Transfer_Learning_VGG_16_&_VGG_19_.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Zain-Pirzada/Diabetic-Reinopathy-Kaggle-Competition/blob/main/Transfer_Learning_VGG_16_%26_VGG_19_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mXVgTWQN5o0"
      },
      "source": [
        "**Transfer Learning VGG 16 and VGG 19 using Keras**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xo-KinWmOFtq"
      },
      "source": [
        "**Import data from gogle drive**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRCVxWZuOKAU",
        "outputId": "a36bfbdf-e3d7-4cdb-e1a9-5fc8a7b0861c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YxVWX7IOOjbr"
      },
      "source": [
        "**Import Keras**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9TM-Fr2pOl-p"
      },
      "outputs": [],
      "source": [
        "!pip install -q keras   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Ti9PlbuJOmuj"
      },
      "outputs": [],
      "source": [
        "import keras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R13Vk-eQOo_6",
        "outputId": "8b99a2dd-7878-41a3-cb2e-0413f7238c13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.8/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow-gpu) (1.3.1)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-gpu) (2022.9.24)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-gpu) (1.24.3)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow-gpu) (3.2.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->tensorflow-gpu) (3.0.9)\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, keras, flatbuffers, tensorflow-gpu\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.9.0\n",
            "    Uninstalling tensorflow-estimator-2.9.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.9.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.9.1\n",
            "    Uninstalling tensorboard-2.9.1:\n",
            "      Successfully uninstalled tensorboard-2.9.1\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.9.0\n",
            "    Uninstalling keras-2.9.0:\n",
            "      Successfully uninstalled keras-2.9.0\n",
            "  Attempting uninstall: flatbuffers\n",
            "    Found existing installation: flatbuffers 1.12\n",
            "    Uninstalling flatbuffers-1.12:\n",
            "      Successfully uninstalled flatbuffers-1.12\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.9.2 requires flatbuffers<2,>=1.12, but you have flatbuffers 22.12.6 which is incompatible.\n",
            "tensorflow 2.9.2 requires keras<2.10.0,>=2.9.0rc0, but you have keras 2.11.0 which is incompatible.\n",
            "tensorflow 2.9.2 requires tensorboard<2.10,>=2.9, but you have tensorboard 2.11.0 which is incompatible.\n",
            "tensorflow 2.9.2 requires tensorflow-estimator<2.10.0,>=2.9.0rc0, but you have tensorflow-estimator 2.11.0 which is incompatible.\u001b[0m\n",
            "Successfully installed flatbuffers-22.12.6 keras-2.11.0 tensorboard-2.11.0 tensorflow-estimator-2.11.0 tensorflow-gpu-2.11.0\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "flatbuffers",
                  "keras",
                  "tensorboard",
                  "tensorflow"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "!pip install tensorflow-gpu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RK6XA7p9OrGB",
        "outputId": "72398790-64fc-454e-e86c-6c2cb4dde346"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Thu Dec 22 12:40:30 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   62C    P0    30W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6DXyioKZO439"
      },
      "source": [
        "**Transfer Learning using VGG 16 & VGG 19 Using Keras**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "im553HJAPDRL"
      },
      "outputs": [],
      "source": [
        "from tensorflow.compat.v1 import ConfigProto\n",
        "from tensorflow.compat.v1 import InteractiveSession\n",
        "\n",
        "config = ConfigProto()\n",
        "config.gpu_options.per_process_gpu_memory_fraction = 0.5\n",
        "config.gpu_options.allow_growth = True\n",
        "session = InteractiveSession(config=config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMs4bBJjPgfv",
        "outputId": "3eb70b0a-ecae-4d1e-92e1-2593d6b09e19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.9.2\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "BY3JnCKYPkVS"
      },
      "outputs": [],
      "source": [
        "# import the libraries as shown below\n",
        "\n",
        "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.applications.vgg19 import VGG19\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
        "from tensorflow.keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "#import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "UoGfFd7KPq5w"
      },
      "outputs": [],
      "source": [
        "# re-size all the images to this\n",
        "IMAGE_SIZE = [224, 224]\n",
        "\n",
        "train_path = '/content/drive/MyDrive/DATASETS__resize_split/val'\n",
        "valid_path = '/content/drive/MyDrive/DATASETS__resize_split/test'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r615NUpRPv44",
        "outputId": "9b3b9b13-8b18-465e-bfef-a513c26b29f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80134624/80134624 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# Import the VGG16 library as shown below and add preprocessing layer to the front of VGG\n",
        "# Here we will be using imagenet weights\n",
        "\n",
        "vgg19 = VGG19(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "uInvGsfQP0gr"
      },
      "outputs": [],
      "source": [
        "# don't train existing weights\n",
        "for layer in vgg19.layers:\n",
        "    layer.trainable = True\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "iDtp20teQFqY"
      },
      "outputs": [],
      "source": [
        "# useful for getting number of output classes\n",
        "folders = glob('/content/drive/MyDrive/DATASETS__resize_split/val*')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_pKIVzsQIRU",
        "outputId": "19720c9e-eb46-4c25-c36c-f2ba77518b92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/DATASETS__resize_split/val']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "folders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "JxHthpkVQJ5U"
      },
      "outputs": [],
      "source": [
        "# our layers - you can add more if you want\n",
        "x = Flatten()(vgg19.output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aNnVeXHQN7i",
        "outputId": "f9586d8e-b10b-430e-f049-3f874563acba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "len(folders)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "vBvTUk5hQQKK"
      },
      "outputs": [],
      "source": [
        "prediction = Dense(5, activation='softmax')(x)\n",
        "\n",
        "# create a model object\n",
        "model = Model(inputs=vgg19.input, outputs=prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WiUb67mgQoIp",
        "outputId": "a4361daf-64c2-4d9c-a743-64bf31e66843"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\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_conv4 (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_conv4 (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_conv4 (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",
            " dense (Dense)               (None, 5)                 125445    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 20,149,829\n",
            "Trainable params: 20,149,829\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# view the structure of the model\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Wpc4bIsaQqJn"
      },
      "outputs": [],
      "source": [
        "# tell the model what cost and optimization method to use\n",
        "model.compile(\n",
        "  loss='categorical_crossentropy',\n",
        "  optimizer='adam',\n",
        "  metrics=['accuracy']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "zFieF2zxQxC2"
      },
      "outputs": [],
      "source": [
        "# Use the Image Data Generator to import the images from the dataset\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "327-ERASQ00s",
        "outputId": "9a6e6c96-8ef5-4b75-d94a-22cc4f6666e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 7033 images belonging to 5 classes.\n"
          ]
        }
      ],
      "source": [
        "# Make sure you provide the same target size as initialied for the image size\n",
        "training_set = train_datagen.flow_from_directory('/content/drive/MyDrive/DATASETS__resize_split/val',\n",
        "                                                 target_size = (224, 224),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'categorical')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vN-CSUpbQ3l5",
        "outputId": "b3da8b73-0ca7-4584-de48-2d8ff6bb8f01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 7030 images belonging to 5 classes.\n"
          ]
        }
      ],
      "source": [
        "test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/DATASETS__resize_split/test',\n",
        "                                            target_size = (224, 224),\n",
        "                                            batch_size = 32,\n",
        "                                            class_mode = 'categorical')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEmu_pehRJl1",
        "outputId": "0cf657a1-969f-408b-fc85-fa5bde07ff30"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-2d02736eff38>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  r = model.fit_generator(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "220/220 [==============================] - 4353s 20s/step - loss: 2.0331 - accuracy: 0.7281 - val_loss: 0.8726 - val_accuracy: 0.7343\n",
            "Epoch 2/20\n",
            "220/220 [==============================] - 154s 700ms/step - loss: 0.8757 - accuracy: 0.7354 - val_loss: 0.8831 - val_accuracy: 0.7343\n",
            "Epoch 3/20\n",
            "220/220 [==============================] - 154s 701ms/step - loss: 0.8786 - accuracy: 0.7354 - val_loss: 0.8697 - val_accuracy: 0.7343\n",
            "Epoch 4/20\n",
            "220/220 [==============================] - 152s 691ms/step - loss: 0.8739 - accuracy: 0.7354 - val_loss: 0.8730 - val_accuracy: 0.7343\n",
            "Epoch 5/20\n",
            "220/220 [==============================] - 153s 694ms/step - loss: 0.8812 - accuracy: 0.7354 - val_loss: 0.8767 - val_accuracy: 0.7343\n",
            "Epoch 6/20\n",
            "220/220 [==============================] - 154s 701ms/step - loss: 1.0305 - accuracy: 0.7321 - val_loss: 0.8753 - val_accuracy: 0.7343\n",
            "Epoch 7/20\n",
            "220/220 [==============================] - 152s 692ms/step - loss: 0.8743 - accuracy: 0.7354 - val_loss: 0.8725 - val_accuracy: 0.7343\n",
            "Epoch 8/20\n",
            "220/220 [==============================] - 152s 690ms/step - loss: 0.8732 - accuracy: 0.7354 - val_loss: 0.8706 - val_accuracy: 0.7343\n",
            "Epoch 9/20\n",
            "220/220 [==============================] - 155s 702ms/step - loss: 0.8705 - accuracy: 0.7354 - val_loss: 0.8741 - val_accuracy: 0.7343\n",
            "Epoch 10/20\n",
            "220/220 [==============================] - 152s 691ms/step - loss: 0.8731 - accuracy: 0.7354 - val_loss: 0.8786 - val_accuracy: 0.7343\n",
            "Epoch 11/20\n",
            "220/220 [==============================] - 152s 689ms/step - loss: 0.8751 - accuracy: 0.7354 - val_loss: 0.9428 - val_accuracy: 0.7343\n",
            "Epoch 12/20\n",
            "220/220 [==============================] - 153s 696ms/step - loss: 0.8725 - accuracy: 0.7354 - val_loss: 0.8762 - val_accuracy: 0.7343\n",
            "Epoch 13/20\n",
            "220/220 [==============================] - 152s 689ms/step - loss: 0.8714 - accuracy: 0.7354 - val_loss: 0.8712 - val_accuracy: 0.7343\n",
            "Epoch 14/20\n",
            "220/220 [==============================] - 151s 685ms/step - loss: 0.8682 - accuracy: 0.7354 - val_loss: 0.8757 - val_accuracy: 0.7343\n",
            "Epoch 15/20\n",
            "220/220 [==============================] - 154s 700ms/step - loss: 0.8709 - accuracy: 0.7354 - val_loss: 0.8704 - val_accuracy: 0.7343\n",
            "Epoch 16/20\n",
            "220/220 [==============================] - 152s 689ms/step - loss: 0.8687 - accuracy: 0.7354 - val_loss: 0.8735 - val_accuracy: 0.7343\n",
            "Epoch 17/20\n",
            "220/220 [==============================] - 152s 688ms/step - loss: 0.8696 - accuracy: 0.7354 - val_loss: 0.8718 - val_accuracy: 0.7343\n",
            "Epoch 18/20\n",
            "220/220 [==============================] - 151s 687ms/step - loss: 0.8702 - accuracy: 0.7354 - val_loss: 0.8769 - val_accuracy: 0.7343\n",
            "Epoch 19/20\n",
            "220/220 [==============================] - 151s 686ms/step - loss: 0.8688 - accuracy: 0.7354 - val_loss: 0.8713 - val_accuracy: 0.7343\n",
            "Epoch 20/20\n",
            "220/220 [==============================] - 152s 689ms/step - loss: 0.8701 - accuracy: 0.7354 - val_loss: 0.8714 - val_accuracy: 0.7343\n"
          ]
        }
      ],
      "source": [
        "# fit the model\n",
        "# Run the cell. It will take some time to execute\n",
        "r = model.fit_generator(\n",
        "  training_set,\n",
        "  validation_data=test_set,\n",
        "  epochs=20,\n",
        "  steps_per_epoch=len(training_set),\n",
        "  validation_steps=len(test_set)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "_MQY8Reo5ojD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "fbab6c1b-5bdb-4260-d1c6-f97d0515221d"
      },
      "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-23-bb6bf5bcc936>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# plot the loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'train loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'val loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\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 'plt' is not defined"
          ]
        }
      ],
      "source": [
        "# plot the loss\n",
        "plt.plot(r.history['loss'], label='train loss')\n",
        "plt.plot(r.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('LossVal_loss')\n",
        "\n",
        "# plot the accuracy\n",
        "plt.plot(r.history['accuracy'], label='train acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('AccVal_acc')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lvbTQ9xZgVsd"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "mount_file_id": "1MvzvEbF8n-My4REtxhijjI0cYIF5j0Ho",
      "authorship_tag": "ABX9TyNTSAcACeXwh7JbkMXVPNN1",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}