example_model/transfer_learningv2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "transfer_learningv2.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "EpF3vDz9-2rK",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "56893294-4b49-4a6d-a544-5df29709702b"
      },
      "source": [
        "# Run this cell and select the kaggle.json file downloaded\n",
        "# from the Kaggle account settings page.\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "\n",
        "# Let's make sure the kaggle.json file is present.\n",
        "!ls -lha kaggle.json\n",
        "\n",
        "# Next, install the Kaggle API client.\n",
        "!pip install -q kaggle\n",
        "\n",
        "# The Kaggle API client expects this file to be in ~/.kaggle,\n",
        "# so move it there.\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "# This permissions change avoids a warning on Kaggle tool startup.\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "! kaggle datasets download -d sriramr/fruits-fresh-and-rotten-for-classification"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-04459f58-5aef-4222-bf5e-c5d92aa817a6\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-04459f58-5aef-4222-bf5e-c5d92aa817a6\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "-rw-r--r-- 1 root root 65 Jun 21 06:08 kaggle.json\n",
            "Downloading fruits-fresh-and-rotten-for-classification.zip to /content\n",
            "100% 3.58G/3.58G [01:23<00:00, 42.6MB/s]\n",
            "100% 3.58G/3.58G [01:23<00:00, 46.1MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2OHfZx17-6sp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "605fe0b1-c55d-47fd-d228-8312387d9251"
      },
      "source": [
        "import zipfile\n",
        "import os\n",
        "\n",
        "curDir = os.getcwd()\n",
        "print(curDir)\n",
        "\n",
        "local_zip = '/content/fruits-fresh-and-rotten-for-classification.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/content')\n",
        "\n",
        "zip_ref.close()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MmXfTJlO_PXV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "23ecf236-85e2-42b5-b4c9-5aa50c44f313"
      },
      "source": [
        "import random\n",
        "from keras.preprocessing.image import load_img\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "\n",
        "filenames = os.listdir('/content/dataset/train/freshoranges')\n",
        "sample = random.choice(filenames)\n",
        "image = load_img('/content/dataset/train/freshoranges/'+sample)\n",
        "plt.imshow(image)\n",
        "print(image.size)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(190, 178)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YrQnkqVn_tKY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "# from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras.applications import ResNet50V2\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, AveragePooling2D\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import Callback, LearningRateScheduler\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from imutils import paths\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import argparse\n",
        "import cv2\n",
        "import os"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rs29SYee_yIQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f46e6141-903c-4d9d-f2ae-40597cb14d53"
      },
      "source": [
        "train_datagen = ImageDataGenerator(rescale=1/255,\n",
        "                                   rotation_range=15,\n",
        "                                   horizontal_flip=True,\n",
        "                                   fill_mode='nearest')\n",
        "validation_datagen = ImageDataGenerator(rescale=1/255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "                    'dataset/train',\n",
        "                    target_size = (150,150),\n",
        "                    batch_size = 15,\n",
        "                    class_mode='categorical')\n",
        "\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "                    'dataset/test',\n",
        "                    target_size=(150,150),\n",
        "                    batch_size= 15,\n",
        "                    class_mode='categorical'\n",
        ")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 10901 images belonging to 6 classes.\n",
            "Found 2698 images belonging to 6 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9h7Sgrmo_6cG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Define a Callback function\n",
        "desired_accuracy = 0.999\n",
        "\n",
        "\n",
        "class myCallback(Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy')>desired_accuracy):\n",
        "      print(\"\\nReached 99.9% accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "my_callback = myCallback()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7LyXLpn_-go",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "79359a1b-712d-4bb4-d35f-90c1c7d27796"
      },
      "source": [
        "baseModel = ResNet50V2(weights=\"imagenet\", include_top=False,\n",
        "\tinput_tensor=Input(shape=(150, 150, 3)))\n",
        "\n",
        "headModel = baseModel.output\n",
        "headModel = AveragePooling2D(pool_size=(4, 4))(headModel)\n",
        "headModel = Flatten(name=\"flatten\")(headModel)\n",
        "headModel = Dense(64, activation=\"relu\")(headModel)\n",
        "headModel = Dropout(0.5)(headModel)\n",
        "headModel = Dense(6, activation=\"softmax\")(headModel)\n",
        "\n",
        "model = Model(inputs=baseModel.input, outputs = headModel)\n",
        "\n",
        "# Freeze the baseModel layers\n",
        "for layer in baseModel.layers :\n",
        "    layer.trainable = False"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94674944/94668760 [==============================] - 2s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpqbL6MKAAW4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3224bcc7-5aed-44ee-fcf8-f0cb8f3b5779"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 150, 150, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 156, 156, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, 75, 75, 64)   9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 77, 77, 64)   0           conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, 38, 38, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_preact_bn (BatchNo (None, 38, 38, 64)   256         pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_preact_relu (Activ (None, 38, 38, 64)   0           conv2_block1_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, 38, 38, 64)   4096        conv2_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, 38, 38, 64)   256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, 38, 38, 64)   0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_pad (ZeroPadding (None, 40, 40, 64)   0           conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, 38, 38, 64)   36864       conv2_block1_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, 38, 38, 64)   256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, 38, 38, 64)   0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, 38, 38, 256)  16640       conv2_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, 38, 38, 256)  16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Add)          (None, 38, 38, 256)  0           conv2_block1_0_conv[0][0]        \n",
            "                                                                 conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_preact_bn (BatchNo (None, 38, 38, 256)  1024        conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_preact_relu (Activ (None, 38, 38, 256)  0           conv2_block2_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, 38, 38, 64)   16384       conv2_block2_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, 38, 38, 64)   256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, 38, 38, 64)   0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_pad (ZeroPadding (None, 40, 40, 64)   0           conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, 38, 38, 64)   36864       conv2_block2_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, 38, 38, 64)   256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, 38, 38, 64)   0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, 38, 38, 256)  16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Add)          (None, 38, 38, 256)  0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_preact_bn (BatchNo (None, 38, 38, 256)  1024        conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_preact_relu (Activ (None, 38, 38, 256)  0           conv2_block3_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, 38, 38, 64)   16384       conv2_block3_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, 38, 38, 64)   256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, 38, 38, 64)   0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_pad (ZeroPadding (None, 40, 40, 64)   0           conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, 19, 19, 64)   36864       conv2_block3_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, 19, 19, 64)   256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, 19, 19, 64)   0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 19, 19, 256)  0           conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, 19, 19, 256)  16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Add)          (None, 19, 19, 256)  0           max_pooling2d[0][0]              \n",
            "                                                                 conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_preact_bn (BatchNo (None, 19, 19, 256)  1024        conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_preact_relu (Activ (None, 19, 19, 256)  0           conv3_block1_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, 19, 19, 128)  32768       conv3_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, 19, 19, 128)  0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_pad (ZeroPadding (None, 21, 21, 128)  0           conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, 19, 19, 128)  147456      conv3_block1_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, 19, 19, 128)  0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, 19, 19, 512)  131584      conv3_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, 19, 19, 512)  66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Add)          (None, 19, 19, 512)  0           conv3_block1_0_conv[0][0]        \n",
            "                                                                 conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_preact_bn (BatchNo (None, 19, 19, 512)  2048        conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_preact_relu (Activ (None, 19, 19, 512)  0           conv3_block2_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, 19, 19, 128)  65536       conv3_block2_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, 19, 19, 128)  0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_pad (ZeroPadding (None, 21, 21, 128)  0           conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, 19, 19, 128)  147456      conv3_block2_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, 19, 19, 128)  0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, 19, 19, 512)  66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Add)          (None, 19, 19, 512)  0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_preact_bn (BatchNo (None, 19, 19, 512)  2048        conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_preact_relu (Activ (None, 19, 19, 512)  0           conv3_block3_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, 19, 19, 128)  65536       conv3_block3_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, 19, 19, 128)  0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_pad (ZeroPadding (None, 21, 21, 128)  0           conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, 19, 19, 128)  147456      conv3_block3_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, 19, 19, 128)  0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, 19, 19, 512)  66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Add)          (None, 19, 19, 512)  0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_preact_bn (BatchNo (None, 19, 19, 512)  2048        conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_preact_relu (Activ (None, 19, 19, 512)  0           conv3_block4_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, 19, 19, 128)  65536       conv3_block4_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, 19, 19, 128)  512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, 19, 19, 128)  0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_pad (ZeroPadding (None, 21, 21, 128)  0           conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, 10, 10, 128)  147456      conv3_block4_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, 10, 10, 128)  512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, 10, 10, 128)  0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 10, 10, 512)  0           conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, 10, 10, 512)  66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Add)          (None, 10, 10, 512)  0           max_pooling2d_1[0][0]            \n",
            "                                                                 conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_preact_bn (BatchNo (None, 10, 10, 512)  2048        conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_preact_relu (Activ (None, 10, 10, 512)  0           conv4_block1_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, 10, 10, 256)  131072      conv4_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, 10, 10, 256)  0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_pad (ZeroPadding (None, 12, 12, 256)  0           conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, 10, 10, 256)  589824      conv4_block1_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, 10, 10, 256)  0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, 10, 10, 1024) 525312      conv4_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, 10, 10, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Add)          (None, 10, 10, 1024) 0           conv4_block1_0_conv[0][0]        \n",
            "                                                                 conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_preact_bn (BatchNo (None, 10, 10, 1024) 4096        conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_preact_relu (Activ (None, 10, 10, 1024) 0           conv4_block2_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, 10, 10, 256)  262144      conv4_block2_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, 10, 10, 256)  0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_pad (ZeroPadding (None, 12, 12, 256)  0           conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, 10, 10, 256)  589824      conv4_block2_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, 10, 10, 256)  0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, 10, 10, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Add)          (None, 10, 10, 1024) 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_preact_bn (BatchNo (None, 10, 10, 1024) 4096        conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_preact_relu (Activ (None, 10, 10, 1024) 0           conv4_block3_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, 10, 10, 256)  262144      conv4_block3_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, 10, 10, 256)  0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_pad (ZeroPadding (None, 12, 12, 256)  0           conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, 10, 10, 256)  589824      conv4_block3_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, 10, 10, 256)  0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, 10, 10, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Add)          (None, 10, 10, 1024) 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_preact_bn (BatchNo (None, 10, 10, 1024) 4096        conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_preact_relu (Activ (None, 10, 10, 1024) 0           conv4_block4_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, 10, 10, 256)  262144      conv4_block4_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, 10, 10, 256)  0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_pad (ZeroPadding (None, 12, 12, 256)  0           conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, 10, 10, 256)  589824      conv4_block4_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, 10, 10, 256)  0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, 10, 10, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Add)          (None, 10, 10, 1024) 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_preact_bn (BatchNo (None, 10, 10, 1024) 4096        conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_preact_relu (Activ (None, 10, 10, 1024) 0           conv4_block5_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, 10, 10, 256)  262144      conv4_block5_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, 10, 10, 256)  0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_pad (ZeroPadding (None, 12, 12, 256)  0           conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, 10, 10, 256)  589824      conv4_block5_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, 10, 10, 256)  0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, 10, 10, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Add)          (None, 10, 10, 1024) 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_preact_bn (BatchNo (None, 10, 10, 1024) 4096        conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_preact_relu (Activ (None, 10, 10, 1024) 0           conv4_block6_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, 10, 10, 256)  262144      conv4_block6_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, 10, 10, 256)  1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, 10, 10, 256)  0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_pad (ZeroPadding (None, 12, 12, 256)  0           conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, 5, 5, 256)    589824      conv4_block6_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, 5, 5, 256)    1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, 5, 5, 256)    0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 5, 5, 1024)   0           conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, 5, 5, 1024)   263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Add)          (None, 5, 5, 1024)   0           max_pooling2d_2[0][0]            \n",
            "                                                                 conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_preact_bn (BatchNo (None, 5, 5, 1024)   4096        conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_preact_relu (Activ (None, 5, 5, 1024)   0           conv5_block1_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, 5, 5, 512)    524288      conv5_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, 5, 5, 512)    2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, 5, 5, 512)    0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_pad (ZeroPadding (None, 7, 7, 512)    0           conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, 5, 5, 512)    2359296     conv5_block1_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, 5, 5, 512)    2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, 5, 5, 512)    0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, 5, 5, 2048)   2099200     conv5_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, 5, 5, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Add)          (None, 5, 5, 2048)   0           conv5_block1_0_conv[0][0]        \n",
            "                                                                 conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_preact_bn (BatchNo (None, 5, 5, 2048)   8192        conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_preact_relu (Activ (None, 5, 5, 2048)   0           conv5_block2_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, 5, 5, 512)    1048576     conv5_block2_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, 5, 5, 512)    2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, 5, 5, 512)    0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_pad (ZeroPadding (None, 7, 7, 512)    0           conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, 5, 5, 512)    2359296     conv5_block2_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, 5, 5, 512)    2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, 5, 5, 512)    0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, 5, 5, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Add)          (None, 5, 5, 2048)   0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_preact_bn (BatchNo (None, 5, 5, 2048)   8192        conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_preact_relu (Activ (None, 5, 5, 2048)   0           conv5_block3_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, 5, 5, 512)    1048576     conv5_block3_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, 5, 5, 512)    2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, 5, 5, 512)    0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_pad (ZeroPadding (None, 7, 7, 512)    0           conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, 5, 5, 512)    2359296     conv5_block3_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, 5, 5, 512)    2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, 5, 5, 512)    0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, 5, 5, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Add)          (None, 5, 5, 2048)   0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "post_bn (BatchNormalization)    (None, 5, 5, 2048)   8192        conv5_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "post_relu (Activation)          (None, 5, 5, 2048)   0           post_bn[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, 1, 1, 2048)   0           post_relu[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 2048)         0           average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 64)           131136      flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 64)           0           dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 6)            390         dropout[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 23,696,326\n",
            "Trainable params: 131,526\n",
            "Non-trainable params: 23,564,800\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnX0xlwhAGIL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Hyperparameter\n",
        "INIT_LR = 1e-3\n",
        "BS = 15\n",
        "EPOCHS = 25"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALBiTXAwAJjA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4facf591-c1f3-4868-aa81-2b249067cb43"
      },
      "source": [
        "print(\"[INFO] compiling model...\")\n",
        "opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] compiling model...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZVQUfZNARmX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "outputId": "27499c16-87c7-478b-9311-ef9abfce9b84"
      },
      "source": [
        "history = model.fit(\n",
        "            train_generator,\n",
        "            validation_data=validation_generator,\n",
        "            steps_per_epoch=len(train_generator) // BS,\n",
        "            validation_steps=len(validation_generator) // BS,\n",
        "            epochs=30,\n",
        "            verbose=1,\n",
        "            callbacks=[my_callback]\n",
        ")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "48/48 [==============================] - 9s 186ms/step - loss: 0.0054 - accuracy: 0.9944 - val_loss: 0.0305 - val_accuracy: 0.9944\n",
            "Epoch 2/30\n",
            "48/48 [==============================] - 9s 177ms/step - loss: 0.0061 - accuracy: 0.9931 - val_loss: 0.0019 - val_accuracy: 0.9944\n",
            "Epoch 3/30\n",
            "48/48 [==============================] - 9s 177ms/step - loss: 0.0141 - accuracy: 0.9861 - val_loss: 0.0110 - val_accuracy: 0.9889\n",
            "Epoch 4/30\n",
            "48/48 [==============================] - 9s 179ms/step - loss: 0.0083 - accuracy: 0.9931 - val_loss: 0.0031 - val_accuracy: 0.9944\n",
            "Epoch 5/30\n",
            "48/48 [==============================] - 9s 183ms/step - loss: 0.0072 - accuracy: 0.9889 - val_loss: 6.4907e-04 - val_accuracy: 1.0000\n",
            "Epoch 6/30\n",
            "48/48 [==============================] - 9s 178ms/step - loss: 0.0077 - accuracy: 0.9931 - val_loss: 0.0068 - val_accuracy: 0.9944\n",
            "Epoch 7/30\n",
            "48/48 [==============================] - 8s 177ms/step - loss: 0.0114 - accuracy: 0.9833 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
            "Epoch 8/30\n",
            "48/48 [==============================] - 9s 178ms/step - loss: 0.0153 - accuracy: 0.9847 - val_loss: 0.0065 - val_accuracy: 0.9889\n",
            "Epoch 9/30\n",
            "48/48 [==============================] - 8s 177ms/step - loss: 0.0087 - accuracy: 0.9875 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 10/30\n",
            "48/48 [==============================] - 8s 177ms/step - loss: 0.0077 - accuracy: 0.9931 - val_loss: 2.8864e-04 - val_accuracy: 1.0000\n",
            "Epoch 11/30\n",
            "48/48 [==============================] - 9s 184ms/step - loss: 0.0064 - accuracy: 0.9931 - val_loss: 0.0089 - val_accuracy: 0.9889\n",
            "Epoch 12/30\n",
            "48/48 [==============================] - 9s 178ms/step - loss: 0.0040 - accuracy: 0.9972 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
            "Epoch 13/30\n",
            "48/48 [==============================] - 9s 179ms/step - loss: 0.0038 - accuracy: 0.9958 - val_loss: 1.2504e-04 - val_accuracy: 1.0000\n",
            "Epoch 14/30\n",
            "48/48 [==============================] - 9s 181ms/step - loss: 0.0050 - accuracy: 0.9944 - val_loss: 0.0253 - val_accuracy: 0.9944\n",
            "Epoch 15/30\n",
            "48/48 [==============================] - 9s 178ms/step - loss: 0.0048 - accuracy: 0.9931 - val_loss: 0.0078 - val_accuracy: 0.9944\n",
            "Epoch 16/30\n",
            "48/48 [==============================] - 9s 178ms/step - loss: 0.0076 - accuracy: 0.9931 - val_loss: 7.4179e-04 - val_accuracy: 1.0000\n",
            "Epoch 17/30\n",
            "48/48 [==============================] - 9s 184ms/step - loss: 0.0100 - accuracy: 0.9917 - val_loss: 0.0107 - val_accuracy: 0.9778\n",
            "Epoch 18/30\n",
            "48/48 [==============================] - 9s 180ms/step - loss: 0.0128 - accuracy: 0.9833 - val_loss: 0.0137 - val_accuracy: 0.9944\n",
            "Epoch 19/30\n",
            "48/48 [==============================] - 9s 179ms/step - loss: 0.0073 - accuracy: 0.9903 - val_loss: 0.0083 - val_accuracy: 0.9889\n",
            "Epoch 20/30\n",
            "48/48 [==============================] - 9s 181ms/step - loss: 0.0054 - accuracy: 0.9917 - val_loss: 7.9467e-04 - val_accuracy: 1.0000\n",
            "Epoch 21/30\n",
            "48/48 [==============================] - 9s 183ms/step - loss: 0.0074 - accuracy: 0.9889 - val_loss: 0.0045 - val_accuracy: 0.9944\n",
            "Epoch 22/30\n",
            "48/48 [==============================] - 9s 183ms/step - loss: 0.0058 - accuracy: 0.9930 - val_loss: 0.0160 - val_accuracy: 0.9889\n",
            "Epoch 23/30\n",
            "48/48 [==============================] - 9s 183ms/step - loss: 0.0056 - accuracy: 0.9958 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 24/30\n",
            "48/48 [==============================] - 9s 179ms/step - loss: 0.0076 - accuracy: 0.9931 - val_loss: 0.0016 - val_accuracy: 0.9944\n",
            "Epoch 25/30\n",
            "48/48 [==============================] - ETA: 0s - loss: 0.0012 - accuracy: 1.0000\n",
            "Reached 99.9% accuracy so cancelling training!\n",
            "48/48 [==============================] - 8s 174ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0148 - val_accuracy: 0.9833\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K84t-BcLBLn8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "f475c813-332d-4e51-8d06-9aea25516647"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2E1sM9VyQqn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('transfer_learningv4.h5')"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5z5-sO7yiFO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}