9043 - val_loss: 0.4651 - val_sparse_categorical_crossentropy: 0.2776 - val_accuracy: 0.9126\n", - "Epoch 741/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4935 - sparse_categorical_crossentropy: 0.3060 - accuracy: 0.8973 - val_loss: 0.4656 - val_sparse_categorical_crossentropy: 0.2784 - val_accuracy: 0.9148\n", - "Epoch 742/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4939 - sparse_categorical_crossentropy: 0.3066 - accuracy: 0.8965 - val_loss: 0.4551 - val_sparse_categorical_crossentropy: 0.2681 - val_accuracy: 0.9204\n", - "Epoch 743/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4826 - sparse_categorical_crossentropy: 0.2958 - accuracy: 0.9057 - val_loss: 0.4601 - val_sparse_categorical_crossentropy: 0.2733 - val_accuracy: 0.9180\n", - "Epoch 744/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4889 - sparse_categorical_crossentropy: 0.3022 - accuracy: 0.9015 - val_loss: 0.4511 - val_sparse_categorical_crossentropy: 0.2642 - val_accuracy: 0.9232\n", - "Epoch 745/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4830 - sparse_categorical_crossentropy: 0.2961 - accuracy: 0.9017 - val_loss: 0.4466 - val_sparse_categorical_crossentropy: 0.2593 - val_accuracy: 0.9258\n", - "Epoch 746/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4836 - sparse_categorical_crossentropy: 0.2965 - accuracy: 0.9055 - val_loss: 0.4514 - val_sparse_categorical_crossentropy: 0.2643 - val_accuracy: 0.9276\n", - "Epoch 747/1000\n", - "4/4 [==============================] - 0s 29ms/step - loss: 0.4849 - sparse_categorical_crossentropy: 0.2977 - accuracy: 0.9049 - val_loss: 0.4558 - val_sparse_categorical_crossentropy: 0.2687 - val_accuracy: 0.9196\n", - "Epoch 748/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4840 - sparse_categorical_crossentropy: 0.2966 - accuracy: 0.9031 - val_loss: 0.4509 - val_sparse_categorical_crossentropy: 0.2635 - val_accuracy: 0.9252\n", - "Epoch 749/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4782 - sparse_categorical_crossentropy: 0.2906 - accuracy: 0.9061 - val_loss: 0.4386 - val_sparse_categorical_crossentropy: 0.2508 - val_accuracy: 0.9324\n", - "Epoch 750/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4778 - sparse_categorical_crossentropy: 0.2898 - accuracy: 0.9078 - val_loss: 0.4383 - val_sparse_categorical_crossentropy: 0.2503 - val_accuracy: 0.9302\n", - "Epoch 751/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4760 - sparse_categorical_crossentropy: 0.2878 - accuracy: 0.9093 - val_loss: 0.4516 - val_sparse_categorical_crossentropy: 0.2632 - val_accuracy: 0.9216\n", - "Epoch 752/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4805 - sparse_categorical_crossentropy: 0.2923 - accuracy: 0.9039 - val_loss: 0.4413 - val_sparse_categorical_crossentropy: 0.2536 - val_accuracy: 0.9288\n", - "Epoch 753/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4806 - sparse_categorical_crossentropy: 0.2928 - accuracy: 0.9059 - val_loss: 0.4385 - val_sparse_categorical_crossentropy: 0.2511 - val_accuracy: 0.9322\n", - "Epoch 754/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4774 - sparse_categorical_crossentropy: 0.2903 - accuracy: 0.9058 - val_loss: 0.4433 - val_sparse_categorical_crossentropy: 0.2564 - val_accuracy: 0.9246\n", - "Epoch 755/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4816 - sparse_categorical_crossentropy: 0.2947 - accuracy: 0.9024 - val_loss: 0.4406 - val_sparse_categorical_crossentropy: 0.2536 - val_accuracy: 0.9298\n", - "Epoch 756/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4778 - sparse_categorical_crossentropy: 0.2909 - accuracy: 0.9069 - val_loss: 0.4435 - val_sparse_categorical_crossentropy: 0.2563 - val_accuracy: 0.9268\n", - "Epoch 757/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4786 - sparse_categorical_crossentropy: 0.2915 - accuracy: 0.9046 - val_loss: 0.4366 - val_sparse_categorical_crossentropy: 0.2499 - val_accuracy: 0.9282\n", - "Epoch 758/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4747 - sparse_categorical_crossentropy: 0.2880 - accuracy: 0.9105 - val_loss: 0.4387 - val_sparse_categorical_crossentropy: 0.2520 - val_accuracy: 0.9292\n", - "Epoch 759/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4780 - sparse_categorical_crossentropy: 0.2914 - accuracy: 0.9029 - val_loss: 0.4426 - val_sparse_categorical_crossentropy: 0.2560 - val_accuracy: 0.9262\n", - "Epoch 760/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4777 - sparse_categorical_crossentropy: 0.2910 - accuracy: 0.9058 - val_loss: 0.4368 - val_sparse_categorical_crossentropy: 0.2499 - val_accuracy: 0.9318\n", - "Epoch 761/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4703 - sparse_categorical_crossentropy: 0.2837 - accuracy: 0.9117 - val_loss: 0.4404 - val_sparse_categorical_crossentropy: 0.2543 - val_accuracy: 0.9268\n", - "Epoch 762/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4745 - sparse_categorical_crossentropy: 0.2885 - accuracy: 0.9073 - val_loss: 0.4517 - val_sparse_categorical_crossentropy: 0.2657 - val_accuracy: 0.9206\n", - "Epoch 763/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4825 - sparse_categorical_crossentropy: 0.2961 - accuracy: 0.9039 - val_loss: 0.4474 - val_sparse_categorical_crossentropy: 0.2611 - val_accuracy: 0.9248\n", - "Epoch 764/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4787 - sparse_categorical_crossentropy: 0.2922 - accuracy: 0.9049 - val_loss: 0.4481 - val_sparse_categorical_crossentropy: 0.2614 - val_accuracy: 0.9252\n", - "Epoch 765/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4776 - sparse_categorical_crossentropy: 0.2908 - accuracy: 0.9061 - val_loss: 0.4573 - val_sparse_categorical_crossentropy: 0.2704 - val_accuracy: 0.9188\n", - "Epoch 766/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4836 - sparse_categorical_crossentropy: 0.2968 - accuracy: 0.9060 - val_loss: 0.4524 - val_sparse_categorical_crossentropy: 0.2662 - val_accuracy: 0.9252\n", - "Epoch 767/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4844 - sparse_categorical_crossentropy: 0.2980 - accuracy: 0.9040 - val_loss: 0.4449 - val_sparse_categorical_crossentropy: 0.2586 - val_accuracy: 0.9244\n", - "Epoch 768/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4779 - sparse_categorical_crossentropy: 0.2917 - accuracy: 0.9043 - val_loss: 0.4444 - val_sparse_categorical_crossentropy: 0.2578 - val_accuracy: 0.9208\n", - "Epoch 769/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4858 - sparse_categorical_crossentropy: 0.2994 - accuracy: 0.9022 - val_loss: 0.4599 - val_sparse_categorical_crossentropy: 0.2728 - val_accuracy: 0.9162\n", - "Epoch 770/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.4940 - sparse_categorical_crossentropy: 0.3074 - accuracy: 0.8966 - val_loss: 0.4500 - val_sparse_categorical_crossentropy: 0.2640 - val_accuracy: 0.9252\n", - "Epoch 771/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4919 - sparse_categorical_crossentropy: 0.3059 - accuracy: 0.8985 - val_loss: 0.4478 - val_sparse_categorical_crossentropy: 0.2618 - val_accuracy: 0.9306\n", - "Epoch 772/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4865 - sparse_categorical_crossentropy: 0.3006 - accuracy: 0.9009 - val_loss: 0.4469 - val_sparse_categorical_crossentropy: 0.2608 - val_accuracy: 0.9258\n", - "Epoch 773/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4850 - sparse_categorical_crossentropy: 0.2990 - accuracy: 0.9029 - val_loss: 0.4623 - val_sparse_categorical_crossentropy: 0.2762 - val_accuracy: 0.9118\n", - "Epoch 774/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4870 - sparse_categorical_crossentropy: 0.3008 - accuracy: 0.9027 - val_loss: 0.4516 - val_sparse_categorical_crossentropy: 0.2660 - val_accuracy: 0.9236\n", - "Epoch 775/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4805 - sparse_categorical_crossentropy: 0.2946 - accuracy: 0.9033 - val_loss: 0.4368 - val_sparse_categorical_crossentropy: 0.2511 - val_accuracy: 0.9322\n", - "Epoch 776/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4660 - sparse_categorical_crossentropy: 0.2801 - accuracy: 0.9143 - val_loss: 0.4406 - val_sparse_categorical_crossentropy: 0.2546 - val_accuracy: 0.9320\n", - "Epoch 777/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4668 - sparse_categorical_crossentropy: 0.2806 - accuracy: 0.9101 - val_loss: 0.4497 - val_sparse_categorical_crossentropy: 0.2632 - val_accuracy: 0.9216\n", - "Epoch 778/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4772 - sparse_categorical_crossentropy: 0.2905 - accuracy: 0.9070 - val_loss: 0.4409 - val_sparse_categorical_crossentropy: 0.2533 - val_accuracy: 0.9296\n", - "Epoch 779/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4758 - sparse_categorical_crossentropy: 0.2885 - accuracy: 0.9094 - val_loss: 0.4436 - val_sparse_categorical_crossentropy: 0.2563 - val_accuracy: 0.9220\n", - "Epoch 780/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4737 - sparse_categorical_crossentropy: 0.2864 - accuracy: 0.9085 - val_loss: 0.4462 - val_sparse_categorical_crossentropy: 0.2590 - val_accuracy: 0.9258\n", - "Epoch 781/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4785 - sparse_categorical_crossentropy: 0.2913 - accuracy: 0.9069 - val_loss: 0.4317 - val_sparse_categorical_crossentropy: 0.2446 - val_accuracy: 0.9320\n", - "Epoch 782/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4730 - sparse_categorical_crossentropy: 0.2859 - accuracy: 0.9066 - val_loss: 0.4405 - val_sparse_categorical_crossentropy: 0.2533 - val_accuracy: 0.9292\n", - "Epoch 783/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4714 - sparse_categorical_crossentropy: 0.2843 - accuracy: 0.9089 - val_loss: 0.4328 - val_sparse_categorical_crossentropy: 0.2458 - val_accuracy: 0.9300\n", - "Epoch 784/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4766 - sparse_categorical_crossentropy: 0.2899 - accuracy: 0.9058 - val_loss: 0.4400 - val_sparse_categorical_crossentropy: 0.2535 - val_accuracy: 0.9282\n", - "Epoch 785/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4755 - sparse_categorical_crossentropy: 0.2890 - accuracy: 0.9083 - val_loss: 0.4672 - val_sparse_categorical_crossentropy: 0.2815 - val_accuracy: 0.9136\n", - "Epoch 786/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4870 - sparse_categorical_crossentropy: 0.3008 - accuracy: 0.8997 - val_loss: 0.4667 - val_sparse_categorical_crossentropy: 0.2807 - val_accuracy: 0.9098\n", - "Epoch 787/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4975 - sparse_categorical_crossentropy: 0.3115 - accuracy: 0.8927 - val_loss: 0.4647 - val_sparse_categorical_crossentropy: 0.2785 - val_accuracy: 0.9104\n", - "Epoch 788/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4833 - sparse_categorical_crossentropy: 0.2972 - accuracy: 0.9037 - val_loss: 0.4522 - val_sparse_categorical_crossentropy: 0.2668 - val_accuracy: 0.9230\n", - "Epoch 789/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4799 - sparse_categorical_crossentropy: 0.2939 - accuracy: 0.9046 - val_loss: 0.4366 - val_sparse_categorical_crossentropy: 0.2509 - val_accuracy: 0.9324\n", - "Epoch 790/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4773 - sparse_categorical_crossentropy: 0.2914 - accuracy: 0.9059 - val_loss: 0.4424 - val_sparse_categorical_crossentropy: 0.2565 - val_accuracy: 0.9286\n", - "Epoch 791/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4724 - sparse_categorical_crossentropy: 0.2866 - accuracy: 0.9081 - val_loss: 0.4440 - val_sparse_categorical_crossentropy: 0.2585 - val_accuracy: 0.9288\n", - "Epoch 792/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4763 - sparse_categorical_crossentropy: 0.2907 - accuracy: 0.9035 - val_loss: 0.4355 - val_sparse_categorical_crossentropy: 0.2498 - val_accuracy: 0.9316\n", - "Epoch 793/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4760 - sparse_categorical_crossentropy: 0.2900 - accuracy: 0.9063 - val_loss: 0.4379 - val_sparse_categorical_crossentropy: 0.2525 - val_accuracy: 0.9296\n", - "Epoch 794/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4747 - sparse_categorical_crossentropy: 0.2890 - accuracy: 0.9045 - val_loss: 0.4398 - val_sparse_categorical_crossentropy: 0.2541 - val_accuracy: 0.9310\n", - "Epoch 795/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4760 - sparse_categorical_crossentropy: 0.2908 - accuracy: 0.9055 - val_loss: 0.4404 - val_sparse_categorical_crossentropy: 0.2548 - val_accuracy: 0.9266\n", - "Epoch 796/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4769 - sparse_categorical_crossentropy: 0.2915 - accuracy: 0.9069 - val_loss: 0.4396 - val_sparse_categorical_crossentropy: 0.2542 - val_accuracy: 0.9280\n", - "Epoch 797/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4713 - sparse_categorical_crossentropy: 0.2857 - accuracy: 0.9106 - val_loss: 0.4274 - val_sparse_categorical_crossentropy: 0.2415 - val_accuracy: 0.9356\n", - "Epoch 798/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4659 - sparse_categorical_crossentropy: 0.2800 - accuracy: 0.9123 - val_loss: 0.4343 - val_sparse_categorical_crossentropy: 0.2479 - val_accuracy: 0.9354\n", - "Epoch 799/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4652 - sparse_categorical_crossentropy: 0.2788 - accuracy: 0.9137 - val_loss: 0.4309 - val_sparse_categorical_crossentropy: 0.2442 - val_accuracy: 0.9310\n", - "Epoch 800/1000\n", - "4/4 [==============================] - 0s 23ms/step - loss: 0.4655 - sparse_categorical_crossentropy: 0.2789 - accuracy: 0.9125 - val_loss: 0.4288 - val_sparse_categorical_crossentropy: 0.2428 - val_accuracy: 0.9332\n", - "Epoch 801/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4691 - sparse_categorical_crossentropy: 0.2830 - accuracy: 0.9093 - val_loss: 0.4312 - val_sparse_categorical_crossentropy: 0.2451 - val_accuracy: 0.9328\n", - "Epoch 802/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4634 - sparse_categorical_crossentropy: 0.2773 - accuracy: 0.9129 - val_loss: 0.4329 - val_sparse_categorical_crossentropy: 0.2466 - val_accuracy: 0.9312\n", - "Epoch 803/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4615 - sparse_categorical_crossentropy: 0.2753 - accuracy: 0.9145 - val_loss: 0.4284 - val_sparse_categorical_crossentropy: 0.2423 - val_accuracy: 0.9342\n", - "Epoch 804/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4676 - sparse_categorical_crossentropy: 0.2817 - accuracy: 0.9121 - val_loss: 0.4294 - val_sparse_categorical_crossentropy: 0.2439 - val_accuracy: 0.9326\n", - "Epoch 805/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4667 - sparse_categorical_crossentropy: 0.2812 - accuracy: 0.9099 - val_loss: 0.4382 - val_sparse_categorical_crossentropy: 0.2531 - val_accuracy: 0.9252\n", - "Epoch 806/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4671 - sparse_categorical_crossentropy: 0.2821 - accuracy: 0.9105 - val_loss: 0.4345 - val_sparse_categorical_crossentropy: 0.2493 - val_accuracy: 0.9250\n", - "Epoch 807/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4694 - sparse_categorical_crossentropy: 0.2843 - accuracy: 0.9067 - val_loss: 0.4301 - val_sparse_categorical_crossentropy: 0.2456 - val_accuracy: 0.9324\n", - "Epoch 808/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4715 - sparse_categorical_crossentropy: 0.2868 - accuracy: 0.9057 - val_loss: 0.4460 - val_sparse_categorical_crossentropy: 0.2613 - val_accuracy: 0.9180\n", - "Epoch 809/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4701 - sparse_categorical_crossentropy: 0.2854 - accuracy: 0.9059 - val_loss: 0.4287 - val_sparse_categorical_crossentropy: 0.2441 - val_accuracy: 0.9334\n", - "Epoch 810/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4678 - sparse_categorical_crossentropy: 0.2830 - accuracy: 0.9080 - val_loss: 0.4301 - val_sparse_categorical_crossentropy: 0.2454 - val_accuracy: 0.9278\n", - "Epoch 811/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4756 - sparse_categorical_crossentropy: 0.2908 - accuracy: 0.9057 - val_loss: 0.4294 - val_sparse_categorical_crossentropy: 0.2448 - val_accuracy: 0.9308\n", - "Epoch 812/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4685 - sparse_categorical_crossentropy: 0.2843 - accuracy: 0.9092 - val_loss: 0.4418 - val_sparse_categorical_crossentropy: 0.2578 - val_accuracy: 0.9204\n", - "Epoch 813/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4735 - sparse_categorical_crossentropy: 0.2895 - accuracy: 0.9053 - val_loss: 0.4469 - val_sparse_categorical_crossentropy: 0.2627 - val_accuracy: 0.9214\n", - "Epoch 814/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4820 - sparse_categorical_crossentropy: 0.2982 - accuracy: 0.9031 - val_loss: 0.4538 - val_sparse_categorical_crossentropy: 0.2700 - val_accuracy: 0.9174\n", - "Epoch 815/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4807 - sparse_categorical_crossentropy: 0.2972 - accuracy: 0.9004 - val_loss: 0.4280 - val_sparse_categorical_crossentropy: 0.2448 - val_accuracy: 0.9312\n", - "Epoch 816/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4724 - sparse_categorical_crossentropy: 0.2892 - accuracy: 0.9070 - val_loss: 0.4373 - val_sparse_categorical_crossentropy: 0.2543 - val_accuracy: 0.9300\n", - "Epoch 817/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4697 - sparse_categorical_crossentropy: 0.2866 - accuracy: 0.9102 - val_loss: 0.4326 - val_sparse_categorical_crossentropy: 0.2490 - val_accuracy: 0.9304\n", - "Epoch 818/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4644 - sparse_categorical_crossentropy: 0.2807 - accuracy: 0.9124 - val_loss: 0.4360 - val_sparse_categorical_crossentropy: 0.2525 - val_accuracy: 0.9314\n", - "Epoch 819/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4718 - sparse_categorical_crossentropy: 0.2880 - accuracy: 0.9071 - val_loss: 0.4469 - val_sparse_categorical_crossentropy: 0.2633 - val_accuracy: 0.9222\n", - "Epoch 820/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4709 - sparse_categorical_crossentropy: 0.2872 - accuracy: 0.9105 - val_loss: 0.4448 - val_sparse_categorical_crossentropy: 0.2613 - val_accuracy: 0.9252\n", - "Epoch 821/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4684 - sparse_categorical_crossentropy: 0.2846 - accuracy: 0.9083 - val_loss: 0.4309 - val_sparse_categorical_crossentropy: 0.2470 - val_accuracy: 0.9300\n", - "Epoch 822/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4673 - sparse_categorical_crossentropy: 0.2833 - accuracy: 0.9081 - val_loss: 0.4276 - val_sparse_categorical_crossentropy: 0.2435 - val_accuracy: 0.9316\n", - "Epoch 823/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4654 - sparse_categorical_crossentropy: 0.2814 - accuracy: 0.9092 - val_loss: 0.4353 - val_sparse_categorical_crossentropy: 0.2512 - val_accuracy: 0.9268\n", - "Epoch 824/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4637 - sparse_categorical_crossentropy: 0.2796 - accuracy: 0.9102 - val_loss: 0.4273 - val_sparse_categorical_crossentropy: 0.2433 - val_accuracy: 0.9318\n", - "Epoch 825/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4748 - sparse_categorical_crossentropy: 0.2910 - accuracy: 0.9038 - val_loss: 0.4493 - val_sparse_categorical_crossentropy: 0.2656 - val_accuracy: 0.9204\n", - "Epoch 826/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4740 - sparse_categorical_crossentropy: 0.2906 - accuracy: 0.9031 - val_loss: 0.4428 - val_sparse_categorical_crossentropy: 0.2591 - val_accuracy: 0.9222\n", - "Epoch 827/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4719 - sparse_categorical_crossentropy: 0.2885 - accuracy: 0.9070 - val_loss: 0.4380 - val_sparse_categorical_crossentropy: 0.2541 - val_accuracy: 0.9264\n", - "Epoch 828/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4691 - sparse_categorical_crossentropy: 0.2850 - accuracy: 0.9061 - val_loss: 0.4260 - val_sparse_categorical_crossentropy: 0.2423 - val_accuracy: 0.9318\n", - "Epoch 829/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4656 - sparse_categorical_crossentropy: 0.2817 - accuracy: 0.9113 - val_loss: 0.4309 - val_sparse_categorical_crossentropy: 0.2469 - val_accuracy: 0.9324\n", - "Epoch 830/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4640 - sparse_categorical_crossentropy: 0.2801 - accuracy: 0.9108 - val_loss: 0.4263 - val_sparse_categorical_crossentropy: 0.2423 - val_accuracy: 0.9342\n", - "Epoch 831/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4608 - sparse_categorical_crossentropy: 0.2771 - accuracy: 0.9131 - val_loss: 0.4246 - val_sparse_categorical_crossentropy: 0.2413 - val_accuracy: 0.9328\n", - "Epoch 832/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4649 - sparse_categorical_crossentropy: 0.2816 - accuracy: 0.9094 - val_loss: 0.4262 - val_sparse_categorical_crossentropy: 0.2438 - val_accuracy: 0.9326\n", - "Epoch 833/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4602 - sparse_categorical_crossentropy: 0.2775 - accuracy: 0.9131 - val_loss: 0.4408 - val_sparse_categorical_crossentropy: 0.2578 - val_accuracy: 0.9226\n", - "Epoch 834/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4683 - sparse_categorical_crossentropy: 0.2855 - accuracy: 0.9080 - val_loss: 0.4275 - val_sparse_categorical_crossentropy: 0.2448 - val_accuracy: 0.9326\n", - "Epoch 835/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4652 - sparse_categorical_crossentropy: 0.2823 - accuracy: 0.9083 - val_loss: 0.4233 - val_sparse_categorical_crossentropy: 0.2402 - val_accuracy: 0.9298\n", - "Epoch 836/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4586 - sparse_categorical_crossentropy: 0.2755 - accuracy: 0.9143 - val_loss: 0.4373 - val_sparse_categorical_crossentropy: 0.2545 - val_accuracy: 0.9272\n", - "Epoch 837/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4620 - sparse_categorical_crossentropy: 0.2790 - accuracy: 0.9121 - val_loss: 0.4285 - val_sparse_categorical_crossentropy: 0.2461 - val_accuracy: 0.9324\n", - "Epoch 838/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4641 - sparse_categorical_crossentropy: 0.2815 - accuracy: 0.9098 - val_loss: 0.4284 - val_sparse_categorical_crossentropy: 0.2457 - val_accuracy: 0.9322\n", - "Epoch 839/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4586 - sparse_categorical_crossentropy: 0.2760 - accuracy: 0.9129 - val_loss: 0.4239 - val_sparse_categorical_crossentropy: 0.2409 - val_accuracy: 0.9310\n", - "Epoch 840/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4586 - sparse_categorical_crossentropy: 0.2759 - accuracy: 0.9129 - val_loss: 0.4335 - val_sparse_categorical_crossentropy: 0.2510 - val_accuracy: 0.9234\n", - "Epoch 841/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4714 - sparse_categorical_crossentropy: 0.2887 - accuracy: 0.9063 - val_loss: 0.4317 - val_sparse_categorical_crossentropy: 0.2490 - val_accuracy: 0.9294\n", - "Epoch 842/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4668 - sparse_categorical_crossentropy: 0.2838 - accuracy: 0.9055 - val_loss: 0.4547 - val_sparse_categorical_crossentropy: 0.2720 - val_accuracy: 0.9154\n", - "Epoch 843/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4719 - sparse_categorical_crossentropy: 0.2893 - accuracy: 0.9022 - val_loss: 0.4389 - val_sparse_categorical_crossentropy: 0.2567 - val_accuracy: 0.9228\n", - "Epoch 844/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4725 - sparse_categorical_crossentropy: 0.2905 - accuracy: 0.9001 - val_loss: 0.4336 - val_sparse_categorical_crossentropy: 0.2516 - val_accuracy: 0.9292\n", - "Epoch 845/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4684 - sparse_categorical_crossentropy: 0.2864 - accuracy: 0.9070 - val_loss: 0.4275 - val_sparse_categorical_crossentropy: 0.2459 - val_accuracy: 0.9250\n", - "Epoch 846/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.4651 - sparse_categorical_crossentropy: 0.2835 - accuracy: 0.9065 - val_loss: 0.4357 - val_sparse_categorical_crossentropy: 0.2542 - val_accuracy: 0.9282\n", - "Epoch 847/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4611 - sparse_categorical_crossentropy: 0.2799 - accuracy: 0.9115 - val_loss: 0.4240 - val_sparse_categorical_crossentropy: 0.2430 - val_accuracy: 0.9330\n", - "Epoch 848/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4685 - sparse_categorical_crossentropy: 0.2875 - accuracy: 0.9047 - val_loss: 0.4223 - val_sparse_categorical_crossentropy: 0.2410 - val_accuracy: 0.9384\n", - "Epoch 849/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4573 - sparse_categorical_crossentropy: 0.2754 - accuracy: 0.9152 - val_loss: 0.4246 - val_sparse_categorical_crossentropy: 0.2424 - val_accuracy: 0.9298\n", - "Epoch 850/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4613 - sparse_categorical_crossentropy: 0.2790 - accuracy: 0.9111 - val_loss: 0.4251 - val_sparse_categorical_crossentropy: 0.2423 - val_accuracy: 0.9292\n", - "Epoch 851/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.4621 - sparse_categorical_crossentropy: 0.2795 - accuracy: 0.9093 - val_loss: 0.4365 - val_sparse_categorical_crossentropy: 0.2539 - val_accuracy: 0.9182\n", - "Epoch 852/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4576 - sparse_categorical_crossentropy: 0.2747 - accuracy: 0.9114 - val_loss: 0.4320 - val_sparse_categorical_crossentropy: 0.2492 - val_accuracy: 0.9264\n", - "Epoch 853/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4591 - sparse_categorical_crossentropy: 0.2762 - accuracy: 0.9113 - val_loss: 0.4290 - val_sparse_categorical_crossentropy: 0.2464 - val_accuracy: 0.9248\n", - "Epoch 854/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4581 - sparse_categorical_crossentropy: 0.2755 - accuracy: 0.9094 - val_loss: 0.4229 - val_sparse_categorical_crossentropy: 0.2402 - val_accuracy: 0.9322\n", - "Epoch 855/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4581 - sparse_categorical_crossentropy: 0.2755 - accuracy: 0.9137 - val_loss: 0.4318 - val_sparse_categorical_crossentropy: 0.2494 - val_accuracy: 0.9312\n", - "Epoch 856/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4586 - sparse_categorical_crossentropy: 0.2764 - accuracy: 0.9140 - val_loss: 0.4331 - val_sparse_categorical_crossentropy: 0.2513 - val_accuracy: 0.9274\n", - "Epoch 857/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4635 - sparse_categorical_crossentropy: 0.2817 - accuracy: 0.9092 - val_loss: 0.4272 - val_sparse_categorical_crossentropy: 0.2456 - val_accuracy: 0.9298\n", - "Epoch 858/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4556 - sparse_categorical_crossentropy: 0.2739 - accuracy: 0.9111 - val_loss: 0.4429 - val_sparse_categorical_crossentropy: 0.2616 - val_accuracy: 0.9182\n", - "Epoch 859/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4653 - sparse_categorical_crossentropy: 0.2838 - accuracy: 0.9058 - val_loss: 0.4380 - val_sparse_categorical_crossentropy: 0.2566 - val_accuracy: 0.9246\n", - "Epoch 860/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4662 - sparse_categorical_crossentropy: 0.2847 - accuracy: 0.9070 - val_loss: 0.4251 - val_sparse_categorical_crossentropy: 0.2435 - val_accuracy: 0.9362\n", - "Epoch 861/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4592 - sparse_categorical_crossentropy: 0.2775 - accuracy: 0.9126 - val_loss: 0.4283 - val_sparse_categorical_crossentropy: 0.2467 - val_accuracy: 0.9296\n", - "Epoch 862/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4590 - sparse_categorical_crossentropy: 0.2774 - accuracy: 0.9128 - val_loss: 0.4164 - val_sparse_categorical_crossentropy: 0.2349 - val_accuracy: 0.9306\n", - "Epoch 863/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4548 - sparse_categorical_crossentropy: 0.2733 - accuracy: 0.9121 - val_loss: 0.4246 - val_sparse_categorical_crossentropy: 0.2430 - val_accuracy: 0.9296\n", - "Epoch 864/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4557 - sparse_categorical_crossentropy: 0.2743 - accuracy: 0.9137 - val_loss: 0.4210 - val_sparse_categorical_crossentropy: 0.2392 - val_accuracy: 0.9330\n", - "Epoch 865/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4529 - sparse_categorical_crossentropy: 0.2713 - accuracy: 0.9141 - val_loss: 0.4149 - val_sparse_categorical_crossentropy: 0.2338 - val_accuracy: 0.9386\n", - "Epoch 866/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4551 - sparse_categorical_crossentropy: 0.2741 - accuracy: 0.9147 - val_loss: 0.4330 - val_sparse_categorical_crossentropy: 0.2519 - val_accuracy: 0.9232\n", - "Epoch 867/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4603 - sparse_categorical_crossentropy: 0.2797 - accuracy: 0.9090 - val_loss: 0.4377 - val_sparse_categorical_crossentropy: 0.2569 - val_accuracy: 0.9272\n", - "Epoch 868/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4685 - sparse_categorical_crossentropy: 0.2877 - accuracy: 0.9066 - val_loss: 0.4232 - val_sparse_categorical_crossentropy: 0.2426 - val_accuracy: 0.9320\n", - "Epoch 869/1000\n", - "4/4 [==============================] - 0s 22ms/step - loss: 0.4661 - sparse_categorical_crossentropy: 0.2856 - accuracy: 0.9058 - val_loss: 0.4286 - val_sparse_categorical_crossentropy: 0.2483 - val_accuracy: 0.9270\n", - "Epoch 870/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4647 - sparse_categorical_crossentropy: 0.2842 - accuracy: 0.9054 - val_loss: 0.4378 - val_sparse_categorical_crossentropy: 0.2571 - val_accuracy: 0.9216\n", - "Epoch 871/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4635 - sparse_categorical_crossentropy: 0.2829 - accuracy: 0.9085 - val_loss: 0.4296 - val_sparse_categorical_crossentropy: 0.2492 - val_accuracy: 0.9288\n", - "Epoch 872/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4624 - sparse_categorical_crossentropy: 0.2824 - accuracy: 0.9094 - val_loss: 0.4241 - val_sparse_categorical_crossentropy: 0.2441 - val_accuracy: 0.9320\n", - "Epoch 873/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4571 - sparse_categorical_crossentropy: 0.2771 - accuracy: 0.9147 - val_loss: 0.4265 - val_sparse_categorical_crossentropy: 0.2464 - val_accuracy: 0.9286\n", - "Epoch 874/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4598 - sparse_categorical_crossentropy: 0.2795 - accuracy: 0.9103 - val_loss: 0.4348 - val_sparse_categorical_crossentropy: 0.2547 - val_accuracy: 0.9252\n", - "Epoch 875/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4588 - sparse_categorical_crossentropy: 0.2784 - accuracy: 0.9120 - val_loss: 0.4364 - val_sparse_categorical_crossentropy: 0.2558 - val_accuracy: 0.9254\n", - "Epoch 876/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4618 - sparse_categorical_crossentropy: 0.2811 - accuracy: 0.9105 - val_loss: 0.4387 - val_sparse_categorical_crossentropy: 0.2582 - val_accuracy: 0.9236\n", - "Epoch 877/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4650 - sparse_categorical_crossentropy: 0.2844 - accuracy: 0.9082 - val_loss: 0.4172 - val_sparse_categorical_crossentropy: 0.2365 - val_accuracy: 0.9354\n", - "Epoch 878/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4501 - sparse_categorical_crossentropy: 0.2692 - accuracy: 0.9161 - val_loss: 0.4231 - val_sparse_categorical_crossentropy: 0.2423 - val_accuracy: 0.9290\n", - "Epoch 879/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4515 - sparse_categorical_crossentropy: 0.2706 - accuracy: 0.9126 - val_loss: 0.4153 - val_sparse_categorical_crossentropy: 0.2348 - val_accuracy: 0.9346\n", - "Epoch 880/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4519 - sparse_categorical_crossentropy: 0.2713 - accuracy: 0.9155 - val_loss: 0.4246 - val_sparse_categorical_crossentropy: 0.2439 - val_accuracy: 0.9288\n", - "Epoch 881/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4523 - sparse_categorical_crossentropy: 0.2718 - accuracy: 0.9152 - val_loss: 0.4130 - val_sparse_categorical_crossentropy: 0.2326 - val_accuracy: 0.9340\n", - "Epoch 882/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4466 - sparse_categorical_crossentropy: 0.2661 - accuracy: 0.9155 - val_loss: 0.4185 - val_sparse_categorical_crossentropy: 0.2384 - val_accuracy: 0.9332\n", - "Epoch 883/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4492 - sparse_categorical_crossentropy: 0.2690 - accuracy: 0.9120 - val_loss: 0.4239 - val_sparse_categorical_crossentropy: 0.2435 - val_accuracy: 0.9268\n", - "Epoch 884/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4537 - sparse_categorical_crossentropy: 0.2737 - accuracy: 0.9139 - val_loss: 0.4218 - val_sparse_categorical_crossentropy: 0.2416 - val_accuracy: 0.9306\n", - "Epoch 885/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4453 - sparse_categorical_crossentropy: 0.2651 - accuracy: 0.9175 - val_loss: 0.4104 - val_sparse_categorical_crossentropy: 0.2303 - val_accuracy: 0.9366\n", - "Epoch 886/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4482 - sparse_categorical_crossentropy: 0.2679 - accuracy: 0.9164 - val_loss: 0.4136 - val_sparse_categorical_crossentropy: 0.2332 - val_accuracy: 0.9378\n", - "Epoch 887/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4537 - sparse_categorical_crossentropy: 0.2732 - accuracy: 0.9128 - val_loss: 0.4210 - val_sparse_categorical_crossentropy: 0.2404 - val_accuracy: 0.9310\n", - "Epoch 888/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4552 - sparse_categorical_crossentropy: 0.2747 - accuracy: 0.9098 - val_loss: 0.4143 - val_sparse_categorical_crossentropy: 0.2339 - val_accuracy: 0.9362\n", - "Epoch 889/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4510 - sparse_categorical_crossentropy: 0.2707 - accuracy: 0.9153 - val_loss: 0.4193 - val_sparse_categorical_crossentropy: 0.2388 - val_accuracy: 0.9336\n", - "Epoch 890/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4530 - sparse_categorical_crossentropy: 0.2728 - accuracy: 0.9111 - val_loss: 0.4165 - val_sparse_categorical_crossentropy: 0.2364 - val_accuracy: 0.9356\n", - "Epoch 891/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4446 - sparse_categorical_crossentropy: 0.2647 - accuracy: 0.9183 - val_loss: 0.4137 - val_sparse_categorical_crossentropy: 0.2346 - val_accuracy: 0.9380\n", - "Epoch 892/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4467 - sparse_categorical_crossentropy: 0.2674 - accuracy: 0.9143 - val_loss: 0.4237 - val_sparse_categorical_crossentropy: 0.2453 - val_accuracy: 0.9296\n", - "Epoch 893/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4510 - sparse_categorical_crossentropy: 0.2723 - accuracy: 0.9137 - val_loss: 0.4249 - val_sparse_categorical_crossentropy: 0.2461 - val_accuracy: 0.9262\n", - "Epoch 894/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4535 - sparse_categorical_crossentropy: 0.2744 - accuracy: 0.9130 - val_loss: 0.4251 - val_sparse_categorical_crossentropy: 0.2458 - val_accuracy: 0.9236\n", - "Epoch 895/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4499 - sparse_categorical_crossentropy: 0.2705 - accuracy: 0.9140 - val_loss: 0.4134 - val_sparse_categorical_crossentropy: 0.2339 - val_accuracy: 0.9344\n", - "Epoch 896/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4470 - sparse_categorical_crossentropy: 0.2675 - accuracy: 0.9154 - val_loss: 0.4172 - val_sparse_categorical_crossentropy: 0.2380 - val_accuracy: 0.9334\n", - "Epoch 897/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4472 - sparse_categorical_crossentropy: 0.2678 - accuracy: 0.9183 - val_loss: 0.4147 - val_sparse_categorical_crossentropy: 0.2354 - val_accuracy: 0.9354\n", - "Epoch 898/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4513 - sparse_categorical_crossentropy: 0.2720 - accuracy: 0.9130 - val_loss: 0.4139 - val_sparse_categorical_crossentropy: 0.2344 - val_accuracy: 0.9354\n", - "Epoch 899/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4477 - sparse_categorical_crossentropy: 0.2687 - accuracy: 0.9144 - val_loss: 0.4143 - val_sparse_categorical_crossentropy: 0.2358 - val_accuracy: 0.9314\n", - "Epoch 900/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4537 - sparse_categorical_crossentropy: 0.2753 - accuracy: 0.9097 - val_loss: 0.4306 - val_sparse_categorical_crossentropy: 0.2523 - val_accuracy: 0.9270\n", - "Epoch 901/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4542 - sparse_categorical_crossentropy: 0.2760 - accuracy: 0.9127 - val_loss: 0.4214 - val_sparse_categorical_crossentropy: 0.2432 - val_accuracy: 0.9312\n", - "Epoch 902/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4561 - sparse_categorical_crossentropy: 0.2780 - accuracy: 0.9102 - val_loss: 0.4242 - val_sparse_categorical_crossentropy: 0.2465 - val_accuracy: 0.9258\n", - "Epoch 903/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4595 - sparse_categorical_crossentropy: 0.2816 - accuracy: 0.9091 - val_loss: 0.4224 - val_sparse_categorical_crossentropy: 0.2441 - val_accuracy: 0.9294\n", - "Epoch 904/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4502 - sparse_categorical_crossentropy: 0.2720 - accuracy: 0.9134 - val_loss: 0.4244 - val_sparse_categorical_crossentropy: 0.2464 - val_accuracy: 0.9316\n", - "Epoch 905/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4527 - sparse_categorical_crossentropy: 0.2745 - accuracy: 0.9129 - val_loss: 0.4276 - val_sparse_categorical_crossentropy: 0.2500 - val_accuracy: 0.9256\n", - "Epoch 906/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4562 - sparse_categorical_crossentropy: 0.2782 - accuracy: 0.9088 - val_loss: 0.4302 - val_sparse_categorical_crossentropy: 0.2512 - val_accuracy: 0.9266\n", - "Epoch 907/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4606 - sparse_categorical_crossentropy: 0.2821 - accuracy: 0.9095 - val_loss: 0.4269 - val_sparse_categorical_crossentropy: 0.2487 - val_accuracy: 0.9252\n", - "Epoch 908/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4561 - sparse_categorical_crossentropy: 0.2778 - accuracy: 0.9110 - val_loss: 0.4136 - val_sparse_categorical_crossentropy: 0.2353 - val_accuracy: 0.9326\n", - "Epoch 909/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4509 - sparse_categorical_crossentropy: 0.2723 - accuracy: 0.9131 - val_loss: 0.4162 - val_sparse_categorical_crossentropy: 0.2378 - val_accuracy: 0.9286\n", - "Epoch 910/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4551 - sparse_categorical_crossentropy: 0.2765 - accuracy: 0.9125 - val_loss: 0.4153 - val_sparse_categorical_crossentropy: 0.2367 - val_accuracy: 0.9322\n", - "Epoch 911/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4478 - sparse_categorical_crossentropy: 0.2693 - accuracy: 0.9122 - val_loss: 0.4296 - val_sparse_categorical_crossentropy: 0.2510 - val_accuracy: 0.9226\n", - "Epoch 912/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4572 - sparse_categorical_crossentropy: 0.2786 - accuracy: 0.9085 - val_loss: 0.4249 - val_sparse_categorical_crossentropy: 0.2464 - val_accuracy: 0.9258\n", - "Epoch 913/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4584 - sparse_categorical_crossentropy: 0.2800 - accuracy: 0.9114 - val_loss: 0.4194 - val_sparse_categorical_crossentropy: 0.2412 - val_accuracy: 0.9304\n", - "Epoch 914/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4517 - sparse_categorical_crossentropy: 0.2736 - accuracy: 0.9118 - val_loss: 0.4185 - val_sparse_categorical_crossentropy: 0.2408 - val_accuracy: 0.9312\n", - "Epoch 915/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4532 - sparse_categorical_crossentropy: 0.2755 - accuracy: 0.9128 - val_loss: 0.4223 - val_sparse_categorical_crossentropy: 0.2449 - val_accuracy: 0.9308\n", - "Epoch 916/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4496 - sparse_categorical_crossentropy: 0.2723 - accuracy: 0.9131 - val_loss: 0.4139 - val_sparse_categorical_crossentropy: 0.2371 - val_accuracy: 0.9358\n", - "Epoch 917/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4529 - sparse_categorical_crossentropy: 0.2759 - accuracy: 0.9098 - val_loss: 0.4336 - val_sparse_categorical_crossentropy: 0.2564 - val_accuracy: 0.9204\n", - "Epoch 918/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4623 - sparse_categorical_crossentropy: 0.2851 - accuracy: 0.9055 - val_loss: 0.4397 - val_sparse_categorical_crossentropy: 0.2621 - val_accuracy: 0.9198\n", - "Epoch 919/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4630 - sparse_categorical_crossentropy: 0.2854 - accuracy: 0.9043 - val_loss: 0.4425 - val_sparse_categorical_crossentropy: 0.2652 - val_accuracy: 0.9124\n", - "Epoch 920/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4700 - sparse_categorical_crossentropy: 0.2924 - accuracy: 0.8970 - val_loss: 0.4235 - val_sparse_categorical_crossentropy: 0.2464 - val_accuracy: 0.9268\n", - "Epoch 921/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4540 - sparse_categorical_crossentropy: 0.2769 - accuracy: 0.9110 - val_loss: 0.4326 - val_sparse_categorical_crossentropy: 0.2549 - val_accuracy: 0.9242\n", - "Epoch 922/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4525 - sparse_categorical_crossentropy: 0.2753 - accuracy: 0.9100 - val_loss: 0.4254 - val_sparse_categorical_crossentropy: 0.2482 - val_accuracy: 0.9258\n", - "Epoch 923/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4467 - sparse_categorical_crossentropy: 0.2696 - accuracy: 0.9127 - val_loss: 0.4128 - val_sparse_categorical_crossentropy: 0.2357 - val_accuracy: 0.9346\n", - "Epoch 924/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4456 - sparse_categorical_crossentropy: 0.2684 - accuracy: 0.9137 - val_loss: 0.4122 - val_sparse_categorical_crossentropy: 0.2352 - val_accuracy: 0.9316\n", - "Epoch 925/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4451 - sparse_categorical_crossentropy: 0.2681 - accuracy: 0.9151 - val_loss: 0.4115 - val_sparse_categorical_crossentropy: 0.2345 - val_accuracy: 0.9342\n", - "Epoch 926/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4460 - sparse_categorical_crossentropy: 0.2689 - accuracy: 0.9119 - val_loss: 0.4181 - val_sparse_categorical_crossentropy: 0.2404 - val_accuracy: 0.9318\n", - "Epoch 927/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4438 - sparse_categorical_crossentropy: 0.2662 - accuracy: 0.9172 - val_loss: 0.4247 - val_sparse_categorical_crossentropy: 0.2472 - val_accuracy: 0.9258\n", - "Epoch 928/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4483 - sparse_categorical_crossentropy: 0.2704 - accuracy: 0.9123 - val_loss: 0.4072 - val_sparse_categorical_crossentropy: 0.2289 - val_accuracy: 0.9358\n", - "Epoch 929/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4468 - sparse_categorical_crossentropy: 0.2685 - accuracy: 0.9135 - val_loss: 0.4178 - val_sparse_categorical_crossentropy: 0.2392 - val_accuracy: 0.9304\n", - "Epoch 930/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4485 - sparse_categorical_crossentropy: 0.2702 - accuracy: 0.9138 - val_loss: 0.4120 - val_sparse_categorical_crossentropy: 0.2339 - val_accuracy: 0.9302\n", - "Epoch 931/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4501 - sparse_categorical_crossentropy: 0.2721 - accuracy: 0.9141 - val_loss: 0.4149 - val_sparse_categorical_crossentropy: 0.2367 - val_accuracy: 0.9302\n", - "Epoch 932/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4472 - sparse_categorical_crossentropy: 0.2691 - accuracy: 0.9127 - val_loss: 0.4329 - val_sparse_categorical_crossentropy: 0.2551 - val_accuracy: 0.9218\n", - "Epoch 933/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4500 - sparse_categorical_crossentropy: 0.2722 - accuracy: 0.9119 - val_loss: 0.4268 - val_sparse_categorical_crossentropy: 0.2496 - val_accuracy: 0.9260\n", - "Epoch 934/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4516 - sparse_categorical_crossentropy: 0.2743 - accuracy: 0.9117 - val_loss: 0.4137 - val_sparse_categorical_crossentropy: 0.2364 - val_accuracy: 0.9316\n", - "Epoch 935/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4507 - sparse_categorical_crossentropy: 0.2736 - accuracy: 0.9099 - val_loss: 0.4337 - val_sparse_categorical_crossentropy: 0.2566 - val_accuracy: 0.9218\n", - "Epoch 936/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4550 - sparse_categorical_crossentropy: 0.2780 - accuracy: 0.9086 - val_loss: 0.4251 - val_sparse_categorical_crossentropy: 0.2489 - val_accuracy: 0.9280\n", - "Epoch 937/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4518 - sparse_categorical_crossentropy: 0.2750 - accuracy: 0.9118 - val_loss: 0.4164 - val_sparse_categorical_crossentropy: 0.2398 - val_accuracy: 0.9376\n", - "Epoch 938/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4462 - sparse_categorical_crossentropy: 0.2696 - accuracy: 0.9135 - val_loss: 0.4249 - val_sparse_categorical_crossentropy: 0.2476 - val_accuracy: 0.9302\n", - "Epoch 939/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4460 - sparse_categorical_crossentropy: 0.2690 - accuracy: 0.9152 - val_loss: 0.4097 - val_sparse_categorical_crossentropy: 0.2324 - val_accuracy: 0.9332\n", - "Epoch 940/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4476 - sparse_categorical_crossentropy: 0.2704 - accuracy: 0.9122 - val_loss: 0.4160 - val_sparse_categorical_crossentropy: 0.2390 - val_accuracy: 0.9280\n", - "Epoch 941/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.4499 - sparse_categorical_crossentropy: 0.2729 - accuracy: 0.9109 - val_loss: 0.4131 - val_sparse_categorical_crossentropy: 0.2360 - val_accuracy: 0.9314\n", - "Epoch 942/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4470 - sparse_categorical_crossentropy: 0.2699 - accuracy: 0.9115 - val_loss: 0.4141 - val_sparse_categorical_crossentropy: 0.2371 - val_accuracy: 0.9332\n", - "Epoch 943/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4520 - sparse_categorical_crossentropy: 0.2750 - accuracy: 0.9107 - val_loss: 0.4178 - val_sparse_categorical_crossentropy: 0.2411 - val_accuracy: 0.9308\n", - "Epoch 944/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4459 - sparse_categorical_crossentropy: 0.2691 - accuracy: 0.9174 - val_loss: 0.4123 - val_sparse_categorical_crossentropy: 0.2356 - val_accuracy: 0.9330\n", - "Epoch 945/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4387 - sparse_categorical_crossentropy: 0.2621 - accuracy: 0.9169 - val_loss: 0.4099 - val_sparse_categorical_crossentropy: 0.2334 - val_accuracy: 0.9364\n", - "Epoch 946/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4422 - sparse_categorical_crossentropy: 0.2656 - accuracy: 0.9157 - val_loss: 0.4104 - val_sparse_categorical_crossentropy: 0.2335 - val_accuracy: 0.9304\n", - "Epoch 947/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4392 - sparse_categorical_crossentropy: 0.2621 - accuracy: 0.9164 - val_loss: 0.4095 - val_sparse_categorical_crossentropy: 0.2323 - val_accuracy: 0.9326\n", - "Epoch 948/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4396 - sparse_categorical_crossentropy: 0.2625 - accuracy: 0.9179 - val_loss: 0.4058 - val_sparse_categorical_crossentropy: 0.2289 - val_accuracy: 0.9348\n", - "Epoch 949/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4445 - sparse_categorical_crossentropy: 0.2677 - accuracy: 0.9137 - val_loss: 0.4255 - val_sparse_categorical_crossentropy: 0.2484 - val_accuracy: 0.9218\n", - "Epoch 950/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4528 - sparse_categorical_crossentropy: 0.2761 - accuracy: 0.9056 - val_loss: 0.4139 - val_sparse_categorical_crossentropy: 0.2375 - val_accuracy: 0.9318\n", - "Epoch 951/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4447 - sparse_categorical_crossentropy: 0.2684 - accuracy: 0.9158 - val_loss: 0.4157 - val_sparse_categorical_crossentropy: 0.2396 - val_accuracy: 0.9252\n", - "Epoch 952/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4465 - sparse_categorical_crossentropy: 0.2704 - accuracy: 0.9129 - val_loss: 0.4094 - val_sparse_categorical_crossentropy: 0.2334 - val_accuracy: 0.9334\n", - "Epoch 953/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4406 - sparse_categorical_crossentropy: 0.2646 - accuracy: 0.9159 - val_loss: 0.4117 - val_sparse_categorical_crossentropy: 0.2359 - val_accuracy: 0.9314\n", - "Epoch 954/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4400 - sparse_categorical_crossentropy: 0.2639 - accuracy: 0.9154 - val_loss: 0.4247 - val_sparse_categorical_crossentropy: 0.2491 - val_accuracy: 0.9224\n", - "Epoch 955/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4416 - sparse_categorical_crossentropy: 0.2659 - accuracy: 0.9154 - val_loss: 0.4179 - val_sparse_categorical_crossentropy: 0.2418 - val_accuracy: 0.9300\n", - "Epoch 956/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4449 - sparse_categorical_crossentropy: 0.2689 - accuracy: 0.9121 - val_loss: 0.4077 - val_sparse_categorical_crossentropy: 0.2317 - val_accuracy: 0.9354\n", - "Epoch 957/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4372 - sparse_categorical_crossentropy: 0.2610 - accuracy: 0.9180 - val_loss: 0.4092 - val_sparse_categorical_crossentropy: 0.2336 - val_accuracy: 0.9320\n", - "Epoch 958/1000\n", - "4/4 [==============================] - 0s 21ms/step - loss: 0.4478 - sparse_categorical_crossentropy: 0.2719 - accuracy: 0.9138 - val_loss: 0.4088 - val_sparse_categorical_crossentropy: 0.2330 - val_accuracy: 0.9334\n", - "Epoch 959/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4433 - sparse_categorical_crossentropy: 0.2673 - accuracy: 0.9124 - val_loss: 0.4149 - val_sparse_categorical_crossentropy: 0.2383 - val_accuracy: 0.9290\n", - "Epoch 960/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4478 - sparse_categorical_crossentropy: 0.2714 - accuracy: 0.9110 - val_loss: 0.4121 - val_sparse_categorical_crossentropy: 0.2357 - val_accuracy: 0.9298\n", - "Epoch 961/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4419 - sparse_categorical_crossentropy: 0.2656 - accuracy: 0.9151 - val_loss: 0.4040 - val_sparse_categorical_crossentropy: 0.2277 - val_accuracy: 0.9374\n", - "Epoch 962/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4333 - sparse_categorical_crossentropy: 0.2571 - accuracy: 0.9195 - val_loss: 0.4153 - val_sparse_categorical_crossentropy: 0.2396 - val_accuracy: 0.9294\n", - "Epoch 963/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4392 - sparse_categorical_crossentropy: 0.2636 - accuracy: 0.9143 - val_loss: 0.4107 - val_sparse_categorical_crossentropy: 0.2351 - val_accuracy: 0.9336\n", - "Epoch 964/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4372 - sparse_categorical_crossentropy: 0.2618 - accuracy: 0.9175 - val_loss: 0.4074 - val_sparse_categorical_crossentropy: 0.2321 - val_accuracy: 0.9338\n", - "Epoch 965/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4417 - sparse_categorical_crossentropy: 0.2663 - accuracy: 0.9134 - val_loss: 0.4137 - val_sparse_categorical_crossentropy: 0.2385 - val_accuracy: 0.9292\n", - "Epoch 966/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4451 - sparse_categorical_crossentropy: 0.2695 - accuracy: 0.9125 - val_loss: 0.4188 - val_sparse_categorical_crossentropy: 0.2430 - val_accuracy: 0.9296\n", - "Epoch 967/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4425 - sparse_categorical_crossentropy: 0.2668 - accuracy: 0.9144 - val_loss: 0.4072 - val_sparse_categorical_crossentropy: 0.2314 - val_accuracy: 0.9320\n", - "Epoch 968/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4389 - sparse_categorical_crossentropy: 0.2632 - accuracy: 0.9155 - val_loss: 0.4057 - val_sparse_categorical_crossentropy: 0.2301 - val_accuracy: 0.9334\n", - "Epoch 969/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4379 - sparse_categorical_crossentropy: 0.2624 - accuracy: 0.9173 - val_loss: 0.4200 - val_sparse_categorical_crossentropy: 0.2446 - val_accuracy: 0.9256\n", - "Epoch 970/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4454 - sparse_categorical_crossentropy: 0.2702 - accuracy: 0.9106 - val_loss: 0.4162 - val_sparse_categorical_crossentropy: 0.2407 - val_accuracy: 0.9280\n", - "Epoch 971/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4445 - sparse_categorical_crossentropy: 0.2691 - accuracy: 0.9147 - val_loss: 0.4067 - val_sparse_categorical_crossentropy: 0.2316 - val_accuracy: 0.9278\n", - "Epoch 972/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4406 - sparse_categorical_crossentropy: 0.2653 - accuracy: 0.9142 - val_loss: 0.4048 - val_sparse_categorical_crossentropy: 0.2295 - val_accuracy: 0.9340\n", - "Epoch 973/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4349 - sparse_categorical_crossentropy: 0.2596 - accuracy: 0.9158 - val_loss: 0.4081 - val_sparse_categorical_crossentropy: 0.2330 - val_accuracy: 0.9328\n", - "Epoch 974/1000\n", - "4/4 [==============================] - 0s 20ms/step - loss: 0.4374 - sparse_categorical_crossentropy: 0.2624 - accuracy: 0.9166 - val_loss: 0.4137 - val_sparse_categorical_crossentropy: 0.2386 - val_accuracy: 0.9292\n", - "Epoch 975/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4389 - sparse_categorical_crossentropy: 0.2639 - accuracy: 0.9186 - val_loss: 0.4165 - val_sparse_categorical_crossentropy: 0.2414 - val_accuracy: 0.9302\n", - "Epoch 976/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4416 - sparse_categorical_crossentropy: 0.2666 - accuracy: 0.9137 - val_loss: 0.4069 - val_sparse_categorical_crossentropy: 0.2325 - val_accuracy: 0.9352\n", - "Epoch 977/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4367 - sparse_categorical_crossentropy: 0.2621 - accuracy: 0.9164 - val_loss: 0.4038 - val_sparse_categorical_crossentropy: 0.2292 - val_accuracy: 0.9348\n", - "Epoch 978/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4320 - sparse_categorical_crossentropy: 0.2575 - accuracy: 0.9194 - val_loss: 0.4026 - val_sparse_categorical_crossentropy: 0.2281 - val_accuracy: 0.9368\n", - "Epoch 979/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4395 - sparse_categorical_crossentropy: 0.2649 - accuracy: 0.9145 - val_loss: 0.4110 - val_sparse_categorical_crossentropy: 0.2362 - val_accuracy: 0.9314\n", - "Epoch 980/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4425 - sparse_categorical_crossentropy: 0.2677 - accuracy: 0.9131 - val_loss: 0.4099 - val_sparse_categorical_crossentropy: 0.2357 - val_accuracy: 0.9282\n", - "Epoch 981/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4438 - sparse_categorical_crossentropy: 0.2694 - accuracy: 0.9136 - val_loss: 0.4136 - val_sparse_categorical_crossentropy: 0.2393 - val_accuracy: 0.9272\n", - "Epoch 982/1000\n", - "4/4 [==============================] - 0s 24ms/step - loss: 0.4510 - sparse_categorical_crossentropy: 0.2767 - accuracy: 0.9089 - val_loss: 0.4181 - val_sparse_categorical_crossentropy: 0.2436 - val_accuracy: 0.9254\n", - "Epoch 983/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4410 - sparse_categorical_crossentropy: 0.2665 - accuracy: 0.9180 - val_loss: 0.4140 - val_sparse_categorical_crossentropy: 0.2396 - val_accuracy: 0.9350\n", - "Epoch 984/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4427 - sparse_categorical_crossentropy: 0.2682 - accuracy: 0.9128 - val_loss: 0.4079 - val_sparse_categorical_crossentropy: 0.2332 - val_accuracy: 0.9324\n", - "Epoch 985/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4358 - sparse_categorical_crossentropy: 0.2610 - accuracy: 0.9182 - val_loss: 0.4013 - val_sparse_categorical_crossentropy: 0.2261 - val_accuracy: 0.9364\n", - "Epoch 986/1000\n", - "4/4 [==============================] - 0s 18ms/step - loss: 0.4337 - sparse_categorical_crossentropy: 0.2585 - accuracy: 0.9185 - val_loss: 0.4142 - val_sparse_categorical_crossentropy: 0.2391 - val_accuracy: 0.9250\n", - "Epoch 987/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4393 - sparse_categorical_crossentropy: 0.2642 - accuracy: 0.9160 - val_loss: 0.4077 - val_sparse_categorical_crossentropy: 0.2325 - val_accuracy: 0.9340\n", - "Epoch 988/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4359 - sparse_categorical_crossentropy: 0.2607 - accuracy: 0.9180 - val_loss: 0.4141 - val_sparse_categorical_crossentropy: 0.2390 - val_accuracy: 0.9272\n", - "Epoch 989/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4469 - sparse_categorical_crossentropy: 0.2719 - accuracy: 0.9110 - val_loss: 0.4046 - val_sparse_categorical_crossentropy: 0.2296 - val_accuracy: 0.9336\n", - "Epoch 990/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4400 - sparse_categorical_crossentropy: 0.2653 - accuracy: 0.9131 - val_loss: 0.4225 - val_sparse_categorical_crossentropy: 0.2478 - val_accuracy: 0.9236\n", - "Epoch 991/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4478 - sparse_categorical_crossentropy: 0.2731 - accuracy: 0.9118 - val_loss: 0.4079 - val_sparse_categorical_crossentropy: 0.2335 - val_accuracy: 0.9318\n", - "Epoch 992/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4399 - sparse_categorical_crossentropy: 0.2654 - accuracy: 0.9154 - val_loss: 0.4206 - val_sparse_categorical_crossentropy: 0.2467 - val_accuracy: 0.9200\n", - "Epoch 993/1000\n", - "4/4 [==============================] - 0s 19ms/step - loss: 0.4455 - sparse_categorical_crossentropy: 0.2715 - accuracy: 0.9095 - val_loss: 0.4097 - val_sparse_categorical_crossentropy: 0.2357 - val_accuracy: 0.9274\n", - "Epoch 994/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4410 - sparse_categorical_crossentropy: 0.2670 - accuracy: 0.9111 - val_loss: 0.4154 - val_sparse_categorical_crossentropy: 0.2413 - val_accuracy: 0.9294\n", - "Epoch 995/1000\n", - "4/4 [==============================] - 0s 16ms/step - loss: 0.4503 - sparse_categorical_crossentropy: 0.2763 - accuracy: 0.9086 - val_loss: 0.4066 - val_sparse_categorical_crossentropy: 0.2330 - val_accuracy: 0.9322\n", - "Epoch 996/1000\n", - "4/4 [==============================] - 0s 15ms/step - loss: 0.4380 - sparse_categorical_crossentropy: 0.2643 - accuracy: 0.9150 - val_loss: 0.4076 - val_sparse_categorical_crossentropy: 0.2339 - val_accuracy: 0.9322\n", - "Epoch 997/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4428 - sparse_categorical_crossentropy: 0.2689 - accuracy: 0.9153 - val_loss: 0.4215 - val_sparse_categorical_crossentropy: 0.2472 - val_accuracy: 0.9212\n", - "Epoch 998/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4489 - sparse_categorical_crossentropy: 0.2750 - accuracy: 0.9109 - val_loss: 0.4223 - val_sparse_categorical_crossentropy: 0.2482 - val_accuracy: 0.9308\n", - "Epoch 999/1000\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.4464 - sparse_categorical_crossentropy: 0.2728 - accuracy: 0.9090 - val_loss: 0.4257 - val_sparse_categorical_crossentropy: 0.2522 - val_accuracy: 0.9262\n", - "Epoch 1000/1000\n", - "4/4 [==============================] - 0s 17ms/step - loss: 0.4498 - sparse_categorical_crossentropy: 0.2761 - accuracy: 0.9083 - val_loss: 0.4121 - val_sparse_categorical_crossentropy: 0.2390 - val_accuracy: 0.9338\n" - ] - }, - { - "data": { - "text/html": [ - "Waiting for W&B process to finish... (success)." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "61880ee68e3e4d5d92664575807aad1f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='0.002 MB of 0.002 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "

Run history:


accuracy▁▄▆▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇▇█████████████████████
epoch▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
loss█▃▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
sparse_categorical_crossentropy█▅▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
val_accuracy▁▄▅▆▆▆▆▆▇▇▆▇▇▇▇▇▇███████████████████████
val_loss█▃▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
val_sparse_categorical_crossentropy█▅▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁

Run summary:


GFLOPS0.00024
accuracy0.90834
best_epoch984
best_val_sparse_categorical_crossentropy0.22611
epoch999
loss0.44979
sparse_categorical_crossentropy0.27614
val_accuracy0.93379
val_loss0.41214
val_sparse_categorical_crossentropy0.239

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Synced electric-oath-48: https://wandb.ai/eltoncn/SygilDetector/runs/2u9kpjot
Synced 5 W&B file(s), 1 media file(s), 0 artifact file(s) and 1 other file(s)" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Find logs at: .\\wandb\\run-20221017_184647-2u9kpjot\\logs" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "units = [400, 400]\n", "regularizer = [0.0075, 0.0075]\n", @@ -2744,44 +604,9 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(model.history.history[\"loss\"])\n", "plt.plot(model.history.history[\"val_loss\"])\n", @@ -2793,15 +618,16 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Unity test" + "### Unity test" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2815,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2825,18 +651,9 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[6.2011808e-01 4.9349675e-03 1.7831384e-05 3.9548450e-08 3.7492913e-01]\n", - "0\n" - ] - } - ], + "outputs": [], "source": [ "points = sample_points_fromlist(all_points_tr, 100)\n", "points = center_points(points)\n", @@ -2850,34 +667,9 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(points[:,0], points[:,1], \"o\")\n", "plt.show()\n", @@ -2889,18 +681,9 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.8976728e-02 9.7843343e-01 7.0569420e-04 5.2762334e-07 1.8835656e-03]\n", - "1\n" - ] - } - ], + "outputs": [], "source": [ "df = pd.read_csv(\"input.csv\", sep=\";\", decimal=\",\", names=[\"x\", \"y\"])\n", "points = df.to_numpy()[100:]\n", @@ -2915,34 +698,9 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(points[:,0], points[:,1], \"o\")\n", "plt.show()\n", @@ -2953,15 +711,16 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Image" + "### Image" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2994,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3019,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3064,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3079,7 +838,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3089,836 +848,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "wandb version 0.13.4 is available! To upgrade, please run:\n", - " $ pip install wandb --upgrade" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Tracking run with wandb version 0.12.21" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Run data is saved locally in d:\\Github\\JogosTeP\\Dev\\wandb\\run-20221017_193034-2ex88lij" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Syncing run usual-dust-56 to Weights & Biases (docs)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The save_model argument by default saves the model in the HDF5 format that cannot save custom objects like subclassed models and custom layers. This behavior will be deprecated in a future release in favor of the SavedModel format. Meanwhile, the HDF5 model is saved as W&B files and the SavedModel as W&B Artifacts.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From c:\\Python38\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3777: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n", - "Epoch 1/1000\n", - "5/5 [==============================] - 2s 107ms/step - loss: 46.1189 - sparse_categorical_crossentropy: 38.0083 - accuracy: 0.3978 - val_loss: 9.9294 - val_sparse_categorical_crossentropy: 2.9501 - val_accuracy: 0.6889\n", - "Epoch 2/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 13.8067 - sparse_categorical_crossentropy: 7.2002 - accuracy: 0.6241 - val_loss: 7.2873 - val_sparse_categorical_crossentropy: 1.2979 - val_accuracy: 0.7765\n", - "Epoch 3/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 9.4117 - sparse_categorical_crossentropy: 3.6071 - accuracy: 0.6917 - val_loss: 6.4466 - val_sparse_categorical_crossentropy: 0.9295 - val_accuracy: 0.7750\n", - "Epoch 4/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 7.7718 - sparse_categorical_crossentropy: 2.3293 - accuracy: 0.7169 - val_loss: 6.0342 - val_sparse_categorical_crossentropy: 0.6987 - val_accuracy: 0.7908\n", - "Epoch 5/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 6.9343 - sparse_categorical_crossentropy: 1.6225 - accuracy: 0.7314 - val_loss: 5.8575 - val_sparse_categorical_crossentropy: 0.5792 - val_accuracy: 0.8048\n", - "Epoch 6/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 6.4673 - sparse_categorical_crossentropy: 1.1987 - accuracy: 0.7429 - val_loss: 5.7753 - val_sparse_categorical_crossentropy: 0.5275 - val_accuracy: 0.8066\n", - "Epoch 7/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 6.1821 - sparse_categorical_crossentropy: 0.9467 - accuracy: 0.7488 - val_loss: 5.7127 - val_sparse_categorical_crossentropy: 0.5079 - val_accuracy: 0.8095\n", - "Epoch 8/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 5.9892 - sparse_categorical_crossentropy: 0.8026 - accuracy: 0.7626 - val_loss: 5.6468 - val_sparse_categorical_crossentropy: 0.5013 - val_accuracy: 0.8120\n", - "Epoch 9/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 5.8356 - sparse_categorical_crossentropy: 0.7115 - accuracy: 0.7723 - val_loss: 5.5732 - val_sparse_categorical_crossentropy: 0.4947 - val_accuracy: 0.8130\n", - "Epoch 10/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 5.7105 - sparse_categorical_crossentropy: 0.6538 - accuracy: 0.7847 - val_loss: 5.4998 - val_sparse_categorical_crossentropy: 0.4882 - val_accuracy: 0.8181\n", - "Epoch 11/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 5.5880 - sparse_categorical_crossentropy: 0.5973 - accuracy: 0.7987 - val_loss: 5.4314 - val_sparse_categorical_crossentropy: 0.4838 - val_accuracy: 0.8230\n", - "Epoch 12/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 5.4951 - sparse_categorical_crossentropy: 0.5677 - accuracy: 0.8062 - val_loss: 5.3651 - val_sparse_categorical_crossentropy: 0.4795 - val_accuracy: 0.8263\n", - "Epoch 13/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 5.4023 - sparse_categorical_crossentropy: 0.5366 - accuracy: 0.8199 - val_loss: 5.2940 - val_sparse_categorical_crossentropy: 0.4697 - val_accuracy: 0.8326\n", - "Epoch 14/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 5.2963 - sparse_categorical_crossentropy: 0.4918 - accuracy: 0.8281 - val_loss: 5.2246 - val_sparse_categorical_crossentropy: 0.4615 - val_accuracy: 0.8350\n", - "Epoch 15/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 5.2030 - sparse_categorical_crossentropy: 0.4597 - accuracy: 0.8351 - val_loss: 5.1565 - val_sparse_categorical_crossentropy: 0.4546 - val_accuracy: 0.8398\n", - "Epoch 16/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 5.1132 - sparse_categorical_crossentropy: 0.4311 - accuracy: 0.8449 - val_loss: 5.0925 - val_sparse_categorical_crossentropy: 0.4515 - val_accuracy: 0.8410\n", - "Epoch 17/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 5.0474 - sparse_categorical_crossentropy: 0.4260 - accuracy: 0.8483 - val_loss: 5.0312 - val_sparse_categorical_crossentropy: 0.4507 - val_accuracy: 0.8456\n", - "Epoch 18/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 4.9592 - sparse_categorical_crossentropy: 0.3982 - accuracy: 0.8566 - val_loss: 4.9669 - val_sparse_categorical_crossentropy: 0.4463 - val_accuracy: 0.8493\n", - "Epoch 19/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 4.8788 - sparse_categorical_crossentropy: 0.3775 - accuracy: 0.8630 - val_loss: 4.9036 - val_sparse_categorical_crossentropy: 0.4424 - val_accuracy: 0.8520\n", - "Epoch 20/1000\n", - "5/5 [==============================] - 0s 54ms/step - loss: 4.7949 - sparse_categorical_crossentropy: 0.3527 - accuracy: 0.8680 - val_loss: 4.8407 - val_sparse_categorical_crossentropy: 0.4383 - val_accuracy: 0.8545\n", - "Epoch 21/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 4.7214 - sparse_categorical_crossentropy: 0.3379 - accuracy: 0.8732 - val_loss: 4.7769 - val_sparse_categorical_crossentropy: 0.4328 - val_accuracy: 0.8621\n", - "Epoch 22/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 4.6458 - sparse_categorical_crossentropy: 0.3204 - accuracy: 0.8807 - val_loss: 4.7046 - val_sparse_categorical_crossentropy: 0.4180 - val_accuracy: 0.8663\n", - "Epoch 23/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 4.5707 - sparse_categorical_crossentropy: 0.3026 - accuracy: 0.8836 - val_loss: 4.6377 - val_sparse_categorical_crossentropy: 0.4081 - val_accuracy: 0.8688\n", - "Epoch 24/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 4.5126 - sparse_categorical_crossentropy: 0.3013 - accuracy: 0.8871 - val_loss: 4.5796 - val_sparse_categorical_crossentropy: 0.4063 - val_accuracy: 0.8711\n", - "Epoch 25/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 4.4367 - sparse_categorical_crossentropy: 0.2815 - accuracy: 0.8938 - val_loss: 4.5270 - val_sparse_categorical_crossentropy: 0.4094 - val_accuracy: 0.8743\n", - "Epoch 26/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 4.3696 - sparse_categorical_crossentropy: 0.2699 - accuracy: 0.8960 - val_loss: 4.4712 - val_sparse_categorical_crossentropy: 0.4085 - val_accuracy: 0.8771\n", - "Epoch 27/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 4.3004 - sparse_categorical_crossentropy: 0.2555 - accuracy: 0.9012 - val_loss: 4.4126 - val_sparse_categorical_crossentropy: 0.4045 - val_accuracy: 0.8790\n", - "Epoch 28/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 4.2345 - sparse_categorical_crossentropy: 0.2439 - accuracy: 0.9053 - val_loss: 4.3577 - val_sparse_categorical_crossentropy: 0.4036 - val_accuracy: 0.8820\n", - "Epoch 29/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 4.1737 - sparse_categorical_crossentropy: 0.2370 - accuracy: 0.9084 - val_loss: 4.3033 - val_sparse_categorical_crossentropy: 0.4027 - val_accuracy: 0.8826\n", - "Epoch 30/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 4.1129 - sparse_categorical_crossentropy: 0.2294 - accuracy: 0.9105 - val_loss: 4.2457 - val_sparse_categorical_crossentropy: 0.3980 - val_accuracy: 0.8843\n", - "Epoch 31/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 4.0520 - sparse_categorical_crossentropy: 0.2212 - accuracy: 0.9161 - val_loss: 4.1895 - val_sparse_categorical_crossentropy: 0.3940 - val_accuracy: 0.8873\n", - "Epoch 32/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 3.9834 - sparse_categorical_crossentropy: 0.2046 - accuracy: 0.9180 - val_loss: 4.1344 - val_sparse_categorical_crossentropy: 0.3904 - val_accuracy: 0.8873\n", - "Epoch 33/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 3.9282 - sparse_categorical_crossentropy: 0.2008 - accuracy: 0.9194 - val_loss: 4.0859 - val_sparse_categorical_crossentropy: 0.3929 - val_accuracy: 0.8880\n", - "Epoch 34/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 3.8691 - sparse_categorical_crossentropy: 0.1924 - accuracy: 0.9206 - val_loss: 4.0359 - val_sparse_categorical_crossentropy: 0.3933 - val_accuracy: 0.8890\n", - "Epoch 35/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 3.8059 - sparse_categorical_crossentropy: 0.1795 - accuracy: 0.9287 - val_loss: 3.9786 - val_sparse_categorical_crossentropy: 0.3859 - val_accuracy: 0.8908\n", - "Epoch 36/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 3.7616 - sparse_categorical_crossentropy: 0.1849 - accuracy: 0.9259 - val_loss: 3.9222 - val_sparse_categorical_crossentropy: 0.3789 - val_accuracy: 0.8935\n", - "Epoch 37/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 3.6991 - sparse_categorical_crossentropy: 0.1716 - accuracy: 0.9304 - val_loss: 3.8675 - val_sparse_categorical_crossentropy: 0.3729 - val_accuracy: 0.8961\n", - "Epoch 38/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 3.6480 - sparse_categorical_crossentropy: 0.1690 - accuracy: 0.9324 - val_loss: 3.8207 - val_sparse_categorical_crossentropy: 0.3742 - val_accuracy: 0.8963\n", - "Epoch 39/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 3.5893 - sparse_categorical_crossentropy: 0.1581 - accuracy: 0.9351 - val_loss: 3.7714 - val_sparse_categorical_crossentropy: 0.3723 - val_accuracy: 0.8985\n", - "Epoch 40/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 3.5457 - sparse_categorical_crossentropy: 0.1618 - accuracy: 0.9342 - val_loss: 3.7198 - val_sparse_categorical_crossentropy: 0.3676 - val_accuracy: 0.8990\n", - "Epoch 41/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 3.4895 - sparse_categorical_crossentropy: 0.1523 - accuracy: 0.9345 - val_loss: 3.6705 - val_sparse_categorical_crossentropy: 0.3647 - val_accuracy: 0.8986\n", - "Epoch 42/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 3.4378 - sparse_categorical_crossentropy: 0.1469 - accuracy: 0.9393 - val_loss: 3.6232 - val_sparse_categorical_crossentropy: 0.3633 - val_accuracy: 0.8996\n", - "Epoch 43/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 3.3883 - sparse_categorical_crossentropy: 0.1432 - accuracy: 0.9409 - val_loss: 3.5771 - val_sparse_categorical_crossentropy: 0.3627 - val_accuracy: 0.9005\n", - "Epoch 44/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 3.3404 - sparse_categorical_crossentropy: 0.1406 - accuracy: 0.9409 - val_loss: 3.5281 - val_sparse_categorical_crossentropy: 0.3585 - val_accuracy: 0.9047\n", - "Epoch 45/1000\n", - "5/5 [==============================] - 0s 51ms/step - loss: 3.2883 - sparse_categorical_crossentropy: 0.1331 - accuracy: 0.9429 - val_loss: 3.4767 - val_sparse_categorical_crossentropy: 0.3514 - val_accuracy: 0.9073\n", - "Epoch 46/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 3.2409 - sparse_categorical_crossentropy: 0.1297 - accuracy: 0.9450 - val_loss: 3.4287 - val_sparse_categorical_crossentropy: 0.3471 - val_accuracy: 0.9067\n", - "Epoch 47/1000\n", - "5/5 [==============================] - 0s 52ms/step - loss: 3.1985 - sparse_categorical_crossentropy: 0.1310 - accuracy: 0.9441 - val_loss: 3.3907 - val_sparse_categorical_crossentropy: 0.3524 - val_accuracy: 0.9048\n", - "Epoch 48/1000\n", - "5/5 [==============================] - 0s 58ms/step - loss: 3.1514 - sparse_categorical_crossentropy: 0.1268 - accuracy: 0.9465 - val_loss: 3.3496 - val_sparse_categorical_crossentropy: 0.3539 - val_accuracy: 0.9062\n", - "Epoch 49/1000\n", - "5/5 [==============================] - 0s 51ms/step - loss: 3.1130 - sparse_categorical_crossentropy: 0.1310 - accuracy: 0.9460 - val_loss: 3.3015 - val_sparse_categorical_crossentropy: 0.3480 - val_accuracy: 0.9072\n", - "Epoch 50/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 3.0612 - sparse_categorical_crossentropy: 0.1211 - accuracy: 0.9480 - val_loss: 3.2530 - val_sparse_categorical_crossentropy: 0.3410 - val_accuracy: 0.9083\n", - "Epoch 51/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 3.0130 - sparse_categorical_crossentropy: 0.1142 - accuracy: 0.9507 - val_loss: 3.2086 - val_sparse_categorical_crossentropy: 0.3375 - val_accuracy: 0.9100\n", - "Epoch 52/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 2.9765 - sparse_categorical_crossentropy: 0.1186 - accuracy: 0.9496 - val_loss: 3.1639 - val_sparse_categorical_crossentropy: 0.3332 - val_accuracy: 0.9093\n", - "Epoch 53/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 2.9238 - sparse_categorical_crossentropy: 0.1061 - accuracy: 0.9543 - val_loss: 3.1172 - val_sparse_categorical_crossentropy: 0.3265 - val_accuracy: 0.9133\n", - "Epoch 54/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 2.8862 - sparse_categorical_crossentropy: 0.1083 - accuracy: 0.9529 - val_loss: 3.0766 - val_sparse_categorical_crossentropy: 0.3253 - val_accuracy: 0.9150\n", - "Epoch 55/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 2.8468 - sparse_categorical_crossentropy: 0.1082 - accuracy: 0.9531 - val_loss: 3.0359 - val_sparse_categorical_crossentropy: 0.3236 - val_accuracy: 0.9145\n", - "Epoch 56/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 2.8068 - sparse_categorical_crossentropy: 0.1069 - accuracy: 0.9534 - val_loss: 2.9913 - val_sparse_categorical_crossentropy: 0.3174 - val_accuracy: 0.9138\n", - "Epoch 57/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 2.7654 - sparse_categorical_crossentropy: 0.1038 - accuracy: 0.9551 - val_loss: 2.9505 - val_sparse_categorical_crossentropy: 0.3144 - val_accuracy: 0.9148\n", - "Epoch 58/1000\n", - "5/5 [==============================] - 0s 51ms/step - loss: 2.7280 - sparse_categorical_crossentropy: 0.1040 - accuracy: 0.9564 - val_loss: 2.9108 - val_sparse_categorical_crossentropy: 0.3120 - val_accuracy: 0.9153\n", - "Epoch 59/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 2.6862 - sparse_categorical_crossentropy: 0.0992 - accuracy: 0.9558 - val_loss: 2.8735 - val_sparse_categorical_crossentropy: 0.3114 - val_accuracy: 0.9160\n", - "Epoch 60/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 2.6415 - sparse_categorical_crossentropy: 0.0912 - accuracy: 0.9583 - val_loss: 2.8380 - val_sparse_categorical_crossentropy: 0.3123 - val_accuracy: 0.9162\n", - "Epoch 61/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 2.6134 - sparse_categorical_crossentropy: 0.0994 - accuracy: 0.9581 - val_loss: 2.8034 - val_sparse_categorical_crossentropy: 0.3136 - val_accuracy: 0.9177\n", - "Epoch 62/1000\n", - "5/5 [==============================] - 0s 51ms/step - loss: 2.5699 - sparse_categorical_crossentropy: 0.0916 - accuracy: 0.9612 - val_loss: 2.7649 - val_sparse_categorical_crossentropy: 0.3107 - val_accuracy: 0.9192\n", - "Epoch 63/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 2.5353 - sparse_categorical_crossentropy: 0.0925 - accuracy: 0.9599 - val_loss: 2.7265 - val_sparse_categorical_crossentropy: 0.3073 - val_accuracy: 0.9198\n", - "Epoch 64/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 2.4939 - sparse_categorical_crossentropy: 0.0860 - accuracy: 0.9637 - val_loss: 2.6851 - val_sparse_categorical_crossentropy: 0.3005 - val_accuracy: 0.9212\n", - "Epoch 65/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 2.4590 - sparse_categorical_crossentropy: 0.0855 - accuracy: 0.9627 - val_loss: 2.6515 - val_sparse_categorical_crossentropy: 0.3008 - val_accuracy: 0.9207\n", - "Epoch 66/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 2.4281 - sparse_categorical_crossentropy: 0.0883 - accuracy: 0.9608 - val_loss: 2.6279 - val_sparse_categorical_crossentropy: 0.3107 - val_accuracy: 0.9180\n", - "Epoch 67/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 2.3901 - sparse_categorical_crossentropy: 0.0837 - accuracy: 0.9642 - val_loss: 2.5983 - val_sparse_categorical_crossentropy: 0.3142 - val_accuracy: 0.9190\n", - "Epoch 68/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 2.3520 - sparse_categorical_crossentropy: 0.0785 - accuracy: 0.9648 - val_loss: 2.5573 - val_sparse_categorical_crossentropy: 0.3058 - val_accuracy: 0.9205\n", - "Epoch 69/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 2.3243 - sparse_categorical_crossentropy: 0.0833 - accuracy: 0.9643 - val_loss: 2.5214 - val_sparse_categorical_crossentropy: 0.3021 - val_accuracy: 0.9223\n", - "Epoch 70/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 2.2869 - sparse_categorical_crossentropy: 0.0779 - accuracy: 0.9660 - val_loss: 2.4885 - val_sparse_categorical_crossentropy: 0.3009 - val_accuracy: 0.9237\n", - "Epoch 71/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 2.2555 - sparse_categorical_crossentropy: 0.0781 - accuracy: 0.9652 - val_loss: 2.4562 - val_sparse_categorical_crossentropy: 0.2998 - val_accuracy: 0.9255\n", - "Epoch 72/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 2.2241 - sparse_categorical_crossentropy: 0.0777 - accuracy: 0.9656 - val_loss: 2.4234 - val_sparse_categorical_crossentropy: 0.2978 - val_accuracy: 0.9247\n", - "Epoch 73/1000\n", - "5/5 [==============================] - 0s 52ms/step - loss: 2.1920 - sparse_categorical_crossentropy: 0.0763 - accuracy: 0.9690 - val_loss: 2.3910 - val_sparse_categorical_crossentropy: 0.2959 - val_accuracy: 0.9258\n", - "Epoch 74/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 2.1643 - sparse_categorical_crossentropy: 0.0789 - accuracy: 0.9675 - val_loss: 2.3607 - val_sparse_categorical_crossentropy: 0.2955 - val_accuracy: 0.9273\n", - "Epoch 75/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 2.1322 - sparse_categorical_crossentropy: 0.0767 - accuracy: 0.9667 - val_loss: 2.3320 - val_sparse_categorical_crossentropy: 0.2965 - val_accuracy: 0.9282\n", - "Epoch 76/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 2.0999 - sparse_categorical_crossentropy: 0.0738 - accuracy: 0.9682 - val_loss: 2.3041 - val_sparse_categorical_crossentropy: 0.2976 - val_accuracy: 0.9263\n", - "Epoch 77/1000\n", - "5/5 [==============================] - 0s 55ms/step - loss: 2.0686 - sparse_categorical_crossentropy: 0.0714 - accuracy: 0.9700 - val_loss: 2.2734 - val_sparse_categorical_crossentropy: 0.2956 - val_accuracy: 0.9265\n", - "Epoch 78/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 2.0390 - sparse_categorical_crossentropy: 0.0703 - accuracy: 0.9707 - val_loss: 2.2413 - val_sparse_categorical_crossentropy: 0.2916 - val_accuracy: 0.9277\n", - "Epoch 79/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 2.0097 - sparse_categorical_crossentropy: 0.0691 - accuracy: 0.9691 - val_loss: 2.2137 - val_sparse_categorical_crossentropy: 0.2919 - val_accuracy: 0.9283\n", - "Epoch 80/1000\n", - "5/5 [==============================] - 0s 54ms/step - loss: 1.9810 - sparse_categorical_crossentropy: 0.0682 - accuracy: 0.9723 - val_loss: 2.1837 - val_sparse_categorical_crossentropy: 0.2894 - val_accuracy: 0.9275\n", - "Epoch 81/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 1.9570 - sparse_categorical_crossentropy: 0.0716 - accuracy: 0.9709 - val_loss: 2.1516 - val_sparse_categorical_crossentropy: 0.2845 - val_accuracy: 0.9298\n", - "Epoch 82/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.9245 - sparse_categorical_crossentropy: 0.0661 - accuracy: 0.9721 - val_loss: 2.1246 - val_sparse_categorical_crossentropy: 0.2842 - val_accuracy: 0.9302\n", - "Epoch 83/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 1.8938 - sparse_categorical_crossentropy: 0.0620 - accuracy: 0.9738 - val_loss: 2.0956 - val_sparse_categorical_crossentropy: 0.2816 - val_accuracy: 0.9302\n", - "Epoch 84/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.8696 - sparse_categorical_crossentropy: 0.0640 - accuracy: 0.9726 - val_loss: 2.0649 - val_sparse_categorical_crossentropy: 0.2768 - val_accuracy: 0.9307\n", - "Epoch 85/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 1.8441 - sparse_categorical_crossentropy: 0.0643 - accuracy: 0.9739 - val_loss: 2.0332 - val_sparse_categorical_crossentropy: 0.2706 - val_accuracy: 0.9320\n", - "Epoch 86/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 1.8150 - sparse_categorical_crossentropy: 0.0605 - accuracy: 0.9739 - val_loss: 2.0052 - val_sparse_categorical_crossentropy: 0.2675 - val_accuracy: 0.9320\n", - "Epoch 87/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 1.7950 - sparse_categorical_crossentropy: 0.0654 - accuracy: 0.9725 - val_loss: 1.9790 - val_sparse_categorical_crossentropy: 0.2660 - val_accuracy: 0.9322\n", - "Epoch 88/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.7655 - sparse_categorical_crossentropy: 0.0604 - accuracy: 0.9748 - val_loss: 1.9551 - val_sparse_categorical_crossentropy: 0.2665 - val_accuracy: 0.9335\n", - "Epoch 89/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 1.7416 - sparse_categorical_crossentropy: 0.0609 - accuracy: 0.9740 - val_loss: 1.9321 - val_sparse_categorical_crossentropy: 0.2678 - val_accuracy: 0.9323\n", - "Epoch 90/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 1.7184 - sparse_categorical_crossentropy: 0.0618 - accuracy: 0.9740 - val_loss: 1.9057 - val_sparse_categorical_crossentropy: 0.2651 - val_accuracy: 0.9317\n", - "Epoch 91/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 1.6907 - sparse_categorical_crossentropy: 0.0577 - accuracy: 0.9758 - val_loss: 1.8767 - val_sparse_categorical_crossentropy: 0.2593 - val_accuracy: 0.9333\n", - "Epoch 92/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 1.6716 - sparse_categorical_crossentropy: 0.0617 - accuracy: 0.9735 - val_loss: 1.8490 - val_sparse_categorical_crossentropy: 0.2545 - val_accuracy: 0.9328\n", - "Epoch 93/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 1.6437 - sparse_categorical_crossentropy: 0.0566 - accuracy: 0.9760 - val_loss: 1.8241 - val_sparse_categorical_crossentropy: 0.2523 - val_accuracy: 0.9335\n", - "Epoch 94/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 1.6199 - sparse_categorical_crossentropy: 0.0554 - accuracy: 0.9771 - val_loss: 1.7950 - val_sparse_categorical_crossentropy: 0.2455 - val_accuracy: 0.9325\n", - "Epoch 95/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 1.6011 - sparse_categorical_crossentropy: 0.0589 - accuracy: 0.9765 - val_loss: 1.7667 - val_sparse_categorical_crossentropy: 0.2394 - val_accuracy: 0.9335\n", - "Epoch 96/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 1.5728 - sparse_categorical_crossentropy: 0.0526 - accuracy: 0.9786 - val_loss: 1.7418 - val_sparse_categorical_crossentropy: 0.2363 - val_accuracy: 0.9348\n", - "Epoch 97/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 1.5539 - sparse_categorical_crossentropy: 0.0554 - accuracy: 0.9770 - val_loss: 1.7227 - val_sparse_categorical_crossentropy: 0.2388 - val_accuracy: 0.9347\n", - "Epoch 98/1000\n", - "5/5 [==============================] - 0s 52ms/step - loss: 1.5308 - sparse_categorical_crossentropy: 0.0538 - accuracy: 0.9785 - val_loss: 1.7051 - val_sparse_categorical_crossentropy: 0.2424 - val_accuracy: 0.9350\n", - "Epoch 99/1000\n", - "5/5 [==============================] - 0s 52ms/step - loss: 1.5063 - sparse_categorical_crossentropy: 0.0504 - accuracy: 0.9795 - val_loss: 1.6850 - val_sparse_categorical_crossentropy: 0.2433 - val_accuracy: 0.9335\n", - "Epoch 100/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 1.4864 - sparse_categorical_crossentropy: 0.0514 - accuracy: 0.9786 - val_loss: 1.6621 - val_sparse_categorical_crossentropy: 0.2410 - val_accuracy: 0.9342\n", - "Epoch 101/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 1.4668 - sparse_categorical_crossentropy: 0.0524 - accuracy: 0.9782 - val_loss: 1.6431 - val_sparse_categorical_crossentropy: 0.2425 - val_accuracy: 0.9342\n", - "Epoch 102/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 1.4430 - sparse_categorical_crossentropy: 0.0490 - accuracy: 0.9801 - val_loss: 1.6208 - val_sparse_categorical_crossentropy: 0.2402 - val_accuracy: 0.9347\n", - "Epoch 103/1000\n", - "5/5 [==============================] - 0s 53ms/step - loss: 1.4253 - sparse_categorical_crossentropy: 0.0511 - accuracy: 0.9798 - val_loss: 1.6005 - val_sparse_categorical_crossentropy: 0.2395 - val_accuracy: 0.9372\n", - "Epoch 104/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.4076 - sparse_categorical_crossentropy: 0.0530 - accuracy: 0.9793 - val_loss: 1.5725 - val_sparse_categorical_crossentropy: 0.2310 - val_accuracy: 0.9365\n", - "Epoch 105/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 1.3825 - sparse_categorical_crossentropy: 0.0472 - accuracy: 0.9808 - val_loss: 1.5470 - val_sparse_categorical_crossentropy: 0.2245 - val_accuracy: 0.9367\n", - "Epoch 106/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 1.3629 - sparse_categorical_crossentropy: 0.0465 - accuracy: 0.9811 - val_loss: 1.5314 - val_sparse_categorical_crossentropy: 0.2275 - val_accuracy: 0.9362\n", - "Epoch 107/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 1.3442 - sparse_categorical_crossentropy: 0.0463 - accuracy: 0.9819 - val_loss: 1.5167 - val_sparse_categorical_crossentropy: 0.2311 - val_accuracy: 0.9365\n", - "Epoch 108/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 1.3241 - sparse_categorical_crossentropy: 0.0442 - accuracy: 0.9823 - val_loss: 1.4996 - val_sparse_categorical_crossentropy: 0.2320 - val_accuracy: 0.9357\n", - "Epoch 109/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.3056 - sparse_categorical_crossentropy: 0.0438 - accuracy: 0.9822 - val_loss: 1.4779 - val_sparse_categorical_crossentropy: 0.2283 - val_accuracy: 0.9368\n", - "Epoch 110/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 1.2874 - sparse_categorical_crossentropy: 0.0435 - accuracy: 0.9841 - val_loss: 1.4608 - val_sparse_categorical_crossentropy: 0.2288 - val_accuracy: 0.9362\n", - "Epoch 111/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 1.2688 - sparse_categorical_crossentropy: 0.0424 - accuracy: 0.9828 - val_loss: 1.4445 - val_sparse_categorical_crossentropy: 0.2298 - val_accuracy: 0.9382\n", - "Epoch 112/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 1.2517 - sparse_categorical_crossentropy: 0.0424 - accuracy: 0.9840 - val_loss: 1.4259 - val_sparse_categorical_crossentropy: 0.2280 - val_accuracy: 0.9385\n", - "Epoch 113/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 1.2375 - sparse_categorical_crossentropy: 0.0451 - accuracy: 0.9824 - val_loss: 1.4066 - val_sparse_categorical_crossentropy: 0.2254 - val_accuracy: 0.9385\n", - "Epoch 114/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 1.2234 - sparse_categorical_crossentropy: 0.0476 - accuracy: 0.9824 - val_loss: 1.3857 - val_sparse_categorical_crossentropy: 0.2209 - val_accuracy: 0.9390\n", - "Epoch 115/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 1.2027 - sparse_categorical_crossentropy: 0.0431 - accuracy: 0.9834 - val_loss: 1.3703 - val_sparse_categorical_crossentropy: 0.2215 - val_accuracy: 0.9392\n", - "Epoch 116/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.1850 - sparse_categorical_crossentropy: 0.0413 - accuracy: 0.9850 - val_loss: 1.3505 - val_sparse_categorical_crossentropy: 0.2175 - val_accuracy: 0.9407\n", - "Epoch 117/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.1694 - sparse_categorical_crossentropy: 0.0414 - accuracy: 0.9836 - val_loss: 1.3318 - val_sparse_categorical_crossentropy: 0.2143 - val_accuracy: 0.9413\n", - "Epoch 118/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 1.1514 - sparse_categorical_crossentropy: 0.0390 - accuracy: 0.9860 - val_loss: 1.3075 - val_sparse_categorical_crossentropy: 0.2054 - val_accuracy: 0.9418\n", - "Epoch 119/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 1.1402 - sparse_categorical_crossentropy: 0.0431 - accuracy: 0.9832 - val_loss: 1.2870 - val_sparse_categorical_crossentropy: 0.2001 - val_accuracy: 0.9438\n", - "Epoch 120/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.1192 - sparse_categorical_crossentropy: 0.0371 - accuracy: 0.9859 - val_loss: 1.2756 - val_sparse_categorical_crossentropy: 0.2036 - val_accuracy: 0.9437\n", - "Epoch 121/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 1.1071 - sparse_categorical_crossentropy: 0.0400 - accuracy: 0.9857 - val_loss: 1.2600 - val_sparse_categorical_crossentropy: 0.2029 - val_accuracy: 0.9430\n", - "Epoch 122/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 1.0889 - sparse_categorical_crossentropy: 0.0367 - accuracy: 0.9868 - val_loss: 1.2456 - val_sparse_categorical_crossentropy: 0.2034 - val_accuracy: 0.9438\n", - "Epoch 123/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 1.0716 - sparse_categorical_crossentropy: 0.0342 - accuracy: 0.9868 - val_loss: 1.2267 - val_sparse_categorical_crossentropy: 0.1992 - val_accuracy: 0.9448\n", - "Epoch 124/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.0593 - sparse_categorical_crossentropy: 0.0365 - accuracy: 0.9869 - val_loss: 1.2163 - val_sparse_categorical_crossentropy: 0.2033 - val_accuracy: 0.9453\n", - "Epoch 125/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.0417 - sparse_categorical_crossentropy: 0.0334 - accuracy: 0.9871 - val_loss: 1.2045 - val_sparse_categorical_crossentropy: 0.2059 - val_accuracy: 0.9450\n", - "Epoch 126/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 1.0292 - sparse_categorical_crossentropy: 0.0351 - accuracy: 0.9871 - val_loss: 1.1918 - val_sparse_categorical_crossentropy: 0.2072 - val_accuracy: 0.9433\n", - "Epoch 127/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 1.0143 - sparse_categorical_crossentropy: 0.0342 - accuracy: 0.9872 - val_loss: 1.1683 - val_sparse_categorical_crossentropy: 0.1973 - val_accuracy: 0.9448\n", - "Epoch 128/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.9998 - sparse_categorical_crossentropy: 0.0331 - accuracy: 0.9879 - val_loss: 1.1462 - val_sparse_categorical_crossentropy: 0.1885 - val_accuracy: 0.9462\n", - "Epoch 129/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 0.9895 - sparse_categorical_crossentropy: 0.0361 - accuracy: 0.9870 - val_loss: 1.1355 - val_sparse_categorical_crossentropy: 0.1909 - val_accuracy: 0.9458\n", - "Epoch 130/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 0.9706 - sparse_categorical_crossentropy: 0.0303 - accuracy: 0.9886 - val_loss: 1.1230 - val_sparse_categorical_crossentropy: 0.1915 - val_accuracy: 0.9478\n", - "Epoch 131/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.9590 - sparse_categorical_crossentropy: 0.0317 - accuracy: 0.9886 - val_loss: 1.1052 - val_sparse_categorical_crossentropy: 0.1867 - val_accuracy: 0.9497\n", - "Epoch 132/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.9482 - sparse_categorical_crossentropy: 0.0338 - accuracy: 0.9879 - val_loss: 1.0964 - val_sparse_categorical_crossentropy: 0.1906 - val_accuracy: 0.9475\n", - "Epoch 133/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.9336 - sparse_categorical_crossentropy: 0.0317 - accuracy: 0.9892 - val_loss: 1.0877 - val_sparse_categorical_crossentropy: 0.1941 - val_accuracy: 0.9465\n", - "Epoch 134/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.9174 - sparse_categorical_crossentropy: 0.0279 - accuracy: 0.9900 - val_loss: 1.0661 - val_sparse_categorical_crossentropy: 0.1848 - val_accuracy: 0.9483\n", - "Epoch 135/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.9075 - sparse_categorical_crossentropy: 0.0302 - accuracy: 0.9890 - val_loss: 1.0551 - val_sparse_categorical_crossentropy: 0.1859 - val_accuracy: 0.9495\n", - "Epoch 136/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.8947 - sparse_categorical_crossentropy: 0.0293 - accuracy: 0.9886 - val_loss: 1.0517 - val_sparse_categorical_crossentropy: 0.1944 - val_accuracy: 0.9485\n", - "Epoch 137/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.8851 - sparse_categorical_crossentropy: 0.0315 - accuracy: 0.9889 - val_loss: 1.0354 - val_sparse_categorical_crossentropy: 0.1897 - val_accuracy: 0.9488\n", - "Epoch 138/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.8698 - sparse_categorical_crossentropy: 0.0278 - accuracy: 0.9896 - val_loss: 1.0158 - val_sparse_categorical_crossentropy: 0.1817 - val_accuracy: 0.9520\n", - "Epoch 139/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.8594 - sparse_categorical_crossentropy: 0.0291 - accuracy: 0.9891 - val_loss: 1.0022 - val_sparse_categorical_crossentropy: 0.1797 - val_accuracy: 0.9513\n", - "Epoch 140/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.8468 - sparse_categorical_crossentropy: 0.0280 - accuracy: 0.9894 - val_loss: 0.9911 - val_sparse_categorical_crossentropy: 0.1801 - val_accuracy: 0.9532\n", - "Epoch 141/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.8343 - sparse_categorical_crossentropy: 0.0270 - accuracy: 0.9911 - val_loss: 0.9850 - val_sparse_categorical_crossentropy: 0.1853 - val_accuracy: 0.9535\n", - "Epoch 142/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.8227 - sparse_categorical_crossentropy: 0.0267 - accuracy: 0.9899 - val_loss: 0.9786 - val_sparse_categorical_crossentropy: 0.1903 - val_accuracy: 0.9518\n", - "Epoch 143/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.8099 - sparse_categorical_crossentropy: 0.0252 - accuracy: 0.9909 - val_loss: 0.9653 - val_sparse_categorical_crossentropy: 0.1882 - val_accuracy: 0.9522\n", - "Epoch 144/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.8014 - sparse_categorical_crossentropy: 0.0280 - accuracy: 0.9906 - val_loss: 0.9456 - val_sparse_categorical_crossentropy: 0.1796 - val_accuracy: 0.9530\n", - "Epoch 145/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.7873 - sparse_categorical_crossentropy: 0.0249 - accuracy: 0.9915 - val_loss: 0.9280 - val_sparse_categorical_crossentropy: 0.1727 - val_accuracy: 0.9553\n", - "Epoch 146/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.7768 - sparse_categorical_crossentropy: 0.0249 - accuracy: 0.9907 - val_loss: 0.9143 - val_sparse_categorical_crossentropy: 0.1693 - val_accuracy: 0.9543\n", - "Epoch 147/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.7659 - sparse_categorical_crossentropy: 0.0241 - accuracy: 0.9915 - val_loss: 0.9083 - val_sparse_categorical_crossentropy: 0.1732 - val_accuracy: 0.9532\n", - "Epoch 148/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.7541 - sparse_categorical_crossentropy: 0.0222 - accuracy: 0.9918 - val_loss: 0.9012 - val_sparse_categorical_crossentropy: 0.1760 - val_accuracy: 0.9528\n", - "Epoch 149/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.7443 - sparse_categorical_crossentropy: 0.0224 - accuracy: 0.9914 - val_loss: 0.8955 - val_sparse_categorical_crossentropy: 0.1804 - val_accuracy: 0.9528\n", - "Epoch 150/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.7378 - sparse_categorical_crossentropy: 0.0259 - accuracy: 0.9909 - val_loss: 0.8815 - val_sparse_categorical_crossentropy: 0.1763 - val_accuracy: 0.9548\n", - "Epoch 151/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.7246 - sparse_categorical_crossentropy: 0.0225 - accuracy: 0.9918 - val_loss: 0.8757 - val_sparse_categorical_crossentropy: 0.1801 - val_accuracy: 0.9525\n", - "Epoch 152/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.7146 - sparse_categorical_crossentropy: 0.0220 - accuracy: 0.9921 - val_loss: 0.8662 - val_sparse_categorical_crossentropy: 0.1799 - val_accuracy: 0.9533\n", - "Epoch 153/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.7062 - sparse_categorical_crossentropy: 0.0230 - accuracy: 0.9924 - val_loss: 0.8516 - val_sparse_categorical_crossentropy: 0.1746 - val_accuracy: 0.9555\n", - "Epoch 154/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.6956 - sparse_categorical_crossentropy: 0.0215 - accuracy: 0.9930 - val_loss: 0.8353 - val_sparse_categorical_crossentropy: 0.1673 - val_accuracy: 0.9545\n", - "Epoch 155/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.6852 - sparse_categorical_crossentropy: 0.0201 - accuracy: 0.9933 - val_loss: 0.8277 - val_sparse_categorical_crossentropy: 0.1688 - val_accuracy: 0.9540\n", - "Epoch 156/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.6776 - sparse_categorical_crossentropy: 0.0216 - accuracy: 0.9931 - val_loss: 0.8193 - val_sparse_categorical_crossentropy: 0.1694 - val_accuracy: 0.9567\n", - "Epoch 157/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.6697 - sparse_categorical_crossentropy: 0.0227 - accuracy: 0.9930 - val_loss: 0.8111 - val_sparse_categorical_crossentropy: 0.1701 - val_accuracy: 0.9572\n", - "Epoch 158/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.6605 - sparse_categorical_crossentropy: 0.0223 - accuracy: 0.9920 - val_loss: 0.8075 - val_sparse_categorical_crossentropy: 0.1752 - val_accuracy: 0.9543\n", - "Epoch 159/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.6487 - sparse_categorical_crossentropy: 0.0192 - accuracy: 0.9935 - val_loss: 0.7974 - val_sparse_categorical_crossentropy: 0.1736 - val_accuracy: 0.9543\n", - "Epoch 160/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.6402 - sparse_categorical_crossentropy: 0.0192 - accuracy: 0.9938 - val_loss: 0.7890 - val_sparse_categorical_crossentropy: 0.1737 - val_accuracy: 0.9530\n", - "Epoch 161/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.6336 - sparse_categorical_crossentropy: 0.0211 - accuracy: 0.9926 - val_loss: 0.7806 - val_sparse_categorical_crossentropy: 0.1738 - val_accuracy: 0.9523\n", - "Epoch 162/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.6258 - sparse_categorical_crossentropy: 0.0218 - accuracy: 0.9930 - val_loss: 0.7686 - val_sparse_categorical_crossentropy: 0.1702 - val_accuracy: 0.9547\n", - "Epoch 163/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.6147 - sparse_categorical_crossentropy: 0.0190 - accuracy: 0.9932 - val_loss: 0.7563 - val_sparse_categorical_crossentropy: 0.1662 - val_accuracy: 0.9543\n", - "Epoch 164/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.6067 - sparse_categorical_crossentropy: 0.0192 - accuracy: 0.9930 - val_loss: 0.7440 - val_sparse_categorical_crossentropy: 0.1619 - val_accuracy: 0.9555\n", - "Epoch 165/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.5992 - sparse_categorical_crossentropy: 0.0197 - accuracy: 0.9937 - val_loss: 0.7358 - val_sparse_categorical_crossentropy: 0.1615 - val_accuracy: 0.9553\n", - "Epoch 166/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.5893 - sparse_categorical_crossentropy: 0.0174 - accuracy: 0.9947 - val_loss: 0.7320 - val_sparse_categorical_crossentropy: 0.1650 - val_accuracy: 0.9557\n", - "Epoch 167/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.5825 - sparse_categorical_crossentropy: 0.0179 - accuracy: 0.9942 - val_loss: 0.7311 - val_sparse_categorical_crossentropy: 0.1713 - val_accuracy: 0.9573\n", - "Epoch 168/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.5778 - sparse_categorical_crossentropy: 0.0203 - accuracy: 0.9927 - val_loss: 0.7290 - val_sparse_categorical_crossentropy: 0.1763 - val_accuracy: 0.9577\n", - "Epoch 169/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.5680 - sparse_categorical_crossentropy: 0.0175 - accuracy: 0.9940 - val_loss: 0.7124 - val_sparse_categorical_crossentropy: 0.1667 - val_accuracy: 0.9563\n", - "Epoch 170/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.5627 - sparse_categorical_crossentropy: 0.0191 - accuracy: 0.9939 - val_loss: 0.6996 - val_sparse_categorical_crossentropy: 0.1607 - val_accuracy: 0.9548\n", - "Epoch 171/1000\n", - "5/5 [==============================] - 0s 42ms/step - loss: 0.5575 - sparse_categorical_crossentropy: 0.0208 - accuracy: 0.9937 - val_loss: 0.6964 - val_sparse_categorical_crossentropy: 0.1641 - val_accuracy: 0.9567\n", - "Epoch 172/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.5482 - sparse_categorical_crossentropy: 0.0180 - accuracy: 0.9941 - val_loss: 0.6885 - val_sparse_categorical_crossentropy: 0.1628 - val_accuracy: 0.9570\n", - "Epoch 173/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.5452 - sparse_categorical_crossentropy: 0.0216 - accuracy: 0.9931 - val_loss: 0.6831 - val_sparse_categorical_crossentropy: 0.1636 - val_accuracy: 0.9573\n", - "Epoch 174/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.5344 - sparse_categorical_crossentropy: 0.0168 - accuracy: 0.9948 - val_loss: 0.6734 - val_sparse_categorical_crossentropy: 0.1598 - val_accuracy: 0.9587\n", - "Epoch 175/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.5275 - sparse_categorical_crossentropy: 0.0160 - accuracy: 0.9951 - val_loss: 0.6632 - val_sparse_categorical_crossentropy: 0.1560 - val_accuracy: 0.9590\n", - "Epoch 176/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.5230 - sparse_categorical_crossentropy: 0.0178 - accuracy: 0.9942 - val_loss: 0.6605 - val_sparse_categorical_crossentropy: 0.1595 - val_accuracy: 0.9567\n", - "Epoch 177/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.5152 - sparse_categorical_crossentropy: 0.0162 - accuracy: 0.9946 - val_loss: 0.6589 - val_sparse_categorical_crossentropy: 0.1639 - val_accuracy: 0.9565\n", - "Epoch 178/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.5119 - sparse_categorical_crossentropy: 0.0188 - accuracy: 0.9936 - val_loss: 0.6589 - val_sparse_categorical_crossentropy: 0.1700 - val_accuracy: 0.9555\n", - "Epoch 179/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.5052 - sparse_categorical_crossentropy: 0.0182 - accuracy: 0.9945 - val_loss: 0.6461 - val_sparse_categorical_crossentropy: 0.1634 - val_accuracy: 0.9555\n", - "Epoch 180/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.4967 - sparse_categorical_crossentropy: 0.0159 - accuracy: 0.9952 - val_loss: 0.6330 - val_sparse_categorical_crossentropy: 0.1563 - val_accuracy: 0.9578\n", - "Epoch 181/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.4928 - sparse_categorical_crossentropy: 0.0180 - accuracy: 0.9948 - val_loss: 0.6275 - val_sparse_categorical_crossentropy: 0.1566 - val_accuracy: 0.9572\n", - "Epoch 182/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.4856 - sparse_categorical_crossentropy: 0.0166 - accuracy: 0.9950 - val_loss: 0.6233 - val_sparse_categorical_crossentropy: 0.1583 - val_accuracy: 0.9572\n", - "Epoch 183/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.4806 - sparse_categorical_crossentropy: 0.0176 - accuracy: 0.9948 - val_loss: 0.6131 - val_sparse_categorical_crossentropy: 0.1542 - val_accuracy: 0.9573\n", - "Epoch 184/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.4754 - sparse_categorical_crossentropy: 0.0185 - accuracy: 0.9940 - val_loss: 0.5986 - val_sparse_categorical_crossentropy: 0.1456 - val_accuracy: 0.9580\n", - "Epoch 185/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.4671 - sparse_categorical_crossentropy: 0.0157 - accuracy: 0.9949 - val_loss: 0.5934 - val_sparse_categorical_crossentropy: 0.1455 - val_accuracy: 0.9570\n", - "Epoch 186/1000\n", - "5/5 [==============================] - 0s 52ms/step - loss: 0.4627 - sparse_categorical_crossentropy: 0.0164 - accuracy: 0.9946 - val_loss: 0.5978 - val_sparse_categorical_crossentropy: 0.1547 - val_accuracy: 0.9547\n", - "Epoch 187/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.4571 - sparse_categorical_crossentropy: 0.0156 - accuracy: 0.9955 - val_loss: 0.5881 - val_sparse_categorical_crossentropy: 0.1501 - val_accuracy: 0.9560\n", - "Epoch 188/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.4549 - sparse_categorical_crossentropy: 0.0184 - accuracy: 0.9943 - val_loss: 0.5758 - val_sparse_categorical_crossentropy: 0.1428 - val_accuracy: 0.9580\n", - "Epoch 189/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.4469 - sparse_categorical_crossentropy: 0.0155 - accuracy: 0.9950 - val_loss: 0.5707 - val_sparse_categorical_crossentropy: 0.1428 - val_accuracy: 0.9580\n", - "Epoch 190/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.4390 - sparse_categorical_crossentropy: 0.0129 - accuracy: 0.9959 - val_loss: 0.5693 - val_sparse_categorical_crossentropy: 0.1469 - val_accuracy: 0.9588\n", - "Epoch 191/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.4359 - sparse_categorical_crossentropy: 0.0153 - accuracy: 0.9951 - val_loss: 0.5691 - val_sparse_categorical_crossentropy: 0.1524 - val_accuracy: 0.9592\n", - "Epoch 192/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.4297 - sparse_categorical_crossentropy: 0.0147 - accuracy: 0.9954 - val_loss: 0.5622 - val_sparse_categorical_crossentropy: 0.1509 - val_accuracy: 0.9590\n", - "Epoch 193/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.4243 - sparse_categorical_crossentropy: 0.0146 - accuracy: 0.9951 - val_loss: 0.5529 - val_sparse_categorical_crossentropy: 0.1467 - val_accuracy: 0.9600\n", - "Epoch 194/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.4191 - sparse_categorical_crossentropy: 0.0145 - accuracy: 0.9958 - val_loss: 0.5428 - val_sparse_categorical_crossentropy: 0.1415 - val_accuracy: 0.9602\n", - "Epoch 195/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.4155 - sparse_categorical_crossentropy: 0.0158 - accuracy: 0.9958 - val_loss: 0.5298 - val_sparse_categorical_crossentropy: 0.1332 - val_accuracy: 0.9613\n", - "Epoch 196/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.4093 - sparse_categorical_crossentropy: 0.0142 - accuracy: 0.9960 - val_loss: 0.5304 - val_sparse_categorical_crossentropy: 0.1382 - val_accuracy: 0.9602\n", - "Epoch 197/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.4034 - sparse_categorical_crossentropy: 0.0126 - accuracy: 0.9960 - val_loss: 0.5326 - val_sparse_categorical_crossentropy: 0.1448 - val_accuracy: 0.9598\n", - "Epoch 198/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.3991 - sparse_categorical_crossentropy: 0.0128 - accuracy: 0.9960 - val_loss: 0.5263 - val_sparse_categorical_crossentropy: 0.1431 - val_accuracy: 0.9615\n", - "Epoch 199/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.3964 - sparse_categorical_crossentropy: 0.0145 - accuracy: 0.9956 - val_loss: 0.5242 - val_sparse_categorical_crossentropy: 0.1451 - val_accuracy: 0.9615\n", - "Epoch 200/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.3943 - sparse_categorical_crossentropy: 0.0165 - accuracy: 0.9945 - val_loss: 0.5217 - val_sparse_categorical_crossentropy: 0.1465 - val_accuracy: 0.9595\n", - "Epoch 201/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.3889 - sparse_categorical_crossentropy: 0.0150 - accuracy: 0.9951 - val_loss: 0.5238 - val_sparse_categorical_crossentropy: 0.1525 - val_accuracy: 0.9593\n", - "Epoch 202/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.3842 - sparse_categorical_crossentropy: 0.0140 - accuracy: 0.9956 - val_loss: 0.5149 - val_sparse_categorical_crossentropy: 0.1471 - val_accuracy: 0.9593\n", - "Epoch 203/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.3826 - sparse_categorical_crossentropy: 0.0160 - accuracy: 0.9951 - val_loss: 0.5090 - val_sparse_categorical_crossentropy: 0.1448 - val_accuracy: 0.9582\n", - "Epoch 204/1000\n", - "5/5 [==============================] - 0s 56ms/step - loss: 0.3785 - sparse_categorical_crossentropy: 0.0154 - accuracy: 0.9950 - val_loss: 0.5034 - val_sparse_categorical_crossentropy: 0.1429 - val_accuracy: 0.9592\n", - "Epoch 205/1000\n", - "5/5 [==============================] - 0s 57ms/step - loss: 0.3738 - sparse_categorical_crossentropy: 0.0146 - accuracy: 0.9946 - val_loss: 0.5001 - val_sparse_categorical_crossentropy: 0.1436 - val_accuracy: 0.9600\n", - "Epoch 206/1000\n", - "5/5 [==============================] - 0s 53ms/step - loss: 0.3700 - sparse_categorical_crossentropy: 0.0150 - accuracy: 0.9955 - val_loss: 0.4993 - val_sparse_categorical_crossentropy: 0.1473 - val_accuracy: 0.9590\n", - "Epoch 207/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 0.3631 - sparse_categorical_crossentropy: 0.0125 - accuracy: 0.9963 - val_loss: 0.4893 - val_sparse_categorical_crossentropy: 0.1418 - val_accuracy: 0.9602\n", - "Epoch 208/1000\n", - "5/5 [==============================] - 0s 53ms/step - loss: 0.3593 - sparse_categorical_crossentropy: 0.0134 - accuracy: 0.9962 - val_loss: 0.4856 - val_sparse_categorical_crossentropy: 0.1431 - val_accuracy: 0.9613\n", - "Epoch 209/1000\n", - "5/5 [==============================] - 0s 52ms/step - loss: 0.3521 - sparse_categorical_crossentropy: 0.0113 - accuracy: 0.9966 - val_loss: 0.4861 - val_sparse_categorical_crossentropy: 0.1486 - val_accuracy: 0.9605\n", - "Epoch 210/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.3491 - sparse_categorical_crossentropy: 0.0131 - accuracy: 0.9962 - val_loss: 0.4804 - val_sparse_categorical_crossentropy: 0.1477 - val_accuracy: 0.9610\n", - "Epoch 211/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.3445 - sparse_categorical_crossentropy: 0.0132 - accuracy: 0.9964 - val_loss: 0.4702 - val_sparse_categorical_crossentropy: 0.1420 - val_accuracy: 0.9618\n", - "Epoch 212/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.3366 - sparse_categorical_crossentropy: 0.0098 - accuracy: 0.9971 - val_loss: 0.4643 - val_sparse_categorical_crossentropy: 0.1406 - val_accuracy: 0.9622\n", - "Epoch 213/1000\n", - "5/5 [==============================] - 0s 52ms/step - loss: 0.3361 - sparse_categorical_crossentropy: 0.0139 - accuracy: 0.9957 - val_loss: 0.4524 - val_sparse_categorical_crossentropy: 0.1333 - val_accuracy: 0.9640\n", - "Epoch 214/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.3289 - sparse_categorical_crossentropy: 0.0112 - accuracy: 0.9965 - val_loss: 0.4571 - val_sparse_categorical_crossentropy: 0.1423 - val_accuracy: 0.9625\n", - "Epoch 215/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.3252 - sparse_categorical_crossentropy: 0.0118 - accuracy: 0.9964 - val_loss: 0.4554 - val_sparse_categorical_crossentropy: 0.1449 - val_accuracy: 0.9642\n", - "Epoch 216/1000\n", - "5/5 [==============================] - 0s 51ms/step - loss: 0.3211 - sparse_categorical_crossentropy: 0.0118 - accuracy: 0.9967 - val_loss: 0.4424 - val_sparse_categorical_crossentropy: 0.1358 - val_accuracy: 0.9637\n", - "Epoch 217/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 0.3180 - sparse_categorical_crossentropy: 0.0126 - accuracy: 0.9962 - val_loss: 0.4375 - val_sparse_categorical_crossentropy: 0.1346 - val_accuracy: 0.9625\n", - "Epoch 218/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 0.3161 - sparse_categorical_crossentropy: 0.0142 - accuracy: 0.9952 - val_loss: 0.4380 - val_sparse_categorical_crossentropy: 0.1382 - val_accuracy: 0.9615\n", - "Epoch 219/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.3128 - sparse_categorical_crossentropy: 0.0140 - accuracy: 0.9958 - val_loss: 0.4390 - val_sparse_categorical_crossentropy: 0.1421 - val_accuracy: 0.9593\n", - "Epoch 220/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.3070 - sparse_categorical_crossentropy: 0.0109 - accuracy: 0.9970 - val_loss: 0.4286 - val_sparse_categorical_crossentropy: 0.1344 - val_accuracy: 0.9622\n", - "Epoch 221/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.3079 - sparse_categorical_crossentropy: 0.0147 - accuracy: 0.9953 - val_loss: 0.4231 - val_sparse_categorical_crossentropy: 0.1321 - val_accuracy: 0.9617\n", - "Epoch 222/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.3016 - sparse_categorical_crossentropy: 0.0116 - accuracy: 0.9960 - val_loss: 0.4182 - val_sparse_categorical_crossentropy: 0.1303 - val_accuracy: 0.9623\n", - "Epoch 223/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.3001 - sparse_categorical_crossentropy: 0.0131 - accuracy: 0.9965 - val_loss: 0.4144 - val_sparse_categorical_crossentropy: 0.1291 - val_accuracy: 0.9625\n", - "Epoch 224/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.2985 - sparse_categorical_crossentropy: 0.0140 - accuracy: 0.9962 - val_loss: 0.4159 - val_sparse_categorical_crossentropy: 0.1331 - val_accuracy: 0.9608\n", - "Epoch 225/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.2952 - sparse_categorical_crossentropy: 0.0132 - accuracy: 0.9955 - val_loss: 0.4180 - val_sparse_categorical_crossentropy: 0.1378 - val_accuracy: 0.9612\n", - "Epoch 226/1000\n", - "5/5 [==============================] - 0s 51ms/step - loss: 0.2918 - sparse_categorical_crossentropy: 0.0125 - accuracy: 0.9959 - val_loss: 0.4099 - val_sparse_categorical_crossentropy: 0.1324 - val_accuracy: 0.9640\n", - "Epoch 227/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 0.2906 - sparse_categorical_crossentropy: 0.0139 - accuracy: 0.9954 - val_loss: 0.4055 - val_sparse_categorical_crossentropy: 0.1304 - val_accuracy: 0.9632\n", - "Epoch 228/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2866 - sparse_categorical_crossentropy: 0.0124 - accuracy: 0.9963 - val_loss: 0.3982 - val_sparse_categorical_crossentropy: 0.1259 - val_accuracy: 0.9632\n", - "Epoch 229/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2842 - sparse_categorical_crossentropy: 0.0128 - accuracy: 0.9957 - val_loss: 0.3946 - val_sparse_categorical_crossentropy: 0.1251 - val_accuracy: 0.9628\n", - "Epoch 230/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2816 - sparse_categorical_crossentropy: 0.0131 - accuracy: 0.9957 - val_loss: 0.3986 - val_sparse_categorical_crossentropy: 0.1323 - val_accuracy: 0.9628\n", - "Epoch 231/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2779 - sparse_categorical_crossentropy: 0.0127 - accuracy: 0.9960 - val_loss: 0.3919 - val_sparse_categorical_crossentropy: 0.1291 - val_accuracy: 0.9642\n", - "Epoch 232/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2728 - sparse_categorical_crossentropy: 0.0111 - accuracy: 0.9961 - val_loss: 0.3884 - val_sparse_categorical_crossentropy: 0.1290 - val_accuracy: 0.9642\n", - "Epoch 233/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2706 - sparse_categorical_crossentropy: 0.0123 - accuracy: 0.9963 - val_loss: 0.3892 - val_sparse_categorical_crossentropy: 0.1332 - val_accuracy: 0.9635\n", - "Epoch 234/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2662 - sparse_categorical_crossentropy: 0.0112 - accuracy: 0.9967 - val_loss: 0.3824 - val_sparse_categorical_crossentropy: 0.1295 - val_accuracy: 0.9657\n", - "Epoch 235/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.2624 - sparse_categorical_crossentropy: 0.0104 - accuracy: 0.9967 - val_loss: 0.3806 - val_sparse_categorical_crossentropy: 0.1304 - val_accuracy: 0.9662\n", - "Epoch 236/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2622 - sparse_categorical_crossentropy: 0.0128 - accuracy: 0.9957 - val_loss: 0.3845 - val_sparse_categorical_crossentropy: 0.1365 - val_accuracy: 0.9635\n", - "Epoch 237/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2595 - sparse_categorical_crossentropy: 0.0124 - accuracy: 0.9959 - val_loss: 0.3797 - val_sparse_categorical_crossentropy: 0.1342 - val_accuracy: 0.9622\n", - "Epoch 238/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2555 - sparse_categorical_crossentropy: 0.0109 - accuracy: 0.9966 - val_loss: 0.3793 - val_sparse_categorical_crossentropy: 0.1364 - val_accuracy: 0.9625\n", - "Epoch 239/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.2531 - sparse_categorical_crossentropy: 0.0110 - accuracy: 0.9965 - val_loss: 0.3811 - val_sparse_categorical_crossentropy: 0.1405 - val_accuracy: 0.9612\n", - "Epoch 240/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.2510 - sparse_categorical_crossentropy: 0.0111 - accuracy: 0.9965 - val_loss: 0.3763 - val_sparse_categorical_crossentropy: 0.1379 - val_accuracy: 0.9630\n", - "Epoch 241/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2495 - sparse_categorical_crossentropy: 0.0118 - accuracy: 0.9963 - val_loss: 0.3823 - val_sparse_categorical_crossentropy: 0.1459 - val_accuracy: 0.9590\n", - "Epoch 242/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.2478 - sparse_categorical_crossentropy: 0.0121 - accuracy: 0.9962 - val_loss: 0.3703 - val_sparse_categorical_crossentropy: 0.1362 - val_accuracy: 0.9617\n", - "Epoch 243/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.2446 - sparse_categorical_crossentropy: 0.0111 - accuracy: 0.9968 - val_loss: 0.3627 - val_sparse_categorical_crossentropy: 0.1307 - val_accuracy: 0.9628\n", - "Epoch 244/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2426 - sparse_categorical_crossentropy: 0.0114 - accuracy: 0.9965 - val_loss: 0.3667 - val_sparse_categorical_crossentropy: 0.1371 - val_accuracy: 0.9623\n", - "Epoch 245/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2399 - sparse_categorical_crossentropy: 0.0110 - accuracy: 0.9962 - val_loss: 0.3700 - val_sparse_categorical_crossentropy: 0.1427 - val_accuracy: 0.9615\n", - "Epoch 246/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2381 - sparse_categorical_crossentropy: 0.0116 - accuracy: 0.9964 - val_loss: 0.3520 - val_sparse_categorical_crossentropy: 0.1271 - val_accuracy: 0.9640\n", - "Epoch 247/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2339 - sparse_categorical_crossentropy: 0.0098 - accuracy: 0.9969 - val_loss: 0.3549 - val_sparse_categorical_crossentropy: 0.1324 - val_accuracy: 0.9617\n", - "Epoch 248/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2304 - sparse_categorical_crossentropy: 0.0087 - accuracy: 0.9971 - val_loss: 0.3596 - val_sparse_categorical_crossentropy: 0.1397 - val_accuracy: 0.9603\n", - "Epoch 249/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2291 - sparse_categorical_crossentropy: 0.0099 - accuracy: 0.9969 - val_loss: 0.3555 - val_sparse_categorical_crossentropy: 0.1377 - val_accuracy: 0.9615\n", - "Epoch 250/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2270 - sparse_categorical_crossentropy: 0.0098 - accuracy: 0.9970 - val_loss: 0.3588 - val_sparse_categorical_crossentropy: 0.1430 - val_accuracy: 0.9608\n", - "Epoch 251/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2262 - sparse_categorical_crossentropy: 0.0111 - accuracy: 0.9962 - val_loss: 0.3527 - val_sparse_categorical_crossentropy: 0.1391 - val_accuracy: 0.9632\n", - "Epoch 252/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.2220 - sparse_categorical_crossentropy: 0.0092 - accuracy: 0.9973 - val_loss: 0.3444 - val_sparse_categorical_crossentropy: 0.1333 - val_accuracy: 0.9643\n", - "Epoch 253/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2195 - sparse_categorical_crossentropy: 0.0093 - accuracy: 0.9969 - val_loss: 0.3379 - val_sparse_categorical_crossentropy: 0.1296 - val_accuracy: 0.9642\n", - "Epoch 254/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2171 - sparse_categorical_crossentropy: 0.0098 - accuracy: 0.9969 - val_loss: 0.3363 - val_sparse_categorical_crossentropy: 0.1309 - val_accuracy: 0.9645\n", - "Epoch 255/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2149 - sparse_categorical_crossentropy: 0.0104 - accuracy: 0.9971 - val_loss: 0.3400 - val_sparse_categorical_crossentropy: 0.1372 - val_accuracy: 0.9638\n", - "Epoch 256/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2120 - sparse_categorical_crossentropy: 0.0099 - accuracy: 0.9968 - val_loss: 0.3329 - val_sparse_categorical_crossentropy: 0.1323 - val_accuracy: 0.9642\n", - "Epoch 257/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2120 - sparse_categorical_crossentropy: 0.0119 - accuracy: 0.9962 - val_loss: 0.3293 - val_sparse_categorical_crossentropy: 0.1300 - val_accuracy: 0.9645\n", - "Epoch 258/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.2084 - sparse_categorical_crossentropy: 0.0095 - accuracy: 0.9973 - val_loss: 0.3261 - val_sparse_categorical_crossentropy: 0.1279 - val_accuracy: 0.9620\n", - "Epoch 259/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2092 - sparse_categorical_crossentropy: 0.0114 - accuracy: 0.9967 - val_loss: 0.3316 - val_sparse_categorical_crossentropy: 0.1345 - val_accuracy: 0.9637\n", - "Epoch 260/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2099 - sparse_categorical_crossentropy: 0.0130 - accuracy: 0.9958 - val_loss: 0.3316 - val_sparse_categorical_crossentropy: 0.1351 - val_accuracy: 0.9618\n", - "Epoch 261/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2093 - sparse_categorical_crossentropy: 0.0129 - accuracy: 0.9961 - val_loss: 0.3260 - val_sparse_categorical_crossentropy: 0.1300 - val_accuracy: 0.9655\n", - "Epoch 262/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.2086 - sparse_categorical_crossentropy: 0.0127 - accuracy: 0.9964 - val_loss: 0.3342 - val_sparse_categorical_crossentropy: 0.1384 - val_accuracy: 0.9628\n", - "Epoch 263/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.2096 - sparse_categorical_crossentropy: 0.0138 - accuracy: 0.9956 - val_loss: 0.3320 - val_sparse_categorical_crossentropy: 0.1365 - val_accuracy: 0.9648\n", - "Epoch 264/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.2071 - sparse_categorical_crossentropy: 0.0118 - accuracy: 0.9965 - val_loss: 0.3291 - val_sparse_categorical_crossentropy: 0.1343 - val_accuracy: 0.9657\n", - "Epoch 265/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2047 - sparse_categorical_crossentropy: 0.0102 - accuracy: 0.9972 - val_loss: 0.3258 - val_sparse_categorical_crossentropy: 0.1324 - val_accuracy: 0.9650\n", - "Epoch 266/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.2040 - sparse_categorical_crossentropy: 0.0110 - accuracy: 0.9965 - val_loss: 0.3180 - val_sparse_categorical_crossentropy: 0.1259 - val_accuracy: 0.9653\n", - "Epoch 267/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2021 - sparse_categorical_crossentropy: 0.0105 - accuracy: 0.9967 - val_loss: 0.3267 - val_sparse_categorical_crossentropy: 0.1362 - val_accuracy: 0.9628\n", - "Epoch 268/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.2033 - sparse_categorical_crossentropy: 0.0134 - accuracy: 0.9961 - val_loss: 0.3138 - val_sparse_categorical_crossentropy: 0.1247 - val_accuracy: 0.9637\n", - "Epoch 269/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.2005 - sparse_categorical_crossentropy: 0.0116 - accuracy: 0.9965 - val_loss: 0.3142 - val_sparse_categorical_crossentropy: 0.1259 - val_accuracy: 0.9627\n", - "Epoch 270/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 0.1994 - sparse_categorical_crossentropy: 0.0114 - accuracy: 0.9964 - val_loss: 0.3249 - val_sparse_categorical_crossentropy: 0.1375 - val_accuracy: 0.9633\n", - "Epoch 271/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1989 - sparse_categorical_crossentropy: 0.0118 - accuracy: 0.9961 - val_loss: 0.3228 - val_sparse_categorical_crossentropy: 0.1365 - val_accuracy: 0.9650\n", - "Epoch 272/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.1991 - sparse_categorical_crossentropy: 0.0132 - accuracy: 0.9962 - val_loss: 0.3078 - val_sparse_categorical_crossentropy: 0.1227 - val_accuracy: 0.9643\n", - "Epoch 273/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 0.1972 - sparse_categorical_crossentropy: 0.0125 - accuracy: 0.9956 - val_loss: 0.3129 - val_sparse_categorical_crossentropy: 0.1293 - val_accuracy: 0.9642\n", - "Epoch 274/1000\n", - "5/5 [==============================] - 0s 50ms/step - loss: 0.1936 - sparse_categorical_crossentropy: 0.0106 - accuracy: 0.9965 - val_loss: 0.3106 - val_sparse_categorical_crossentropy: 0.1288 - val_accuracy: 0.9640\n", - "Epoch 275/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1931 - sparse_categorical_crossentropy: 0.0119 - accuracy: 0.9960 - val_loss: 0.3034 - val_sparse_categorical_crossentropy: 0.1234 - val_accuracy: 0.9647\n", - "Epoch 276/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1900 - sparse_categorical_crossentropy: 0.0105 - accuracy: 0.9965 - val_loss: 0.3022 - val_sparse_categorical_crossentropy: 0.1237 - val_accuracy: 0.9658\n", - "Epoch 277/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1885 - sparse_categorical_crossentropy: 0.0104 - accuracy: 0.9965 - val_loss: 0.2986 - val_sparse_categorical_crossentropy: 0.1215 - val_accuracy: 0.9670\n", - "Epoch 278/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.1885 - sparse_categorical_crossentropy: 0.0120 - accuracy: 0.9962 - val_loss: 0.2928 - val_sparse_categorical_crossentropy: 0.1174 - val_accuracy: 0.9675\n", - "Epoch 279/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1839 - sparse_categorical_crossentropy: 0.0090 - accuracy: 0.9972 - val_loss: 0.2905 - val_sparse_categorical_crossentropy: 0.1168 - val_accuracy: 0.9678\n", - "Epoch 280/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1822 - sparse_categorical_crossentropy: 0.0091 - accuracy: 0.9972 - val_loss: 0.2895 - val_sparse_categorical_crossentropy: 0.1177 - val_accuracy: 0.9683\n", - "Epoch 281/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1812 - sparse_categorical_crossentropy: 0.0101 - accuracy: 0.9968 - val_loss: 0.2872 - val_sparse_categorical_crossentropy: 0.1176 - val_accuracy: 0.9670\n", - "Epoch 282/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.1777 - sparse_categorical_crossentropy: 0.0089 - accuracy: 0.9971 - val_loss: 0.2863 - val_sparse_categorical_crossentropy: 0.1189 - val_accuracy: 0.9683\n", - "Epoch 283/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1760 - sparse_categorical_crossentropy: 0.0091 - accuracy: 0.9975 - val_loss: 0.2889 - val_sparse_categorical_crossentropy: 0.1232 - val_accuracy: 0.9668\n", - "Epoch 284/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1749 - sparse_categorical_crossentropy: 0.0097 - accuracy: 0.9970 - val_loss: 0.2868 - val_sparse_categorical_crossentropy: 0.1228 - val_accuracy: 0.9673\n", - "Epoch 285/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1735 - sparse_categorical_crossentropy: 0.0101 - accuracy: 0.9970 - val_loss: 0.2775 - val_sparse_categorical_crossentropy: 0.1153 - val_accuracy: 0.9685\n", - "Epoch 286/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1696 - sparse_categorical_crossentropy: 0.0080 - accuracy: 0.9978 - val_loss: 0.2746 - val_sparse_categorical_crossentropy: 0.1144 - val_accuracy: 0.9687\n", - "Epoch 287/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1676 - sparse_categorical_crossentropy: 0.0081 - accuracy: 0.9977 - val_loss: 0.2735 - val_sparse_categorical_crossentropy: 0.1156 - val_accuracy: 0.9688\n", - "Epoch 288/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1650 - sparse_categorical_crossentropy: 0.0079 - accuracy: 0.9975 - val_loss: 0.2728 - val_sparse_categorical_crossentropy: 0.1174 - val_accuracy: 0.9700\n", - "Epoch 289/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1646 - sparse_categorical_crossentropy: 0.0099 - accuracy: 0.9967 - val_loss: 0.2667 - val_sparse_categorical_crossentropy: 0.1135 - val_accuracy: 0.9698\n", - "Epoch 290/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.1596 - sparse_categorical_crossentropy: 0.0070 - accuracy: 0.9980 - val_loss: 0.2663 - val_sparse_categorical_crossentropy: 0.1151 - val_accuracy: 0.9682\n", - "Epoch 291/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.1598 - sparse_categorical_crossentropy: 0.0092 - accuracy: 0.9969 - val_loss: 0.2687 - val_sparse_categorical_crossentropy: 0.1193 - val_accuracy: 0.9680\n", - "Epoch 292/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.1577 - sparse_categorical_crossentropy: 0.0088 - accuracy: 0.9973 - val_loss: 0.2652 - val_sparse_categorical_crossentropy: 0.1174 - val_accuracy: 0.9663\n", - "Epoch 293/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1558 - sparse_categorical_crossentropy: 0.0084 - accuracy: 0.9975 - val_loss: 0.2598 - val_sparse_categorical_crossentropy: 0.1131 - val_accuracy: 0.9677\n", - "Epoch 294/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1570 - sparse_categorical_crossentropy: 0.0105 - accuracy: 0.9967 - val_loss: 0.2648 - val_sparse_categorical_crossentropy: 0.1190 - val_accuracy: 0.9687\n", - "Epoch 295/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1534 - sparse_categorical_crossentropy: 0.0079 - accuracy: 0.9977 - val_loss: 0.2535 - val_sparse_categorical_crossentropy: 0.1091 - val_accuracy: 0.9688\n", - "Epoch 296/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1527 - sparse_categorical_crossentropy: 0.0088 - accuracy: 0.9972 - val_loss: 0.2557 - val_sparse_categorical_crossentropy: 0.1132 - val_accuracy: 0.9685\n", - "Epoch 297/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1499 - sparse_categorical_crossentropy: 0.0081 - accuracy: 0.9976 - val_loss: 0.2484 - val_sparse_categorical_crossentropy: 0.1080 - val_accuracy: 0.9700\n", - "Epoch 298/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1471 - sparse_categorical_crossentropy: 0.0074 - accuracy: 0.9980 - val_loss: 0.2475 - val_sparse_categorical_crossentropy: 0.1093 - val_accuracy: 0.9698\n", - "Epoch 299/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1449 - sparse_categorical_crossentropy: 0.0074 - accuracy: 0.9980 - val_loss: 0.2525 - val_sparse_categorical_crossentropy: 0.1164 - val_accuracy: 0.9695\n", - "Epoch 300/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1433 - sparse_categorical_crossentropy: 0.0079 - accuracy: 0.9974 - val_loss: 0.2493 - val_sparse_categorical_crossentropy: 0.1153 - val_accuracy: 0.9692\n", - "Epoch 301/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1422 - sparse_categorical_crossentropy: 0.0088 - accuracy: 0.9972 - val_loss: 0.2488 - val_sparse_categorical_crossentropy: 0.1164 - val_accuracy: 0.9685\n", - "Epoch 302/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1422 - sparse_categorical_crossentropy: 0.0101 - accuracy: 0.9968 - val_loss: 0.2440 - val_sparse_categorical_crossentropy: 0.1122 - val_accuracy: 0.9678\n", - "Epoch 303/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1427 - sparse_categorical_crossentropy: 0.0110 - accuracy: 0.9969 - val_loss: 0.2454 - val_sparse_categorical_crossentropy: 0.1141 - val_accuracy: 0.9685\n", - "Epoch 304/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1412 - sparse_categorical_crossentropy: 0.0101 - accuracy: 0.9972 - val_loss: 0.2423 - val_sparse_categorical_crossentropy: 0.1119 - val_accuracy: 0.9692\n", - "Epoch 305/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1392 - sparse_categorical_crossentropy: 0.0091 - accuracy: 0.9972 - val_loss: 0.2436 - val_sparse_categorical_crossentropy: 0.1142 - val_accuracy: 0.9682\n", - "Epoch 306/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1378 - sparse_categorical_crossentropy: 0.0088 - accuracy: 0.9972 - val_loss: 0.2407 - val_sparse_categorical_crossentropy: 0.1123 - val_accuracy: 0.9685\n", - "Epoch 307/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1382 - sparse_categorical_crossentropy: 0.0099 - accuracy: 0.9967 - val_loss: 0.2440 - val_sparse_categorical_crossentropy: 0.1159 - val_accuracy: 0.9675\n", - "Epoch 308/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1377 - sparse_categorical_crossentropy: 0.0094 - accuracy: 0.9971 - val_loss: 0.2499 - val_sparse_categorical_crossentropy: 0.1213 - val_accuracy: 0.9667\n", - "Epoch 309/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1405 - sparse_categorical_crossentropy: 0.0116 - accuracy: 0.9963 - val_loss: 0.2452 - val_sparse_categorical_crossentropy: 0.1158 - val_accuracy: 0.9672\n", - "Epoch 310/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1408 - sparse_categorical_crossentropy: 0.0112 - accuracy: 0.9969 - val_loss: 0.2500 - val_sparse_categorical_crossentropy: 0.1199 - val_accuracy: 0.9658\n", - "Epoch 311/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 0.1415 - sparse_categorical_crossentropy: 0.0113 - accuracy: 0.9965 - val_loss: 0.2632 - val_sparse_categorical_crossentropy: 0.1329 - val_accuracy: 0.9650\n", - "Epoch 312/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1418 - sparse_categorical_crossentropy: 0.0114 - accuracy: 0.9962 - val_loss: 0.2488 - val_sparse_categorical_crossentropy: 0.1182 - val_accuracy: 0.9667\n", - "Epoch 313/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1417 - sparse_categorical_crossentropy: 0.0110 - accuracy: 0.9965 - val_loss: 0.2519 - val_sparse_categorical_crossentropy: 0.1210 - val_accuracy: 0.9668\n", - "Epoch 314/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1429 - sparse_categorical_crossentropy: 0.0120 - accuracy: 0.9966 - val_loss: 0.2546 - val_sparse_categorical_crossentropy: 0.1235 - val_accuracy: 0.9675\n", - "Epoch 315/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1430 - sparse_categorical_crossentropy: 0.0118 - accuracy: 0.9967 - val_loss: 0.2558 - val_sparse_categorical_crossentropy: 0.1243 - val_accuracy: 0.9640\n", - "Epoch 316/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1433 - sparse_categorical_crossentropy: 0.0116 - accuracy: 0.9965 - val_loss: 0.2501 - val_sparse_categorical_crossentropy: 0.1181 - val_accuracy: 0.9665\n", - "Epoch 317/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1433 - sparse_categorical_crossentropy: 0.0111 - accuracy: 0.9964 - val_loss: 0.2437 - val_sparse_categorical_crossentropy: 0.1116 - val_accuracy: 0.9682\n", - "Epoch 318/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1437 - sparse_categorical_crossentropy: 0.0115 - accuracy: 0.9969 - val_loss: 0.2465 - val_sparse_categorical_crossentropy: 0.1143 - val_accuracy: 0.9670\n", - "Epoch 319/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1434 - sparse_categorical_crossentropy: 0.0112 - accuracy: 0.9969 - val_loss: 0.2489 - val_sparse_categorical_crossentropy: 0.1169 - val_accuracy: 0.9693\n", - "Epoch 320/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1426 - sparse_categorical_crossentropy: 0.0106 - accuracy: 0.9966 - val_loss: 0.2469 - val_sparse_categorical_crossentropy: 0.1153 - val_accuracy: 0.9683\n", - "Epoch 321/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.1417 - sparse_categorical_crossentropy: 0.0102 - accuracy: 0.9967 - val_loss: 0.2487 - val_sparse_categorical_crossentropy: 0.1179 - val_accuracy: 0.9687\n", - "Epoch 322/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.1411 - sparse_categorical_crossentropy: 0.0106 - accuracy: 0.9968 - val_loss: 0.2492 - val_sparse_categorical_crossentropy: 0.1194 - val_accuracy: 0.9683\n", - "Epoch 323/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1382 - sparse_categorical_crossentropy: 0.0087 - accuracy: 0.9972 - val_loss: 0.2491 - val_sparse_categorical_crossentropy: 0.1202 - val_accuracy: 0.9685\n", - "Epoch 324/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1394 - sparse_categorical_crossentropy: 0.0108 - accuracy: 0.9966 - val_loss: 0.2378 - val_sparse_categorical_crossentropy: 0.1096 - val_accuracy: 0.9705\n", - "Epoch 325/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1381 - sparse_categorical_crossentropy: 0.0100 - accuracy: 0.9968 - val_loss: 0.2559 - val_sparse_categorical_crossentropy: 0.1279 - val_accuracy: 0.9627\n", - "Epoch 326/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1401 - sparse_categorical_crossentropy: 0.0122 - accuracy: 0.9959 - val_loss: 0.2383 - val_sparse_categorical_crossentropy: 0.1107 - val_accuracy: 0.9698\n", - "Epoch 327/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1381 - sparse_categorical_crossentropy: 0.0104 - accuracy: 0.9970 - val_loss: 0.2451 - val_sparse_categorical_crossentropy: 0.1170 - val_accuracy: 0.9677\n", - "Epoch 328/1000\n", - "5/5 [==============================] - 0s 45ms/step - loss: 0.1382 - sparse_categorical_crossentropy: 0.0100 - accuracy: 0.9972 - val_loss: 0.2481 - val_sparse_categorical_crossentropy: 0.1199 - val_accuracy: 0.9668\n", - "Epoch 329/1000\n", - "5/5 [==============================] - 0s 49ms/step - loss: 0.1382 - sparse_categorical_crossentropy: 0.0101 - accuracy: 0.9965 - val_loss: 0.2469 - val_sparse_categorical_crossentropy: 0.1191 - val_accuracy: 0.9698\n", - "Epoch 330/1000\n", - "5/5 [==============================] - 0s 48ms/step - loss: 0.1368 - sparse_categorical_crossentropy: 0.0092 - accuracy: 0.9972 - val_loss: 0.2417 - val_sparse_categorical_crossentropy: 0.1145 - val_accuracy: 0.9690\n", - "Epoch 331/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1371 - sparse_categorical_crossentropy: 0.0103 - accuracy: 0.9967 - val_loss: 0.2435 - val_sparse_categorical_crossentropy: 0.1176 - val_accuracy: 0.9682\n", - "Epoch 332/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1359 - sparse_categorical_crossentropy: 0.0105 - accuracy: 0.9967 - val_loss: 0.2459 - val_sparse_categorical_crossentropy: 0.1214 - val_accuracy: 0.9668\n", - "Epoch 333/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1339 - sparse_categorical_crossentropy: 0.0099 - accuracy: 0.9973 - val_loss: 0.2384 - val_sparse_categorical_crossentropy: 0.1152 - val_accuracy: 0.9675\n", - "Epoch 334/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1329 - sparse_categorical_crossentropy: 0.0098 - accuracy: 0.9969 - val_loss: 0.2385 - val_sparse_categorical_crossentropy: 0.1153 - val_accuracy: 0.9688\n", - "Epoch 335/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1340 - sparse_categorical_crossentropy: 0.0108 - accuracy: 0.9967 - val_loss: 0.2420 - val_sparse_categorical_crossentropy: 0.1185 - val_accuracy: 0.9687\n", - "Epoch 336/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1379 - sparse_categorical_crossentropy: 0.0141 - accuracy: 0.9956 - val_loss: 0.2468 - val_sparse_categorical_crossentropy: 0.1218 - val_accuracy: 0.9660\n", - "Epoch 337/1000\n", - "5/5 [==============================] - 0s 43ms/step - loss: 0.1399 - sparse_categorical_crossentropy: 0.0143 - accuracy: 0.9953 - val_loss: 0.2523 - val_sparse_categorical_crossentropy: 0.1254 - val_accuracy: 0.9652\n", - "Epoch 338/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1409 - sparse_categorical_crossentropy: 0.0131 - accuracy: 0.9957 - val_loss: 0.2416 - val_sparse_categorical_crossentropy: 0.1120 - val_accuracy: 0.9687\n", - "Epoch 339/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1402 - sparse_categorical_crossentropy: 0.0098 - accuracy: 0.9972 - val_loss: 0.2555 - val_sparse_categorical_crossentropy: 0.1238 - val_accuracy: 0.9660\n", - "Epoch 340/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1436 - sparse_categorical_crossentropy: 0.0116 - accuracy: 0.9960 - val_loss: 0.2524 - val_sparse_categorical_crossentropy: 0.1200 - val_accuracy: 0.9665\n", - "Epoch 341/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1435 - sparse_categorical_crossentropy: 0.0110 - accuracy: 0.9965 - val_loss: 0.2473 - val_sparse_categorical_crossentropy: 0.1151 - val_accuracy: 0.9675\n", - "Epoch 342/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1430 - sparse_categorical_crossentropy: 0.0111 - accuracy: 0.9964 - val_loss: 0.2492 - val_sparse_categorical_crossentropy: 0.1181 - val_accuracy: 0.9685\n", - "Epoch 343/1000\n", - "5/5 [==============================] - 0s 46ms/step - loss: 0.1411 - sparse_categorical_crossentropy: 0.0103 - accuracy: 0.9972 - val_loss: 0.2523 - val_sparse_categorical_crossentropy: 0.1221 - val_accuracy: 0.9668\n", - "Epoch 344/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1392 - sparse_categorical_crossentropy: 0.0093 - accuracy: 0.9971 - val_loss: 0.2409 - val_sparse_categorical_crossentropy: 0.1121 - val_accuracy: 0.9692\n", - "Epoch 345/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1375 - sparse_categorical_crossentropy: 0.0092 - accuracy: 0.9972 - val_loss: 0.2496 - val_sparse_categorical_crossentropy: 0.1227 - val_accuracy: 0.9675\n", - "Epoch 346/1000\n", - "5/5 [==============================] - 0s 44ms/step - loss: 0.1353 - sparse_categorical_crossentropy: 0.0091 - accuracy: 0.9972 - val_loss: 0.2423 - val_sparse_categorical_crossentropy: 0.1178 - val_accuracy: 0.9695\n", - "Epoch 347/1000\n", - "5/5 [==============================] - 0s 47ms/step - loss: 0.1334 - sparse_categorical_crossentropy: 0.0098 - accuracy: 0.9968 - val_loss: 0.2382 - val_sparse_categorical_crossentropy: 0.1164 - val_accuracy: 0.9690\n" - ] - }, - { - "data": { - "text/html": [ - "Waiting for W&B process to finish... (success)." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "656697c81d8a4308931ddfa47a929f45", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='23.002 MB of 23.002 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, m…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "

Run history:


accuracy▁▃▅▆▆▇▇▇▇▇▇█████████████████████████████
epoch▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
loss█▆▅▅▄▄▃▃▃▃▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
sparse_categorical_crossentropy█▃▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
val_accuracy▁▂▃▅▅▅▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇███████████████████
val_loss█▇▇▆▅▅▄▄▄▃▃▃▃▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
val_sparse_categorical_crossentropy█▅▅▄▄▄▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁

Run summary:


GFLOPS0.00416
accuracy0.99676
best_epoch296
best_val_sparse_categorical_crossentropy0.10799
epoch346
loss0.13342
sparse_categorical_crossentropy0.00981
val_accuracy0.96899
val_loss0.23815
val_sparse_categorical_crossentropy0.1164

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Synced usual-dust-56: https://wandb.ai/eltoncn/SygilDetector/runs/2ex88lij
Synced 5 W&B file(s), 1 media file(s), 0 artifact file(s) and 1 other file(s)" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Find logs at: .\\wandb\\run-20221017_193034-2ex88lij\\logs" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "units = [400, 400]\n", "regularizer = [0.0075, 0.0075]\n", @@ -3939,27 +871,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[6.5890199e-01 2.6562536e-06 2.5961262e-07 1.0639899e-07 3.4109497e-01]\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "points = sample_points_fromlist(all_points_tr, 100)\n", "points = center_points(points)\n", @@ -3985,7 +899,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4017,96 +931,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "wandb version 0.13.4 is available! To upgrade, please run:\n", - " $ pip install wandb --upgrade" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Tracking run with wandb version 0.12.21" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Run data is saved locally in d:\\Github\\JogosTeP\\Dev\\wandb\\run-20221017_192853-s47y0u3r" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Syncing run rich-pond-55 to Weights & Biases (docs)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The save_model argument by default saves the model in the HDF5 format that cannot save custom objects like subclassed models and custom layers. This behavior will be deprecated in a future release in favor of the SavedModel format. Meanwhile, the HDF5 model is saved as W&B files and the SavedModel as W&B Artifacts.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From c:\\Python38\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py:5063: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n", - "Epoch 1/1000\n" - ] - }, - { - "ename": "ResourceExhaustedError", - "evalue": " OOM when allocating tensor with shape[5000,33,33,16] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[{{node sequential/dense/Tensordot/transpose-0-1-TransposeNCHWToNHWC-LayoutOptimizer}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n [Op:__inference_train_function_1088]\n\nFunction call stack:\ntrain_function\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mResourceExhaustedError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;31m#optimizer = keras.optimizers.SGD(learning_rate=0.02)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[0mhist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompile_fit_cnn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10000\u001b[0m\u001b[1;33m//\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mregularizer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"dropout[0.5, 0.5]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m\u001b[0m in \u001b[0;36mcompile_fit_cnn\u001b[1;34m(model, epochs, optimizer, batch_size, patience, units, regularizer, others)\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"sparse_categorical_crossentropy\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"sparse_categorical_crossentropy\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"accuracy\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m hist = model.fit(train_features, train_targets, validation_data=(val_features, val_targets), epochs=epochs, batch_size=batch_size,\n\u001b[0m\u001b[0;32m 20\u001b[0m callbacks=callbacks)\n\u001b[0;32m 21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Python38\\lib\\site-packages\\wandb\\integration\\keras\\keras.py\u001b[0m in \u001b[0;36mnew_v2\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 171\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcbk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcbks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 172\u001b[0m \u001b[0mset_wandb_attrs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcbk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 173\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mold_v2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 174\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 175\u001b[0m \u001b[0mtraining_arrays\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0morig_fit_loop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mold_arrays\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Python38\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m 1182\u001b[0m _r=1):\n\u001b[0;32m 1183\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1184\u001b[1;33m \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1185\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1186\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Python38\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 883\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 884\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 885\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 886\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 887\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Python38\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 948\u001b[0m \u001b[1;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 949\u001b[0m \u001b[1;31m# stateless function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 950\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 951\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 952\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfiltered_flat_args\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Python38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 3037\u001b[0m (graph_function,\n\u001b[0;32m 3038\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m-> 3039\u001b[1;33m return graph_function._call_flat(\n\u001b[0m\u001b[0;32m 3040\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[0;32m 3041\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Python38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m 1961\u001b[0m and executing_eagerly):\n\u001b[0;32m 1962\u001b[0m \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1963\u001b[1;33m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m 1964\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m 1965\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n", - "\u001b[1;32mc:\\Python38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m 589\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 590\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 591\u001b[1;33m outputs = execute.execute(\n\u001b[0m\u001b[0;32m 592\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 593\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Python38\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m 60\u001b[0m inputs, attrs, num_outputs)\n\u001b[0;32m 61\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[5000,33,33,16] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[{{node sequential/dense/Tensordot/transpose-0-1-TransposeNCHWToNHWC-LayoutOptimizer}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n [Op:__inference_train_function_1088]\n\nFunction call stack:\ntrain_function\n" - ] - } - ], + "outputs": [], "source": [ "units = [400, 400]\n", "regularizer = [0.0075, 0.0075]\n", @@ -4135,28 +962,18 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Export" + "### Export" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From c:\\Python38\\lib\\site-packages\\tf2onnx\\tf_loader.py:706: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use `tf.compat.v1.graph_util.extract_sub_graph`\n", - "INFO:tensorflow:Assets written to: sygil_detector_img1\\assets\n" - ] - } - ], + "outputs": [], "source": [ "onnx_model = tf2onnx.convert.from_keras(model)\n", "\n", @@ -4190,7 +1007,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.5 (tags/v3.8.5:580fbb0, Jul 20 2020, 15:57:54) [MSC v.1924 64 bit (AMD64)]" }, "orig_nbformat": 4, "vscode": { diff --git a/Media/image_Shadow_Loop2_a_2022-12-16_003_0000.png b/Media/image_Shadow_Loop2_a_2022-12-16_003_0000.png new file mode 100644 index 0000000..e37b15a Binary files /dev/null and b/Media/image_Shadow_Loop2_a_2022-12-16_003_0000.png differ diff --git a/README.md b/README.md new file mode 100644 index 0000000..b1cf1b1 --- /dev/null +++ b/README.md @@ -0,0 +1,50 @@ +# Submissão + +Become an exorcist graffiti student trapped in a college from another dimension as you investigate your advisor's whereabouts and try to stay safe from a strange shadow. + +[![](https://img.shields.io/badge/-Try%20it%20now!-red?style=for-the-badge)]() + +![](Media/image_Shadow_Loop2_a_2022-12-16_003_0000.png) + +3D Suspense game prototype made by undergraduate and graduate students for the discipline "Jogos Digitais - Teoria e Prática" (FEEC-IA/Unicamp, 2s2022, Prof. Alfredo Suppia and Prof. José Mario De Martino). + +## Team + +- [Elton Cardoso do Nascimento](https://github.com/EltonCN) (IC/Unicamp): Programming, 3D Artist (Technical, VFX, Lighting), Animation, UI, 2D Artist +- [Gilberto de Ataide Batista Faria](https://github.com/Tensaip) (IA/Unicamp): Script, 3D Artist (Materials, Lighting), Mocap, UI +- [Lucas Hideki Ueda](https://github.com/lucashueda) (FEEC/Unicamp): Mocap, 3D Artist (Modeling), Character Rigging +- [Pedro Henrique Suguino](https://github.com/Pedro-Suguino) (IC-FEEC/Unicamp): Programming, 3D Artist (Modeling) +- Ricardo Alves Moreira Mazzeo (IFCH/Unicamp): Art and story references, 3D Artist (Materials) +- [Sam Gomes](https://github.com/samrgomes): Programming +- [Vinicius Yglesias Pereira](https://github.com/Vinicius-Yglesias) (IA/Unicamp): Music and SFX, Programming + +## Folder structure +- Shadow: Unity project +- Dev: sigil detection synthetic data generation and model training +- Media: images + +## Footage and Sounds + +### Footage +- [Gameplay](https://drive.google.com/file/d/19tNfZ6DuG2Hfh5Cl6yJem_EcDp4TyiN2/view?usp=share_link) +- [Level Blocking](https://drive.google.com/file/d/13LzRkUbgq_AVBIQ6LWuY8MRiruvfO2YK/view?usp=share_link) +- [Animation and Shadow VFX Test](https://drive.google.com/file/d/18gvGmwPz2eJ5nXAGKibLqSaM-Of44Mow/view?usp=share_link) +- [Animation Test](https://drive.google.com/file/d/1CW5NLLJDmjEFb5PdMnoKLyqVtJDohiPS/view?usp=share_link) +- [End Game VFX](https://drive.google.com/file/d/16IrmL1-csUix4AuajMKVv43MWDGuPC-a/view?usp=share_link) +- [Grab and Sigil Detection Test](https://drive.google.com/file/d/18cL3RwkdwYUn3LHX_2SEc9eQfemfpdXv/view?usp=share_link) + + +### Sounds + +## Tools Used + +- Game Engine: ![](https://img.shields.io/badge/Unity-100000?style=for-the-badge&logo=unity&logoColor=white) +- 2D Art: ![](https://img.shields.io/badge/Autodesk%20SketchBook-e45b36?style=for-the-badge&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADIAAAAyCAYAAAAeP4ixAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAACxMAAAsTAQCanBgAAAUbaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8P3hwYWNrZXQgYmVnaW49Iu+7vyIgaWQ9Ilc1TTBNcENlaGlIenJlU3pOVGN6a2M5ZCI/PiA8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJBZG9iZSBYTVAgQ29yZSA2LjAtYzAwNiA3OS5kYWJhY2JiLCAyMDIxLzA0LzE0LTAwOjM5OjQ0ICAgICAgICAiPiA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPiA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iIHhtbG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyIgeG1sbnM6cGhvdG9zaG9wPSJodHRwOi8vbnMuYWRvYmUuY29tL3Bob3Rvc2hvcC8xLjAvIiB4bWxuczp4bXBNTT0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL21tLyIgeG1sbnM6c3RFdnQ9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZUV2ZW50IyIgeG1wOkNyZWF0b3JUb29sPSJBZG9iZSBQaG90b3Nob3AgMjIuNCAoTWFjaW50b3NoKSIgeG1wOkNyZWF0ZURhdGU9IjIwMjEtMDYtMzBUMTY6MDM6MzYtMDQ6MDAiIHhtcDpNb2RpZnlEYXRlPSIyMDIxLTA2LTMwVDE2OjA1OjAyLTA0OjAwIiB4bXA6TWV0YWRhdGFEYXRlPSIyMDIxLTA2LTMwVDE2OjA1OjAyLTA0OjAwIiBkYzpmb3JtYXQ9ImltYWdlL3BuZyIgcGhvdG9zaG9wOkNvbG9yTW9kZT0iMyIgcGhvdG9zaG9wOklDQ1Byb2ZpbGU9InNSR0IgSUVDNjE5NjYtMi4xIiB4bXBNTTpJbnN0YW5jZUlEPSJ4bXAuaWlkOjA4YWY0NzhmLWQ0YWQtNGFiOC04YmM3LTdkMGM5MjVlMGNiMiIgeG1wTU06RG9jdW1lbnRJRD0ieG1wLmRpZDowOGFmNDc4Zi1kNGFkLTRhYjgtOGJjNy03ZDBjOTI1ZTBjYjIiIHhtcE1NOk9yaWdpbmFsRG9jdW1lbnRJRD0ieG1wLmRpZDowOGFmNDc4Zi1kNGFkLTRhYjgtOGJjNy03ZDBjOTI1ZTBjYjIiPiA8eG1wTU06SGlzdG9yeT4gPHJkZjpTZXE+IDxyZGY6bGkgc3RFdnQ6YWN0aW9uPSJjcmVhdGVkIiBzdEV2dDppbnN0YW5jZUlEPSJ4bXAuaWlkOjA4YWY0NzhmLWQ0YWQtNGFiOC04YmM3LTdkMGM5MjVlMGNiMiIgc3RFdnQ6d2hlbj0iMjAyMS0wNi0zMFQxNjowMzozNi0wNDowMCIgc3RFdnQ6c29mdHdhcmVBZ2VudD0iQWRvYmUgUGhvdG9zaG9wIDIyLjQgKE1hY2ludG9zaCkiLz4gPC9yZGY6U2VxPiA8L3htcE1NOkhpc3Rvcnk+IDwvcmRmOkRlc2NyaXB0aW9uPiA8L3JkZjpSREY+IDwveDp4bXBtZXRhPiA8P3hwYWNrZXQgZW5kPSJyIj8+0l1hRgAACPlJREFUaEOtWV1zVMcR7d3VsisRGxsBAuykHJddBBuMk7fk7+QxL3nOf8jvyA/IQ17iSlX8ElJULFThARyIYwO2kYQkkHa1X9r06Tk9t+fqrgQun6KnT5+eO3N7Z+7cK7v17R9+MxORlsy1BaInn2euTdALT171hVeSOQ0gtzDrFIyX3sYkr3QknMu8ra7Vqtu84tqJfJ61whr7Qg/9C137qbd5o95k7Ou80suxMW/bqqoqK3j1SyCAbzBAffmrMTCeqHMLCx0iEzWDyzFgnEHUFViRhJhQX90YAuc0wLla2ZcBcwZyCwvdRXAaANnjoDWN7VNWK8IEvPdPvZzTgMCtr5k2mdMAcgsL3UVwGgC5rpshEWK4EJeFqKUkCThgWqK5H1zWXQCnDxYnTAZRPeAaALlRR8I5Xcwr0tYqkh7UDCC3MOsUjNMH/ufdn8idPX0ss66N86ypg0we9ZRwTlfkk9mKGM8NPA1wTsuDwExwTh/41nRJ/rR3Xv74aC5PXo6SWOtjrq5lriTqcEW+snZ1Y2icJ1rn3iXpEMiBGkf6HwcrFr6QvvzlyUA2B5OyPxx81DJXUtNz32iGuT8jVGKyxr1L0hkUWqKx7+1BKqTd6cj9UU/uPtuXzSGL8X7s65oBiZpuIbnBOfvqM8KMJ4Aa9xtLOoNCSzT23Z525OH4jPFOZ0m+ni3LYHokdze1GF0Z6+bXugGvUwQa8sbjN/KFRUQD1Me+d7gaAArZm3fl+7FOdyRydysVE69NBcA8TgaXYyBzbZyrVYUANX5iEQC534OB2j+HVSHYWsCXk54caQ62gWLyNkMDTwPUm1zTEtcm6HDV8RsS8OWNMTCeqHMLXae9mLXtmXBgRYDHR8synOo3qvZBMbYyw7Hl8vVw6sv5o2njnCF4WhHAk2rVICFgzkBuYaFDnMvm+XflVzffk+sfXJEP37sk196/LD9de0vOXbksB219bngNum9sHciWrwyHqMajAcYZUM991VpPf//rI3xFmgDNk9bTec0z5dyAC8nf+u3vZOmDazIdT2QyGstE/Wd/uy2DwVB2/3VHzj35Str4dmV/bItbq2dltdcN49EA4wyoW+QSP+Nz4sQigpnsMRCKaPVX5MyH11Lg0NzZlb7R3tV37fSqrvVthpWZZi3mTyrCNdtaOeEkc/rAc7HZIKoH1Pc/vqkPRfp9IpaXUyHd86sy6ugvD4Tr7ADY3i+2mflFRdQsvNnV0GROH7iPWekQQ6zWu/lLJI5hhYXYn0NrV2WGO/friFSMrsxh82lmEbnBuVo4tbTxBFDjJxYBqG/1+tL7xUcUSiwvV6dY78pVGUx0ezl8HLWqmPI0g3NuiFwJ3yNUmjqpf5UigN5H2FbpnVFHXhHF0uoFGbbYrzYGyJFOuPH8QLa5MpZCE/tmrkT/aSFUcoIGqH+lIiCp9T/5NGkNWO7rivg1ur1aa5dlMqPgug2UGFZmXYuptlnSS66EMVekZnDqX7kIda3uGel9fMPkJiwtdWSpW61W9/IVPb3SyzGBAxGY+wifMzu1YryPFwGqVj0j7pGnN2SOhHN6Smh6129Ia4mn0QKcXV4m00IuXJQhp68GSojzo5gNFDOKxWhD7n3TigCeCLwybZwD5BaS9281n1YR8YGXdltaFy/JeBbeKYpjP6LCDoCdgTwf4T2jAftYmrzYWnA5BqwABq7T8oRqWIn+DX3QT8EKnpOAtL14esUxAfeA8iOtZn1nPxWDvtS9X/Vmd5GJVIRzGgC5puPIbfXKm2yCvxQdSxf09OIYNqQ1wQPMo8Fptq7bbHucisn91NuK1G/sdYoA+p+evq0AO7ki2h1prV6UQ99eYUyDxZUIOtMGxaRtBjFZerMDFKxxnjV1kMmj3tK93v/kVgpOwQqeER+D6Kytld9egI+fJwxUvW2zXRbDvuXxiybzytsgUct8LmeuXZcjPR5nLwcUS8wmE50Y/51c5Ey3q+9LnlR67Tc7Yxm+sSqHsY4wtiPP76bANruLYrDNFOH41SZ0dJ8HAQquRP8tvfNz2V//Uva/uC+zgyGTCs1/vXFP/v3Z53L/77dl77tNk/0Nf+/pUD5/eCB/fTSS0Zvnq+0F2KQJx+Z3KMf32vouDoAJVwQNvHckP60IHKHzNy4mTQc9fPQ0ccXT+/+RZ//9Rv9Gb8lMt86397+SwYt9e07GU/0EeZxWcKxv9wett/UvRz+9fBLSPCc9EHRbmb19FKJKSDi30PVs2nh/RefSO1pM+jMWmD5/IUf6h9R4OJLH9x5YEdYdP7ba5qMn9pw8fDaUKV4OxOPueRlqQXN0Jox6WMmJ51iJ/sOXTvWMAOQWHtMZuK7WufwzkyKmW3vy3YNHugq4Me2GIuapoMOXh7oEU/nftvqAiX5AbnXfTKuCazi+wT0QdetYUf9GyJ0sR24wziDokFrn3k5BwHTnpXz/ULeU5mMRvjrDnaE82+MnesCL7oqMdZVseDRujiJW4px6cWrB5RgwziDoJhnXP5JqGDx7LqPBYWMR0HZ3R/bJUUf6H09GkkUUmhLnQc+F5JsrDI1z0sCbYOkFRbjWBJPtYpCAQlPivNC5tfLgMWmzw9PgAs++DtUXFYEv2TxXHdBjzuOsKXFe6ArlDW92eCVRhyvytAVIN99cBPip8LHNo8GFFTVzMC6eEcPrFOG+hoVFUD8RxdgM4AqdCDy82eGVxAsQ0hvqPAclTixC/UL4cOYZwBU64Tq1tCJAUxGBZwOsrwclrNuCIqAtuEyTwXyiHAcPRB1QroVoa1aJMAvJDc69r+t1IB2L4M2bV+1kcGA6M8A9EHVAOcLqGQHILWzQrYn6AhRFcHVOL4LwsX2eOFctjr9/9YzQLOExkLk2Qccgzuv40YqIqGk2v0N5fkbS5Ek8ZmgyVxd4E35QEY6msWtavQjACilu7BjXJminFYHcj1aEx0FrKgK+bTwIJWdA/VjBDbAuP7SIiIZ5FhUBhOOXBhhnQN0iSq4twmsX4YjjR684VoQbgULw90wwR6XZTglxaXWoluZtNvRQf9zSpamhUcTXfdWBhpjWEpn/H2FPpP/8elS6AAAAAElFTkSuQmCC) +- Coding: ![](https://img.shields.io/badge/VSCode-0078D4?style=for-the-badge&logo=visual%20studio%20code&logoColor=white) +- GDD/Managing: ![](https://img.shields.io/badge/Google%20Sheets-34A853?style=for-the-badge&logo=google-sheets&logoColor=white) +- Materials: ![](https://img.shields.io/badge/Adobe%20Substance%203D%20Sampler-1E3101?style=for-the-badge&logo=data:image/svg+xml;base64,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) ![](https://img.shields.io/badge/Adobe%20Photoshop-31A8FF?style=for-the-badge&logo=Adobe%20Photoshop&logoColor=black) +- Models: ![](https://img.shields.io/badge/blender-%23F5792A.svg?style=for-the-badge&logo=blender&logoColor=white) +- Mocap: ![](https://img.shields.io/badge/Vicon%20Shōgun-000000?style=for-the-badge&logo=data:image/png;base64,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) +=- Model training: ![](https://img.shields.io/badge/TensorFlow-FF6F00?style=for-the-badge&logo=tensorflow&logoColor=white) +- Music and SFX: ![](https://img.shields.io/badge/Studio%20One-white?style=for-the-badge&logo=data:image/png;base64,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) ![](https://img.shields.io/badge/Soundly-e2e3e5?style=for-the-badge&logo=data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4gKgSUNDX1BST0ZJTEUAAQEAAAKQbGNtcwQwAABtbnRyUkdCIFhZWiAAAAAAAAAAAAAAAABhY3NwQVBQTAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA9tYAAQAAAADTLWxjbXMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAtkZXNjAAABCAAAADhjcHJ0AAABQAAAAE53dHB0AAABkAAAABRjaGFkAAABpAAAACxyWFlaAAAB0AAAABRiWFlaAAAB5AAAABRnWFlaAAAB+AAAABRyVFJDAAACDAAAACBnVFJDAAACLAAAACBiVFJDAAACTAAAACBjaHJtAAACbAAAACRtbHVjAAAAAAAAAAEAAAAMZW5VUwAAABwAAAAcAHMAUgBHAEIAIABiAHUAaQBsAHQALQBpAG4AAG1sdWMAAAAAAAAAAQAAAAxlblVTAAAAMgAAABwATgBvACAAYwBvAHAAeQByAGkAZwBoAHQALAAgAHUAcwBlACAAZgByAGUAZQBsAHkAAAAAWFlaIAAAAAAAAPbWAAEAAAAA0y1zZjMyAAAAAAABDEoAAAXj///zKgAAB5sAAP2H///7ov///aMAAAPYAADAlFhZWiAAAAAAAABvlAAAOO4AAAOQWFlaIAAAAAAAACSdAAAPgwAAtr5YWVogAAAAAAAAYqUAALeQAAAY3nBhcmEAAAAAAAMAAAACZmYAAPKnAAANWQAAE9AAAApbcGFyYQAAAAAAAwAAAAJmZgAA8qcAAA1ZAAAT0AAACltwYXJhAAAAAAADAAAAAmZmAADypwAADVkAABPQAAAKW2Nocm0AAAAAAAMAAAAAo9cAAFR7AABMzQAAmZoAACZmAAAPXP/bAEMAAwICAwICAwMDAwQDAwQFCAUFBAQFCgcHBggMCgwMCwoLCw0OEhANDhEOCwsQFhARExQVFRUMDxcYFhQYEhQVFP/bAEMBAwQEBQQFCQUFCRQNCw0UFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFP/AABEIADIAMgMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/AP0IoooqyAorB8c6pNpXgfxFqFlKEurTT7maKQYO2RI2IP4EVwX7M3irVfFnw9urrWL6W/uIdQkhWac5bYEjbBPflj1rRU24OfY5JYmMcRHD21ab+49bopFYMoZSCp5BHelrM6wooooAK4fw78VtF8cTeIrDSZLgXWlKyyNLHsDfeG5Dnpkd8H2ruK8h+G/wfvfhzqnjLU7q/t7qLUlf7OkIbcEyzZfI4PIGBn61rBQcZOW/Q4cRKvGpTVNXi783lpp+J5h8CbiWX4OfFJXkZ1FlM+GYn5jby5P1OB+VL8O5nh/ZX8bNG7I322RcqcHBW3BH4gkfjUfwF/5I98Uv+vCT/wBJ5aX4f/8AJqvjX/r+b/0G3r1p/FL/ABI+Kw7fsqT/AOndT82d98HfHNj4F/Z9sdZ1d53tbeaWMLEu9zmZgqqCQO/cgV7B4c1+08U6HY6tYMzWl5EJY967WwexHqOleDeAvAlz8Rv2ZbbRbO4itbp7qSWN587MrOTg4BI4zzg17V4A8MyeDfBmkaLNMtxLZwCN5EBCs3U4z2ya8+uoXk+t2fTZbOu404tfu+SOvn/wx0FFFFch7oUjKHUqwDKwwQRwaWigDzrXPAGh+A/hf41t9B08WSXWnXcsiq7OWbyXAGWJOB2FcR+y/odn4i+Dus6bqVuLmxutSmSSJiQGXyoe45HI6juK97ZQylWAIIwQe9Q2On2umW629nbQ2luuSsUEYRRnk4A4ro9s+Rxe7e55jwMPbwqxsoxTVraalLwz4Z03wfotvpOk2wtbG3zsjDFupJJJJJJJJ61qUUVzttu7PRjFQSjFWSCiiigoKKKKACiiigAooooAKKKKAP/Z) +- Versioning: ![](https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white) \ No newline at end of file