KTJ_No_ball_Legal_ball_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KTJ_No_ball_Legal_ball_Prediction.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOZShqHa5F25",
        "colab_type": "text"
      },
      "source": [
        "Note : This code is developed using google colaboratory with the help of necessary gpu support. It uses Google Drive at various instances to load and save necessary data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COepQ-Tur_Pg",
        "colab_type": "code",
        "outputId": "dd61a22f-b506-48ec-c701-1fee656f99e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0-rc0\n",
        "!pip install opencv-python"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow-gpu==2.0.0-rc0 in /usr/local/lib/python3.6/dist-packages (2.0.0rc0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (0.9.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (1.15.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (0.1.8)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (1.0.8)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (3.1.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (1.12.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (3.10.0)\n",
            "Requirement already satisfied: tb-nightly<1.15.0a20190807,>=1.15.0a20190806 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (1.15.0a20190806)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (1.11.2)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (0.2.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (0.33.6)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (0.8.1)\n",
            "Requirement already satisfied: tf-estimator-nightly<1.14.0.dev2019080602,>=1.14.0.dev2019080601 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (1.14.0.dev2019080601)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (1.1.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (1.1.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-rc0) (1.17.5)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu==2.0.0-rc0) (2.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0-rc0) (42.0.2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow-gpu==2.0.0-rc0) (0.16.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow-gpu==2.0.0-rc0) (3.1.1)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.6/dist-packages (4.1.2.30)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from opencv-python) (1.17.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJQ8dS973MOC",
        "colab_type": "text"
      },
      "source": [
        "#Importing necessary liberaries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOAxfdMVsga5",
        "colab_type": "code",
        "outputId": "35f12e8e-ad02-4ad2-9d8e-99997a01d121",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential, layers, models\n",
        "from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras.preprocessing import image\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm import tqdm\n",
        "\n",
        "from google.colab import files\n",
        "from IPython.display import Image\n",
        "from google.colab import drive\n",
        "from os import listdir\n",
        "from keras import datasets\n",
        "\n",
        "from tensorflow.python.keras.applications import vgg19\n",
        "\n",
        "drive.mount('/content/gdrive', force_remount='true')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GN-XzDDw3jtq",
        "colab_type": "text"
      },
      "source": [
        "#Importing Dataset from drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hecZgJlLEhOm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=[]\n",
        "y=[]\n",
        "\n",
        "path = '/content/gdrive/My Drive/training_100x100/legal/'\n",
        "data = [os.path.join(path, file) for file in os.listdir(path) if file.endswith('.jpg')]\n",
        "\n",
        "for i in range(len(data)):\n",
        "  X.append(cv2.imread(data[i], cv2.IMREAD_UNCHANGED))\n",
        "  y.append(0)\n",
        "  #print(i)\n",
        "  \n",
        "path = '/content/gdrive/My Drive/training_100x100/no/'\n",
        "data = [os.path.join(path, file) for file in os.listdir(path) if file.endswith('.jpg')]\n",
        "\n",
        "for i in range(len(data)):\n",
        "  X.append(cv2.imread(data[i], cv2.IMREAD_UNCHANGED))\n",
        "  y.append(1)\n",
        "  #print(i)\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6xHR-fw3vtn",
        "colab_type": "text"
      },
      "source": [
        "#Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjV91cMiIj5a",
        "colab_type": "code",
        "outputId": "d8059ee8-f894-450e-e319-ac0a88c692a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "X_max = np.max(X)\n",
        "X=X/X_max\n",
        "\n",
        "X=np.array(X)\n",
        "y=np.array(y)\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5690, 100, 100, 3)\n",
            "(5690,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUDuEqyX3zey",
        "colab_type": "text"
      },
      "source": [
        "#Image Specifications"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ENSV2rRxELb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "NCLASSES = 2\n",
        "HEIGHT = 100\n",
        "WIDTH = 100\n",
        "NUM_CHANNELS = 3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fJMrocQ386S",
        "colab_type": "text"
      },
      "source": [
        "#Model Initialisation using Transfer learning (VGG19)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbsdkVzpuxIF",
        "colab_type": "code",
        "outputId": "4fdd7e61-9234-4ba4-ec18-89e418706a76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "model=Sequential()\n",
        "model.add(vgg19.VGG19(include_top = False, input_shape = (HEIGHT, WIDTH, NUM_CHANNELS), weights ='imagenet', classes = 2, pooling  = MaxPool2D))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(4096, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.layers[0].trainable=False\n",
        "\n",
        "print(model.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg19 (Model)                (None, 3, 3, 512)         20024384  \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 4096)              18878464  \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 4097      \n",
            "=================================================================\n",
            "Total params: 38,906,945\n",
            "Trainable params: 18,882,561\n",
            "Non-trainable params: 20,024,384\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIzyii4yx1kI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_o6vOO1M4LiO",
        "colab_type": "text"
      },
      "source": [
        "#Training Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4uj4bw-pz65j",
        "colab_type": "code",
        "outputId": "13afd879-45c7-4516-b4a6-d526421e7483",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "checkpoint_path = \"/content/gdrive/My Drive/ktj/saves/checkpoints.pkl\"\n",
        "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,save_weights_only=True,verbose=1)\n",
        "history = model.fit(X_train, y_train, batch_size = 50, epochs = 30,callbacks=[cp_callback])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 5121 samples\n",
            "Epoch 1/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.9024 - accuracy: 0.7041\n",
            "Epoch 00001: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.9000 - accuracy: 0.7045\n",
            "Epoch 2/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.3284 - accuracy: 0.8339\n",
            "Epoch 00002: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 9s 2ms/sample - loss: 0.3280 - accuracy: 0.8340\n",
            "Epoch 3/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.2574 - accuracy: 0.8769\n",
            "Epoch 00003: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 9s 2ms/sample - loss: 0.2572 - accuracy: 0.8766\n",
            "Epoch 4/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.2142 - accuracy: 0.9024\n",
            "Epoch 00004: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.2140 - accuracy: 0.9026\n",
            "Epoch 5/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.2233 - accuracy: 0.8916\n",
            "Epoch 00005: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 9s 2ms/sample - loss: 0.2238 - accuracy: 0.8914\n",
            "Epoch 6/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1709 - accuracy: 0.9149\n",
            "Epoch 00006: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1707 - accuracy: 0.9149\n",
            "Epoch 7/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1706 - accuracy: 0.9171\n",
            "Epoch 00007: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1704 - accuracy: 0.9172\n",
            "Epoch 8/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1649 - accuracy: 0.9208\n",
            "Epoch 00008: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 9s 2ms/sample - loss: 0.1649 - accuracy: 0.9209\n",
            "Epoch 9/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1506 - accuracy: 0.9253\n",
            "Epoch 00009: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1507 - accuracy: 0.9250\n",
            "Epoch 10/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1630 - accuracy: 0.9261\n",
            "Epoch 00010: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1627 - accuracy: 0.9264\n",
            "Epoch 11/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1340 - accuracy: 0.9388\n",
            "Epoch 00011: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1337 - accuracy: 0.9391\n",
            "Epoch 12/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1390 - accuracy: 0.9320\n",
            "Epoch 00012: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1387 - accuracy: 0.9320\n",
            "Epoch 13/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1524 - accuracy: 0.9251\n",
            "Epoch 00013: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 9s 2ms/sample - loss: 0.1525 - accuracy: 0.9250\n",
            "Epoch 14/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1469 - accuracy: 0.9288\n",
            "Epoch 00014: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1469 - accuracy: 0.9291\n",
            "Epoch 15/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1196 - accuracy: 0.9424\n",
            "Epoch 00015: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 9s 2ms/sample - loss: 0.1192 - accuracy: 0.9426\n",
            "Epoch 16/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1120 - accuracy: 0.9475\n",
            "Epoch 00016: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 9s 2ms/sample - loss: 0.1118 - accuracy: 0.9477\n",
            "Epoch 17/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1260 - accuracy: 0.9371\n",
            "Epoch 00017: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1261 - accuracy: 0.9371\n",
            "Epoch 18/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1327 - accuracy: 0.9353\n",
            "Epoch 00018: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 9s 2ms/sample - loss: 0.1326 - accuracy: 0.9354\n",
            "Epoch 19/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1146 - accuracy: 0.9408\n",
            "Epoch 00019: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1147 - accuracy: 0.9406\n",
            "Epoch 20/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1280 - accuracy: 0.9367\n",
            "Epoch 00020: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1287 - accuracy: 0.9361\n",
            "Epoch 21/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1122 - accuracy: 0.9437\n",
            "Epoch 00021: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1122 - accuracy: 0.9438\n",
            "Epoch 22/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1130 - accuracy: 0.9463\n",
            "Epoch 00022: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1127 - accuracy: 0.9465\n",
            "Epoch 23/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1092 - accuracy: 0.9478\n",
            "Epoch 00023: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1091 - accuracy: 0.9479\n",
            "Epoch 24/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1101 - accuracy: 0.9475\n",
            "Epoch 00024: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1102 - accuracy: 0.9475\n",
            "Epoch 25/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1082 - accuracy: 0.9476\n",
            "Epoch 00025: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1085 - accuracy: 0.9475\n",
            "Epoch 26/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1104 - accuracy: 0.9461\n",
            "Epoch 00026: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1105 - accuracy: 0.9461\n",
            "Epoch 27/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1031 - accuracy: 0.9512\n",
            "Epoch 00027: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1027 - accuracy: 0.9514\n",
            "Epoch 28/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1059 - accuracy: 0.9473\n",
            "Epoch 00028: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1055 - accuracy: 0.9475\n",
            "Epoch 29/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1406 - accuracy: 0.9378\n",
            "Epoch 00029: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1409 - accuracy: 0.9377\n",
            "Epoch 30/30\n",
            "5100/5121 [============================>.] - ETA: 0s - loss: 0.1048 - accuracy: 0.9486\n",
            "Epoch 00030: saving model to /content/gdrive/My Drive/ktj/saves/checkpoints.pkl\n",
            "5121/5121 [==============================] - 10s 2ms/sample - loss: 0.1045 - accuracy: 0.9488\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1o2qKR54RcC",
        "colab_type": "text"
      },
      "source": [
        "#Model Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6mIiDJiz7QQ",
        "colab_type": "code",
        "outputId": "6f456179-0848-430e-a628-9fe3f775207f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Model Accuracy')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Model Accuracy')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BWYkMqj1bVVrJhh171ctaRNLCIX8Ku3spwZNJggb0E5H0csgkYWbZBP0ZJhA8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\nxlNQdwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWYNUdnk4UeB",
        "colab_type": "text"
      },
      "source": [
        "#Model Loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lF903L2Fz8Q9",
        "colab_type": "code",
        "outputId": "7b416f11-b3da-409f-abef-a8d544c28b20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history.history['loss'])\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Model Loss')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Model Loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lb/t+xWoZiEjMdemcgbv/CvhV0vP1wGWHeNlmgovT2owOlyXvd0fS058Cd3Wlnt6Wl5Ug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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9sXxbXV4Za-",
        "colab_type": "text"
      },
      "source": [
        "#Testing Model against Test Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrmWrKJdz8Vm",
        "colab_type": "code",
        "outputId": "6fcbac98-4b97-40fd-e595-65b9e26f04ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "y_pred = model.predict_classes(X_test)\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score\n",
        "print(\"accuracy score - \",accuracy_score(y_pred, y_test[:len(y_pred)]))\n",
        "print(\"Precision Score - \",precision_score(y_pred,y_test[:len(y_pred)]))\n",
        "print(\"Recall Score - \",recall_score(y_pred, y_test[:len(y_pred)]))\n",
        "print(\"f1 Score - \",f1_score(y_pred, y_test[:len(y_pred)]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy score -  0.8945518453427065\n",
            "Precision Score -  0.7722007722007722\n",
            "Recall Score -  0.9950248756218906\n",
            "f1 Score -  0.8695652173913043\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTOCJcAn4kC5",
        "colab_type": "text"
      },
      "source": [
        "#Saving Trained model to Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUO8SlUMPRFx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.backend import manual_variable_initialization\n",
        "manual_variable_initialization(True)\n",
        "model.save(\"/content/gdrive/My Drive/ktj/saves/model_save.h5\")\n",
        "model.save_weights(\"/content/gdrive/My Drive/ktj/saves/model_weights.pkl\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1S-Qb-kFjj06",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}