Training.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Training.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xw0w2HtCObp8"
      },
      "source": [
        "# ***First we need to download the Dog vs Cat Dataset from Kaggle using Kaggle API***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUfzf9J1wHoX",
        "outputId": "59057ced-332f-49e5-bfeb-d667b0aacb74",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install --upgrade --force-reinstall --no-deps kaggle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting kaggle\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fe/52/3d13208c0f24c72b886c400e94748076222d5ffa4913fb410af50cb09219/kaggle-1.5.9.tar.gz (58kB)\n",
            "\r\u001b[K     |█████▋                          | 10kB 4.7MB/s eta 0:00:01\r\u001b[K     |███████████▎                    | 20kB 2.1MB/s eta 0:00:01\r\u001b[K     |████████████████▉               | 30kB 2.6MB/s eta 0:00:01\r\u001b[K     |██████████████████████▌         | 40kB 2.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▏   | 51kB 2.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 61kB 2.0MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: kaggle\n",
            "  Building wheel for kaggle (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaggle: filename=kaggle-1.5.9-cp36-none-any.whl size=73265 sha256=f113844ad571e5ba2620a52cdaf9ca3e2891639236fcfc096856cbc203813521\n",
            "  Stored in directory: /root/.cache/pip/wheels/68/6d/9b/7a98271454edcba3b56328cbc78c037286e787d004c8afee71\n",
            "Successfully built kaggle\n",
            "Installing collected packages: kaggle\n",
            "  Found existing installation: kaggle 1.5.9\n",
            "    Uninstalling kaggle-1.5.9:\n",
            "      Successfully uninstalled kaggle-1.5.9\n",
            "Successfully installed kaggle-1.5.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCs9oLwsxC7X"
      },
      "source": [
        "!mkdir -p ~/.kaggle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MZrLUXIxEog"
      },
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAMNufXoxIPS",
        "outputId": "cdf67fdd-4be0-4932-afd0-7b0fb3e2eb5d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!kaggle competitions download -c dogs-vs-cats"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading dogs-vs-cats.zip to /content/drive/My Drive/Task\n",
            " 99% 802M/812M [00:07<00:00, 141MB/s]\n",
            "100% 812M/812M [00:07<00:00, 118MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8gLgzScpxNHU",
        "outputId": "1d230e32-a379-451c-cc93-a6ab79563408",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "mkdir input"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘input’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ra2yX35nWbQz",
        "outputId": "c406f3e0-0891-4577-c585-8338fc1c0294",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!unzip dogs-vs-cats.zip -d input/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  dogs-vs-cats.zip\n",
            "  inflating: input/sampleSubmission.csv  \n",
            "  inflating: input/test1.zip         \n",
            "  inflating: input/train.zip         \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZXtbQ67WtAX",
        "outputId": "294a14e5-c186-4968-a6d6-3448fbe535a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!unzip test1.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKbuBhRaXbNc",
        "outputId": "e07f5e6b-786e-4438-fd35-fa70c40cf4ca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!unzip train.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tB6f5qz8aLD3",
        "outputId": "362ecd49-9aff-4961-9535-4b30edac600f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Task\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgYEZMZXQtiC"
      },
      "source": [
        "# ***As all the images are mixed in the original dataset, we need to filter the dog and category and place it in different folders as we will be using Keras Data Generators for loading images for training***\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ubb2RqwgxPRU"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"train\"):\n",
        "    os.mkdir(\"train\")\n",
        "if not os.path.exists(\"train/dog\"):\n",
        "    os.mkdir(\"train/dog\")\n",
        "if not os.path.exists(\"train/cat\"):\n",
        "    os.mkdir(\"train/cat\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOHSegXzyUir"
      },
      "source": [
        "import shutil\n",
        "from tqdm import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDCeFRTRyYyW",
        "outputId": "de253517-318a-48bc-ad4d-c06e18a864e2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "for i in tqdm(os.listdir(\"input/train\")):\n",
        "    if i.split(\".\")[0] == \"dog\":\n",
        "        shutil.copy2(os.path.join(\"input/train\",i),os.path.join(\"train/dog\",i))\n",
        "    elif i.split(\".\")[0] == \"cat\":\n",
        "        shutil.copy2(os.path.join(\"input/train\",i),os.path.join(\"train/cat\",i))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 25000/25000 [04:29<00:00, 92.81it/s] \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENJw9CdKywxp"
      },
      "source": [
        "if not os.path.exists(\"test\"):\n",
        "    os.mkdir(\"test\")\n",
        "if not os.path.exists(\"test/dog\"):\n",
        "    os.mkdir(\"test/dog\")\n",
        "if not os.path.exists(\"test/cat\"):\n",
        "    os.mkdir(\"test/cat\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZk4DvMDy4Gr",
        "outputId": "c996546c-d7e8-470c-e9f8-5c1bc9c7f453",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "for i in tqdm(os.listdir(\"train/dog\")[:3000]):\n",
        "    shutil.move(os.path.join(\"train/dog\",i),os.path.join(\"test/dog\",i)) \n",
        "for i in tqdm(os.listdir(\"train/cat\")[:3000]):\n",
        "    shutil.move(os.path.join(\"train/cat\",i),os.path.join(\"test/cat\",i)) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 3000/3000 [00:10<00:00, 289.61it/s]\n",
            "100%|██████████| 3000/3000 [00:11<00:00, 270.31it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MsI_zc8JTZiN"
      },
      "source": [
        "# ***Importing libraries needed for Training Model***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z43-AUi2zDqR"
      },
      "source": [
        "import keras\n",
        "from keras.models import Model\n",
        "from keras.layers import Dense\n",
        "from keras import optimizers\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.preprocessing import image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1Xn1xP3TnbQ"
      },
      "source": [
        "# ***19000 training images***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukvXtl6NzH0n",
        "outputId": "5ab4463a-d537-4eee-c874-6bc6744c1de0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "trdata = ImageDataGenerator()\n",
        "traindata = trdata.flow_from_directory(directory=\"train\",target_size=(224,224))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 19000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjfUq-2LTvCN"
      },
      "source": [
        "# ***6000 testing images***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_m8w5S9zRRM",
        "outputId": "102fd6ae-8cbf-4fde-f83c-868a921f3cc0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "tsdata = ImageDataGenerator()\n",
        "testdata = tsdata.flow_from_directory(directory=\"test\", target_size=(224,224))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 6000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F7uU8KTjT0zn"
      },
      "source": [
        "# ***Importing VGG16 Model from the Keras Library and using imagenet weights for the convolutional layers***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLZoIHHuzUGT"
      },
      "source": [
        "from keras.applications.vgg16 import VGG16"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-e2FlbVZzWJ4"
      },
      "source": [
        "vggmodel = VGG16(weights='imagenet', include_top=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0d1lj52NzeUh",
        "outputId": "b83aba8f-9083-4593-8293-a7efbb2f610c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "vggmodel.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "predictions (Dense)          (None, 1000)              4097000   \n",
            "=================================================================\n",
            "Total params: 138,357,544\n",
            "Trainable params: 138,357,544\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CsJSbfrEUC6d"
      },
      "source": [
        "# ***Freezing the first 19 layers of VGG16***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4Sfl4n6ziDT",
        "outputId": "231c1aab-7703-43bf-aeca-bba8887b1418",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "for layers in (vggmodel.layers)[:19]:\n",
        "    print(layers)\n",
        "    layers.trainable = False"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff01f18e4e0>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff01f18eba8>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff02015d828>\n",
            "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7ff01f183c50>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7ff02009a2b0>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fefe2be75f8>\n",
            "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7fefe2be79e8>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fefdc4e8588>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fefdc4ed400>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fefdc4ed828>\n",
            "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7fefdc4f1400>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fefdc4f1be0>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fefdc4ffa58>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fefdc4ffb38>\n",
            "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7fefdc508748>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fefdc510278>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fefdc510e80>\n",
            "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fefdc517518>\n",
            "<tensorflow.python.keras.layers.pooling.MaxPooling2D object at 0x7fefdc517d68>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_aGvJ3A6UPYb"
      },
      "source": [
        "# ***As our dataset only have 2 possible classese, Dog and Cat, we need to change the last layer output size from 1000 (Imagenet Categories) to 2***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nndihUhLzjT8"
      },
      "source": [
        "X= vggmodel.layers[-2].output\n",
        "predictions = Dense(2, activation=\"softmax\", name = 'predictions')(X)\n",
        "model_final = Model(inputs = vggmodel.input, outputs = predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDEa59y8UtGJ"
      },
      "source": [
        "# ***Defining the loss function, optimizer and the metric***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDY9cyiw41yK"
      },
      "source": [
        "model_final.compile(loss = \"categorical_crossentropy\", optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), metrics=[\"accuracy\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IR6FauzL45A-",
        "outputId": "f7baf124-9d03-4db7-a4d1-a1bd3d2205f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model_final.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "predictions (Dense)          (None, 2)                 8194      \n",
            "=================================================================\n",
            "Total params: 134,268,738\n",
            "Trainable params: 119,554,050\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-myZpgBd4-4a"
      },
      "source": [
        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLoK5zfhU9jS"
      },
      "source": [
        "# ***Using Model Callbacks, Model Checkpoint and Early Stopping***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3lstrxj5fwU",
        "outputId": "6069c024-41f0-468c-a002-bbb3f0f12126",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "checkpoint = ModelCheckpoint(\"vgg16_1.h5\", monitor='val_accuracy', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n",
        "early = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=6, verbose=1, mode='auto')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CP2X7X6GVOYB"
      },
      "source": [
        "# ***Setting epoch = 100, step per epoch = 2***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bt1FDDV95hbV",
        "outputId": "223d6143-d5f8-4dfa-a3df-6ee585890bd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model_final.fit_generator(generator= traindata, steps_per_epoch= 2, epochs= 100, validation_data= testdata, validation_steps=1, callbacks=[checkpoint,early])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-42-b4ccdc769145>:1: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 1.0368 - accuracy: 0.6094\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.81250, saving model to vgg16_1.h5\n",
            "2/2 [==============================] - 43s 22s/step - loss: 1.0368 - accuracy: 0.6094 - val_loss: 0.2987 - val_accuracy: 0.8125\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.6138 - accuracy: 0.6875\n",
            "Epoch 00002: val_accuracy improved from 0.81250 to 0.84375, saving model to vgg16_1.h5\n",
            "2/2 [==============================] - 39s 20s/step - loss: 0.6138 - accuracy: 0.6875 - val_loss: 0.3501 - val_accuracy: 0.8438\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.2388 - accuracy: 0.9062\n",
            "Epoch 00003: val_accuracy improved from 0.84375 to 0.87500, saving model to vgg16_1.h5\n",
            "2/2 [==============================] - 39s 19s/step - loss: 0.2388 - accuracy: 0.9062 - val_loss: 0.2776 - val_accuracy: 0.8750\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.4087 - accuracy: 0.8438\n",
            "Epoch 00004: val_accuracy did not improve from 0.87500\n",
            "2/2 [==============================] - 34s 17s/step - loss: 0.4087 - accuracy: 0.8438 - val_loss: 0.5712 - val_accuracy: 0.8125\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.2525 - accuracy: 0.8906\n",
            "Epoch 00005: val_accuracy improved from 0.87500 to 0.96875, saving model to vgg16_1.h5\n",
            "2/2 [==============================] - 37s 19s/step - loss: 0.2525 - accuracy: 0.8906 - val_loss: 0.1617 - val_accuracy: 0.9688\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1071 - accuracy: 0.9531\n",
            "Epoch 00006: val_accuracy did not improve from 0.96875\n",
            "2/2 [==============================] - 33s 17s/step - loss: 0.1071 - accuracy: 0.9531 - val_loss: 0.2229 - val_accuracy: 0.9375\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.2027 - accuracy: 0.9219\n",
            "Epoch 00007: val_accuracy did not improve from 0.96875\n",
            "2/2 [==============================] - 33s 17s/step - loss: 0.2027 - accuracy: 0.9219 - val_loss: 0.1520 - val_accuracy: 0.9375\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1410 - accuracy: 0.9375\n",
            "Epoch 00008: val_accuracy improved from 0.96875 to 1.00000, saving model to vgg16_1.h5\n",
            "2/2 [==============================] - 40s 20s/step - loss: 0.1410 - accuracy: 0.9375 - val_loss: 0.0304 - val_accuracy: 1.0000\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.3070 - accuracy: 0.9062\n",
            "Epoch 00009: val_accuracy did not improve from 1.00000\n",
            "2/2 [==============================] - 33s 17s/step - loss: 0.3070 - accuracy: 0.9062 - val_loss: 0.1860 - val_accuracy: 0.9688\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.2413 - accuracy: 0.8906\n",
            "Epoch 00010: val_accuracy did not improve from 1.00000\n",
            "2/2 [==============================] - 33s 17s/step - loss: 0.2413 - accuracy: 0.8906 - val_loss: 0.0372 - val_accuracy: 0.9688\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1398 - accuracy: 0.9688\n",
            "Epoch 00011: val_accuracy did not improve from 1.00000\n",
            "2/2 [==============================] - 32s 16s/step - loss: 0.1398 - accuracy: 0.9688 - val_loss: 0.0323 - val_accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1969 - accuracy: 0.9375\n",
            "Epoch 00012: val_accuracy did not improve from 1.00000\n",
            "2/2 [==============================] - 32s 16s/step - loss: 0.1969 - accuracy: 0.9375 - val_loss: 0.0948 - val_accuracy: 0.9688\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1924 - accuracy: 0.9375\n",
            "Epoch 00013: val_accuracy did not improve from 1.00000\n",
            "2/2 [==============================] - 32s 16s/step - loss: 0.1924 - accuracy: 0.9375 - val_loss: 0.1555 - val_accuracy: 0.9062\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1504 - accuracy: 0.9062\n",
            "Epoch 00014: val_accuracy did not improve from 1.00000\n",
            "2/2 [==============================] - 33s 16s/step - loss: 0.1504 - accuracy: 0.9062 - val_loss: 0.2692 - val_accuracy: 0.9062\n",
            "Epoch 00014: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd75b153e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D1ugSHBVVfYS"
      },
      "source": [
        "# ***I got almost 100 percent Accuracy on 8th Epoch***"
      ]
    }
  ]
}