1_measure_files/Automatic_subj_training-test_HR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ccc04654",
   "metadata": {},
   "source": [
    "## Var name setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d887cfd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input file:  5inputs.mat\n"
     ]
    }
   ],
   "source": [
    "subjectID='3'\n",
    "noEpochs=1000\n",
    "noLSTMUnits=100\n",
    "\n",
    "# Change to true if input file 's features are sorted by time (False by default) \n",
    "orderedInput=True\n",
    "\n",
    "# Change at least one of these 2 variable to true for reading inputFeatures (Can be both)\n",
    "fourInputFeats=True\n",
    "HRInputFeats=True\n",
    "assert fourInputFeats == True or HRInputFeats==True, \"Both condition are false!! Need at least one of these 2 variables to true\"\n",
    "\n",
    "# Change number of steps according to Matlab file for testing input data\n",
    "test_noTimeSteps=2 \n",
    "\n",
    "# Change to 1 if want to export to CSV O2 precict against test (0 by default)\n",
    "exportCSV=0\n",
    "\n",
    "# Chabge to 1 if want to save the training model (0 by default)\n",
    "savedModel=0\n",
    "\n",
    "# Set seed value for uniform (NOT RECOMMEND TO CHANGE, by default 12321)\n",
    "seed_value = 12321\n",
    "\n",
    "# Getting input file name\n",
    "if orderedInput:\n",
    "    orderedPrefix = 'order-'\n",
    "    sortedPrefix = '_sored'\n",
    "    fileName='inputFeatures'+subjectID+'_sorted.mat'\n",
    "else:    \n",
    "    # Unordered input features\n",
    "    orderedPrefix = ''\n",
    "    sortedPrefix = ''\n",
    "    fileName='inputFeatures'+subjectID+'.mat'\n",
    "\n",
    "## For testinng purpose\n",
    "fileName=\"5inputs.mat\"\n",
    "print(\"Input file: \",fileName)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64db4a1",
   "metadata": {},
   "source": [
    "## Include lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f273d91",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "import scipy.io as sio \n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import random\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tensorflow.keras.models import Model\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Input, Bidirectional\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.python.client import device_lib\n",
    "from datetime import datetime as dt\n",
    "from keras import backend as K\n",
    "#from comet_ml import Experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d232b155",
   "metadata": {},
   "source": [
    "## Set seed for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d846f5ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['PYTHONHASHSEED']=str(seed_value)\n",
    "random.seed(seed_value)\n",
    "np.random.seed(seed_value)\n",
    "tf.random.set_seed(seed_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48fdef95",
   "metadata": {},
   "source": [
    "## Defilne parser function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e95b0608",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_test=[]\n",
    "\n",
    "\"\"\"\n",
    "The function take number of feature according to data \n",
    "and return a list normalized input features data\n",
    "\"\"\"\n",
    "def parse_input(noFeatures):\n",
    "    global list_test\n",
    "    print(\"Number of features: \",noFeatures)\n",
    "    if noFeatures==5:\n",
    "        print(\"Keep all data\")\n",
    "        for i in range(len(test)):\n",
    "            temp_trainData=np.transpose(np.array(test[i]))\n",
    "            list_test.append(temp_trainData)\n",
    "        for j in range(len(test2)):  \n",
    "            temp_testData=np.transpose(np.array(test2[j]))\n",
    "            list_test.append(temp_testData)\n",
    "    elif noFeatures==4:\n",
    "        print(\"Remove HR data\")\n",
    "        for i in range(len(test)):\n",
    "            temp_trainData=np.transpose(np.array(test[i]))\n",
    "            list_test.append(temp_trainData[:,:4])\n",
    "        for j in range(len(test2)):  \n",
    "            temp_testData=np.transpose(np.array(test2[j]))\n",
    "            list_test.append(temp_testData[:,:4])\n",
    "    elif noFeatures==1:\n",
    "        print(\"Remove 4 input features\")\n",
    "        for i in range(len(test)):\n",
    "            temp_trainData=np.transpose(np.array(test[i]))\n",
    "            list_test.append(temp_trainData[:,-1])\n",
    "        for j in range(len(test2)):  \n",
    "            temp_testData=np.transpose(np.array(test2[j]))\n",
    "            list_test.append(temp_testData[:,-1])\n",
    "    else:\n",
    "        # Code will runinto this part\n",
    "        print(\"Unknown number of features\")\n",
    "        \n",
    "    print(\"---------\")\n",
    "    print(np.shape(list_test))    \n",
    "    list_test=np.vstack(list_test)\n",
    "    print(np.shape(list_test))\n",
    "    scaler = StandardScaler()\n",
    "    scaler=scaler.fit(list_test)\n",
    "    scaled_list_test=scaler.transform(list_test)\n",
    "    \n",
    "    return scaled_list_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9410f08",
   "metadata": {},
   "source": [
    "## Read input from Matlab file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9be7b2e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time step now should be  2\n",
      "5 input features included: speed, speedChange, stepDuration, vertOscillation_dist_amp, heart rate\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "for device in gpu_devices:\n",
    "    tf.config.experimental.set_memory_growth(device, True)\n",
    "\n",
    "i=sio.loadmat(fileName)\n",
    "\n",
    "TTest=i['TTest']\n",
    "TTrain=i['TTrain']\n",
    "XTrain=i['XTrain']\n",
    "XTest=i['XTest']\n",
    "\n",
    "XTrain=XTrain.reshape(-1)\n",
    "TTrain=TTrain.reshape(-1)\n",
    "XTest=XTest.reshape(-1)\n",
    "TTest=TTest.reshape(-1)\n",
    "\n",
    "test=np.transpose(XTrain).reshape(-1)\n",
    "test2=np.transpose(XTest).reshape(-1)\n",
    "\n",
    "# Get number of features and number of number of time steps\n",
    "noFeatures = np.shape(test[0])[0]\n",
    "noTimeSteps = np.shape(test[0])[1]\n",
    "\n",
    "# Validate the input data's number of feature and number of time step\n",
    "assert noTimeSteps == test_noTimeSteps, \"The number of time steps does not match our configuration!! Check either of them\"\n",
    "print(\"The time step now should be \",noTimeSteps)\n",
    "\n",
    "if fourInputFeats and HRInputFeats:\n",
    "    assert noFeatures==5, \"The number of input features is not 5! Check setting again\"\n",
    "    print(\"5 input features included: speed, speedChange, stepDuration, vertOscillation_dist_amp, heart rate\")\n",
    "    confPrefix=\"withHR_\"\n",
    "elif fourInputFeats:\n",
    "    assert noFeatures==4, \"The number of input features is not 4! Check setting again\"\n",
    "    print(\"4 input features included: speed, speedChange, stepDuration, vertOscillation_dist_amp\")\n",
    "    confPrefix=\"NoHR_\"\n",
    "elif HRInputFeats:\n",
    "    assert noFeatures==1, \"The number of input features is not 1! Check setting again\"\n",
    "    print(\"1 input feture included: Heart rate only\")\n",
    "    confPrefix=\"onlyHR_\"\n",
    "else:\n",
    "    ## Both conditions are false, code will not run into this section\n",
    "    print(\"Both condition are false!! Need at least \")\n",
    "    \n",
    "# Automatically setting name of output files based on configuration    \n",
    "modelSaveName=confPrefix+'Subj-'+subjectID+'-'+str(noEpochs)+'pochs-'+orderedPrefix+str(noLSTMUnits)+'-LSTM_u.h5'\n",
    "figPredictName=confPrefix+'Subj'+subjectID+'-'+str(noEpochs)+'pochs-'+orderedPrefix+str(noLSTMUnits)+'-LSTM_u.jpg'\n",
    "figRMSEName=confPrefix+'RMSE_Subj'+subjectID+'-'+str(noEpochs)+'pochs-'+orderedPrefix+str(noLSTMUnits)+'-LSTM_u.jpg'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c78ae0",
   "metadata": {},
   "source": [
    "## Call parser function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d2a27fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features:  5\n",
      "Keep all data\n",
      "---------\n",
      "(45, 2, 5)\n",
      "(90, 5)\n"
     ]
    }
   ],
   "source": [
    "scaled_list_test = parse_input(noFeatures)\n",
    "\n",
    "# Return back input data with scaled value\n",
    "XT_s_stack=list()\n",
    "i=0\n",
    "timeSteps=noTimeSteps\n",
    "while i+timeSteps <= (len(scaled_list_test)):\n",
    "    XT_s_stack.append(scaled_list_test[i:i+timeSteps])\n",
    "    i=i+timeSteps\n",
    "\n",
    "XT_s_stack=np.stack(XT_s_stack)\n",
    "cutPoint=np.shape(TTest.reshape(-1))[0]\n",
    "\n",
    "\n",
    "XTrain_s=XT_s_stack[:-cutPoint,:,:]\n",
    "XTest_s=XT_s_stack[-cutPoint:,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55305abc",
   "metadata": {},
   "source": [
    "## Configure the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91546b25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 100)               42400     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 42,501\n",
      "Trainable params: 42,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "length = noTimeSteps\n",
    "n_features = noFeatures\n",
    "model = Sequential()\n",
    "model.add(LSTM(noLSTMUnits,input_shape=(length,n_features)))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mean_squared_logarithmic_error')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f31e4d",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a62b3fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "2/2 - 3s - loss: 3.6031 - 3s/epoch - 1s/step\n",
      "Epoch 2/1000\n",
      "2/2 - 0s - loss: 3.5307 - 11ms/epoch - 5ms/step\n",
      "Epoch 3/1000\n",
      "2/2 - 0s - loss: 3.4528 - 11ms/epoch - 5ms/step\n",
      "Epoch 4/1000\n",
      "2/2 - 0s - loss: 3.3857 - 11ms/epoch - 6ms/step\n",
      "Epoch 5/1000\n",
      "2/2 - 0s - loss: 3.3211 - 9ms/epoch - 4ms/step\n",
      "Epoch 6/1000\n",
      "2/2 - 0s - loss: 3.2577 - 10ms/epoch - 5ms/step\n",
      "Epoch 7/1000\n",
      "2/2 - 0s - loss: 3.1948 - 12ms/epoch - 6ms/step\n",
      "Epoch 8/1000\n",
      "2/2 - 0s - loss: 3.1349 - 12ms/epoch - 6ms/step\n",
      "Epoch 9/1000\n",
      "2/2 - 0s - loss: 3.0690 - 13ms/epoch - 7ms/step\n",
      "Epoch 10/1000\n",
      "2/2 - 0s - loss: 3.0053 - 12ms/epoch - 6ms/step\n",
      "Epoch 11/1000\n",
      "2/2 - 0s - loss: 2.9425 - 11ms/epoch - 5ms/step\n",
      "Epoch 12/1000\n",
      "2/2 - 0s - loss: 2.8793 - 11ms/epoch - 5ms/step\n",
      "Epoch 13/1000\n",
      "2/2 - 0s - loss: 2.8187 - 11ms/epoch - 6ms/step\n",
      "Epoch 14/1000\n",
      "2/2 - 0s - loss: 2.7599 - 9ms/epoch - 5ms/step\n",
      "Epoch 15/1000\n",
      "2/2 - 0s - loss: 2.6998 - 10ms/epoch - 5ms/step\n",
      "Epoch 16/1000\n",
      "2/2 - 0s - loss: 2.6425 - 10ms/epoch - 5ms/step\n",
      "Epoch 17/1000\n",
      "2/2 - 0s - loss: 2.5797 - 10ms/epoch - 5ms/step\n",
      "Epoch 18/1000\n",
      "2/2 - 0s - loss: 2.5214 - 10ms/epoch - 5ms/step\n",
      "Epoch 19/1000\n",
      "2/2 - 0s - loss: 2.4558 - 11ms/epoch - 5ms/step\n",
      "Epoch 20/1000\n",
      "2/2 - 0s - loss: 2.3963 - 12ms/epoch - 6ms/step\n",
      "Epoch 21/1000\n",
      "2/2 - 0s - loss: 2.3320 - 9ms/epoch - 5ms/step\n",
      "Epoch 22/1000\n",
      "2/2 - 0s - loss: 2.2695 - 9ms/epoch - 4ms/step\n",
      "Epoch 23/1000\n",
      "2/2 - 0s - loss: 2.2118 - 11ms/epoch - 5ms/step\n",
      "Epoch 24/1000\n",
      "2/2 - 0s - loss: 2.1491 - 13ms/epoch - 7ms/step\n",
      "Epoch 25/1000\n",
      "2/2 - 0s - loss: 2.0874 - 11ms/epoch - 6ms/step\n",
      "Epoch 26/1000\n",
      "2/2 - 0s - loss: 2.0287 - 9ms/epoch - 4ms/step\n",
      "Epoch 27/1000\n",
      "2/2 - 0s - loss: 1.9677 - 10ms/epoch - 5ms/step\n",
      "Epoch 28/1000\n",
      "2/2 - 0s - loss: 1.9024 - 10ms/epoch - 5ms/step\n",
      "Epoch 29/1000\n",
      "2/2 - 0s - loss: 1.8397 - 10ms/epoch - 5ms/step\n",
      "Epoch 30/1000\n",
      "2/2 - 0s - loss: 1.7752 - 11ms/epoch - 5ms/step\n",
      "Epoch 31/1000\n",
      "2/2 - 0s - loss: 1.7133 - 11ms/epoch - 5ms/step\n",
      "Epoch 32/1000\n",
      "2/2 - 0s - loss: 1.6491 - 12ms/epoch - 6ms/step\n",
      "Epoch 33/1000\n",
      "2/2 - 0s - loss: 1.5894 - 11ms/epoch - 5ms/step\n",
      "Epoch 34/1000\n",
      "2/2 - 0s - loss: 1.5285 - 10ms/epoch - 5ms/step\n",
      "Epoch 35/1000\n",
      "2/2 - 0s - loss: 1.4711 - 11ms/epoch - 5ms/step\n",
      "Epoch 36/1000\n",
      "2/2 - 0s - loss: 1.4154 - 11ms/epoch - 6ms/step\n",
      "Epoch 37/1000\n",
      "2/2 - 0s - loss: 1.3577 - 10ms/epoch - 5ms/step\n",
      "Epoch 38/1000\n",
      "2/2 - 0s - loss: 1.3032 - 11ms/epoch - 5ms/step\n",
      "Epoch 39/1000\n",
      "2/2 - 0s - loss: 1.2520 - 11ms/epoch - 6ms/step\n",
      "Epoch 40/1000\n",
      "2/2 - 0s - loss: 1.2007 - 11ms/epoch - 5ms/step\n",
      "Epoch 41/1000\n",
      "2/2 - 0s - loss: 1.1519 - 10ms/epoch - 5ms/step\n",
      "Epoch 42/1000\n",
      "2/2 - 0s - loss: 1.1060 - 14ms/epoch - 7ms/step\n",
      "Epoch 43/1000\n",
      "2/2 - 0s - loss: 1.0586 - 14ms/epoch - 7ms/step\n",
      "Epoch 44/1000\n",
      "2/2 - 0s - loss: 1.0115 - 13ms/epoch - 6ms/step\n",
      "Epoch 45/1000\n",
      "2/2 - 0s - loss: 0.9610 - 12ms/epoch - 6ms/step\n",
      "Epoch 46/1000\n",
      "2/2 - 0s - loss: 0.9102 - 10ms/epoch - 5ms/step\n",
      "Epoch 47/1000\n",
      "2/2 - 0s - loss: 0.8552 - 11ms/epoch - 5ms/step\n",
      "Epoch 48/1000\n",
      "2/2 - 0s - loss: 0.8016 - 11ms/epoch - 5ms/step\n",
      "Epoch 49/1000\n",
      "2/2 - 0s - loss: 0.7489 - 13ms/epoch - 6ms/step\n",
      "Epoch 50/1000\n",
      "2/2 - 0s - loss: 0.7001 - 10ms/epoch - 5ms/step\n",
      "Epoch 51/1000\n",
      "2/2 - 0s - loss: 0.6491 - 12ms/epoch - 6ms/step\n",
      "Epoch 52/1000\n",
      "2/2 - 0s - loss: 0.5982 - 11ms/epoch - 5ms/step\n",
      "Epoch 53/1000\n",
      "2/2 - 0s - loss: 0.5494 - 12ms/epoch - 6ms/step\n",
      "Epoch 54/1000\n",
      "2/2 - 0s - loss: 0.5025 - 12ms/epoch - 6ms/step\n",
      "Epoch 55/1000\n",
      "2/2 - 0s - loss: 0.4621 - 13ms/epoch - 7ms/step\n",
      "Epoch 56/1000\n",
      "2/2 - 0s - loss: 0.4270 - 13ms/epoch - 7ms/step\n",
      "Epoch 57/1000\n",
      "2/2 - 0s - loss: 0.3969 - 12ms/epoch - 6ms/step\n",
      "Epoch 58/1000\n",
      "2/2 - 0s - loss: 0.3662 - 11ms/epoch - 5ms/step\n",
      "Epoch 59/1000\n",
      "2/2 - 0s - loss: 0.3379 - 12ms/epoch - 6ms/step\n",
      "Epoch 60/1000\n",
      "2/2 - 0s - loss: 0.3116 - 15ms/epoch - 7ms/step\n",
      "Epoch 61/1000\n",
      "2/2 - 0s - loss: 0.2894 - 12ms/epoch - 6ms/step\n",
      "Epoch 62/1000\n",
      "2/2 - 0s - loss: 0.2683 - 11ms/epoch - 6ms/step\n",
      "Epoch 63/1000\n",
      "2/2 - 0s - loss: 0.2506 - 11ms/epoch - 5ms/step\n",
      "Epoch 64/1000\n",
      "2/2 - 0s - loss: 0.2341 - 12ms/epoch - 6ms/step\n",
      "Epoch 65/1000\n",
      "2/2 - 0s - loss: 0.2204 - 11ms/epoch - 6ms/step\n",
      "Epoch 66/1000\n",
      "2/2 - 0s - loss: 0.2083 - 12ms/epoch - 6ms/step\n",
      "Epoch 67/1000\n",
      "2/2 - 0s - loss: 0.1973 - 11ms/epoch - 5ms/step\n",
      "Epoch 68/1000\n",
      "2/2 - 0s - loss: 0.1884 - 27ms/epoch - 13ms/step\n",
      "Epoch 69/1000\n",
      "2/2 - 0s - loss: 0.1811 - 15ms/epoch - 7ms/step\n",
      "Epoch 70/1000\n",
      "2/2 - 0s - loss: 0.1746 - 12ms/epoch - 6ms/step\n",
      "Epoch 71/1000\n",
      "2/2 - 0s - loss: 0.1685 - 11ms/epoch - 6ms/step\n",
      "Epoch 72/1000\n",
      "2/2 - 0s - loss: 0.1639 - 10ms/epoch - 5ms/step\n",
      "Epoch 73/1000\n",
      "2/2 - 0s - loss: 0.1599 - 11ms/epoch - 5ms/step\n",
      "Epoch 74/1000\n",
      "2/2 - 0s - loss: 0.1561 - 14ms/epoch - 7ms/step\n",
      "Epoch 75/1000\n",
      "2/2 - 0s - loss: 0.1530 - 13ms/epoch - 6ms/step\n",
      "Epoch 76/1000\n",
      "2/2 - 0s - loss: 0.1499 - 13ms/epoch - 6ms/step\n",
      "Epoch 77/1000\n",
      "2/2 - 0s - loss: 0.1473 - 12ms/epoch - 6ms/step\n",
      "Epoch 78/1000\n",
      "2/2 - 0s - loss: 0.1448 - 12ms/epoch - 6ms/step\n",
      "Epoch 79/1000\n",
      "2/2 - 0s - loss: 0.1427 - 12ms/epoch - 6ms/step\n",
      "Epoch 80/1000\n",
      "2/2 - 0s - loss: 0.1407 - 11ms/epoch - 6ms/step\n",
      "Epoch 81/1000\n",
      "2/2 - 0s - loss: 0.1391 - 12ms/epoch - 6ms/step\n",
      "Epoch 82/1000\n",
      "2/2 - 0s - loss: 0.1377 - 11ms/epoch - 6ms/step\n",
      "Epoch 83/1000\n",
      "2/2 - 0s - loss: 0.1363 - 11ms/epoch - 5ms/step\n",
      "Epoch 84/1000\n",
      "2/2 - 0s - loss: 0.1351 - 13ms/epoch - 6ms/step\n",
      "Epoch 85/1000\n",
      "2/2 - 0s - loss: 0.1341 - 14ms/epoch - 7ms/step\n",
      "Epoch 86/1000\n",
      "2/2 - 0s - loss: 0.1331 - 12ms/epoch - 6ms/step\n",
      "Epoch 87/1000\n",
      "2/2 - 0s - loss: 0.1322 - 11ms/epoch - 5ms/step\n",
      "Epoch 88/1000\n",
      "2/2 - 0s - loss: 0.1315 - 10ms/epoch - 5ms/step\n",
      "Epoch 89/1000\n",
      "2/2 - 0s - loss: 0.1306 - 11ms/epoch - 5ms/step\n",
      "Epoch 90/1000\n",
      "2/2 - 0s - loss: 0.1299 - 11ms/epoch - 5ms/step\n",
      "Epoch 91/1000\n",
      "2/2 - 0s - loss: 0.1293 - 14ms/epoch - 7ms/step\n",
      "Epoch 92/1000\n",
      "2/2 - 0s - loss: 0.1285 - 10ms/epoch - 5ms/step\n",
      "Epoch 93/1000\n",
      "2/2 - 0s - loss: 0.1280 - 12ms/epoch - 6ms/step\n",
      "Epoch 94/1000\n",
      "2/2 - 0s - loss: 0.1274 - 11ms/epoch - 5ms/step\n",
      "Epoch 95/1000\n",
      "2/2 - 0s - loss: 0.1268 - 12ms/epoch - 6ms/step\n",
      "Epoch 96/1000\n",
      "2/2 - 0s - loss: 0.1262 - 11ms/epoch - 6ms/step\n",
      "Epoch 97/1000\n",
      "2/2 - 0s - loss: 0.1256 - 12ms/epoch - 6ms/step\n",
      "Epoch 98/1000\n",
      "2/2 - 0s - loss: 0.1251 - 13ms/epoch - 7ms/step\n",
      "Epoch 99/1000\n",
      "2/2 - 0s - loss: 0.1245 - 11ms/epoch - 5ms/step\n",
      "Epoch 100/1000\n",
      "2/2 - 0s - loss: 0.1241 - 12ms/epoch - 6ms/step\n",
      "Epoch 101/1000\n",
      "2/2 - 0s - loss: 0.1237 - 11ms/epoch - 5ms/step\n",
      "Epoch 102/1000\n",
      "2/2 - 0s - loss: 0.1233 - 10ms/epoch - 5ms/step\n",
      "Epoch 103/1000\n",
      "2/2 - 0s - loss: 0.1230 - 12ms/epoch - 6ms/step\n",
      "Epoch 104/1000\n",
      "2/2 - 0s - loss: 0.1224 - 13ms/epoch - 6ms/step\n",
      "Epoch 105/1000\n",
      "2/2 - 0s - loss: 0.1218 - 11ms/epoch - 5ms/step\n",
      "Epoch 106/1000\n",
      "2/2 - 0s - loss: 0.1214 - 15ms/epoch - 7ms/step\n",
      "Epoch 107/1000\n",
      "2/2 - 0s - loss: 0.1210 - 12ms/epoch - 6ms/step\n",
      "Epoch 108/1000\n",
      "2/2 - 0s - loss: 0.1206 - 12ms/epoch - 6ms/step\n",
      "Epoch 109/1000\n",
      "2/2 - 0s - loss: 0.1203 - 11ms/epoch - 5ms/step\n",
      "Epoch 110/1000\n",
      "2/2 - 0s - loss: 0.1200 - 11ms/epoch - 5ms/step\n",
      "Epoch 111/1000\n",
      "2/2 - 0s - loss: 0.1197 - 10ms/epoch - 5ms/step\n",
      "Epoch 112/1000\n",
      "2/2 - 0s - loss: 0.1195 - 11ms/epoch - 5ms/step\n",
      "Epoch 113/1000\n",
      "2/2 - 0s - loss: 0.1192 - 12ms/epoch - 6ms/step\n",
      "Epoch 114/1000\n",
      "2/2 - 0s - loss: 0.1190 - 12ms/epoch - 6ms/step\n",
      "Epoch 115/1000\n",
      "2/2 - 0s - loss: 0.1187 - 12ms/epoch - 6ms/step\n",
      "Epoch 116/1000\n",
      "2/2 - 0s - loss: 0.1186 - 12ms/epoch - 6ms/step\n",
      "Epoch 117/1000\n",
      "2/2 - 0s - loss: 0.1183 - 10ms/epoch - 5ms/step\n",
      "Epoch 118/1000\n",
      "2/2 - 0s - loss: 0.1181 - 12ms/epoch - 6ms/step\n",
      "Epoch 119/1000\n",
      "2/2 - 0s - loss: 0.1180 - 10ms/epoch - 5ms/step\n",
      "Epoch 120/1000\n",
      "2/2 - 0s - loss: 0.1178 - 12ms/epoch - 6ms/step\n",
      "Epoch 121/1000\n",
      "2/2 - 0s - loss: 0.1176 - 12ms/epoch - 6ms/step\n",
      "Epoch 122/1000\n",
      "2/2 - 0s - loss: 0.1174 - 12ms/epoch - 6ms/step\n",
      "Epoch 123/1000\n",
      "2/2 - 0s - loss: 0.1173 - 10ms/epoch - 5ms/step\n",
      "Epoch 124/1000\n",
      "2/2 - 0s - loss: 0.1172 - 12ms/epoch - 6ms/step\n",
      "Epoch 125/1000\n",
      "2/2 - 0s - loss: 0.1171 - 13ms/epoch - 6ms/step\n",
      "Epoch 126/1000\n",
      "2/2 - 0s - loss: 0.1169 - 10ms/epoch - 5ms/step\n",
      "Epoch 127/1000\n",
      "2/2 - 0s - loss: 0.1168 - 11ms/epoch - 6ms/step\n",
      "Epoch 128/1000\n",
      "2/2 - 0s - loss: 0.1167 - 14ms/epoch - 7ms/step\n",
      "Epoch 129/1000\n",
      "2/2 - 0s - loss: 0.1165 - 11ms/epoch - 5ms/step\n",
      "Epoch 130/1000\n",
      "2/2 - 0s - loss: 0.1163 - 10ms/epoch - 5ms/step\n",
      "Epoch 131/1000\n",
      "2/2 - 0s - loss: 0.1163 - 12ms/epoch - 6ms/step\n",
      "Epoch 132/1000\n",
      "2/2 - 0s - loss: 0.1161 - 12ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 133/1000\n",
      "2/2 - 0s - loss: 0.1159 - 12ms/epoch - 6ms/step\n",
      "Epoch 134/1000\n",
      "2/2 - 0s - loss: 0.1158 - 12ms/epoch - 6ms/step\n",
      "Epoch 135/1000\n",
      "2/2 - 0s - loss: 0.1157 - 11ms/epoch - 5ms/step\n",
      "Epoch 136/1000\n",
      "2/2 - 0s - loss: 0.1157 - 12ms/epoch - 6ms/step\n",
      "Epoch 137/1000\n",
      "2/2 - 0s - loss: 0.1157 - 11ms/epoch - 6ms/step\n",
      "Epoch 138/1000\n",
      "2/2 - 0s - loss: 0.1156 - 9ms/epoch - 4ms/step\n",
      "Epoch 139/1000\n",
      "2/2 - 0s - loss: 0.1157 - 10ms/epoch - 5ms/step\n",
      "Epoch 140/1000\n",
      "2/2 - 0s - loss: 0.1156 - 11ms/epoch - 6ms/step\n",
      "Epoch 141/1000\n",
      "2/2 - 0s - loss: 0.1156 - 10ms/epoch - 5ms/step\n",
      "Epoch 142/1000\n",
      "2/2 - 0s - loss: 0.1156 - 11ms/epoch - 5ms/step\n",
      "Epoch 143/1000\n",
      "2/2 - 0s - loss: 0.1155 - 13ms/epoch - 6ms/step\n",
      "Epoch 144/1000\n",
      "2/2 - 0s - loss: 0.1154 - 11ms/epoch - 5ms/step\n",
      "Epoch 145/1000\n",
      "2/2 - 0s - loss: 0.1153 - 9ms/epoch - 5ms/step\n",
      "Epoch 146/1000\n",
      "2/2 - 0s - loss: 0.1153 - 10ms/epoch - 5ms/step\n",
      "Epoch 147/1000\n",
      "2/2 - 0s - loss: 0.1152 - 11ms/epoch - 5ms/step\n",
      "Epoch 148/1000\n",
      "2/2 - 0s - loss: 0.1151 - 12ms/epoch - 6ms/step\n",
      "Epoch 149/1000\n",
      "2/2 - 0s - loss: 0.1149 - 11ms/epoch - 6ms/step\n",
      "Epoch 150/1000\n",
      "2/2 - 0s - loss: 0.1148 - 11ms/epoch - 5ms/step\n",
      "Epoch 151/1000\n",
      "2/2 - 0s - loss: 0.1146 - 11ms/epoch - 5ms/step\n",
      "Epoch 152/1000\n",
      "2/2 - 0s - loss: 0.1144 - 11ms/epoch - 5ms/step\n",
      "Epoch 153/1000\n",
      "2/2 - 0s - loss: 0.1142 - 10ms/epoch - 5ms/step\n",
      "Epoch 154/1000\n",
      "2/2 - 0s - loss: 0.1140 - 11ms/epoch - 5ms/step\n",
      "Epoch 155/1000\n",
      "2/2 - 0s - loss: 0.1139 - 13ms/epoch - 6ms/step\n",
      "Epoch 156/1000\n",
      "2/2 - 0s - loss: 0.1137 - 12ms/epoch - 6ms/step\n",
      "Epoch 157/1000\n",
      "2/2 - 0s - loss: 0.1137 - 11ms/epoch - 5ms/step\n",
      "Epoch 158/1000\n",
      "2/2 - 0s - loss: 0.1135 - 9ms/epoch - 4ms/step\n",
      "Epoch 159/1000\n",
      "2/2 - 0s - loss: 0.1133 - 12ms/epoch - 6ms/step\n",
      "Epoch 160/1000\n",
      "2/2 - 0s - loss: 0.1133 - 12ms/epoch - 6ms/step\n",
      "Epoch 161/1000\n",
      "2/2 - 0s - loss: 0.1131 - 12ms/epoch - 6ms/step\n",
      "Epoch 162/1000\n",
      "2/2 - 0s - loss: 0.1129 - 11ms/epoch - 6ms/step\n",
      "Epoch 163/1000\n",
      "2/2 - 0s - loss: 0.1129 - 10ms/epoch - 5ms/step\n",
      "Epoch 164/1000\n",
      "2/2 - 0s - loss: 0.1127 - 11ms/epoch - 5ms/step\n",
      "Epoch 165/1000\n",
      "2/2 - 0s - loss: 0.1126 - 12ms/epoch - 6ms/step\n",
      "Epoch 166/1000\n",
      "2/2 - 0s - loss: 0.1125 - 11ms/epoch - 6ms/step\n",
      "Epoch 167/1000\n",
      "2/2 - 0s - loss: 0.1124 - 11ms/epoch - 5ms/step\n",
      "Epoch 168/1000\n",
      "2/2 - 0s - loss: 0.1124 - 12ms/epoch - 6ms/step\n",
      "Epoch 169/1000\n",
      "2/2 - 0s - loss: 0.1123 - 12ms/epoch - 6ms/step\n",
      "Epoch 170/1000\n",
      "2/2 - 0s - loss: 0.1124 - 12ms/epoch - 6ms/step\n",
      "Epoch 171/1000\n",
      "2/2 - 0s - loss: 0.1123 - 12ms/epoch - 6ms/step\n",
      "Epoch 172/1000\n",
      "2/2 - 0s - loss: 0.1122 - 10ms/epoch - 5ms/step\n",
      "Epoch 173/1000\n",
      "2/2 - 0s - loss: 0.1121 - 10ms/epoch - 5ms/step\n",
      "Epoch 174/1000\n",
      "2/2 - 0s - loss: 0.1120 - 11ms/epoch - 5ms/step\n",
      "Epoch 175/1000\n",
      "2/2 - 0s - loss: 0.1118 - 13ms/epoch - 7ms/step\n",
      "Epoch 176/1000\n",
      "2/2 - 0s - loss: 0.1117 - 11ms/epoch - 5ms/step\n",
      "Epoch 177/1000\n",
      "2/2 - 0s - loss: 0.1116 - 11ms/epoch - 5ms/step\n",
      "Epoch 178/1000\n",
      "2/2 - 0s - loss: 0.1115 - 11ms/epoch - 6ms/step\n",
      "Epoch 179/1000\n",
      "2/2 - 0s - loss: 0.1114 - 11ms/epoch - 6ms/step\n",
      "Epoch 180/1000\n",
      "2/2 - 0s - loss: 0.1114 - 10ms/epoch - 5ms/step\n",
      "Epoch 181/1000\n",
      "2/2 - 0s - loss: 0.1114 - 11ms/epoch - 6ms/step\n",
      "Epoch 182/1000\n",
      "2/2 - 0s - loss: 0.1113 - 10ms/epoch - 5ms/step\n",
      "Epoch 183/1000\n",
      "2/2 - 0s - loss: 0.1112 - 11ms/epoch - 5ms/step\n",
      "Epoch 184/1000\n",
      "2/2 - 0s - loss: 0.1111 - 13ms/epoch - 6ms/step\n",
      "Epoch 185/1000\n",
      "2/2 - 0s - loss: 0.1111 - 12ms/epoch - 6ms/step\n",
      "Epoch 186/1000\n",
      "2/2 - 0s - loss: 0.1110 - 11ms/epoch - 5ms/step\n",
      "Epoch 187/1000\n",
      "2/2 - 0s - loss: 0.1110 - 15ms/epoch - 7ms/step\n",
      "Epoch 188/1000\n",
      "2/2 - 0s - loss: 0.1109 - 12ms/epoch - 6ms/step\n",
      "Epoch 189/1000\n",
      "2/2 - 0s - loss: 0.1108 - 14ms/epoch - 7ms/step\n",
      "Epoch 190/1000\n",
      "2/2 - 0s - loss: 0.1108 - 12ms/epoch - 6ms/step\n",
      "Epoch 191/1000\n",
      "2/2 - 0s - loss: 0.1107 - 11ms/epoch - 5ms/step\n",
      "Epoch 192/1000\n",
      "2/2 - 0s - loss: 0.1107 - 13ms/epoch - 6ms/step\n",
      "Epoch 193/1000\n",
      "2/2 - 0s - loss: 0.1106 - 12ms/epoch - 6ms/step\n",
      "Epoch 194/1000\n",
      "2/2 - 0s - loss: 0.1106 - 13ms/epoch - 6ms/step\n",
      "Epoch 195/1000\n",
      "2/2 - 0s - loss: 0.1106 - 12ms/epoch - 6ms/step\n",
      "Epoch 196/1000\n",
      "2/2 - 0s - loss: 0.1105 - 14ms/epoch - 7ms/step\n",
      "Epoch 197/1000\n",
      "2/2 - 0s - loss: 0.1105 - 12ms/epoch - 6ms/step\n",
      "Epoch 198/1000\n",
      "2/2 - 0s - loss: 0.1104 - 13ms/epoch - 6ms/step\n",
      "Epoch 199/1000\n",
      "2/2 - 0s - loss: 0.1104 - 13ms/epoch - 6ms/step\n",
      "Epoch 200/1000\n",
      "2/2 - 0s - loss: 0.1103 - 14ms/epoch - 7ms/step\n",
      "Epoch 201/1000\n",
      "2/2 - 0s - loss: 0.1103 - 12ms/epoch - 6ms/step\n",
      "Epoch 202/1000\n",
      "2/2 - 0s - loss: 0.1102 - 13ms/epoch - 7ms/step\n",
      "Epoch 203/1000\n",
      "2/2 - 0s - loss: 0.1101 - 12ms/epoch - 6ms/step\n",
      "Epoch 204/1000\n",
      "2/2 - 0s - loss: 0.1100 - 11ms/epoch - 5ms/step\n",
      "Epoch 205/1000\n",
      "2/2 - 0s - loss: 0.1100 - 11ms/epoch - 5ms/step\n",
      "Epoch 206/1000\n",
      "2/2 - 0s - loss: 0.1099 - 12ms/epoch - 6ms/step\n",
      "Epoch 207/1000\n",
      "2/2 - 0s - loss: 0.1098 - 12ms/epoch - 6ms/step\n",
      "Epoch 208/1000\n",
      "2/2 - 0s - loss: 0.1098 - 12ms/epoch - 6ms/step\n",
      "Epoch 209/1000\n",
      "2/2 - 0s - loss: 0.1097 - 13ms/epoch - 7ms/step\n",
      "Epoch 210/1000\n",
      "2/2 - 0s - loss: 0.1097 - 12ms/epoch - 6ms/step\n",
      "Epoch 211/1000\n",
      "2/2 - 0s - loss: 0.1097 - 11ms/epoch - 5ms/step\n",
      "Epoch 212/1000\n",
      "2/2 - 0s - loss: 0.1096 - 10ms/epoch - 5ms/step\n",
      "Epoch 213/1000\n",
      "2/2 - 0s - loss: 0.1096 - 12ms/epoch - 6ms/step\n",
      "Epoch 214/1000\n",
      "2/2 - 0s - loss: 0.1096 - 10ms/epoch - 5ms/step\n",
      "Epoch 215/1000\n",
      "2/2 - 0s - loss: 0.1095 - 11ms/epoch - 5ms/step\n",
      "Epoch 216/1000\n",
      "2/2 - 0s - loss: 0.1094 - 12ms/epoch - 6ms/step\n",
      "Epoch 217/1000\n",
      "2/2 - 0s - loss: 0.1096 - 10ms/epoch - 5ms/step\n",
      "Epoch 218/1000\n",
      "2/2 - 0s - loss: 0.1096 - 10ms/epoch - 5ms/step\n",
      "Epoch 219/1000\n",
      "2/2 - 0s - loss: 0.1095 - 13ms/epoch - 7ms/step\n",
      "Epoch 220/1000\n",
      "2/2 - 0s - loss: 0.1094 - 12ms/epoch - 6ms/step\n",
      "Epoch 221/1000\n",
      "2/2 - 0s - loss: 0.1093 - 11ms/epoch - 6ms/step\n",
      "Epoch 222/1000\n",
      "2/2 - 0s - loss: 0.1093 - 12ms/epoch - 6ms/step\n",
      "Epoch 223/1000\n",
      "2/2 - 0s - loss: 0.1092 - 9ms/epoch - 4ms/step\n",
      "Epoch 224/1000\n",
      "2/2 - 0s - loss: 0.1091 - 14ms/epoch - 7ms/step\n",
      "Epoch 225/1000\n",
      "2/2 - 0s - loss: 0.1090 - 14ms/epoch - 7ms/step\n",
      "Epoch 226/1000\n",
      "2/2 - 0s - loss: 0.1090 - 12ms/epoch - 6ms/step\n",
      "Epoch 227/1000\n",
      "2/2 - 0s - loss: 0.1089 - 13ms/epoch - 7ms/step\n",
      "Epoch 228/1000\n",
      "2/2 - 0s - loss: 0.1088 - 14ms/epoch - 7ms/step\n",
      "Epoch 229/1000\n",
      "2/2 - 0s - loss: 0.1087 - 12ms/epoch - 6ms/step\n",
      "Epoch 230/1000\n",
      "2/2 - 0s - loss: 0.1087 - 12ms/epoch - 6ms/step\n",
      "Epoch 231/1000\n",
      "2/2 - 0s - loss: 0.1086 - 11ms/epoch - 6ms/step\n",
      "Epoch 232/1000\n",
      "2/2 - 0s - loss: 0.1086 - 12ms/epoch - 6ms/step\n",
      "Epoch 233/1000\n",
      "2/2 - 0s - loss: 0.1085 - 12ms/epoch - 6ms/step\n",
      "Epoch 234/1000\n",
      "2/2 - 0s - loss: 0.1085 - 21ms/epoch - 11ms/step\n",
      "Epoch 235/1000\n",
      "2/2 - 0s - loss: 0.1084 - 13ms/epoch - 7ms/step\n",
      "Epoch 236/1000\n",
      "2/2 - 0s - loss: 0.1083 - 12ms/epoch - 6ms/step\n",
      "Epoch 237/1000\n",
      "2/2 - 0s - loss: 0.1083 - 12ms/epoch - 6ms/step\n",
      "Epoch 238/1000\n",
      "2/2 - 0s - loss: 0.1082 - 13ms/epoch - 6ms/step\n",
      "Epoch 239/1000\n",
      "2/2 - 0s - loss: 0.1082 - 12ms/epoch - 6ms/step\n",
      "Epoch 240/1000\n",
      "2/2 - 0s - loss: 0.1083 - 12ms/epoch - 6ms/step\n",
      "Epoch 241/1000\n",
      "2/2 - 0s - loss: 0.1084 - 13ms/epoch - 7ms/step\n",
      "Epoch 242/1000\n",
      "2/2 - 0s - loss: 0.1083 - 11ms/epoch - 5ms/step\n",
      "Epoch 243/1000\n",
      "2/2 - 0s - loss: 0.1085 - 11ms/epoch - 5ms/step\n",
      "Epoch 244/1000\n",
      "2/2 - 0s - loss: 0.1086 - 13ms/epoch - 7ms/step\n",
      "Epoch 245/1000\n",
      "2/2 - 0s - loss: 0.1088 - 15ms/epoch - 7ms/step\n",
      "Epoch 246/1000\n",
      "2/2 - 0s - loss: 0.1088 - 12ms/epoch - 6ms/step\n",
      "Epoch 247/1000\n",
      "2/2 - 0s - loss: 0.1088 - 12ms/epoch - 6ms/step\n",
      "Epoch 248/1000\n",
      "2/2 - 0s - loss: 0.1087 - 11ms/epoch - 5ms/step\n",
      "Epoch 249/1000\n",
      "2/2 - 0s - loss: 0.1085 - 12ms/epoch - 6ms/step\n",
      "Epoch 250/1000\n",
      "2/2 - 0s - loss: 0.1082 - 11ms/epoch - 6ms/step\n",
      "Epoch 251/1000\n",
      "2/2 - 0s - loss: 0.1080 - 10ms/epoch - 5ms/step\n",
      "Epoch 252/1000\n",
      "2/2 - 0s - loss: 0.1079 - 10ms/epoch - 5ms/step\n",
      "Epoch 253/1000\n",
      "2/2 - 0s - loss: 0.1077 - 11ms/epoch - 6ms/step\n",
      "Epoch 254/1000\n",
      "2/2 - 0s - loss: 0.1076 - 11ms/epoch - 5ms/step\n",
      "Epoch 255/1000\n",
      "2/2 - 0s - loss: 0.1075 - 13ms/epoch - 6ms/step\n",
      "Epoch 256/1000\n",
      "2/2 - 0s - loss: 0.1075 - 11ms/epoch - 5ms/step\n",
      "Epoch 257/1000\n",
      "2/2 - 0s - loss: 0.1073 - 15ms/epoch - 7ms/step\n",
      "Epoch 258/1000\n",
      "2/2 - 0s - loss: 0.1073 - 14ms/epoch - 7ms/step\n",
      "Epoch 259/1000\n",
      "2/2 - 0s - loss: 0.1073 - 13ms/epoch - 6ms/step\n",
      "Epoch 260/1000\n",
      "2/2 - 0s - loss: 0.1073 - 14ms/epoch - 7ms/step\n",
      "Epoch 261/1000\n",
      "2/2 - 0s - loss: 0.1072 - 15ms/epoch - 8ms/step\n",
      "Epoch 262/1000\n",
      "2/2 - 0s - loss: 0.1072 - 14ms/epoch - 7ms/step\n",
      "Epoch 263/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 - 0s - loss: 0.1071 - 14ms/epoch - 7ms/step\n",
      "Epoch 264/1000\n",
      "2/2 - 0s - loss: 0.1070 - 14ms/epoch - 7ms/step\n",
      "Epoch 265/1000\n",
      "2/2 - 0s - loss: 0.1070 - 14ms/epoch - 7ms/step\n",
      "Epoch 266/1000\n",
      "2/2 - 0s - loss: 0.1069 - 11ms/epoch - 5ms/step\n",
      "Epoch 267/1000\n",
      "2/2 - 0s - loss: 0.1068 - 11ms/epoch - 6ms/step\n",
      "Epoch 268/1000\n",
      "2/2 - 0s - loss: 0.1069 - 13ms/epoch - 7ms/step\n",
      "Epoch 269/1000\n",
      "2/2 - 0s - loss: 0.1068 - 12ms/epoch - 6ms/step\n",
      "Epoch 270/1000\n",
      "2/2 - 0s - loss: 0.1068 - 11ms/epoch - 5ms/step\n",
      "Epoch 271/1000\n",
      "2/2 - 0s - loss: 0.1067 - 13ms/epoch - 7ms/step\n",
      "Epoch 272/1000\n",
      "2/2 - 0s - loss: 0.1066 - 10ms/epoch - 5ms/step\n",
      "Epoch 273/1000\n",
      "2/2 - 0s - loss: 0.1066 - 12ms/epoch - 6ms/step\n",
      "Epoch 274/1000\n",
      "2/2 - 0s - loss: 0.1065 - 11ms/epoch - 5ms/step\n",
      "Epoch 275/1000\n",
      "2/2 - 0s - loss: 0.1065 - 12ms/epoch - 6ms/step\n",
      "Epoch 276/1000\n",
      "2/2 - 0s - loss: 0.1064 - 15ms/epoch - 8ms/step\n",
      "Epoch 277/1000\n",
      "2/2 - 0s - loss: 0.1065 - 11ms/epoch - 6ms/step\n",
      "Epoch 278/1000\n",
      "2/2 - 0s - loss: 0.1065 - 10ms/epoch - 5ms/step\n",
      "Epoch 279/1000\n",
      "2/2 - 0s - loss: 0.1066 - 11ms/epoch - 5ms/step\n",
      "Epoch 280/1000\n",
      "2/2 - 0s - loss: 0.1066 - 13ms/epoch - 6ms/step\n",
      "Epoch 281/1000\n",
      "2/2 - 0s - loss: 0.1068 - 13ms/epoch - 6ms/step\n",
      "Epoch 282/1000\n",
      "2/2 - 0s - loss: 0.1069 - 11ms/epoch - 6ms/step\n",
      "Epoch 283/1000\n",
      "2/2 - 0s - loss: 0.1070 - 13ms/epoch - 6ms/step\n",
      "Epoch 284/1000\n",
      "2/2 - 0s - loss: 0.1071 - 11ms/epoch - 5ms/step\n",
      "Epoch 285/1000\n",
      "2/2 - 0s - loss: 0.1072 - 12ms/epoch - 6ms/step\n",
      "Epoch 286/1000\n",
      "2/2 - 0s - loss: 0.1072 - 12ms/epoch - 6ms/step\n",
      "Epoch 287/1000\n",
      "2/2 - 0s - loss: 0.1072 - 14ms/epoch - 7ms/step\n",
      "Epoch 288/1000\n",
      "2/2 - 0s - loss: 0.1071 - 12ms/epoch - 6ms/step\n",
      "Epoch 289/1000\n",
      "2/2 - 0s - loss: 0.1069 - 12ms/epoch - 6ms/step\n",
      "Epoch 290/1000\n",
      "2/2 - 0s - loss: 0.1068 - 12ms/epoch - 6ms/step\n",
      "Epoch 291/1000\n",
      "2/2 - 0s - loss: 0.1066 - 13ms/epoch - 6ms/step\n",
      "Epoch 292/1000\n",
      "2/2 - 0s - loss: 0.1065 - 12ms/epoch - 6ms/step\n",
      "Epoch 293/1000\n",
      "2/2 - 0s - loss: 0.1063 - 11ms/epoch - 6ms/step\n",
      "Epoch 294/1000\n",
      "2/2 - 0s - loss: 0.1061 - 13ms/epoch - 6ms/step\n",
      "Epoch 295/1000\n",
      "2/2 - 0s - loss: 0.1060 - 14ms/epoch - 7ms/step\n",
      "Epoch 296/1000\n",
      "2/2 - 0s - loss: 0.1059 - 12ms/epoch - 6ms/step\n",
      "Epoch 297/1000\n",
      "2/2 - 0s - loss: 0.1058 - 12ms/epoch - 6ms/step\n",
      "Epoch 298/1000\n",
      "2/2 - 0s - loss: 0.1057 - 12ms/epoch - 6ms/step\n",
      "Epoch 299/1000\n",
      "2/2 - 0s - loss: 0.1057 - 10ms/epoch - 5ms/step\n",
      "Epoch 300/1000\n",
      "2/2 - 0s - loss: 0.1056 - 10ms/epoch - 5ms/step\n",
      "Epoch 301/1000\n",
      "2/2 - 0s - loss: 0.1056 - 12ms/epoch - 6ms/step\n",
      "Epoch 302/1000\n",
      "2/2 - 0s - loss: 0.1056 - 12ms/epoch - 6ms/step\n",
      "Epoch 303/1000\n",
      "2/2 - 0s - loss: 0.1056 - 10ms/epoch - 5ms/step\n",
      "Epoch 304/1000\n",
      "2/2 - 0s - loss: 0.1055 - 11ms/epoch - 5ms/step\n",
      "Epoch 305/1000\n",
      "2/2 - 0s - loss: 0.1055 - 12ms/epoch - 6ms/step\n",
      "Epoch 306/1000\n",
      "2/2 - 0s - loss: 0.1055 - 11ms/epoch - 6ms/step\n",
      "Epoch 307/1000\n",
      "2/2 - 0s - loss: 0.1054 - 11ms/epoch - 6ms/step\n",
      "Epoch 308/1000\n",
      "2/2 - 0s - loss: 0.1054 - 12ms/epoch - 6ms/step\n",
      "Epoch 309/1000\n",
      "2/2 - 0s - loss: 0.1054 - 13ms/epoch - 7ms/step\n",
      "Epoch 310/1000\n",
      "2/2 - 0s - loss: 0.1053 - 12ms/epoch - 6ms/step\n",
      "Epoch 311/1000\n",
      "2/2 - 0s - loss: 0.1053 - 12ms/epoch - 6ms/step\n",
      "Epoch 312/1000\n",
      "2/2 - 0s - loss: 0.1052 - 11ms/epoch - 6ms/step\n",
      "Epoch 313/1000\n",
      "2/2 - 0s - loss: 0.1052 - 12ms/epoch - 6ms/step\n",
      "Epoch 314/1000\n",
      "2/2 - 0s - loss: 0.1052 - 13ms/epoch - 6ms/step\n",
      "Epoch 315/1000\n",
      "2/2 - 0s - loss: 0.1052 - 11ms/epoch - 5ms/step\n",
      "Epoch 316/1000\n",
      "2/2 - 0s - loss: 0.1051 - 12ms/epoch - 6ms/step\n",
      "Epoch 317/1000\n",
      "2/2 - 0s - loss: 0.1051 - 12ms/epoch - 6ms/step\n",
      "Epoch 318/1000\n",
      "2/2 - 0s - loss: 0.1051 - 12ms/epoch - 6ms/step\n",
      "Epoch 319/1000\n",
      "2/2 - 0s - loss: 0.1051 - 11ms/epoch - 5ms/step\n",
      "Epoch 320/1000\n",
      "2/2 - 0s - loss: 0.1051 - 11ms/epoch - 5ms/step\n",
      "Epoch 321/1000\n",
      "2/2 - 0s - loss: 0.1051 - 12ms/epoch - 6ms/step\n",
      "Epoch 322/1000\n",
      "2/2 - 0s - loss: 0.1051 - 11ms/epoch - 5ms/step\n",
      "Epoch 323/1000\n",
      "2/2 - 0s - loss: 0.1051 - 12ms/epoch - 6ms/step\n",
      "Epoch 324/1000\n",
      "2/2 - 0s - loss: 0.1050 - 10ms/epoch - 5ms/step\n",
      "Epoch 325/1000\n",
      "2/2 - 0s - loss: 0.1050 - 11ms/epoch - 5ms/step\n",
      "Epoch 326/1000\n",
      "2/2 - 0s - loss: 0.1050 - 12ms/epoch - 6ms/step\n",
      "Epoch 327/1000\n",
      "2/2 - 0s - loss: 0.1049 - 10ms/epoch - 5ms/step\n",
      "Epoch 328/1000\n",
      "2/2 - 0s - loss: 0.1049 - 10ms/epoch - 5ms/step\n",
      "Epoch 329/1000\n",
      "2/2 - 0s - loss: 0.1048 - 11ms/epoch - 6ms/step\n",
      "Epoch 330/1000\n",
      "2/2 - 0s - loss: 0.1048 - 13ms/epoch - 7ms/step\n",
      "Epoch 331/1000\n",
      "2/2 - 0s - loss: 0.1048 - 10ms/epoch - 5ms/step\n",
      "Epoch 332/1000\n",
      "2/2 - 0s - loss: 0.1048 - 12ms/epoch - 6ms/step\n",
      "Epoch 333/1000\n",
      "2/2 - 0s - loss: 0.1049 - 11ms/epoch - 5ms/step\n",
      "Epoch 334/1000\n",
      "2/2 - 0s - loss: 0.1049 - 14ms/epoch - 7ms/step\n",
      "Epoch 335/1000\n",
      "2/2 - 0s - loss: 0.1049 - 12ms/epoch - 6ms/step\n",
      "Epoch 336/1000\n",
      "2/2 - 0s - loss: 0.1049 - 11ms/epoch - 6ms/step\n",
      "Epoch 337/1000\n",
      "2/2 - 0s - loss: 0.1049 - 10ms/epoch - 5ms/step\n",
      "Epoch 338/1000\n",
      "2/2 - 0s - loss: 0.1049 - 12ms/epoch - 6ms/step\n",
      "Epoch 339/1000\n",
      "2/2 - 0s - loss: 0.1049 - 12ms/epoch - 6ms/step\n",
      "Epoch 340/1000\n",
      "2/2 - 0s - loss: 0.1048 - 12ms/epoch - 6ms/step\n",
      "Epoch 341/1000\n",
      "2/2 - 0s - loss: 0.1047 - 10ms/epoch - 5ms/step\n",
      "Epoch 342/1000\n",
      "2/2 - 0s - loss: 0.1046 - 11ms/epoch - 5ms/step\n",
      "Epoch 343/1000\n",
      "2/2 - 0s - loss: 0.1046 - 11ms/epoch - 6ms/step\n",
      "Epoch 344/1000\n",
      "2/2 - 0s - loss: 0.1045 - 11ms/epoch - 6ms/step\n",
      "Epoch 345/1000\n",
      "2/2 - 0s - loss: 0.1045 - 10ms/epoch - 5ms/step\n",
      "Epoch 346/1000\n",
      "2/2 - 0s - loss: 0.1044 - 11ms/epoch - 5ms/step\n",
      "Epoch 347/1000\n",
      "2/2 - 0s - loss: 0.1044 - 11ms/epoch - 6ms/step\n",
      "Epoch 348/1000\n",
      "2/2 - 0s - loss: 0.1043 - 12ms/epoch - 6ms/step\n",
      "Epoch 349/1000\n",
      "2/2 - 0s - loss: 0.1042 - 12ms/epoch - 6ms/step\n",
      "Epoch 350/1000\n",
      "2/2 - 0s - loss: 0.1042 - 10ms/epoch - 5ms/step\n",
      "Epoch 351/1000\n",
      "2/2 - 0s - loss: 0.1042 - 11ms/epoch - 6ms/step\n",
      "Epoch 352/1000\n",
      "2/2 - 0s - loss: 0.1041 - 11ms/epoch - 6ms/step\n",
      "Epoch 353/1000\n",
      "2/2 - 0s - loss: 0.1041 - 10ms/epoch - 5ms/step\n",
      "Epoch 354/1000\n",
      "2/2 - 0s - loss: 0.1041 - 9ms/epoch - 4ms/step\n",
      "Epoch 355/1000\n",
      "2/2 - 0s - loss: 0.1041 - 12ms/epoch - 6ms/step\n",
      "Epoch 356/1000\n",
      "2/2 - 0s - loss: 0.1041 - 12ms/epoch - 6ms/step\n",
      "Epoch 357/1000\n",
      "2/2 - 0s - loss: 0.1042 - 10ms/epoch - 5ms/step\n",
      "Epoch 358/1000\n",
      "2/2 - 0s - loss: 0.1042 - 10ms/epoch - 5ms/step\n",
      "Epoch 359/1000\n",
      "2/2 - 0s - loss: 0.1042 - 11ms/epoch - 5ms/step\n",
      "Epoch 360/1000\n",
      "2/2 - 0s - loss: 0.1042 - 11ms/epoch - 5ms/step\n",
      "Epoch 361/1000\n",
      "2/2 - 0s - loss: 0.1043 - 9ms/epoch - 4ms/step\n",
      "Epoch 362/1000\n",
      "2/2 - 0s - loss: 0.1042 - 10ms/epoch - 5ms/step\n",
      "Epoch 363/1000\n",
      "2/2 - 0s - loss: 0.1042 - 11ms/epoch - 5ms/step\n",
      "Epoch 364/1000\n",
      "2/2 - 0s - loss: 0.1042 - 12ms/epoch - 6ms/step\n",
      "Epoch 365/1000\n",
      "2/2 - 0s - loss: 0.1042 - 11ms/epoch - 6ms/step\n",
      "Epoch 366/1000\n",
      "2/2 - 0s - loss: 0.1041 - 11ms/epoch - 5ms/step\n",
      "Epoch 367/1000\n",
      "2/2 - 0s - loss: 0.1040 - 11ms/epoch - 5ms/step\n",
      "Epoch 368/1000\n",
      "2/2 - 0s - loss: 0.1039 - 11ms/epoch - 6ms/step\n",
      "Epoch 369/1000\n",
      "2/2 - 0s - loss: 0.1037 - 11ms/epoch - 6ms/step\n",
      "Epoch 370/1000\n",
      "2/2 - 0s - loss: 0.1036 - 11ms/epoch - 5ms/step\n",
      "Epoch 371/1000\n",
      "2/2 - 0s - loss: 0.1035 - 12ms/epoch - 6ms/step\n",
      "Epoch 372/1000\n",
      "2/2 - 0s - loss: 0.1035 - 12ms/epoch - 6ms/step\n",
      "Epoch 373/1000\n",
      "2/2 - 0s - loss: 0.1035 - 12ms/epoch - 6ms/step\n",
      "Epoch 374/1000\n",
      "2/2 - 0s - loss: 0.1035 - 11ms/epoch - 5ms/step\n",
      "Epoch 375/1000\n",
      "2/2 - 0s - loss: 0.1034 - 12ms/epoch - 6ms/step\n",
      "Epoch 376/1000\n",
      "2/2 - 0s - loss: 0.1034 - 12ms/epoch - 6ms/step\n",
      "Epoch 377/1000\n",
      "2/2 - 0s - loss: 0.1034 - 13ms/epoch - 6ms/step\n",
      "Epoch 378/1000\n",
      "2/2 - 0s - loss: 0.1034 - 13ms/epoch - 6ms/step\n",
      "Epoch 379/1000\n",
      "2/2 - 0s - loss: 0.1033 - 14ms/epoch - 7ms/step\n",
      "Epoch 380/1000\n",
      "2/2 - 0s - loss: 0.1033 - 13ms/epoch - 7ms/step\n",
      "Epoch 381/1000\n",
      "2/2 - 0s - loss: 0.1033 - 12ms/epoch - 6ms/step\n",
      "Epoch 382/1000\n",
      "2/2 - 0s - loss: 0.1033 - 12ms/epoch - 6ms/step\n",
      "Epoch 383/1000\n",
      "2/2 - 0s - loss: 0.1033 - 13ms/epoch - 7ms/step\n",
      "Epoch 384/1000\n",
      "2/2 - 0s - loss: 0.1034 - 13ms/epoch - 7ms/step\n",
      "Epoch 385/1000\n",
      "2/2 - 0s - loss: 0.1038 - 12ms/epoch - 6ms/step\n",
      "Epoch 386/1000\n",
      "2/2 - 0s - loss: 0.1037 - 13ms/epoch - 6ms/step\n",
      "Epoch 387/1000\n",
      "2/2 - 0s - loss: 0.1038 - 13ms/epoch - 7ms/step\n",
      "Epoch 388/1000\n",
      "2/2 - 0s - loss: 0.1038 - 12ms/epoch - 6ms/step\n",
      "Epoch 389/1000\n",
      "2/2 - 0s - loss: 0.1039 - 12ms/epoch - 6ms/step\n",
      "Epoch 390/1000\n",
      "2/2 - 0s - loss: 0.1040 - 13ms/epoch - 7ms/step\n",
      "Epoch 391/1000\n",
      "2/2 - 0s - loss: 0.1042 - 14ms/epoch - 7ms/step\n",
      "Epoch 392/1000\n",
      "2/2 - 0s - loss: 0.1046 - 14ms/epoch - 7ms/step\n",
      "Epoch 393/1000\n",
      "2/2 - 0s - loss: 0.1047 - 13ms/epoch - 7ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 394/1000\n",
      "2/2 - 0s - loss: 0.1047 - 12ms/epoch - 6ms/step\n",
      "Epoch 395/1000\n",
      "2/2 - 0s - loss: 0.1044 - 12ms/epoch - 6ms/step\n",
      "Epoch 396/1000\n",
      "2/2 - 0s - loss: 0.1042 - 13ms/epoch - 7ms/step\n",
      "Epoch 397/1000\n",
      "2/2 - 0s - loss: 0.1037 - 12ms/epoch - 6ms/step\n",
      "Epoch 398/1000\n",
      "2/2 - 0s - loss: 0.1034 - 12ms/epoch - 6ms/step\n",
      "Epoch 399/1000\n",
      "2/2 - 0s - loss: 0.1032 - 11ms/epoch - 6ms/step\n",
      "Epoch 400/1000\n",
      "2/2 - 0s - loss: 0.1030 - 11ms/epoch - 5ms/step\n",
      "Epoch 401/1000\n",
      "2/2 - 0s - loss: 0.1028 - 12ms/epoch - 6ms/step\n",
      "Epoch 402/1000\n",
      "2/2 - 0s - loss: 0.1027 - 10ms/epoch - 5ms/step\n",
      "Epoch 403/1000\n",
      "2/2 - 0s - loss: 0.1027 - 11ms/epoch - 6ms/step\n",
      "Epoch 404/1000\n",
      "2/2 - 0s - loss: 0.1027 - 12ms/epoch - 6ms/step\n",
      "Epoch 405/1000\n",
      "2/2 - 0s - loss: 0.1026 - 12ms/epoch - 6ms/step\n",
      "Epoch 406/1000\n",
      "2/2 - 0s - loss: 0.1026 - 13ms/epoch - 7ms/step\n",
      "Epoch 407/1000\n",
      "2/2 - 0s - loss: 0.1025 - 10ms/epoch - 5ms/step\n",
      "Epoch 408/1000\n",
      "2/2 - 0s - loss: 0.1025 - 12ms/epoch - 6ms/step\n",
      "Epoch 409/1000\n",
      "2/2 - 0s - loss: 0.1024 - 12ms/epoch - 6ms/step\n",
      "Epoch 410/1000\n",
      "2/2 - 0s - loss: 0.1024 - 13ms/epoch - 6ms/step\n",
      "Epoch 411/1000\n",
      "2/2 - 0s - loss: 0.1024 - 11ms/epoch - 6ms/step\n",
      "Epoch 412/1000\n",
      "2/2 - 0s - loss: 0.1024 - 11ms/epoch - 5ms/step\n",
      "Epoch 413/1000\n",
      "2/2 - 0s - loss: 0.1023 - 12ms/epoch - 6ms/step\n",
      "Epoch 414/1000\n",
      "2/2 - 0s - loss: 0.1023 - 12ms/epoch - 6ms/step\n",
      "Epoch 415/1000\n",
      "2/2 - 0s - loss: 0.1023 - 12ms/epoch - 6ms/step\n",
      "Epoch 416/1000\n",
      "2/2 - 0s - loss: 0.1023 - 11ms/epoch - 5ms/step\n",
      "Epoch 417/1000\n",
      "2/2 - 0s - loss: 0.1023 - 11ms/epoch - 5ms/step\n",
      "Epoch 418/1000\n",
      "2/2 - 0s - loss: 0.1023 - 13ms/epoch - 7ms/step\n",
      "Epoch 419/1000\n",
      "2/2 - 0s - loss: 0.1023 - 11ms/epoch - 6ms/step\n",
      "Epoch 420/1000\n",
      "2/2 - 0s - loss: 0.1024 - 12ms/epoch - 6ms/step\n",
      "Epoch 421/1000\n",
      "2/2 - 0s - loss: 0.1023 - 11ms/epoch - 5ms/step\n",
      "Epoch 422/1000\n",
      "2/2 - 0s - loss: 0.1022 - 14ms/epoch - 7ms/step\n",
      "Epoch 423/1000\n",
      "2/2 - 0s - loss: 0.1022 - 13ms/epoch - 6ms/step\n",
      "Epoch 424/1000\n",
      "2/2 - 0s - loss: 0.1021 - 12ms/epoch - 6ms/step\n",
      "Epoch 425/1000\n",
      "2/2 - 0s - loss: 0.1021 - 20ms/epoch - 10ms/step\n",
      "Epoch 426/1000\n",
      "2/2 - 0s - loss: 0.1021 - 13ms/epoch - 6ms/step\n",
      "Epoch 427/1000\n",
      "2/2 - 0s - loss: 0.1019 - 12ms/epoch - 6ms/step\n",
      "Epoch 428/1000\n",
      "2/2 - 0s - loss: 0.1020 - 19ms/epoch - 9ms/step\n",
      "Epoch 429/1000\n",
      "2/2 - 0s - loss: 0.1022 - 11ms/epoch - 5ms/step\n",
      "Epoch 430/1000\n",
      "2/2 - 0s - loss: 0.1023 - 11ms/epoch - 6ms/step\n",
      "Epoch 431/1000\n",
      "2/2 - 0s - loss: 0.1025 - 11ms/epoch - 6ms/step\n",
      "Epoch 432/1000\n",
      "2/2 - 0s - loss: 0.1027 - 11ms/epoch - 5ms/step\n",
      "Epoch 433/1000\n",
      "2/2 - 0s - loss: 0.1027 - 11ms/epoch - 6ms/step\n",
      "Epoch 434/1000\n",
      "2/2 - 0s - loss: 0.1027 - 10ms/epoch - 5ms/step\n",
      "Epoch 435/1000\n",
      "2/2 - 0s - loss: 0.1027 - 11ms/epoch - 6ms/step\n",
      "Epoch 436/1000\n",
      "2/2 - 0s - loss: 0.1026 - 11ms/epoch - 5ms/step\n",
      "Epoch 437/1000\n",
      "2/2 - 0s - loss: 0.1026 - 11ms/epoch - 6ms/step\n",
      "Epoch 438/1000\n",
      "2/2 - 0s - loss: 0.1026 - 11ms/epoch - 5ms/step\n",
      "Epoch 439/1000\n",
      "2/2 - 0s - loss: 0.1025 - 14ms/epoch - 7ms/step\n",
      "Epoch 440/1000\n",
      "2/2 - 0s - loss: 0.1024 - 9ms/epoch - 4ms/step\n",
      "Epoch 441/1000\n",
      "2/2 - 0s - loss: 0.1023 - 12ms/epoch - 6ms/step\n",
      "Epoch 442/1000\n",
      "2/2 - 0s - loss: 0.1022 - 12ms/epoch - 6ms/step\n",
      "Epoch 443/1000\n",
      "2/2 - 0s - loss: 0.1022 - 11ms/epoch - 6ms/step\n",
      "Epoch 444/1000\n",
      "2/2 - 0s - loss: 0.1021 - 11ms/epoch - 5ms/step\n",
      "Epoch 445/1000\n",
      "2/2 - 0s - loss: 0.1020 - 11ms/epoch - 5ms/step\n",
      "Epoch 446/1000\n",
      "2/2 - 0s - loss: 0.1019 - 12ms/epoch - 6ms/step\n",
      "Epoch 447/1000\n",
      "2/2 - 0s - loss: 0.1018 - 12ms/epoch - 6ms/step\n",
      "Epoch 448/1000\n",
      "2/2 - 0s - loss: 0.1017 - 13ms/epoch - 7ms/step\n",
      "Epoch 449/1000\n",
      "2/2 - 0s - loss: 0.1015 - 9ms/epoch - 4ms/step\n",
      "Epoch 450/1000\n",
      "2/2 - 0s - loss: 0.1014 - 11ms/epoch - 5ms/step\n",
      "Epoch 451/1000\n",
      "2/2 - 0s - loss: 0.1012 - 10ms/epoch - 5ms/step\n",
      "Epoch 452/1000\n",
      "2/2 - 0s - loss: 0.1012 - 11ms/epoch - 6ms/step\n",
      "Epoch 453/1000\n",
      "2/2 - 0s - loss: 0.1011 - 11ms/epoch - 5ms/step\n",
      "Epoch 454/1000\n",
      "2/2 - 0s - loss: 0.1010 - 11ms/epoch - 5ms/step\n",
      "Epoch 455/1000\n",
      "2/2 - 0s - loss: 0.1010 - 10ms/epoch - 5ms/step\n",
      "Epoch 456/1000\n",
      "2/2 - 0s - loss: 0.1009 - 12ms/epoch - 6ms/step\n",
      "Epoch 457/1000\n",
      "2/2 - 0s - loss: 0.1010 - 13ms/epoch - 6ms/step\n",
      "Epoch 458/1000\n",
      "2/2 - 0s - loss: 0.1012 - 12ms/epoch - 6ms/step\n",
      "Epoch 459/1000\n",
      "2/2 - 0s - loss: 0.1011 - 13ms/epoch - 6ms/step\n",
      "Epoch 460/1000\n",
      "2/2 - 0s - loss: 0.1010 - 14ms/epoch - 7ms/step\n",
      "Epoch 461/1000\n",
      "2/2 - 0s - loss: 0.1008 - 12ms/epoch - 6ms/step\n",
      "Epoch 462/1000\n",
      "2/2 - 0s - loss: 0.1008 - 13ms/epoch - 6ms/step\n",
      "Epoch 463/1000\n",
      "2/2 - 0s - loss: 0.1007 - 13ms/epoch - 6ms/step\n",
      "Epoch 464/1000\n",
      "2/2 - 0s - loss: 0.1007 - 12ms/epoch - 6ms/step\n",
      "Epoch 465/1000\n",
      "2/2 - 0s - loss: 0.1007 - 14ms/epoch - 7ms/step\n",
      "Epoch 466/1000\n",
      "2/2 - 0s - loss: 0.1006 - 12ms/epoch - 6ms/step\n",
      "Epoch 467/1000\n",
      "2/2 - 0s - loss: 0.1006 - 10ms/epoch - 5ms/step\n",
      "Epoch 468/1000\n",
      "2/2 - 0s - loss: 0.1006 - 11ms/epoch - 5ms/step\n",
      "Epoch 469/1000\n",
      "2/2 - 0s - loss: 0.1006 - 11ms/epoch - 6ms/step\n",
      "Epoch 470/1000\n",
      "2/2 - 0s - loss: 0.1006 - 11ms/epoch - 5ms/step\n",
      "Epoch 471/1000\n",
      "2/2 - 0s - loss: 0.1007 - 12ms/epoch - 6ms/step\n",
      "Epoch 472/1000\n",
      "2/2 - 0s - loss: 0.1008 - 12ms/epoch - 6ms/step\n",
      "Epoch 473/1000\n",
      "2/2 - 0s - loss: 0.1011 - 12ms/epoch - 6ms/step\n",
      "Epoch 474/1000\n",
      "2/2 - 0s - loss: 0.1011 - 11ms/epoch - 6ms/step\n",
      "Epoch 475/1000\n",
      "2/2 - 0s - loss: 0.1011 - 11ms/epoch - 6ms/step\n",
      "Epoch 476/1000\n",
      "2/2 - 0s - loss: 0.1012 - 11ms/epoch - 5ms/step\n",
      "Epoch 477/1000\n",
      "2/2 - 0s - loss: 0.1014 - 11ms/epoch - 6ms/step\n",
      "Epoch 478/1000\n",
      "2/2 - 0s - loss: 0.1014 - 10ms/epoch - 5ms/step\n",
      "Epoch 479/1000\n",
      "2/2 - 0s - loss: 0.1014 - 12ms/epoch - 6ms/step\n",
      "Epoch 480/1000\n",
      "2/2 - 0s - loss: 0.1012 - 12ms/epoch - 6ms/step\n",
      "Epoch 481/1000\n",
      "2/2 - 0s - loss: 0.1011 - 12ms/epoch - 6ms/step\n",
      "Epoch 482/1000\n",
      "2/2 - 0s - loss: 0.1010 - 12ms/epoch - 6ms/step\n",
      "Epoch 483/1000\n",
      "2/2 - 0s - loss: 0.1007 - 11ms/epoch - 5ms/step\n",
      "Epoch 484/1000\n",
      "2/2 - 0s - loss: 0.1006 - 13ms/epoch - 7ms/step\n",
      "Epoch 485/1000\n",
      "2/2 - 0s - loss: 0.1005 - 10ms/epoch - 5ms/step\n",
      "Epoch 486/1000\n",
      "2/2 - 0s - loss: 0.1003 - 11ms/epoch - 5ms/step\n",
      "Epoch 487/1000\n",
      "2/2 - 0s - loss: 0.1002 - 11ms/epoch - 5ms/step\n",
      "Epoch 488/1000\n",
      "2/2 - 0s - loss: 0.1001 - 12ms/epoch - 6ms/step\n",
      "Epoch 489/1000\n",
      "2/2 - 0s - loss: 0.1000 - 10ms/epoch - 5ms/step\n",
      "Epoch 490/1000\n",
      "2/2 - 0s - loss: 0.0999 - 13ms/epoch - 6ms/step\n",
      "Epoch 491/1000\n",
      "2/2 - 0s - loss: 0.0998 - 11ms/epoch - 5ms/step\n",
      "Epoch 492/1000\n",
      "2/2 - 0s - loss: 0.0997 - 10ms/epoch - 5ms/step\n",
      "Epoch 493/1000\n",
      "2/2 - 0s - loss: 0.0996 - 12ms/epoch - 6ms/step\n",
      "Epoch 494/1000\n",
      "2/2 - 0s - loss: 0.0996 - 13ms/epoch - 6ms/step\n",
      "Epoch 495/1000\n",
      "2/2 - 0s - loss: 0.0997 - 15ms/epoch - 8ms/step\n",
      "Epoch 496/1000\n",
      "2/2 - 0s - loss: 0.0997 - 11ms/epoch - 5ms/step\n",
      "Epoch 497/1000\n",
      "2/2 - 0s - loss: 0.0996 - 12ms/epoch - 6ms/step\n",
      "Epoch 498/1000\n",
      "2/2 - 0s - loss: 0.0996 - 11ms/epoch - 6ms/step\n",
      "Epoch 499/1000\n",
      "2/2 - 0s - loss: 0.0994 - 10ms/epoch - 5ms/step\n",
      "Epoch 500/1000\n",
      "2/2 - 0s - loss: 0.0993 - 12ms/epoch - 6ms/step\n",
      "Epoch 501/1000\n",
      "2/2 - 0s - loss: 0.0994 - 11ms/epoch - 5ms/step\n",
      "Epoch 502/1000\n",
      "2/2 - 0s - loss: 0.0995 - 11ms/epoch - 5ms/step\n",
      "Epoch 503/1000\n",
      "2/2 - 0s - loss: 0.0994 - 10ms/epoch - 5ms/step\n",
      "Epoch 504/1000\n",
      "2/2 - 0s - loss: 0.0993 - 13ms/epoch - 6ms/step\n",
      "Epoch 505/1000\n",
      "2/2 - 0s - loss: 0.0992 - 12ms/epoch - 6ms/step\n",
      "Epoch 506/1000\n",
      "2/2 - 0s - loss: 0.0991 - 11ms/epoch - 6ms/step\n",
      "Epoch 507/1000\n",
      "2/2 - 0s - loss: 0.0989 - 11ms/epoch - 5ms/step\n",
      "Epoch 508/1000\n",
      "2/2 - 0s - loss: 0.0988 - 11ms/epoch - 5ms/step\n",
      "Epoch 509/1000\n",
      "2/2 - 0s - loss: 0.0990 - 12ms/epoch - 6ms/step\n",
      "Epoch 510/1000\n",
      "2/2 - 0s - loss: 0.0992 - 11ms/epoch - 5ms/step\n",
      "Epoch 511/1000\n",
      "2/2 - 0s - loss: 0.0995 - 14ms/epoch - 7ms/step\n",
      "Epoch 512/1000\n",
      "2/2 - 0s - loss: 0.0995 - 11ms/epoch - 6ms/step\n",
      "Epoch 513/1000\n",
      "2/2 - 0s - loss: 0.0995 - 12ms/epoch - 6ms/step\n",
      "Epoch 514/1000\n",
      "2/2 - 0s - loss: 0.0994 - 13ms/epoch - 7ms/step\n",
      "Epoch 515/1000\n",
      "2/2 - 0s - loss: 0.0993 - 12ms/epoch - 6ms/step\n",
      "Epoch 516/1000\n",
      "2/2 - 0s - loss: 0.0992 - 11ms/epoch - 6ms/step\n",
      "Epoch 517/1000\n",
      "2/2 - 0s - loss: 0.0989 - 12ms/epoch - 6ms/step\n",
      "Epoch 518/1000\n",
      "2/2 - 0s - loss: 0.0988 - 12ms/epoch - 6ms/step\n",
      "Epoch 519/1000\n",
      "2/2 - 0s - loss: 0.0985 - 11ms/epoch - 5ms/step\n",
      "Epoch 520/1000\n",
      "2/2 - 0s - loss: 0.0985 - 12ms/epoch - 6ms/step\n",
      "Epoch 521/1000\n",
      "2/2 - 0s - loss: 0.0983 - 12ms/epoch - 6ms/step\n",
      "Epoch 522/1000\n",
      "2/2 - 0s - loss: 0.0983 - 12ms/epoch - 6ms/step\n",
      "Epoch 523/1000\n",
      "2/2 - 0s - loss: 0.0983 - 12ms/epoch - 6ms/step\n",
      "Epoch 524/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 - 0s - loss: 0.0982 - 11ms/epoch - 5ms/step\n",
      "Epoch 525/1000\n",
      "2/2 - 0s - loss: 0.0982 - 14ms/epoch - 7ms/step\n",
      "Epoch 526/1000\n",
      "2/2 - 0s - loss: 0.0982 - 11ms/epoch - 5ms/step\n",
      "Epoch 527/1000\n",
      "2/2 - 0s - loss: 0.0981 - 12ms/epoch - 6ms/step\n",
      "Epoch 528/1000\n",
      "2/2 - 0s - loss: 0.0982 - 11ms/epoch - 6ms/step\n",
      "Epoch 529/1000\n",
      "2/2 - 0s - loss: 0.0983 - 11ms/epoch - 6ms/step\n",
      "Epoch 530/1000\n",
      "2/2 - 0s - loss: 0.0984 - 12ms/epoch - 6ms/step\n",
      "Epoch 531/1000\n",
      "2/2 - 0s - loss: 0.0985 - 10ms/epoch - 5ms/step\n",
      "Epoch 532/1000\n",
      "2/2 - 0s - loss: 0.0987 - 11ms/epoch - 5ms/step\n",
      "Epoch 533/1000\n",
      "2/2 - 0s - loss: 0.0987 - 11ms/epoch - 5ms/step\n",
      "Epoch 534/1000\n",
      "2/2 - 0s - loss: 0.0986 - 13ms/epoch - 6ms/step\n",
      "Epoch 535/1000\n",
      "2/2 - 0s - loss: 0.0985 - 11ms/epoch - 6ms/step\n",
      "Epoch 536/1000\n",
      "2/2 - 0s - loss: 0.0983 - 10ms/epoch - 5ms/step\n",
      "Epoch 537/1000\n",
      "2/2 - 0s - loss: 0.0982 - 13ms/epoch - 6ms/step\n",
      "Epoch 538/1000\n",
      "2/2 - 0s - loss: 0.0980 - 11ms/epoch - 5ms/step\n",
      "Epoch 539/1000\n",
      "2/2 - 0s - loss: 0.0979 - 12ms/epoch - 6ms/step\n",
      "Epoch 540/1000\n",
      "2/2 - 0s - loss: 0.0978 - 10ms/epoch - 5ms/step\n",
      "Epoch 541/1000\n",
      "2/2 - 0s - loss: 0.0977 - 10ms/epoch - 5ms/step\n",
      "Epoch 542/1000\n",
      "2/2 - 0s - loss: 0.0976 - 11ms/epoch - 5ms/step\n",
      "Epoch 543/1000\n",
      "2/2 - 0s - loss: 0.0975 - 11ms/epoch - 5ms/step\n",
      "Epoch 544/1000\n",
      "2/2 - 0s - loss: 0.0975 - 11ms/epoch - 6ms/step\n",
      "Epoch 545/1000\n",
      "2/2 - 0s - loss: 0.0975 - 10ms/epoch - 5ms/step\n",
      "Epoch 546/1000\n",
      "2/2 - 0s - loss: 0.0975 - 13ms/epoch - 6ms/step\n",
      "Epoch 547/1000\n",
      "2/2 - 0s - loss: 0.0974 - 15ms/epoch - 7ms/step\n",
      "Epoch 548/1000\n",
      "2/2 - 0s - loss: 0.0975 - 13ms/epoch - 6ms/step\n",
      "Epoch 549/1000\n",
      "2/2 - 0s - loss: 0.0974 - 13ms/epoch - 6ms/step\n",
      "Epoch 550/1000\n",
      "2/2 - 0s - loss: 0.0974 - 13ms/epoch - 7ms/step\n",
      "Epoch 551/1000\n",
      "2/2 - 0s - loss: 0.0974 - 12ms/epoch - 6ms/step\n",
      "Epoch 552/1000\n",
      "2/2 - 0s - loss: 0.0973 - 13ms/epoch - 7ms/step\n",
      "Epoch 553/1000\n",
      "2/2 - 0s - loss: 0.0973 - 14ms/epoch - 7ms/step\n",
      "Epoch 554/1000\n",
      "2/2 - 0s - loss: 0.0972 - 14ms/epoch - 7ms/step\n",
      "Epoch 555/1000\n",
      "2/2 - 0s - loss: 0.0971 - 13ms/epoch - 6ms/step\n",
      "Epoch 556/1000\n",
      "2/2 - 0s - loss: 0.0970 - 10ms/epoch - 5ms/step\n",
      "Epoch 557/1000\n",
      "2/2 - 0s - loss: 0.0970 - 13ms/epoch - 6ms/step\n",
      "Epoch 558/1000\n",
      "2/2 - 0s - loss: 0.0969 - 14ms/epoch - 7ms/step\n",
      "Epoch 559/1000\n",
      "2/2 - 0s - loss: 0.0970 - 13ms/epoch - 6ms/step\n",
      "Epoch 560/1000\n",
      "2/2 - 0s - loss: 0.0971 - 11ms/epoch - 6ms/step\n",
      "Epoch 561/1000\n",
      "2/2 - 0s - loss: 0.0972 - 12ms/epoch - 6ms/step\n",
      "Epoch 562/1000\n",
      "2/2 - 0s - loss: 0.0972 - 11ms/epoch - 5ms/step\n",
      "Epoch 563/1000\n",
      "2/2 - 0s - loss: 0.0972 - 13ms/epoch - 6ms/step\n",
      "Epoch 564/1000\n",
      "2/2 - 0s - loss: 0.0970 - 12ms/epoch - 6ms/step\n",
      "Epoch 565/1000\n",
      "2/2 - 0s - loss: 0.0967 - 13ms/epoch - 7ms/step\n",
      "Epoch 566/1000\n",
      "2/2 - 0s - loss: 0.0966 - 14ms/epoch - 7ms/step\n",
      "Epoch 567/1000\n",
      "2/2 - 0s - loss: 0.0965 - 12ms/epoch - 6ms/step\n",
      "Epoch 568/1000\n",
      "2/2 - 0s - loss: 0.0966 - 12ms/epoch - 6ms/step\n",
      "Epoch 569/1000\n",
      "2/2 - 0s - loss: 0.0965 - 12ms/epoch - 6ms/step\n",
      "Epoch 570/1000\n",
      "2/2 - 0s - loss: 0.0965 - 14ms/epoch - 7ms/step\n",
      "Epoch 571/1000\n",
      "2/2 - 0s - loss: 0.0964 - 11ms/epoch - 5ms/step\n",
      "Epoch 572/1000\n",
      "2/2 - 0s - loss: 0.0964 - 11ms/epoch - 5ms/step\n",
      "Epoch 573/1000\n",
      "2/2 - 0s - loss: 0.0964 - 12ms/epoch - 6ms/step\n",
      "Epoch 574/1000\n",
      "2/2 - 0s - loss: 0.0963 - 12ms/epoch - 6ms/step\n",
      "Epoch 575/1000\n",
      "2/2 - 0s - loss: 0.0967 - 13ms/epoch - 6ms/step\n",
      "Epoch 576/1000\n",
      "2/2 - 0s - loss: 0.0967 - 12ms/epoch - 6ms/step\n",
      "Epoch 577/1000\n",
      "2/2 - 0s - loss: 0.0968 - 14ms/epoch - 7ms/step\n",
      "Epoch 578/1000\n",
      "2/2 - 0s - loss: 0.0967 - 13ms/epoch - 6ms/step\n",
      "Epoch 579/1000\n",
      "2/2 - 0s - loss: 0.0966 - 10ms/epoch - 5ms/step\n",
      "Epoch 580/1000\n",
      "2/2 - 0s - loss: 0.0964 - 11ms/epoch - 5ms/step\n",
      "Epoch 581/1000\n",
      "2/2 - 0s - loss: 0.0963 - 13ms/epoch - 6ms/step\n",
      "Epoch 582/1000\n",
      "2/2 - 0s - loss: 0.0961 - 12ms/epoch - 6ms/step\n",
      "Epoch 583/1000\n",
      "2/2 - 0s - loss: 0.0959 - 10ms/epoch - 5ms/step\n",
      "Epoch 584/1000\n",
      "2/2 - 0s - loss: 0.0959 - 11ms/epoch - 5ms/step\n",
      "Epoch 585/1000\n",
      "2/2 - 0s - loss: 0.0958 - 10ms/epoch - 5ms/step\n",
      "Epoch 586/1000\n",
      "2/2 - 0s - loss: 0.0958 - 10ms/epoch - 5ms/step\n",
      "Epoch 587/1000\n",
      "2/2 - 0s - loss: 0.0959 - 10ms/epoch - 5ms/step\n",
      "Epoch 588/1000\n",
      "2/2 - 0s - loss: 0.0960 - 10ms/epoch - 5ms/step\n",
      "Epoch 589/1000\n",
      "2/2 - 0s - loss: 0.0960 - 11ms/epoch - 5ms/step\n",
      "Epoch 590/1000\n",
      "2/2 - 0s - loss: 0.0959 - 12ms/epoch - 6ms/step\n",
      "Epoch 591/1000\n",
      "2/2 - 0s - loss: 0.0958 - 10ms/epoch - 5ms/step\n",
      "Epoch 592/1000\n",
      "2/2 - 0s - loss: 0.0957 - 11ms/epoch - 5ms/step\n",
      "Epoch 593/1000\n",
      "2/2 - 0s - loss: 0.0955 - 10ms/epoch - 5ms/step\n",
      "Epoch 594/1000\n",
      "2/2 - 0s - loss: 0.0954 - 10ms/epoch - 5ms/step\n",
      "Epoch 595/1000\n",
      "2/2 - 0s - loss: 0.0954 - 10ms/epoch - 5ms/step\n",
      "Epoch 596/1000\n",
      "2/2 - 0s - loss: 0.0953 - 10ms/epoch - 5ms/step\n",
      "Epoch 597/1000\n",
      "2/2 - 0s - loss: 0.0954 - 11ms/epoch - 5ms/step\n",
      "Epoch 598/1000\n",
      "2/2 - 0s - loss: 0.0957 - 12ms/epoch - 6ms/step\n",
      "Epoch 599/1000\n",
      "2/2 - 0s - loss: 0.0960 - 9ms/epoch - 5ms/step\n",
      "Epoch 600/1000\n",
      "2/2 - 0s - loss: 0.0961 - 9ms/epoch - 4ms/step\n",
      "Epoch 601/1000\n",
      "2/2 - 0s - loss: 0.0961 - 11ms/epoch - 5ms/step\n",
      "Epoch 602/1000\n",
      "2/2 - 0s - loss: 0.0958 - 10ms/epoch - 5ms/step\n",
      "Epoch 603/1000\n",
      "2/2 - 0s - loss: 0.0955 - 11ms/epoch - 5ms/step\n",
      "Epoch 604/1000\n",
      "2/2 - 0s - loss: 0.0952 - 11ms/epoch - 5ms/step\n",
      "Epoch 605/1000\n",
      "2/2 - 0s - loss: 0.0950 - 11ms/epoch - 5ms/step\n",
      "Epoch 606/1000\n",
      "2/2 - 0s - loss: 0.0949 - 11ms/epoch - 6ms/step\n",
      "Epoch 607/1000\n",
      "2/2 - 0s - loss: 0.0948 - 12ms/epoch - 6ms/step\n",
      "Epoch 608/1000\n",
      "2/2 - 0s - loss: 0.0947 - 13ms/epoch - 6ms/step\n",
      "Epoch 609/1000\n",
      "2/2 - 0s - loss: 0.0948 - 11ms/epoch - 5ms/step\n",
      "Epoch 610/1000\n",
      "2/2 - 0s - loss: 0.0948 - 11ms/epoch - 5ms/step\n",
      "Epoch 611/1000\n",
      "2/2 - 0s - loss: 0.0948 - 10ms/epoch - 5ms/step\n",
      "Epoch 612/1000\n",
      "2/2 - 0s - loss: 0.0948 - 10ms/epoch - 5ms/step\n",
      "Epoch 613/1000\n",
      "2/2 - 0s - loss: 0.0948 - 11ms/epoch - 6ms/step\n",
      "Epoch 614/1000\n",
      "2/2 - 0s - loss: 0.0948 - 11ms/epoch - 5ms/step\n",
      "Epoch 615/1000\n",
      "2/2 - 0s - loss: 0.0948 - 9ms/epoch - 4ms/step\n",
      "Epoch 616/1000\n",
      "2/2 - 0s - loss: 0.0947 - 12ms/epoch - 6ms/step\n",
      "Epoch 617/1000\n",
      "2/2 - 0s - loss: 0.0946 - 12ms/epoch - 6ms/step\n",
      "Epoch 618/1000\n",
      "2/2 - 0s - loss: 0.0945 - 11ms/epoch - 5ms/step\n",
      "Epoch 619/1000\n",
      "2/2 - 0s - loss: 0.0944 - 10ms/epoch - 5ms/step\n",
      "Epoch 620/1000\n",
      "2/2 - 0s - loss: 0.0943 - 11ms/epoch - 5ms/step\n",
      "Epoch 621/1000\n",
      "2/2 - 0s - loss: 0.0942 - 11ms/epoch - 6ms/step\n",
      "Epoch 622/1000\n",
      "2/2 - 0s - loss: 0.0942 - 13ms/epoch - 6ms/step\n",
      "Epoch 623/1000\n",
      "2/2 - 0s - loss: 0.0943 - 9ms/epoch - 4ms/step\n",
      "Epoch 624/1000\n",
      "2/2 - 0s - loss: 0.0941 - 10ms/epoch - 5ms/step\n",
      "Epoch 625/1000\n",
      "2/2 - 0s - loss: 0.0941 - 23ms/epoch - 12ms/step\n",
      "Epoch 626/1000\n",
      "2/2 - 0s - loss: 0.0940 - 13ms/epoch - 6ms/step\n",
      "Epoch 627/1000\n",
      "2/2 - 0s - loss: 0.0939 - 15ms/epoch - 8ms/step\n",
      "Epoch 628/1000\n",
      "2/2 - 0s - loss: 0.0941 - 13ms/epoch - 6ms/step\n",
      "Epoch 629/1000\n",
      "2/2 - 0s - loss: 0.0940 - 11ms/epoch - 5ms/step\n",
      "Epoch 630/1000\n",
      "2/2 - 0s - loss: 0.0940 - 12ms/epoch - 6ms/step\n",
      "Epoch 631/1000\n",
      "2/2 - 0s - loss: 0.0940 - 12ms/epoch - 6ms/step\n",
      "Epoch 632/1000\n",
      "2/2 - 0s - loss: 0.0939 - 10ms/epoch - 5ms/step\n",
      "Epoch 633/1000\n",
      "2/2 - 0s - loss: 0.0938 - 11ms/epoch - 5ms/step\n",
      "Epoch 634/1000\n",
      "2/2 - 0s - loss: 0.0936 - 12ms/epoch - 6ms/step\n",
      "Epoch 635/1000\n",
      "2/2 - 0s - loss: 0.0936 - 12ms/epoch - 6ms/step\n",
      "Epoch 636/1000\n",
      "2/2 - 0s - loss: 0.0937 - 12ms/epoch - 6ms/step\n",
      "Epoch 637/1000\n",
      "2/2 - 0s - loss: 0.0936 - 11ms/epoch - 6ms/step\n",
      "Epoch 638/1000\n",
      "2/2 - 0s - loss: 0.0935 - 12ms/epoch - 6ms/step\n",
      "Epoch 639/1000\n",
      "2/2 - 0s - loss: 0.0934 - 10ms/epoch - 5ms/step\n",
      "Epoch 640/1000\n",
      "2/2 - 0s - loss: 0.0934 - 12ms/epoch - 6ms/step\n",
      "Epoch 641/1000\n",
      "2/2 - 0s - loss: 0.0933 - 11ms/epoch - 5ms/step\n",
      "Epoch 642/1000\n",
      "2/2 - 0s - loss: 0.0933 - 9ms/epoch - 4ms/step\n",
      "Epoch 643/1000\n",
      "2/2 - 0s - loss: 0.0932 - 12ms/epoch - 6ms/step\n",
      "Epoch 644/1000\n",
      "2/2 - 0s - loss: 0.0932 - 10ms/epoch - 5ms/step\n",
      "Epoch 645/1000\n",
      "2/2 - 0s - loss: 0.0932 - 10ms/epoch - 5ms/step\n",
      "Epoch 646/1000\n",
      "2/2 - 0s - loss: 0.0932 - 11ms/epoch - 5ms/step\n",
      "Epoch 647/1000\n",
      "2/2 - 0s - loss: 0.0931 - 12ms/epoch - 6ms/step\n",
      "Epoch 648/1000\n",
      "2/2 - 0s - loss: 0.0930 - 10ms/epoch - 5ms/step\n",
      "Epoch 649/1000\n",
      "2/2 - 0s - loss: 0.0930 - 12ms/epoch - 6ms/step\n",
      "Epoch 650/1000\n",
      "2/2 - 0s - loss: 0.0930 - 11ms/epoch - 5ms/step\n",
      "Epoch 651/1000\n",
      "2/2 - 0s - loss: 0.0929 - 13ms/epoch - 6ms/step\n",
      "Epoch 652/1000\n",
      "2/2 - 0s - loss: 0.0929 - 11ms/epoch - 6ms/step\n",
      "Epoch 653/1000\n",
      "2/2 - 0s - loss: 0.0929 - 12ms/epoch - 6ms/step\n",
      "Epoch 654/1000\n",
      "2/2 - 0s - loss: 0.0928 - 11ms/epoch - 5ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 655/1000\n",
      "2/2 - 0s - loss: 0.0928 - 11ms/epoch - 5ms/step\n",
      "Epoch 656/1000\n",
      "2/2 - 0s - loss: 0.0928 - 10ms/epoch - 5ms/step\n",
      "Epoch 657/1000\n",
      "2/2 - 0s - loss: 0.0928 - 11ms/epoch - 6ms/step\n",
      "Epoch 658/1000\n",
      "2/2 - 0s - loss: 0.0927 - 10ms/epoch - 5ms/step\n",
      "Epoch 659/1000\n",
      "2/2 - 0s - loss: 0.0927 - 12ms/epoch - 6ms/step\n",
      "Epoch 660/1000\n",
      "2/2 - 0s - loss: 0.0926 - 12ms/epoch - 6ms/step\n",
      "Epoch 661/1000\n",
      "2/2 - 0s - loss: 0.0925 - 11ms/epoch - 6ms/step\n",
      "Epoch 662/1000\n",
      "2/2 - 0s - loss: 0.0924 - 11ms/epoch - 5ms/step\n",
      "Epoch 663/1000\n",
      "2/2 - 0s - loss: 0.0923 - 10ms/epoch - 5ms/step\n",
      "Epoch 664/1000\n",
      "2/2 - 0s - loss: 0.0922 - 12ms/epoch - 6ms/step\n",
      "Epoch 665/1000\n",
      "2/2 - 0s - loss: 0.0923 - 13ms/epoch - 7ms/step\n",
      "Epoch 666/1000\n",
      "2/2 - 0s - loss: 0.0922 - 11ms/epoch - 6ms/step\n",
      "Epoch 667/1000\n",
      "2/2 - 0s - loss: 0.0921 - 11ms/epoch - 5ms/step\n",
      "Epoch 668/1000\n",
      "2/2 - 0s - loss: 0.0921 - 13ms/epoch - 6ms/step\n",
      "Epoch 669/1000\n",
      "2/2 - 0s - loss: 0.0921 - 12ms/epoch - 6ms/step\n",
      "Epoch 670/1000\n",
      "2/2 - 0s - loss: 0.0921 - 11ms/epoch - 6ms/step\n",
      "Epoch 671/1000\n",
      "2/2 - 0s - loss: 0.0920 - 11ms/epoch - 6ms/step\n",
      "Epoch 672/1000\n",
      "2/2 - 0s - loss: 0.0921 - 12ms/epoch - 6ms/step\n",
      "Epoch 673/1000\n",
      "2/2 - 0s - loss: 0.0921 - 10ms/epoch - 5ms/step\n",
      "Epoch 674/1000\n",
      "2/2 - 0s - loss: 0.0920 - 11ms/epoch - 6ms/step\n",
      "Epoch 675/1000\n",
      "2/2 - 0s - loss: 0.0919 - 9ms/epoch - 4ms/step\n",
      "Epoch 676/1000\n",
      "2/2 - 0s - loss: 0.0918 - 12ms/epoch - 6ms/step\n",
      "Epoch 677/1000\n",
      "2/2 - 0s - loss: 0.0918 - 12ms/epoch - 6ms/step\n",
      "Epoch 678/1000\n",
      "2/2 - 0s - loss: 0.0917 - 10ms/epoch - 5ms/step\n",
      "Epoch 679/1000\n",
      "2/2 - 0s - loss: 0.0917 - 12ms/epoch - 6ms/step\n",
      "Epoch 680/1000\n",
      "2/2 - 0s - loss: 0.0917 - 11ms/epoch - 5ms/step\n",
      "Epoch 681/1000\n",
      "2/2 - 0s - loss: 0.0916 - 11ms/epoch - 5ms/step\n",
      "Epoch 682/1000\n",
      "2/2 - 0s - loss: 0.0916 - 10ms/epoch - 5ms/step\n",
      "Epoch 683/1000\n",
      "2/2 - 0s - loss: 0.0916 - 11ms/epoch - 5ms/step\n",
      "Epoch 684/1000\n",
      "2/2 - 0s - loss: 0.0917 - 12ms/epoch - 6ms/step\n",
      "Epoch 685/1000\n",
      "2/2 - 0s - loss: 0.0917 - 11ms/epoch - 6ms/step\n",
      "Epoch 686/1000\n",
      "2/2 - 0s - loss: 0.0918 - 12ms/epoch - 6ms/step\n",
      "Epoch 687/1000\n",
      "2/2 - 0s - loss: 0.0916 - 10ms/epoch - 5ms/step\n",
      "Epoch 688/1000\n",
      "2/2 - 0s - loss: 0.0915 - 11ms/epoch - 6ms/step\n",
      "Epoch 689/1000\n",
      "2/2 - 0s - loss: 0.0913 - 10ms/epoch - 5ms/step\n",
      "Epoch 690/1000\n",
      "2/2 - 0s - loss: 0.0912 - 10ms/epoch - 5ms/step\n",
      "Epoch 691/1000\n",
      "2/2 - 0s - loss: 0.0911 - 11ms/epoch - 5ms/step\n",
      "Epoch 692/1000\n",
      "2/2 - 0s - loss: 0.0910 - 12ms/epoch - 6ms/step\n",
      "Epoch 693/1000\n",
      "2/2 - 0s - loss: 0.0910 - 11ms/epoch - 5ms/step\n",
      "Epoch 694/1000\n",
      "2/2 - 0s - loss: 0.0910 - 9ms/epoch - 4ms/step\n",
      "Epoch 695/1000\n",
      "2/2 - 0s - loss: 0.0910 - 12ms/epoch - 6ms/step\n",
      "Epoch 696/1000\n",
      "2/2 - 0s - loss: 0.0909 - 14ms/epoch - 7ms/step\n",
      "Epoch 697/1000\n",
      "2/2 - 0s - loss: 0.0909 - 13ms/epoch - 6ms/step\n",
      "Epoch 698/1000\n",
      "2/2 - 0s - loss: 0.0909 - 14ms/epoch - 7ms/step\n",
      "Epoch 699/1000\n",
      "2/2 - 0s - loss: 0.0908 - 12ms/epoch - 6ms/step\n",
      "Epoch 700/1000\n",
      "2/2 - 0s - loss: 0.0908 - 14ms/epoch - 7ms/step\n",
      "Epoch 701/1000\n",
      "2/2 - 0s - loss: 0.0907 - 13ms/epoch - 7ms/step\n",
      "Epoch 702/1000\n",
      "2/2 - 0s - loss: 0.0907 - 16ms/epoch - 8ms/step\n",
      "Epoch 703/1000\n",
      "2/2 - 0s - loss: 0.0906 - 16ms/epoch - 8ms/step\n",
      "Epoch 704/1000\n",
      "2/2 - 0s - loss: 0.0906 - 14ms/epoch - 7ms/step\n",
      "Epoch 705/1000\n",
      "2/2 - 0s - loss: 0.0905 - 13ms/epoch - 7ms/step\n",
      "Epoch 706/1000\n",
      "2/2 - 0s - loss: 0.0906 - 14ms/epoch - 7ms/step\n",
      "Epoch 707/1000\n",
      "2/2 - 0s - loss: 0.0907 - 12ms/epoch - 6ms/step\n",
      "Epoch 708/1000\n",
      "2/2 - 0s - loss: 0.0907 - 13ms/epoch - 7ms/step\n",
      "Epoch 709/1000\n",
      "2/2 - 0s - loss: 0.0907 - 13ms/epoch - 6ms/step\n",
      "Epoch 710/1000\n",
      "2/2 - 0s - loss: 0.0907 - 12ms/epoch - 6ms/step\n",
      "Epoch 711/1000\n",
      "2/2 - 0s - loss: 0.0907 - 12ms/epoch - 6ms/step\n",
      "Epoch 712/1000\n",
      "2/2 - 0s - loss: 0.0907 - 14ms/epoch - 7ms/step\n",
      "Epoch 713/1000\n",
      "2/2 - 0s - loss: 0.0908 - 12ms/epoch - 6ms/step\n",
      "Epoch 714/1000\n",
      "2/2 - 0s - loss: 0.0908 - 14ms/epoch - 7ms/step\n",
      "Epoch 715/1000\n",
      "2/2 - 0s - loss: 0.0909 - 13ms/epoch - 7ms/step\n",
      "Epoch 716/1000\n",
      "2/2 - 0s - loss: 0.0909 - 13ms/epoch - 7ms/step\n",
      "Epoch 717/1000\n",
      "2/2 - 0s - loss: 0.0908 - 13ms/epoch - 6ms/step\n",
      "Epoch 718/1000\n",
      "2/2 - 0s - loss: 0.0907 - 13ms/epoch - 6ms/step\n",
      "Epoch 719/1000\n",
      "2/2 - 0s - loss: 0.0904 - 13ms/epoch - 6ms/step\n",
      "Epoch 720/1000\n",
      "2/2 - 0s - loss: 0.0903 - 12ms/epoch - 6ms/step\n",
      "Epoch 721/1000\n",
      "2/2 - 0s - loss: 0.0902 - 15ms/epoch - 7ms/step\n",
      "Epoch 722/1000\n",
      "2/2 - 0s - loss: 0.0904 - 13ms/epoch - 7ms/step\n",
      "Epoch 723/1000\n",
      "2/2 - 0s - loss: 0.0907 - 12ms/epoch - 6ms/step\n",
      "Epoch 724/1000\n",
      "2/2 - 0s - loss: 0.0911 - 13ms/epoch - 6ms/step\n",
      "Epoch 725/1000\n",
      "2/2 - 0s - loss: 0.0911 - 15ms/epoch - 8ms/step\n",
      "Epoch 726/1000\n",
      "2/2 - 0s - loss: 0.0906 - 13ms/epoch - 7ms/step\n",
      "Epoch 727/1000\n",
      "2/2 - 0s - loss: 0.0899 - 12ms/epoch - 6ms/step\n",
      "Epoch 728/1000\n",
      "2/2 - 0s - loss: 0.0897 - 13ms/epoch - 6ms/step\n",
      "Epoch 729/1000\n",
      "2/2 - 0s - loss: 0.0896 - 11ms/epoch - 6ms/step\n",
      "Epoch 730/1000\n",
      "2/2 - 0s - loss: 0.0896 - 12ms/epoch - 6ms/step\n",
      "Epoch 731/1000\n",
      "2/2 - 0s - loss: 0.0898 - 12ms/epoch - 6ms/step\n",
      "Epoch 732/1000\n",
      "2/2 - 0s - loss: 0.0902 - 14ms/epoch - 7ms/step\n",
      "Epoch 733/1000\n",
      "2/2 - 0s - loss: 0.0905 - 11ms/epoch - 5ms/step\n",
      "Epoch 734/1000\n",
      "2/2 - 0s - loss: 0.0907 - 12ms/epoch - 6ms/step\n",
      "Epoch 735/1000\n",
      "2/2 - 0s - loss: 0.0907 - 12ms/epoch - 6ms/step\n",
      "Epoch 736/1000\n",
      "2/2 - 0s - loss: 0.0905 - 12ms/epoch - 6ms/step\n",
      "Epoch 737/1000\n",
      "2/2 - 0s - loss: 0.0901 - 12ms/epoch - 6ms/step\n",
      "Epoch 738/1000\n",
      "2/2 - 0s - loss: 0.0897 - 14ms/epoch - 7ms/step\n",
      "Epoch 739/1000\n",
      "2/2 - 0s - loss: 0.0892 - 12ms/epoch - 6ms/step\n",
      "Epoch 740/1000\n",
      "2/2 - 0s - loss: 0.0891 - 12ms/epoch - 6ms/step\n",
      "Epoch 741/1000\n",
      "2/2 - 0s - loss: 0.0890 - 12ms/epoch - 6ms/step\n",
      "Epoch 742/1000\n",
      "2/2 - 0s - loss: 0.0892 - 12ms/epoch - 6ms/step\n",
      "Epoch 743/1000\n",
      "2/2 - 0s - loss: 0.0893 - 12ms/epoch - 6ms/step\n",
      "Epoch 744/1000\n",
      "2/2 - 0s - loss: 0.0895 - 13ms/epoch - 7ms/step\n",
      "Epoch 745/1000\n",
      "2/2 - 0s - loss: 0.0896 - 12ms/epoch - 6ms/step\n",
      "Epoch 746/1000\n",
      "2/2 - 0s - loss: 0.0896 - 12ms/epoch - 6ms/step\n",
      "Epoch 747/1000\n",
      "2/2 - 0s - loss: 0.0894 - 14ms/epoch - 7ms/step\n",
      "Epoch 748/1000\n",
      "2/2 - 0s - loss: 0.0892 - 12ms/epoch - 6ms/step\n",
      "Epoch 749/1000\n",
      "2/2 - 0s - loss: 0.0889 - 11ms/epoch - 6ms/step\n",
      "Epoch 750/1000\n",
      "2/2 - 0s - loss: 0.0887 - 11ms/epoch - 5ms/step\n",
      "Epoch 751/1000\n",
      "2/2 - 0s - loss: 0.0887 - 12ms/epoch - 6ms/step\n",
      "Epoch 752/1000\n",
      "2/2 - 0s - loss: 0.0887 - 11ms/epoch - 5ms/step\n",
      "Epoch 753/1000\n",
      "2/2 - 0s - loss: 0.0887 - 14ms/epoch - 7ms/step\n",
      "Epoch 754/1000\n",
      "2/2 - 0s - loss: 0.0888 - 13ms/epoch - 6ms/step\n",
      "Epoch 755/1000\n",
      "2/2 - 0s - loss: 0.0888 - 12ms/epoch - 6ms/step\n",
      "Epoch 756/1000\n",
      "2/2 - 0s - loss: 0.0887 - 12ms/epoch - 6ms/step\n",
      "Epoch 757/1000\n",
      "2/2 - 0s - loss: 0.0886 - 11ms/epoch - 6ms/step\n",
      "Epoch 758/1000\n",
      "2/2 - 0s - loss: 0.0885 - 12ms/epoch - 6ms/step\n",
      "Epoch 759/1000\n",
      "2/2 - 0s - loss: 0.0884 - 12ms/epoch - 6ms/step\n",
      "Epoch 760/1000\n",
      "2/2 - 0s - loss: 0.0884 - 14ms/epoch - 7ms/step\n",
      "Epoch 761/1000\n",
      "2/2 - 0s - loss: 0.0884 - 12ms/epoch - 6ms/step\n",
      "Epoch 762/1000\n",
      "2/2 - 0s - loss: 0.0884 - 11ms/epoch - 6ms/step\n",
      "Epoch 763/1000\n",
      "2/2 - 0s - loss: 0.0886 - 11ms/epoch - 5ms/step\n",
      "Epoch 764/1000\n",
      "2/2 - 0s - loss: 0.0885 - 12ms/epoch - 6ms/step\n",
      "Epoch 765/1000\n",
      "2/2 - 0s - loss: 0.0885 - 13ms/epoch - 6ms/step\n",
      "Epoch 766/1000\n",
      "2/2 - 0s - loss: 0.0884 - 12ms/epoch - 6ms/step\n",
      "Epoch 767/1000\n",
      "2/2 - 0s - loss: 0.0884 - 12ms/epoch - 6ms/step\n",
      "Epoch 768/1000\n",
      "2/2 - 0s - loss: 0.0885 - 16ms/epoch - 8ms/step\n",
      "Epoch 769/1000\n",
      "2/2 - 0s - loss: 0.0885 - 12ms/epoch - 6ms/step\n",
      "Epoch 770/1000\n",
      "2/2 - 0s - loss: 0.0884 - 13ms/epoch - 6ms/step\n",
      "Epoch 771/1000\n",
      "2/2 - 0s - loss: 0.0883 - 14ms/epoch - 7ms/step\n",
      "Epoch 772/1000\n",
      "2/2 - 0s - loss: 0.0883 - 12ms/epoch - 6ms/step\n",
      "Epoch 773/1000\n",
      "2/2 - 0s - loss: 0.0882 - 12ms/epoch - 6ms/step\n",
      "Epoch 774/1000\n",
      "2/2 - 0s - loss: 0.0882 - 14ms/epoch - 7ms/step\n",
      "Epoch 775/1000\n",
      "2/2 - 0s - loss: 0.0881 - 13ms/epoch - 6ms/step\n",
      "Epoch 776/1000\n",
      "2/2 - 0s - loss: 0.0881 - 15ms/epoch - 8ms/step\n",
      "Epoch 777/1000\n",
      "2/2 - 0s - loss: 0.0880 - 11ms/epoch - 6ms/step\n",
      "Epoch 778/1000\n",
      "2/2 - 0s - loss: 0.0879 - 12ms/epoch - 6ms/step\n",
      "Epoch 779/1000\n",
      "2/2 - 0s - loss: 0.0879 - 11ms/epoch - 6ms/step\n",
      "Epoch 780/1000\n",
      "2/2 - 0s - loss: 0.0879 - 14ms/epoch - 7ms/step\n",
      "Epoch 781/1000\n",
      "2/2 - 0s - loss: 0.0879 - 12ms/epoch - 6ms/step\n",
      "Epoch 782/1000\n",
      "2/2 - 0s - loss: 0.0878 - 14ms/epoch - 7ms/step\n",
      "Epoch 783/1000\n",
      "2/2 - 0s - loss: 0.0878 - 13ms/epoch - 7ms/step\n",
      "Epoch 784/1000\n",
      "2/2 - 0s - loss: 0.0879 - 15ms/epoch - 8ms/step\n",
      "Epoch 785/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 - 0s - loss: 0.0879 - 14ms/epoch - 7ms/step\n",
      "Epoch 786/1000\n",
      "2/2 - 0s - loss: 0.0878 - 13ms/epoch - 6ms/step\n",
      "Epoch 787/1000\n",
      "2/2 - 0s - loss: 0.0877 - 13ms/epoch - 7ms/step\n",
      "Epoch 788/1000\n",
      "2/2 - 0s - loss: 0.0876 - 13ms/epoch - 6ms/step\n",
      "Epoch 789/1000\n",
      "2/2 - 0s - loss: 0.0876 - 11ms/epoch - 5ms/step\n",
      "Epoch 790/1000\n",
      "2/2 - 0s - loss: 0.0875 - 15ms/epoch - 7ms/step\n",
      "Epoch 791/1000\n",
      "2/2 - 0s - loss: 0.0875 - 15ms/epoch - 8ms/step\n",
      "Epoch 792/1000\n",
      "2/2 - 0s - loss: 0.0875 - 13ms/epoch - 6ms/step\n",
      "Epoch 793/1000\n",
      "2/2 - 0s - loss: 0.0875 - 13ms/epoch - 7ms/step\n",
      "Epoch 794/1000\n",
      "2/2 - 0s - loss: 0.0874 - 12ms/epoch - 6ms/step\n",
      "Epoch 795/1000\n",
      "2/2 - 0s - loss: 0.0874 - 13ms/epoch - 7ms/step\n",
      "Epoch 796/1000\n",
      "2/2 - 0s - loss: 0.0874 - 12ms/epoch - 6ms/step\n",
      "Epoch 797/1000\n",
      "2/2 - 0s - loss: 0.0875 - 12ms/epoch - 6ms/step\n",
      "Epoch 798/1000\n",
      "2/2 - 0s - loss: 0.0877 - 12ms/epoch - 6ms/step\n",
      "Epoch 799/1000\n",
      "2/2 - 0s - loss: 0.0876 - 14ms/epoch - 7ms/step\n",
      "Epoch 800/1000\n",
      "2/2 - 0s - loss: 0.0876 - 14ms/epoch - 7ms/step\n",
      "Epoch 801/1000\n",
      "2/2 - 0s - loss: 0.0875 - 14ms/epoch - 7ms/step\n",
      "Epoch 802/1000\n",
      "2/2 - 0s - loss: 0.0875 - 15ms/epoch - 7ms/step\n",
      "Epoch 803/1000\n",
      "2/2 - 0s - loss: 0.0874 - 14ms/epoch - 7ms/step\n",
      "Epoch 804/1000\n",
      "2/2 - 0s - loss: 0.0874 - 14ms/epoch - 7ms/step\n",
      "Epoch 805/1000\n",
      "2/2 - 0s - loss: 0.0874 - 15ms/epoch - 7ms/step\n",
      "Epoch 806/1000\n",
      "2/2 - 0s - loss: 0.0874 - 14ms/epoch - 7ms/step\n",
      "Epoch 807/1000\n",
      "2/2 - 0s - loss: 0.0872 - 13ms/epoch - 7ms/step\n",
      "Epoch 808/1000\n",
      "2/2 - 0s - loss: 0.0872 - 13ms/epoch - 6ms/step\n",
      "Epoch 809/1000\n",
      "2/2 - 0s - loss: 0.0872 - 15ms/epoch - 7ms/step\n",
      "Epoch 810/1000\n",
      "2/2 - 0s - loss: 0.0871 - 13ms/epoch - 7ms/step\n",
      "Epoch 811/1000\n",
      "2/2 - 0s - loss: 0.0870 - 15ms/epoch - 8ms/step\n",
      "Epoch 812/1000\n",
      "2/2 - 0s - loss: 0.0870 - 14ms/epoch - 7ms/step\n",
      "Epoch 813/1000\n",
      "2/2 - 0s - loss: 0.0869 - 13ms/epoch - 7ms/step\n",
      "Epoch 814/1000\n",
      "2/2 - 0s - loss: 0.0868 - 13ms/epoch - 7ms/step\n",
      "Epoch 815/1000\n",
      "2/2 - 0s - loss: 0.0868 - 12ms/epoch - 6ms/step\n",
      "Epoch 816/1000\n",
      "2/2 - 0s - loss: 0.0867 - 26ms/epoch - 13ms/step\n",
      "Epoch 817/1000\n",
      "2/2 - 0s - loss: 0.0867 - 16ms/epoch - 8ms/step\n",
      "Epoch 818/1000\n",
      "2/2 - 0s - loss: 0.0867 - 15ms/epoch - 7ms/step\n",
      "Epoch 819/1000\n",
      "2/2 - 0s - loss: 0.0866 - 14ms/epoch - 7ms/step\n",
      "Epoch 820/1000\n",
      "2/2 - 0s - loss: 0.0866 - 11ms/epoch - 5ms/step\n",
      "Epoch 821/1000\n",
      "2/2 - 0s - loss: 0.0866 - 13ms/epoch - 6ms/step\n",
      "Epoch 822/1000\n",
      "2/2 - 0s - loss: 0.0866 - 11ms/epoch - 6ms/step\n",
      "Epoch 823/1000\n",
      "2/2 - 0s - loss: 0.0866 - 13ms/epoch - 6ms/step\n",
      "Epoch 824/1000\n",
      "2/2 - 0s - loss: 0.0866 - 13ms/epoch - 6ms/step\n",
      "Epoch 825/1000\n",
      "2/2 - 0s - loss: 0.0866 - 14ms/epoch - 7ms/step\n",
      "Epoch 826/1000\n",
      "2/2 - 0s - loss: 0.0865 - 13ms/epoch - 6ms/step\n",
      "Epoch 827/1000\n",
      "2/2 - 0s - loss: 0.0864 - 13ms/epoch - 6ms/step\n",
      "Epoch 828/1000\n",
      "2/2 - 0s - loss: 0.0864 - 14ms/epoch - 7ms/step\n",
      "Epoch 829/1000\n",
      "2/2 - 0s - loss: 0.0863 - 13ms/epoch - 7ms/step\n",
      "Epoch 830/1000\n",
      "2/2 - 0s - loss: 0.0864 - 15ms/epoch - 8ms/step\n",
      "Epoch 831/1000\n",
      "2/2 - 0s - loss: 0.0864 - 11ms/epoch - 5ms/step\n",
      "Epoch 832/1000\n",
      "2/2 - 0s - loss: 0.0864 - 12ms/epoch - 6ms/step\n",
      "Epoch 833/1000\n",
      "2/2 - 0s - loss: 0.0864 - 13ms/epoch - 6ms/step\n",
      "Epoch 834/1000\n",
      "2/2 - 0s - loss: 0.0864 - 16ms/epoch - 8ms/step\n",
      "Epoch 835/1000\n",
      "2/2 - 0s - loss: 0.0864 - 12ms/epoch - 6ms/step\n",
      "Epoch 836/1000\n",
      "2/2 - 0s - loss: 0.0864 - 14ms/epoch - 7ms/step\n",
      "Epoch 837/1000\n",
      "2/2 - 0s - loss: 0.0864 - 13ms/epoch - 6ms/step\n",
      "Epoch 838/1000\n",
      "2/2 - 0s - loss: 0.0864 - 14ms/epoch - 7ms/step\n",
      "Epoch 839/1000\n",
      "2/2 - 0s - loss: 0.0863 - 13ms/epoch - 7ms/step\n",
      "Epoch 840/1000\n",
      "2/2 - 0s - loss: 0.0864 - 13ms/epoch - 7ms/step\n",
      "Epoch 841/1000\n",
      "2/2 - 0s - loss: 0.0864 - 13ms/epoch - 7ms/step\n",
      "Epoch 842/1000\n",
      "2/2 - 0s - loss: 0.0863 - 14ms/epoch - 7ms/step\n",
      "Epoch 843/1000\n",
      "2/2 - 0s - loss: 0.0863 - 14ms/epoch - 7ms/step\n",
      "Epoch 844/1000\n",
      "2/2 - 0s - loss: 0.0862 - 12ms/epoch - 6ms/step\n",
      "Epoch 845/1000\n",
      "2/2 - 0s - loss: 0.0861 - 11ms/epoch - 6ms/step\n",
      "Epoch 846/1000\n",
      "2/2 - 0s - loss: 0.0860 - 13ms/epoch - 6ms/step\n",
      "Epoch 847/1000\n",
      "2/2 - 0s - loss: 0.0860 - 12ms/epoch - 6ms/step\n",
      "Epoch 848/1000\n",
      "2/2 - 0s - loss: 0.0859 - 10ms/epoch - 5ms/step\n",
      "Epoch 849/1000\n",
      "2/2 - 0s - loss: 0.0859 - 14ms/epoch - 7ms/step\n",
      "Epoch 850/1000\n",
      "2/2 - 0s - loss: 0.0859 - 11ms/epoch - 6ms/step\n",
      "Epoch 851/1000\n",
      "2/2 - 0s - loss: 0.0859 - 10ms/epoch - 5ms/step\n",
      "Epoch 852/1000\n",
      "2/2 - 0s - loss: 0.0858 - 13ms/epoch - 6ms/step\n",
      "Epoch 853/1000\n",
      "2/2 - 0s - loss: 0.0858 - 11ms/epoch - 5ms/step\n",
      "Epoch 854/1000\n",
      "2/2 - 0s - loss: 0.0858 - 13ms/epoch - 6ms/step\n",
      "Epoch 855/1000\n",
      "2/2 - 0s - loss: 0.0858 - 12ms/epoch - 6ms/step\n",
      "Epoch 856/1000\n",
      "2/2 - 0s - loss: 0.0857 - 12ms/epoch - 6ms/step\n",
      "Epoch 857/1000\n",
      "2/2 - 0s - loss: 0.0857 - 11ms/epoch - 5ms/step\n",
      "Epoch 858/1000\n",
      "2/2 - 0s - loss: 0.0857 - 12ms/epoch - 6ms/step\n",
      "Epoch 859/1000\n",
      "2/2 - 0s - loss: 0.0857 - 12ms/epoch - 6ms/step\n",
      "Epoch 860/1000\n",
      "2/2 - 0s - loss: 0.0857 - 12ms/epoch - 6ms/step\n",
      "Epoch 861/1000\n",
      "2/2 - 0s - loss: 0.0857 - 12ms/epoch - 6ms/step\n",
      "Epoch 862/1000\n",
      "2/2 - 0s - loss: 0.0856 - 12ms/epoch - 6ms/step\n",
      "Epoch 863/1000\n",
      "2/2 - 0s - loss: 0.0857 - 12ms/epoch - 6ms/step\n",
      "Epoch 864/1000\n",
      "2/2 - 0s - loss: 0.0856 - 12ms/epoch - 6ms/step\n",
      "Epoch 865/1000\n",
      "2/2 - 0s - loss: 0.0855 - 11ms/epoch - 6ms/step\n",
      "Epoch 866/1000\n",
      "2/2 - 0s - loss: 0.0855 - 11ms/epoch - 5ms/step\n",
      "Epoch 867/1000\n",
      "2/2 - 0s - loss: 0.0855 - 12ms/epoch - 6ms/step\n",
      "Epoch 868/1000\n",
      "2/2 - 0s - loss: 0.0854 - 10ms/epoch - 5ms/step\n",
      "Epoch 869/1000\n",
      "2/2 - 0s - loss: 0.0854 - 11ms/epoch - 5ms/step\n",
      "Epoch 870/1000\n",
      "2/2 - 0s - loss: 0.0854 - 11ms/epoch - 5ms/step\n",
      "Epoch 871/1000\n",
      "2/2 - 0s - loss: 0.0853 - 11ms/epoch - 5ms/step\n",
      "Epoch 872/1000\n",
      "2/2 - 0s - loss: 0.0854 - 12ms/epoch - 6ms/step\n",
      "Epoch 873/1000\n",
      "2/2 - 0s - loss: 0.0854 - 12ms/epoch - 6ms/step\n",
      "Epoch 874/1000\n",
      "2/2 - 0s - loss: 0.0855 - 13ms/epoch - 7ms/step\n",
      "Epoch 875/1000\n",
      "2/2 - 0s - loss: 0.0855 - 10ms/epoch - 5ms/step\n",
      "Epoch 876/1000\n",
      "2/2 - 0s - loss: 0.0855 - 11ms/epoch - 6ms/step\n",
      "Epoch 877/1000\n",
      "2/2 - 0s - loss: 0.0855 - 10ms/epoch - 5ms/step\n",
      "Epoch 878/1000\n",
      "2/2 - 0s - loss: 0.0856 - 10ms/epoch - 5ms/step\n",
      "Epoch 879/1000\n",
      "2/2 - 0s - loss: 0.0857 - 11ms/epoch - 5ms/step\n",
      "Epoch 880/1000\n",
      "2/2 - 0s - loss: 0.0857 - 10ms/epoch - 5ms/step\n",
      "Epoch 881/1000\n",
      "2/2 - 0s - loss: 0.0858 - 10ms/epoch - 5ms/step\n",
      "Epoch 882/1000\n",
      "2/2 - 0s - loss: 0.0857 - 10ms/epoch - 5ms/step\n",
      "Epoch 883/1000\n",
      "2/2 - 0s - loss: 0.0856 - 11ms/epoch - 5ms/step\n",
      "Epoch 884/1000\n",
      "2/2 - 0s - loss: 0.0854 - 12ms/epoch - 6ms/step\n",
      "Epoch 885/1000\n",
      "2/2 - 0s - loss: 0.0854 - 13ms/epoch - 7ms/step\n",
      "Epoch 886/1000\n",
      "2/2 - 0s - loss: 0.0853 - 11ms/epoch - 5ms/step\n",
      "Epoch 887/1000\n",
      "2/2 - 0s - loss: 0.0853 - 11ms/epoch - 5ms/step\n",
      "Epoch 888/1000\n",
      "2/2 - 0s - loss: 0.0852 - 13ms/epoch - 6ms/step\n",
      "Epoch 889/1000\n",
      "2/2 - 0s - loss: 0.0851 - 11ms/epoch - 6ms/step\n",
      "Epoch 890/1000\n",
      "2/2 - 0s - loss: 0.0851 - 11ms/epoch - 5ms/step\n",
      "Epoch 891/1000\n",
      "2/2 - 0s - loss: 0.0850 - 11ms/epoch - 5ms/step\n",
      "Epoch 892/1000\n",
      "2/2 - 0s - loss: 0.0850 - 11ms/epoch - 6ms/step\n",
      "Epoch 893/1000\n",
      "2/2 - 0s - loss: 0.0850 - 11ms/epoch - 6ms/step\n",
      "Epoch 894/1000\n",
      "2/2 - 0s - loss: 0.0850 - 11ms/epoch - 6ms/step\n",
      "Epoch 895/1000\n",
      "2/2 - 0s - loss: 0.0850 - 10ms/epoch - 5ms/step\n",
      "Epoch 896/1000\n",
      "2/2 - 0s - loss: 0.0850 - 11ms/epoch - 5ms/step\n",
      "Epoch 897/1000\n",
      "2/2 - 0s - loss: 0.0849 - 12ms/epoch - 6ms/step\n",
      "Epoch 898/1000\n",
      "2/2 - 0s - loss: 0.0849 - 12ms/epoch - 6ms/step\n",
      "Epoch 899/1000\n",
      "2/2 - 0s - loss: 0.0849 - 12ms/epoch - 6ms/step\n",
      "Epoch 900/1000\n",
      "2/2 - 0s - loss: 0.0849 - 11ms/epoch - 5ms/step\n",
      "Epoch 901/1000\n",
      "2/2 - 0s - loss: 0.0849 - 13ms/epoch - 6ms/step\n",
      "Epoch 902/1000\n",
      "2/2 - 0s - loss: 0.0849 - 12ms/epoch - 6ms/step\n",
      "Epoch 903/1000\n",
      "2/2 - 0s - loss: 0.0849 - 10ms/epoch - 5ms/step\n",
      "Epoch 904/1000\n",
      "2/2 - 0s - loss: 0.0848 - 11ms/epoch - 5ms/step\n",
      "Epoch 905/1000\n",
      "2/2 - 0s - loss: 0.0848 - 11ms/epoch - 5ms/step\n",
      "Epoch 906/1000\n",
      "2/2 - 0s - loss: 0.0848 - 12ms/epoch - 6ms/step\n",
      "Epoch 907/1000\n",
      "2/2 - 0s - loss: 0.0848 - 10ms/epoch - 5ms/step\n",
      "Epoch 908/1000\n",
      "2/2 - 0s - loss: 0.0847 - 11ms/epoch - 5ms/step\n",
      "Epoch 909/1000\n",
      "2/2 - 0s - loss: 0.0847 - 11ms/epoch - 5ms/step\n",
      "Epoch 910/1000\n",
      "2/2 - 0s - loss: 0.0847 - 12ms/epoch - 6ms/step\n",
      "Epoch 911/1000\n",
      "2/2 - 0s - loss: 0.0847 - 13ms/epoch - 7ms/step\n",
      "Epoch 912/1000\n",
      "2/2 - 0s - loss: 0.0847 - 10ms/epoch - 5ms/step\n",
      "Epoch 913/1000\n",
      "2/2 - 0s - loss: 0.0847 - 12ms/epoch - 6ms/step\n",
      "Epoch 914/1000\n",
      "2/2 - 0s - loss: 0.0847 - 12ms/epoch - 6ms/step\n",
      "Epoch 915/1000\n",
      "2/2 - 0s - loss: 0.0847 - 13ms/epoch - 7ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 916/1000\n",
      "2/2 - 0s - loss: 0.0846 - 15ms/epoch - 7ms/step\n",
      "Epoch 917/1000\n",
      "2/2 - 0s - loss: 0.0846 - 15ms/epoch - 8ms/step\n",
      "Epoch 918/1000\n",
      "2/2 - 0s - loss: 0.0846 - 14ms/epoch - 7ms/step\n",
      "Epoch 919/1000\n",
      "2/2 - 0s - loss: 0.0846 - 13ms/epoch - 7ms/step\n",
      "Epoch 920/1000\n",
      "2/2 - 0s - loss: 0.0846 - 16ms/epoch - 8ms/step\n",
      "Epoch 921/1000\n",
      "2/2 - 0s - loss: 0.0846 - 14ms/epoch - 7ms/step\n",
      "Epoch 922/1000\n",
      "2/2 - 0s - loss: 0.0846 - 12ms/epoch - 6ms/step\n",
      "Epoch 923/1000\n",
      "2/2 - 0s - loss: 0.0846 - 13ms/epoch - 7ms/step\n",
      "Epoch 924/1000\n",
      "2/2 - 0s - loss: 0.0846 - 12ms/epoch - 6ms/step\n",
      "Epoch 925/1000\n",
      "2/2 - 0s - loss: 0.0846 - 12ms/epoch - 6ms/step\n",
      "Epoch 926/1000\n",
      "2/2 - 0s - loss: 0.0848 - 13ms/epoch - 7ms/step\n",
      "Epoch 927/1000\n",
      "2/2 - 0s - loss: 0.0848 - 12ms/epoch - 6ms/step\n",
      "Epoch 928/1000\n",
      "2/2 - 0s - loss: 0.0847 - 12ms/epoch - 6ms/step\n",
      "Epoch 929/1000\n",
      "2/2 - 0s - loss: 0.0846 - 12ms/epoch - 6ms/step\n",
      "Epoch 930/1000\n",
      "2/2 - 0s - loss: 0.0845 - 11ms/epoch - 5ms/step\n",
      "Epoch 931/1000\n",
      "2/2 - 0s - loss: 0.0844 - 11ms/epoch - 6ms/step\n",
      "Epoch 932/1000\n",
      "2/2 - 0s - loss: 0.0844 - 12ms/epoch - 6ms/step\n",
      "Epoch 933/1000\n",
      "2/2 - 0s - loss: 0.0843 - 12ms/epoch - 6ms/step\n",
      "Epoch 934/1000\n",
      "2/2 - 0s - loss: 0.0843 - 12ms/epoch - 6ms/step\n",
      "Epoch 935/1000\n",
      "2/2 - 0s - loss: 0.0843 - 12ms/epoch - 6ms/step\n",
      "Epoch 936/1000\n",
      "2/2 - 0s - loss: 0.0843 - 11ms/epoch - 5ms/step\n",
      "Epoch 937/1000\n",
      "2/2 - 0s - loss: 0.0843 - 10ms/epoch - 5ms/step\n",
      "Epoch 938/1000\n",
      "2/2 - 0s - loss: 0.0843 - 11ms/epoch - 5ms/step\n",
      "Epoch 939/1000\n",
      "2/2 - 0s - loss: 0.0843 - 11ms/epoch - 6ms/step\n",
      "Epoch 940/1000\n",
      "2/2 - 0s - loss: 0.0843 - 10ms/epoch - 5ms/step\n",
      "Epoch 941/1000\n",
      "2/2 - 0s - loss: 0.0843 - 11ms/epoch - 5ms/step\n",
      "Epoch 942/1000\n",
      "2/2 - 0s - loss: 0.0842 - 13ms/epoch - 6ms/step\n",
      "Epoch 943/1000\n",
      "2/2 - 0s - loss: 0.0842 - 12ms/epoch - 6ms/step\n",
      "Epoch 944/1000\n",
      "2/2 - 0s - loss: 0.0842 - 12ms/epoch - 6ms/step\n",
      "Epoch 945/1000\n",
      "2/2 - 0s - loss: 0.0843 - 11ms/epoch - 6ms/step\n",
      "Epoch 946/1000\n",
      "2/2 - 0s - loss: 0.0844 - 11ms/epoch - 5ms/step\n",
      "Epoch 947/1000\n",
      "2/2 - 0s - loss: 0.0843 - 12ms/epoch - 6ms/step\n",
      "Epoch 948/1000\n",
      "2/2 - 0s - loss: 0.0842 - 12ms/epoch - 6ms/step\n",
      "Epoch 949/1000\n",
      "2/2 - 0s - loss: 0.0842 - 13ms/epoch - 7ms/step\n",
      "Epoch 950/1000\n",
      "2/2 - 0s - loss: 0.0841 - 10ms/epoch - 5ms/step\n",
      "Epoch 951/1000\n",
      "2/2 - 0s - loss: 0.0842 - 10ms/epoch - 5ms/step\n",
      "Epoch 952/1000\n",
      "2/2 - 0s - loss: 0.0842 - 12ms/epoch - 6ms/step\n",
      "Epoch 953/1000\n",
      "2/2 - 0s - loss: 0.0842 - 11ms/epoch - 5ms/step\n",
      "Epoch 954/1000\n",
      "2/2 - 0s - loss: 0.0843 - 10ms/epoch - 5ms/step\n",
      "Epoch 955/1000\n",
      "2/2 - 0s - loss: 0.0843 - 15ms/epoch - 8ms/step\n",
      "Epoch 956/1000\n",
      "2/2 - 0s - loss: 0.0843 - 12ms/epoch - 6ms/step\n",
      "Epoch 957/1000\n",
      "2/2 - 0s - loss: 0.0842 - 11ms/epoch - 5ms/step\n",
      "Epoch 958/1000\n",
      "2/2 - 0s - loss: 0.0842 - 12ms/epoch - 6ms/step\n",
      "Epoch 959/1000\n",
      "2/2 - 0s - loss: 0.0841 - 10ms/epoch - 5ms/step\n",
      "Epoch 960/1000\n",
      "2/2 - 0s - loss: 0.0841 - 13ms/epoch - 6ms/step\n",
      "Epoch 961/1000\n",
      "2/2 - 0s - loss: 0.0841 - 13ms/epoch - 6ms/step\n",
      "Epoch 962/1000\n",
      "2/2 - 0s - loss: 0.0842 - 12ms/epoch - 6ms/step\n",
      "Epoch 963/1000\n",
      "2/2 - 0s - loss: 0.0841 - 10ms/epoch - 5ms/step\n",
      "Epoch 964/1000\n",
      "2/2 - 0s - loss: 0.0840 - 11ms/epoch - 6ms/step\n",
      "Epoch 965/1000\n",
      "2/2 - 0s - loss: 0.0840 - 14ms/epoch - 7ms/step\n",
      "Epoch 966/1000\n",
      "2/2 - 0s - loss: 0.0839 - 11ms/epoch - 5ms/step\n",
      "Epoch 967/1000\n",
      "2/2 - 0s - loss: 0.0839 - 10ms/epoch - 5ms/step\n",
      "Epoch 968/1000\n",
      "2/2 - 0s - loss: 0.0839 - 10ms/epoch - 5ms/step\n",
      "Epoch 969/1000\n",
      "2/2 - 0s - loss: 0.0840 - 12ms/epoch - 6ms/step\n",
      "Epoch 970/1000\n",
      "2/2 - 0s - loss: 0.0840 - 10ms/epoch - 5ms/step\n",
      "Epoch 971/1000\n",
      "2/2 - 0s - loss: 0.0840 - 11ms/epoch - 6ms/step\n",
      "Epoch 972/1000\n",
      "2/2 - 0s - loss: 0.0840 - 11ms/epoch - 6ms/step\n",
      "Epoch 973/1000\n",
      "2/2 - 0s - loss: 0.0839 - 11ms/epoch - 5ms/step\n",
      "Epoch 974/1000\n",
      "2/2 - 0s - loss: 0.0839 - 11ms/epoch - 5ms/step\n",
      "Epoch 975/1000\n",
      "2/2 - 0s - loss: 0.0838 - 12ms/epoch - 6ms/step\n",
      "Epoch 976/1000\n",
      "2/2 - 0s - loss: 0.0839 - 12ms/epoch - 6ms/step\n",
      "Epoch 977/1000\n",
      "2/2 - 0s - loss: 0.0838 - 14ms/epoch - 7ms/step\n",
      "Epoch 978/1000\n",
      "2/2 - 0s - loss: 0.0838 - 12ms/epoch - 6ms/step\n",
      "Epoch 979/1000\n",
      "2/2 - 0s - loss: 0.0837 - 11ms/epoch - 5ms/step\n",
      "Epoch 980/1000\n",
      "2/2 - 0s - loss: 0.0837 - 12ms/epoch - 6ms/step\n",
      "Epoch 981/1000\n",
      "2/2 - 0s - loss: 0.0837 - 13ms/epoch - 7ms/step\n",
      "Epoch 982/1000\n",
      "2/2 - 0s - loss: 0.0837 - 14ms/epoch - 7ms/step\n",
      "Epoch 983/1000\n",
      "2/2 - 0s - loss: 0.0837 - 12ms/epoch - 6ms/step\n",
      "Epoch 984/1000\n",
      "2/2 - 0s - loss: 0.0837 - 13ms/epoch - 7ms/step\n",
      "Epoch 985/1000\n",
      "2/2 - 0s - loss: 0.0837 - 11ms/epoch - 5ms/step\n",
      "Epoch 986/1000\n",
      "2/2 - 0s - loss: 0.0836 - 13ms/epoch - 7ms/step\n",
      "Epoch 987/1000\n",
      "2/2 - 0s - loss: 0.0837 - 12ms/epoch - 6ms/step\n",
      "Epoch 988/1000\n",
      "2/2 - 0s - loss: 0.0837 - 11ms/epoch - 6ms/step\n",
      "Epoch 989/1000\n",
      "2/2 - 0s - loss: 0.0837 - 12ms/epoch - 6ms/step\n",
      "Epoch 990/1000\n",
      "2/2 - 0s - loss: 0.0837 - 12ms/epoch - 6ms/step\n",
      "Epoch 991/1000\n",
      "2/2 - 0s - loss: 0.0837 - 13ms/epoch - 6ms/step\n",
      "Epoch 992/1000\n",
      "2/2 - 0s - loss: 0.0837 - 12ms/epoch - 6ms/step\n",
      "Epoch 993/1000\n",
      "2/2 - 0s - loss: 0.0838 - 11ms/epoch - 5ms/step\n",
      "Epoch 994/1000\n",
      "2/2 - 0s - loss: 0.0838 - 10ms/epoch - 5ms/step\n",
      "Epoch 995/1000\n",
      "2/2 - 0s - loss: 0.0838 - 12ms/epoch - 6ms/step\n",
      "Epoch 996/1000\n",
      "2/2 - 0s - loss: 0.0837 - 12ms/epoch - 6ms/step\n",
      "Epoch 997/1000\n",
      "2/2 - 0s - loss: 0.0837 - 12ms/epoch - 6ms/step\n",
      "Epoch 998/1000\n",
      "2/2 - 0s - loss: 0.0836 - 11ms/epoch - 6ms/step\n",
      "Epoch 999/1000\n",
      "2/2 - 0s - loss: 0.0836 - 25ms/epoch - 13ms/step\n",
      "Epoch 1000/1000\n",
      "2/2 - 0s - loss: 0.0836 - 13ms/epoch - 6ms/step\n"
     ]
    }
   ],
   "source": [
    "with tf.device('/gpu:0'):\n",
    "    start = dt.now()\n",
    "    model.fit(XTrain_s,np.array(TTrain),epochs=noEpochs,verbose=2)\n",
    "    trainTime=(dt.now() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a852559c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train time:  0:00:16.496817 s\n"
     ]
    }
   ],
   "source": [
    "print(\"Train time: \",trainTime,\"s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e24d20bf",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5df4584d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 354ms/step\n"
     ]
    }
   ],
   "source": [
    "with tf.device('/cpu:0'):\n",
    "    startInf = dt.now()\n",
    "    Y_hat=model.predict(XTest_s)\n",
    "    inferenceTime=(dt.now() - startInf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eda1f3f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference time:  0:00:00.381303 s\n"
     ]
    }
   ],
   "source": [
    "print(\"Inference time: \",inferenceTime,\"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0edfc195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 2, 5)\n",
      "(36,)\n"
     ]
    }
   ],
   "source": [
    "print(XTrain_s.shape)\n",
    "print(TTrain.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d28eda96",
   "metadata": {},
   "source": [
    "## Plot prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e0d91afc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2520x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "plt.figure(figsize=(35,15))\n",
    "plt.plot(TTest,color='blue',label='Actual',alpha=0.5,linewidth=7.0)\n",
    "plt.plot(Y_hat,color='red',label='Predicted')\n",
    "plt.xlabel('Number')\n",
    "plt.ylabel('VO2')\n",
    "plt.title('Actual vs Predicted')\n",
    "plt.tight_layout()\n",
    "plt.legend(prop={'size': 30})\n",
    "plt.savefig(figPredictName,bbox_inches='tight',dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f8ef8bc",
   "metadata": {},
   "source": [
    "## Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95627e67",
   "metadata": {},
   "outputs": [],
   "source": [
    "if savedModel:\n",
    "    model.save(modelSaveName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7441ce70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1476x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Y_hat=np.transpose(Y_hat)\n",
    "TTest=TTest.reshape(len(TTest),1)\n",
    "#print(Y_hat)\n",
    "#diff=np.concatenate((Y_hat,TTest),axis=1)\n",
    "diff=np.subtract(Y_hat,TTest)\n",
    "#print(diff)\n",
    "\n",
    "plt.figure(figsize=(20.5,15.5))\n",
    "plt.plot(diff,color='red')\n",
    "plt.xlabel('Number')\n",
    "plt.ylabel('Diff')\n",
    "plt.title('Difference between grounded and predicted value')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(20.5, 10.5)\n",
    "#fig.savefig('1st-stage-subj-1_L'+results+'.jpg', dpi=150)\n",
    "plt.show()\n",
    "#model.save('1st-stage-subj-1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d4a8537",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.87054145  1.5625     -3.43304145 -0.15402073]\n",
      " [ 5.18986988  6.4981     -1.30823012  5.84398494]\n",
      " [ 7.02770519  6.4981      0.52960519  6.7629026 ]\n",
      " [ 7.01805973  6.4981      0.51995973  6.75807987]\n",
      " [ 8.87109661  8.4593      0.41179661  8.66519831]\n",
      " [ 8.54492283 10.011      -1.46607717  9.27796141]\n",
      " [ 9.43749905 10.011      -0.57350095  9.72424952]\n",
      " [ 8.14532566 10.011      -1.86567434  9.07816283]\n",
      " [ 6.63668156 10.011      -3.37431844  8.32384078]]\n"
     ]
    }
   ],
   "source": [
    "res=np.concatenate((Y_hat,TTest,Y_hat-TTest,(Y_hat+TTest)/2),axis=1)\n",
    "print(res)\n",
    "\n",
    "if exportCSV:\n",
    "    pd.DataFrame(res).to_csv('Subj-'+subjectID+'-predict.csv')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}