2DCNN_v2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.6"
    },
    "colab": {
      "name": "2DCNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sid-vt/DL-Research/blob/main/2DCNN_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZTOsA_Zf-Qf"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import os\n",
        "import glob\n",
        "\n",
        "from PIL import Image\n",
        "import cv2\n",
        "\n",
        "import random\n",
        "import math\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout, BatchNormalization\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "import sklearn as sk\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "pd.options.display.max_rows = 3000\n",
        "\n",
        "# import h5py\n",
        "# import torchvision"
      ],
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euMe_MF43R_c"
      },
      "source": [
        "# f=[]\r\n",
        "# for i in range(100):\r\n",
        "#   f.append(float(\"{0:.4f}\".format(random.uniform(-math.pi/2, math.pi/2))))\r\n",
        "# (unique, counts) = np.unique(f, return_counts=True)\r\n",
        "# frequencies = np.asarray((unique, counts)).T\r\n",
        "# frequencies\r\n",
        "# counts.sort()\r\n",
        "# counts"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSZb2l7cBK03",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1931c725-57be-4c25-e8d1-a68c9927dbaa"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6irloPy7-OvW"
      },
      "source": [
        "# top left corner +1 and bottom corner as it is \r\n",
        "# img_read[y1+1:y2, x1:x2]\r\n",
        "\r\n",
        "image = []\r\n",
        "for image_path in glob.glob('/content/drive/MyDrive/Colab Notebooks/ResearchCNN/MisesFiles_12/*.png'):\r\n",
        "    img_read = cv2.imread(image_path)\r\n",
        "    crop_img = img_read[81:147, 80:147]\r\n",
        "    resize = cv2.resize(crop_img, (64, 64))\r\n",
        "    image.append(resize)\r\n",
        "    # image.append(crop_img)\r\n",
        "image12 = np.asarray(image)"
      ],
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "F3OvtIqy-aSo",
        "outputId": "b3029d7d-dfc6-4299-b416-dc68e1f31ab4"
      },
      "source": [
        "data = image12[15]\r\n",
        "img_show = Image.fromarray(image12[15], 'RGB')\r\n",
        "plt.imshow(data, interpolation='nearest')\r\n",
        "plt.show()"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y74Ckpcr1fuS"
      },
      "source": [
        "image = []\r\n",
        "for image_path in glob.glob('/content/drive/MyDrive/Colab Notebooks/ResearchCNN/MisesFiles_24/*.png'):\r\n",
        "    img_read = cv2.imread(image_path)\r\n",
        "    crop_img = img_read[81:147, 80:147]\r\n",
        "    resize = cv2.resize(crop_img, (64, 64))\r\n",
        "    image.append(resize)\r\n",
        "    # image.append(crop_img)\r\n",
        "image24 = np.asarray(image)"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JaKQcK33Nee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "183cecb8-7d5e-42b0-ceb0-ef93a4d41185"
      },
      "source": [
        "data = image24[15]\r\n",
        "img_show = Image.fromarray(image12[15], 'RGB')\r\n",
        "plt.imshow(data, interpolation='nearest')\r\n",
        "plt.show()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmymEeqXf-Qr"
      },
      "source": [
        "X12 = image12\r\n",
        "X24 = image24"
      ],
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nBnq1dHsCgT1",
        "outputId": "623f3f2d-53fc-4918-d870-fc3a92dea75c"
      },
      "source": [
        "X12.shape\r\n",
        "X24.shape"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 64, 64, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bP9DBbkWIq7",
        "outputId": "68bab901-9017-4921-db6e-ff24a88643b5"
      },
      "source": [
        "X12_single = X12.mean(axis=3).mean(axis=2).mean(axis=1)\r\n",
        "X12_single"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([131.33333333, 131.33333333, 146.66666667, 146.66666667,\n",
              "       115.66666667, 115.66666667, 100.33333333, 146.66666667,\n",
              "       115.66666667, 162.        , 146.66666667, 146.66666667,\n",
              "       146.66666667, 146.66666667, 115.66666667, 115.66666667,\n",
              "       115.66666667, 115.66666667, 162.        , 115.66666667,\n",
              "        85.        , 162.        , 131.33333333, 115.66666667,\n",
              "       162.        , 115.66666667, 115.66666667, 115.66666667,\n",
              "        85.        , 131.33333333, 162.        , 162.        ,\n",
              "       131.33333333, 146.66666667, 146.66666667, 146.66666667,\n",
              "       115.66666667,  85.        , 115.66666667,  85.        ,\n",
              "       115.66666667, 115.66666667,  85.        , 146.66666667,\n",
              "        85.        , 162.        , 146.66666667,  85.        ,\n",
              "       115.66666667,  85.        , 100.33333333, 131.33333333,\n",
              "        85.        , 131.33333333, 131.33333333,  85.        ,\n",
              "        85.        , 146.66666667, 115.66666667, 115.66666667,\n",
              "       115.66666667, 162.        , 100.33333333,  85.        ,\n",
              "       146.66666667, 146.66666667, 115.66666667, 115.66666667,\n",
              "       131.33333333, 115.66666667,  85.        , 100.33333333,\n",
              "       100.33333333, 131.33333333, 100.33333333, 115.66666667,\n",
              "       115.66666667, 162.        ,  85.        , 131.33333333,\n",
              "       115.66666667, 115.66666667, 146.66666667, 115.66666667,\n",
              "       100.33333333, 115.66666667, 162.        , 162.        ,\n",
              "       100.33333333, 115.66666667,  85.        , 100.33333333,\n",
              "       115.66666667, 146.66666667, 146.66666667, 115.66666667,\n",
              "       131.33333333, 115.66666667, 162.        , 162.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNmAWdyaE68l",
        "outputId": "fc681d10-abd6-4720-edcc-37f3eb84ea63"
      },
      "source": [
        "X24_single = X24.mean(axis=3).mean(axis=2).mean(axis=1)\r\n",
        "X24_single"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([129.33333333,  85.        ,  99.66666667, 121.66666667,\n",
              "       151.33333333, 114.33333333, 129.33333333,  85.        ,\n",
              "        99.66666667,  99.66666667, 144.        ,  99.66666667,\n",
              "       129.33333333, 121.66666667, 121.66666667, 114.33333333,\n",
              "        99.66666667, 114.33333333, 121.66666667, 144.        ,\n",
              "       166.        , 129.33333333,  99.66666667, 107.        ,\n",
              "        85.        ,  99.66666667, 144.        , 144.        ,\n",
              "       144.        ,  92.33333333, 114.33333333, 151.33333333,\n",
              "       107.        , 129.33333333, 121.66666667, 129.33333333,\n",
              "       129.33333333, 121.66666667, 107.        , 121.66666667,\n",
              "       136.66666667, 114.33333333, 107.        ,  99.66666667,\n",
              "       158.66666667, 114.33333333, 114.33333333, 158.66666667,\n",
              "       158.66666667, 151.33333333, 114.33333333, 158.66666667,\n",
              "        85.        , 121.66666667, 114.33333333, 129.33333333,\n",
              "       144.        , 114.33333333, 107.        , 151.33333333,\n",
              "       166.        , 114.33333333, 114.33333333, 121.66666667,\n",
              "       114.33333333,  99.66666667, 151.33333333, 114.33333333,\n",
              "       114.33333333, 151.33333333, 114.33333333, 114.33333333,\n",
              "       107.        , 114.33333333, 107.        , 158.66666667,\n",
              "       158.66666667, 166.        , 158.66666667,  92.33333333,\n",
              "        99.66666667, 114.33333333, 129.33333333, 136.66666667,\n",
              "        99.66666667, 114.33333333, 121.66666667, 129.33333333,\n",
              "       166.        , 151.33333333, 114.33333333, 151.33333333,\n",
              "       158.66666667, 129.33333333, 158.66666667, 158.66666667,\n",
              "       144.        , 166.        , 114.33333333, 158.66666667])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbAz1fKef-Qt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8857220b-b436-4399-dbb1-19f043c3af4a"
      },
      "source": [
        "# Scaling with min max\n",
        "X12norm = (X12_single - X12_single.min())/(X12_single.max() - X12_single.min())\n",
        "X12norm"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.6017316, 0.6017316, 0.8008658, 0.8008658, 0.3982684, 0.3982684,\n",
              "       0.1991342, 0.8008658, 0.3982684, 1.       , 0.8008658, 0.8008658,\n",
              "       0.8008658, 0.8008658, 0.3982684, 0.3982684, 0.3982684, 0.3982684,\n",
              "       1.       , 0.3982684, 0.       , 1.       , 0.6017316, 0.3982684,\n",
              "       1.       , 0.3982684, 0.3982684, 0.3982684, 0.       , 0.6017316,\n",
              "       1.       , 1.       , 0.6017316, 0.8008658, 0.8008658, 0.8008658,\n",
              "       0.3982684, 0.       , 0.3982684, 0.       , 0.3982684, 0.3982684,\n",
              "       0.       , 0.8008658, 0.       , 1.       , 0.8008658, 0.       ,\n",
              "       0.3982684, 0.       , 0.1991342, 0.6017316, 0.       , 0.6017316,\n",
              "       0.6017316, 0.       , 0.       , 0.8008658, 0.3982684, 0.3982684,\n",
              "       0.3982684, 1.       , 0.1991342, 0.       , 0.8008658, 0.8008658,\n",
              "       0.3982684, 0.3982684, 0.6017316, 0.3982684, 0.       , 0.1991342,\n",
              "       0.1991342, 0.6017316, 0.1991342, 0.3982684, 0.3982684, 1.       ,\n",
              "       0.       , 0.6017316, 0.3982684, 0.3982684, 0.8008658, 0.3982684,\n",
              "       0.1991342, 0.3982684, 1.       , 1.       , 0.1991342, 0.3982684,\n",
              "       0.       , 0.1991342, 0.3982684, 0.8008658, 0.8008658, 0.3982684,\n",
              "       0.6017316, 0.3982684, 1.       , 1.       ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-u1_ORbaeAyB",
        "outputId": "e9a15689-1f16-4181-e312-f37ba2870617"
      },
      "source": [
        "X24norm = (X24_single - X24_single.min())/(X24_single.max() - X24_single.min())\r\n",
        "X24norm"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.5473251 , 0.        , 0.18106996, 0.4526749 , 0.81893004,\n",
              "       0.36213992, 0.5473251 , 0.        , 0.18106996, 0.18106996,\n",
              "       0.72839506, 0.18106996, 0.5473251 , 0.4526749 , 0.4526749 ,\n",
              "       0.36213992, 0.18106996, 0.36213992, 0.4526749 , 0.72839506,\n",
              "       1.        , 0.5473251 , 0.18106996, 0.27160494, 0.        ,\n",
              "       0.18106996, 0.72839506, 0.72839506, 0.72839506, 0.09053498,\n",
              "       0.36213992, 0.81893004, 0.27160494, 0.5473251 , 0.4526749 ,\n",
              "       0.5473251 , 0.5473251 , 0.4526749 , 0.27160494, 0.4526749 ,\n",
              "       0.63786008, 0.36213992, 0.27160494, 0.18106996, 0.90946502,\n",
              "       0.36213992, 0.36213992, 0.90946502, 0.90946502, 0.81893004,\n",
              "       0.36213992, 0.90946502, 0.        , 0.4526749 , 0.36213992,\n",
              "       0.5473251 , 0.72839506, 0.36213992, 0.27160494, 0.81893004,\n",
              "       1.        , 0.36213992, 0.36213992, 0.4526749 , 0.36213992,\n",
              "       0.18106996, 0.81893004, 0.36213992, 0.36213992, 0.81893004,\n",
              "       0.36213992, 0.36213992, 0.27160494, 0.36213992, 0.27160494,\n",
              "       0.90946502, 0.90946502, 1.        , 0.90946502, 0.09053498,\n",
              "       0.18106996, 0.36213992, 0.5473251 , 0.63786008, 0.18106996,\n",
              "       0.36213992, 0.4526749 , 0.5473251 , 1.        , 0.81893004,\n",
              "       0.36213992, 0.81893004, 0.90946502, 0.5473251 , 0.90946502,\n",
              "       0.90946502, 0.72839506, 1.        , 0.36213992, 0.90946502])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJ-5uRn0f-Qy"
      },
      "source": [
        "# y = []\n",
        "# for i in range (X.shape[0]):\n",
        "#     temp = X[i].mean()\n",
        "#     y.append(temp)"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Sk5sv2Zf-Q1"
      },
      "source": [
        "# X = np.array(y)"
      ],
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yaA_gJIf-Q2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25abe354-0267-48c4-a042-8f48d2312eeb"
      },
      "source": [
        "y12 = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/ResearchCNN/MisesFiles_12/results.txt')\r\n",
        "y12norm = (y12 - y12.min())/(y12.max() - y12.min())\r\n",
        "y12norm"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.31240073, 0.97630487, 0.45717335, 0.32810979, 0.6617581 ,\n",
              "       0.09344794, 0.99019417, 0.99376232, 0.98845972, 0.04173491,\n",
              "       0.98898419, 0.47555996, 0.76196271, 0.97503957, 0.80633126,\n",
              "       0.0034585 , 0.8965379 , 0.64886975, 0.9553336 , 0.15350148,\n",
              "       0.82259759, 0.98645767, 0.76069431, 0.97711326, 0.85377543,\n",
              "       0.06784888, 0.06154645, 0.09075054, 0.20594422, 0.84254595,\n",
              "       0.87331263, 0.77840316, 0.8211904 , 0.0824113 , 0.58474019,\n",
              "       0.67038252, 0.45617057, 0.92991316, 0.63018725, 0.02549518,\n",
              "       0.59352593, 0.36401269, 0.92829103, 0.66661365, 0.9629841 ,\n",
              "       0.75034022, 0.92443306, 0.05350253, 0.0044693 , 0.9029079 ,\n",
              "       0.11765714, 0.63887473, 0.5697476 , 0.18809039, 0.97087079,\n",
              "       0.17249408, 0.04372499, 0.05588195, 0.58301854, 0.79518187,\n",
              "       0.65931561, 0.92954268, 0.39699988, 0.18757647, 0.0977907 ,\n",
              "       0.86977559, 0.02419089, 0.00618377, 0.04425566, 0.02188227,\n",
              "       0.90804117, 0.9608237 , 0.02223685, 0.        , 0.23405066,\n",
              "       0.71854979, 0.22589258, 0.90657092, 0.43163791, 0.2115893 ,\n",
              "       0.94095769, 0.75493494, 0.41380504, 0.04659384, 0.92275744,\n",
              "       0.72905379, 0.99359425, 0.06988963, 0.81032816, 0.36928292,\n",
              "       1.        , 0.98387302, 0.85660768, 0.50659606, 0.25896802,\n",
              "       0.96298353, 0.8215409 , 0.11556233, 0.22023033, 0.6015006 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmh7MYkDc3nl",
        "outputId": "7b1674b7-5efd-4afb-a1d6-634f706bc684"
      },
      "source": [
        "y24 = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/ResearchCNN/MisesFiles_24/results.txt')\r\n",
        "y24norm = (y24 - y24.min())/(y24.max() - y24.min())\r\n",
        "y24norm"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.31240073, 0.97630487, 0.45717335, 0.32810979, 0.6617581 ,\n",
              "       0.09344794, 0.99019417, 0.99376232, 0.98845972, 0.04173491,\n",
              "       0.98898419, 0.47555996, 0.76196271, 0.97503957, 0.80633126,\n",
              "       0.0034585 , 0.8965379 , 0.64886975, 0.9553336 , 0.15350148,\n",
              "       0.82259759, 0.98645767, 0.76069431, 0.97711326, 0.85377543,\n",
              "       0.06784888, 0.06154645, 0.09075054, 0.20594422, 0.84254595,\n",
              "       0.87331263, 0.77840316, 0.8211904 , 0.0824113 , 0.58474019,\n",
              "       0.67038252, 0.45617057, 0.92991316, 0.63018725, 0.02549518,\n",
              "       0.59352593, 0.36401269, 0.92829103, 0.66661365, 0.9629841 ,\n",
              "       0.75034022, 0.92443306, 0.05350253, 0.0044693 , 0.9029079 ,\n",
              "       0.11765714, 0.63887473, 0.5697476 , 0.18809039, 0.97087079,\n",
              "       0.17249408, 0.04372499, 0.05588195, 0.58301854, 0.79518187,\n",
              "       0.65931561, 0.92954268, 0.39699988, 0.18757647, 0.0977907 ,\n",
              "       0.86977559, 0.02419089, 0.00618377, 0.04425566, 0.02188227,\n",
              "       0.90804117, 0.9608237 , 0.02223685, 0.        , 0.23405066,\n",
              "       0.71854979, 0.22589258, 0.90657092, 0.43163791, 0.2115893 ,\n",
              "       0.94095769, 0.75493494, 0.41380504, 0.04659384, 0.92275744,\n",
              "       0.72905379, 0.99359425, 0.06988963, 0.81032816, 0.36928292,\n",
              "       1.        , 0.98387302, 0.85660768, 0.50659606, 0.25896802,\n",
              "       0.96298353, 0.8215409 , 0.11556233, 0.22023033, 0.6015006 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VoFQeqBPf-Q4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "65b15067-640c-4ca4-980e-03b1d35f7806"
      },
      "source": [
        "plt.scatter(X12norm, y12norm)"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f5950256a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bqbJHUqW9XPLRyQj9IuCouz/h7r8F7gC2tjjuU8BngF9n2L/QdFkqqVIKNR+dBPQh4Mmmx8cabSeZ2QXAKne/Z6FfZGY7zGzCzCaefVZ7NixGl6WSqqgp1LydctmimfUBnwM+sNix7r4X2Av1OvRT/dup02WppCxaCrUInYzQa8CqpsdnN9pmvQp4E3C/mf0UuAQYz2NiNBpdlopINzoZoT8CrDWzNdQD+dXA+2afdPfngRWzj83sfuCv3b3zZaDSUtTl0VpwIrI0iwZ0d3/RzK4H9gH9wG3ufsjMbgYm3H08705GFu2ydGyyxuhdB5meqWfkalPHGb3rIKDKHpHFVGYvF4lh483f5bkXpl/WfubyASZvfFcPeiRSLgvt5VKZlaISQ6tgvlC7iLxEAV1EJBEK6FIqg8sGumoXkZcooEup7NqynoE+m9M20Gfs2rK+Rz0SqY7K7IcuMUQt1RTJggK6lE60Us2otN4gewroIlI47SSaD+XQRaRw2kk0Hxqhi5RAtPSDdhLNh0boIj0W8f6aukdwPhTQRXosYvpBO4nmQykXkR6LmH5QeWo+FNBFeizqjUxUnpo9pVxEekzpB8mKRugiPab0g2RFAV2kBJR+kCwo5SIikggFdBGRRCigi4gkQjl0EemJaNsdFEEBXUQKp90W86GUi4gULuJ2B0VQQBeRwkXc7qAICugiUjjttpgPBfSSG5ussWn3ftbsvIdNu/cnvaWqxKHtDvKhSdESizpxpOqH9Gm7g3wooJfYQhNHqb7xo36JRRRxu4O8BytKuZRYxIkjVT9Iqoq4M5UCeolFnDiK+CUGmiuJoIjBSqUCerQ3fcSJo4hfYhHvKRpREYOVjgK6mV1mZkfM7KiZ7Wzx/A1mdtjMHjWz75nZOZn1sCHim37bxiFuuXIDQ4PLMGBocBm3XLkh6bxjxC8xpZliKGKwsuikqJn1A7cC7wSOAY+Y2bi7H246bBIYcfcXzOzDwN8CV2XWS2JOEEK8iaOI1Q9R00zRjG5eN2fCH7IfrHRS5XIRcNTdnwAwszuArcDJgO7u9zUd/xBwbWY9bNCbPo5oX2JR7ykaTRGDlU4C+hDwZNPjY8DFCxx/HfCdVk+Y2Q5gB8Dw8HCHXazTm15SNbp5HaN3HWR6xk+2DfRb0mmmqPIerGQ6KWpm1wIjwJ5Wz7v7XncfcfeRlStXdvW7Lz2v9fHt2kUqxRd5LNKBTgJ6DVjV9PjsRtscZvYO4OPAFnf/TTbde8l9P3q2q3aRqtiz7wjTJ+ZG8OkTrklR6VonAf0RYK2ZrTGz04GrgfHmA8xsI/Al6sH8mey7qRy6pEvvbcnKogHd3V8Ergf2Af8DfNPdD5nZzWa2pXHYHuB3gTvN7AdmNt7m1y3Z4PKBrtpFqiJi7b3ko6O9XNz9XuDeeW03Nv38joz71aIP3bWLVEUR5WwSQ2U253r++HRX7SJVEbH2XvJRmYCussU4Im6fG632XvJRmb1cIi4JjyjiFg8iWalMQI+4r0lE2tdEZOkqk3IBXZZGoBI+kaWrzAhdYlAJn8jSKaBLqWiuRGTpKpVykfSphE9k6RTQSy5iCZ+ILI0CeonNlvDNVn3MlvAByQb1iOcskhXl0EssYglfxHMWyYoCeolFLOGLeM4iWVHKpcQibncQ8Zyj0vxQ9jRCL7GIJXy6M1UM2uIhHwroJRZxu4P/OPh0V+1STZoryYdSLiUXbbuDqTbbIbdrl2rSXEk+NEIXkcLpDmT50AhdSuXM5QM898LLR+Nn6oOelKh3IMt7IlgjdCmVm65YT3+fzWnr7zNuumJ9j3okeYh4B7IiJoIV0KV0/IQv+FiqL+KumkVMBFcqoI9N1ti0ez9rdt7Dpt37VeKUoF3jhzgxr+1Eo13SMbp5HQPzrsQG+izpktwiJoIrE9BVtxqDqlwCsUUeJ6aIq5LKBHTVrUrKol197tl3hOmZuam06RlP+vNcxELBygT0VsvBF2qXalo+0Pot2a49BRGvPiPWoW/bOMT2C4fot/qlSL8Z2y/Mdp1JZT4ls/8ROm2XanrFvBHMYu0piHj1GXFSdGyyxt0Hasw0ajNn3Ln7QC1mlctMmwLVdu1STVMtatAXak9BxNFqxH2KVOXSZKjNN3e7dqmmiCO3iOcccZ+iIr64K7NS9NLzVvJvD/28ZXvKom0xGvF1Ht28bs5dmiD90SrE26eoiK2hKzNCv+9Hz3bVnoKIk2URd1uMOFqNqIg0U2VG6BHzjAvl3FL9sEetQ482Wo1o9vXN84q7MgE94p1sIn6JiaQs7y/ujlIuZnaZmR0xs6NmtrPF868ws280nn/YzFZn3dGIs+IRJ8tO62tdhtquPRXRFhZJPhYN6GbWD9wKvBs4H7jGzM6fd9h1wHPu/vvA3wGfybqjEfOMEb/EXmyzEVe79hREnCuRfHSScrkIOOruTwCY2R3AVuBw0zFbgV2Nn+8CvmBm5p5tkXi0PGMROTfpvYhzJZKPTgL6EPBk0+NjwMXtjnH3F83seeD3gF80H2RmO4AdAMPDw0vscizRvsQi0lyJZKXQskV33+vuI+4+snJlunXFsnSb3vCartpTEHGuRPLRSUCvAauaHp/daGt5jJmdBpwB/DKLDkosX//gW14WvDe94TV8/YNv6VGP8hdxrkTy0UnK5RFgrZmtoR64rwbeN++YceBPge8DfwLszzp/LnGkHLxb0VyJZGXRgN7IiV8P7AP6gdvc/ZCZ3QxMuPs48M/A18zsKPAr6kFfRDqkuRLJQkcLi9z9XuDeeW03Nv38a+A92XZNRES6UZm9XEREZGEK6CIiiVBAFxFJhAK6iEgirFfVhWb2LPCzJf7fVzBvFWoAOucYdM4xnMo5n+PuLVdm9iygnwozm3D3kV73o0g65xh0zjHkdc5KuYiIJEIBXUQkEVUN6Ht73YEe0DnHoHOOIZdzrmQOXUREXq6qI3QREZlHAV1EJBGlDuhluDl10To45xvM7LCZPWpm3zOzc3rRzywtds5Nx203Mzezype4dXLOZvbexmt9yMz+veg+Zq2D9/awmd1nZpON9/flvehnVszsNjN7xsx+2OZ5M7PPN/57PGpmF5zyH3X3Uv6jvlXv48DrgdOBg8D58475c+CLjZ+vBr7R634XcM6XAssbP384wjk3jnsV8ADwEDDS634X8DqvBSaBMxuPX9vrfhdwznuBDzd+Ph/4aa/7fYrn/IfABcAP2zx/OfAdwIBLgIdP9W+WeYR+8ubU7v5bYPbm1M22Av/a+Pku4O1mZgX2MWuLnrO73+fuLzQePkT9DlJV1snrDPAp4DPAr4vsXE46OecPAre6+3MA7v5MwX3MWifn7MCrGz+fATxVYP8y5+4PUL8/RDtbga963UPAoJm97lT+ZpkDequbU8+/A8Ccm1MDszenrqpOzrnZddS/4ats0XNuXIqucvd7iuxYjjp5nc8FzjWzB83sITO7rLDe5aOTc94FXGtmx6jff+EjxXStZ7r9vC+qoxtcSPmY2bXACPBHve5LnsysD/gc8IEed6Vop1FPu7yV+lXYA2a2wd2netqrfF0DfMXdP2tmb6F+F7Q3ufuJXnesKso8Qo94c+pOzhkzewfwcWCLu/+moL7lZbFzfhXwJuB+M/sp9VzjeMUnRjt5nY8B4+4+7e4/AX5MPcBXVSfnfB3wTQB3/z7wSuqbWKWqo897N8oc0E/enNrMTqc+6Tk+75jZm1NDGjenXvSczWwj8CXqwbzqeVVY5Jzd/Xl3X+Huq919NfV5gy3uPtGb7maik/f2GPXROWa2gnoK5okiO5mxTs7558DbAczsjdQD+rOF9rJY48D7G9UulwDPu/vTp/Qbez0TvMgs8eXURyaPAx9vtN1M/QMN9Rf8TuAo8N/A63vd5wLO+b+A/wV+0Pg33us+533O8469n4pXuXT4Ohv1VNNh4DHg6l73uYBzPh94kHoFzA+Ad/W6z6d4vrcDTwPT1K+4rgM+BHyo6TW+tfHf47Es3tda+i8ikogyp1xERKQLCugiIolQQBcRSYQCuohIIhTQRUQSoYAuIpIIBXQRkUT8P1rCACWmDw9CAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "4ksDQk4EJyA3",
        "outputId": "8a66e6b7-58a7-4569-83fa-ef0e747b8945"
      },
      "source": [
        "plt.scatter(X24norm, y12norm)"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f594fa1a828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "ahR5TTxtPmfD",
        "outputId": "ede956b6-c237-4baf-e470-f415ead80a04"
      },
      "source": [
        "# x = np.random.normal(size = 1000)\r\n",
        "plt.hist(y24, bins=100)\r\n",
        "plt.gca().set(title='Frequency Histogram', ylabel='Frequency');"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcwbf5-WUU8c",
        "outputId": "cbe8fa96-b8e1-4db4-a3ee-92698311cfe3"
      },
      "source": [
        "(unique, counts) = np.unique(X24norm, return_counts=True)\r\n",
        "frequencies = np.asarray((unique, counts)).T\r\n",
        "frequencies"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.        ,  4.        ],\n",
              "       [ 0.09053498,  2.        ],\n",
              "       [ 0.18106996, 11.        ],\n",
              "       [ 0.27160494,  7.        ],\n",
              "       [ 0.36213992, 22.        ],\n",
              "       [ 0.4526749 , 10.        ],\n",
              "       [ 0.5473251 , 11.        ],\n",
              "       [ 0.63786008,  2.        ],\n",
              "       [ 0.72839506,  7.        ],\n",
              "       [ 0.81893004,  8.        ],\n",
              "       [ 0.90946502, 11.        ],\n",
              "       [ 1.        ,  5.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXLxRBx9IJuH",
        "outputId": "2c9ace4b-62a2-477c-d021-1befd7fe9d1c"
      },
      "source": [
        "(unique, counts) = np.unique(y24, return_counts=True)\r\n",
        "frequencies = np.asarray((unique, counts)).T\r\n",
        "counts"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTcGNt6ef-Q5"
      },
      "source": [
        "X12_train, X12_test, y12_train, y12_test = train_test_split(X12, y12norm , test_size=0.1)\r\n",
        "X24_train, X24_test, y24_train, y24_test = train_test_split(X24, y24norm , test_size=0.1)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0PM9nInf-Q6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79ef4cd3-bd35-46bc-dc6a-42829f289211"
      },
      "source": [
        "X12_train.shape"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90, 64, 64, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMr-AT9Ef-Q7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f053089f-86aa-46cb-d600-bb52d6f8b524"
      },
      "source": [
        "y12_train.shape"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXhhOiP2M7Cp",
        "outputId": "1f866656-a284-40cb-99c5-1994060f6c03"
      },
      "source": [
        "X24_train.shape"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90, 64, 64, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GUyvIrcM7Ku",
        "outputId": "dfe82479-0040-473c-bff9-d93e59d04490"
      },
      "source": [
        "y24_train.shape"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S1zZtzDGNYpT"
      },
      "source": [
        "model = Sequential()\r\n",
        "\r\n",
        "model.add(Conv2D(64, kernel_size=(2,2 ), activation='relu', kernel_initializer='glorot_uniform', input_shape=(64,64,3)))\r\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\r\n",
        "model.add(Dropout(0.1))\r\n",
        "\r\n",
        "model.add(Flatten())\r\n",
        "\r\n",
        "model.add(Dense(100, activation='relu', kernel_initializer='glorot_uniform'))\r\n",
        "model.add(Dense(1, activation='linear'))\r\n",
        "\r\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzB49ASINfMb"
      },
      "source": [
        "# Compile the model\r\n",
        "model.compile( loss=tf.keras.losses.MeanSquaredError(),\r\n",
        "              optimizer=keras.optimizers.Adam(lr=0.00001),\r\n",
        "              metrics=['mae'])\r\n",
        "\r\n",
        "# Fit data to model\r\n",
        "history = model.fit(X12_train ,y12_train, validation_data=(X12_test,y12_test),\r\n",
        "            batch_size=2,\r\n",
        "            epochs=500,\r\n",
        "            verbose=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCD5i_1wf-Q-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "38d0be28-b621-4443-a7a8-fe31bae24136"
      },
      "source": [
        "plt.semilogy(history.history['loss'])\n",
        "plt.semilogy(history.history['val_loss'])\n",
        "plt.title('Loss vs Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.yscale('log')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq94hhs0f-Q_"
      },
      "source": [
        "predictions_all = model.predict(X12)\n",
        "predictions_train = model.predict(X12_train)\n",
        "predictions_test = model.predict(X12_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acjwVhl7f-Q_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "5b2e0ab9-12a8-4875-ae43-2b67e319886e"
      },
      "source": [
        "xlim = 1\n",
        "x = np.linspace(0,xlim,11)\n",
        "plt.plot(x, x)\n",
        "\n",
        "# x = np.linspace(1,1,10)\n",
        "plt.scatter(predictions_all,y12)\n",
        "# plt.plot(x,x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa3d3732588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKgL1IT7f-Q8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ea1e272-5eee-4c0a-cb71-76169afba433"
      },
      "source": [
        "# model = Sequential()\n",
        "\n",
        "# model.add(Conv2D(64, kernel_size=(2,2 ), activation='relu', kernel_initializer='glorot_uniform', input_shape=(64,64,3)))\n",
        "# # model.add(BatchNormalization(axis = 1))\n",
        "# model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "# model.add(Dropout(0.1))\n",
        "\n",
        "# # model.add(Conv2D(64, kernel_size=(2, 2), activation='relu', kernel_initializer='glorot_uniform'))\n",
        "# # model.add(MaxPooling2D(pool_size=(2, 2,)))\n",
        "# # model.add(Dropout(0.5))\n",
        "\n",
        "# # model.add(Conv2D(128, kernel_size=(2, 2), activation='relu', kernel_initializer='glorot_uniform'))\n",
        "# # model.add(MaxPooling2D(pool_size=(2, 2,)))\n",
        "# # model.add(Dropout(0.5))\n",
        "\n",
        "# # model.add(Conv3D(256, kernel_size=(2, 2, 2), activation='relu', kernel_initializer='glorot_uniform'))\n",
        "# # model.add(MaxPooling3D(pool_size=(2, 2, 2)))\n",
        "# # model.add(BatchNormalization())\n",
        "\n",
        "# # model.add(BatchNormalization(center=True, scale=True))\n",
        "# # model.add(Dropout(0.5))\n",
        "\n",
        "\n",
        "# model.add(Flatten())\n",
        "\n",
        "# # model.add(Dense(256, activation='relu', kernel_initializer='glorot_uniform'))\n",
        "# model.add(Dense(100, activation='relu', kernel_initializer='glorot_uniform'))\n",
        "# model.add(Dense(1, activation='linear'))\n",
        "\n",
        "# model.summary()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 63, 63, 64)        832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 31, 31, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 31, 31, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 61504)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 100)               6150500   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 6,151,433\n",
            "Trainable params: 6,151,433\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "id": "DoBGbYIBf-Q9",
        "outputId": "abe5b1eb-638d-4220-e037-902e133f3121"
      },
      "source": [
        "# # Compile the model\n",
        "# model.compile( loss=tf.keras.losses.MeanSquaredError(),\n",
        "#               optimizer=keras.optimizers.Adam(lr=0.00001),\n",
        "#               metrics=['mae'])\n",
        "\n",
        "# # Fit data to model\n",
        "# history = model.fit(X_train ,y_train, validation_data=(X_test,y_test),\n",
        "#             batch_size=2,\n",
        "#             epochs=500,\n",
        "#             verbose=1)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-81-b2bf56edb6f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Fit data to model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m history = model.fit(X_train ,y_train, validation_data=(X_test,y_test),\n\u001b[0m\u001b[1;32m      8\u001b[0m             \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOALfuzcf-RA"
      },
      "source": [
        "import seaborn as snsn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JV2Z23cMf-RA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "outputId": "be18fd83-b74e-4d9f-e0e3-e3c8d2e77c8d"
      },
      "source": [
        "import seaborn as sns\n",
        "fig,ax=plt.subplots(figsize=(8,6))\n",
        "sns.kdeplot(predictions_test.ravel(),label=\"\",ax=ax,c='b')\n",
        "sns.kdeplot(y_test.ravel(),label=\"\",ax=ax,c='g')\n",
        "ax.legend(['Actual','Pred'],fontsize=14)\n",
        "ax.title.set_text('Test_set')\n",
        "\n",
        "fig,ax=plt.subplots(figsize=(8,6))\n",
        "sns.kdeplot(predictions_train.ravel(),label=\"\",ax=ax,c='b')\n",
        "sns.kdeplot(y_train.ravel(),label=\"\",ax=ax,c='g')\n",
        "ax.legend(['Actual','Pred'],fontsize=14)\n",
        "ax.title.set_text('Test_set')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQnuPsSLf-RB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e67d5b45-e37a-4daf-e014-1c4c9a66465b"
      },
      "source": [
        "sns.kdeplot(y,label=\"\",ax=ax,c='b')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa372197470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxIDnQTZf-RB"
      },
      "source": [
        "fig,ax=plt.subplots(figsize=(8,6))\n",
        "sns.kdeplot(y.ravel(),label=\"\",ax=ax,c='b')\n",
        "# sns.kdeplot(y_test.ravel(),label=\"\",ax=ax,c='g')\n",
        "ax.legend(['Actual','Pred'],fontsize=14)\n",
        "ax.title.set_text('Test_set')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UqGxyVcVBrR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}