material/deep_learning/real_practice_glaucoma_classification.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-fqCbUc8Pyb",
        "outputId": "d9ec85ba-6a5c-4964-82f2-59748c3bec6a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "mYa3bWhI_gTy"
      },
      "outputs": [],
      "source": [
        "!cp '/content/drive/MyDrive/tmp/data.zip' ./"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQ-nTSsXAAZF",
        "outputId": "1be8febc-5493-48f0-e253-39b1444d98b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 121844\n",
            "drwxr-xr-x 1 root root      4096 Nov 26 06:45 .\n",
            "drwxr-xr-x 1 root root      4096 Nov 26 06:39 ..\n",
            "drwxr-xr-x 1 root root      4096 Nov 20 17:15 .config\n",
            "-rw------- 1 root root 124741357 Nov 26 06:46 data.zip\n",
            "drwx------ 6 root root      4096 Nov 26 06:45 drive\n",
            "drwxr-xr-x 1 root root      4096 Nov 13 17:33 sample_data\n"
          ]
        }
      ],
      "source": [
        "!ls -al"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awBWIVpPABHT",
        "outputId": "f2ac8a7a-922d-4626-ab29-d54db1bfe7a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "  inflating: __MACOSX/data/train/normal/._351.png  \n"
          ]
        }
      ],
      "source": [
        "!unzip data.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2RNJlMNAzY6",
        "outputId": "4d8f7ecc-039c-435b-f358-4e2b41b2a7f8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 121852\n",
            "drwxr-xr-x 1 root root      4096 Nov 26 06:46 .\n",
            "drwxr-xr-x 1 root root      4096 Nov 26 06:39 ..\n",
            "drwxr-xr-x 1 root root      4096 Nov 20 17:15 .config\n",
            "drwxr-xr-x 4 root root      4096 Nov 26 04:36 data\n",
            "-rw------- 1 root root 124741357 Nov 26 06:46 data.zip\n",
            "drwx------ 6 root root      4096 Nov 26 06:45 drive\n",
            "drwxr-xr-x 3 root root      4096 Nov 26 06:46 __MACOSX\n",
            "drwxr-xr-x 1 root root      4096 Nov 13 17:33 sample_data\n"
          ]
        }
      ],
      "source": [
        "!ls -al"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4uHU5NoYA3NS",
        "outputId": "7e570301-d543-4c8b-e1da-67b9ba014516"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 24\n",
            "drwxr-xr-x 4 root root 4096 Nov 26 04:36 .\n",
            "drwxr-xr-x 1 root root 4096 Nov 26 06:46 ..\n",
            "-rw-r--r-- 1 root root 6148 Nov 26 04:36 .DS_Store\n",
            "drwxr-xr-x 5 root root 4096 Nov 26 04:36 test\n",
            "drwxr-xr-x 5 root root 4096 Nov 26 04:36 train\n"
          ]
        }
      ],
      "source": [
        "!ls -al data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njIFFz60SVvw"
      },
      "source": [
        "# by ResNet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upi-VGfnA6rM",
        "outputId": "8f71d528-4b73-4aee-b9f3-c9cb347b2596"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94773248/94765736 [==============================] - 0s 0us/step\n",
            "Found 771 images belonging to 3 classes.\n",
            "Found 773 images belonging to 3 classes.\n",
            "WARNING:tensorflow:From <ipython-input-7-4ed468772654>:69: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/10\n",
            "13/12 [================================] - 15s 1s/step - loss: 222.6868 - acc: 0.4968 - val_loss: 22.7146 - val_acc: 0.5938\n",
            "Epoch 2/10\n",
            "13/12 [================================] - 15s 1s/step - loss: 23.2085 - acc: 0.7328 - val_loss: 14.7720 - val_acc: 0.6494\n",
            "Epoch 3/10\n",
            "13/12 [================================] - 15s 1s/step - loss: 30.0983 - acc: 0.6861 - val_loss: 42.9183 - val_acc: 0.5369\n",
            "Epoch 4/10\n",
            "13/12 [================================] - 15s 1s/step - loss: 16.6768 - acc: 0.7419 - val_loss: 23.4295 - val_acc: 0.5705\n",
            "Epoch 5/10\n",
            "13/12 [================================] - 15s 1s/step - loss: 10.5347 - acc: 0.7665 - val_loss: 33.8283 - val_acc: 0.5938\n",
            "Epoch 6/10\n",
            "13/12 [================================] - 15s 1s/step - loss: 10.3005 - acc: 0.7367 - val_loss: 8.1583 - val_acc: 0.6533\n",
            "Epoch 7/10\n",
            "13/12 [================================] - 15s 1s/step - loss: 2.9348 - acc: 0.8145 - val_loss: 7.1639 - val_acc: 0.6753\n",
            "Epoch 8/10\n",
            "13/12 [================================] - 15s 1s/step - loss: 4.0149 - acc: 0.7497 - val_loss: 3.8229 - val_acc: 0.5291\n",
            "Epoch 9/10\n",
            "13/12 [================================] - 15s 1s/step - loss: 1.7077 - acc: 0.7743 - val_loss: 2.1204 - val_acc: 0.6688\n",
            "Epoch 10/10\n",
            "13/12 [================================] - 16s 1s/step - loss: 2.6283 - acc: 0.7328 - val_loss: 5.3991 - val_acc: 0.5446\n",
            "WARNING:tensorflow:From <ipython-input-7-4ed468772654>:74: Model.evaluate_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.evaluate, which supports generators.\n",
            "loss= 5.399132251739502\n",
            "acc= 0.54463130235672\n",
            "WARNING:tensorflow:From <ipython-input-7-4ed468772654>:81: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.predict, which supports generators.\n",
            "1 early\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dropout, Flatten, Dense\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "\n",
        "# from tensorflow.keras.applications import vgg16\n",
        "# from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras.applications import resnet50\n",
        "from tensorflow.keras.applications import ResNet50\n",
        "\n",
        "\n",
        "# conv_layers = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "conv_layers = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "conv_layers.trainable = False\n",
        "\n",
        "model = keras.Sequential()\n",
        "\n",
        "model.add(conv_layers)\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(1024, activation='relu'))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=\"RMSprop\", metrics=['acc'])\n",
        "\n",
        "\n",
        "train_data_generator = ImageDataGenerator(\n",
        "      rotation_range=10,\n",
        "      width_shift_range=0.1,\n",
        "      height_shift_range=0.1,\n",
        "      horizontal_flip=True,\n",
        "      vertical_flip=False,\n",
        "#       preprocessing_function=vgg16.preprocess_input\n",
        "      preprocessing_function=resnet50.preprocess_input\n",
        ").flow_from_directory(\n",
        "      \"data/train\",   # <--------------------------------------\n",
        "      target_size=(224,224),\n",
        "      batch_size=64,\n",
        "      class_mode='sparse'\n",
        ")\n",
        "\n",
        "test_data_generator = ImageDataGenerator(\n",
        "#       preprocessing_function=vgg16.preprocess_input\n",
        "      preprocessing_function=resnet50.preprocess_input\n",
        ").flow_from_directory(\n",
        "      \"data/test\",   # <--------------------------------------\n",
        "      target_size=(224,224),\n",
        "      batch_size=64,\n",
        "      class_mode='sparse'\n",
        ")\n",
        "\n",
        "\n",
        "model.fit(\n",
        "      train_data_generator,\n",
        "      validation_data=test_data_generator,\n",
        "      steps_per_epoch=train_data_generator.samples/64,  \n",
        "      validation_steps=test_data_generator.samples/64,      \n",
        "      epochs=10\n",
        ")\n",
        "\n",
        "loss, acc = model.evaluate(\n",
        "      test_data_generator,\n",
        "      steps=test_data_generator.samples/64\n",
        ")\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "\n",
        "y_ = model.predict(\n",
        "      test_data_generator,\n",
        "      steps=test_data_generator.samples/64\n",
        ")\n",
        "\n",
        "custom_labels = list(test_data_generator.class_indices.keys())\n",
        "predicted = np.argmax(y_, axis=1)\n",
        "print(predicted[0], custom_labels[predicted[0]])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dtjb2BRRSMO1"
      },
      "source": [
        "# by DenseNet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "HVn9hns3SzRn"
      },
      "outputs": [],
      "source": [
        "# copy from https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e\n",
        "\n",
        "from IPython.display import clear_output\n",
        "from tensorflow.keras.callbacks import Callback\n",
        "\n",
        "class PlotLosses(Callback):\n",
        "\n",
        "  def on_train_begin(self, logs={}):\n",
        "\n",
        "    self.i = 0\n",
        "    self.x = []\n",
        "    self.losses = []\n",
        "    self.val_losses = []\n",
        "\n",
        "    self.fig = plt.figure()\n",
        "\n",
        "    self.logs = []\n",
        "\n",
        "\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "\n",
        "    self.logs.append(logs)\n",
        "    self.x.append(self.i)\n",
        "    self.losses.append(logs.get('loss'))\n",
        "    self.val_losses.append(logs.get('val_loss'))\n",
        "    self.i += 1\n",
        "\n",
        "    clear_output(wait=True)\n",
        "    plt.plot(self.x, self.losses, label=\"loss\")\n",
        "    plt.plot(self.x, self.val_losses, label=\"val_loss\")\n",
        "    plt.legend()\n",
        "    plt.show();\n",
        "    print(\"loss = \", self.losses[-1], \", val_loss = \", self.val_losses[-1])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "CAfhAIWJROlp",
        "outputId": "6ace8721-d016-4849-cbc2-d6d0c9b56257"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loss =  0.9686822295188904 , val_loss =  4.769455909729004\n",
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/12 [================================] - 12s 927ms/step - loss: 0.9687 - acc: 0.7147 - val_loss: 4.7695 - val_acc: 0.3687\n",
            "WARNING:tensorflow:From <ipython-input-13-4bf1d47a3ec3>:79: Model.evaluate_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.evaluate, which supports generators.\n",
            "loss= 4.769455909729004\n",
            "acc= 0.36869341135025024\n",
            "WARNING:tensorflow:From <ipython-input-13-4bf1d47a3ec3>:86: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.predict, which supports generators.\n",
            "1 early\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dropout, Flatten, Dense\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "\n",
        "# from tensorflow.keras.applications import vgg16\n",
        "# from tensorflow.keras.applications import VGG16\n",
        "# from tensorflow.keras.applications import resnet50\n",
        "# from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.applications import densenet # <------------------\n",
        "from tensorflow.keras.applications import DenseNet121 # <------------------\n",
        "\n",
        "\n",
        "# conv_layers = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "# conv_layers = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "conv_layers = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "conv_layers.trainable = False\n",
        "\n",
        "model = keras.Sequential()\n",
        "\n",
        "model.add(conv_layers)\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(1024, activation='relu'))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(3, activation='softmax')) # <------------------\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=\"RMSprop\", metrics=['acc'])\n",
        "\n",
        "\n",
        "train_data_generator = ImageDataGenerator(\n",
        "      rotation_range=10,\n",
        "      width_shift_range=0.1,\n",
        "      height_shift_range=0.1,\n",
        "      horizontal_flip=True,\n",
        "      vertical_flip=False,\n",
        "#       preprocessing_function=vgg16.preprocess_input\n",
        "      preprocessing_function=densenet.preprocess_input    # <--------------------------------------\n",
        ").flow_from_directory(\n",
        "      \"data/train\",   \n",
        "      target_size=(224,224),\n",
        "      batch_size=64,\n",
        "      class_mode='sparse'\n",
        ")\n",
        "\n",
        "test_data_generator = ImageDataGenerator(\n",
        "#       preprocessing_function=vgg16.preprocess_input\n",
        "      preprocessing_function=densenet.preprocess_input   # <--------------------------------------\n",
        ").flow_from_directory(\n",
        "      \"data/test\",\n",
        "      target_size=(224,224),\n",
        "      batch_size=64,\n",
        "      class_mode='sparse'\n",
        ")\n",
        "\n",
        "plot_losses = PlotLosses()     # <--------------------------------------\n",
        "\n",
        "model.fit(\n",
        "      train_data_generator,\n",
        "      validation_data=test_data_generator,\n",
        "      steps_per_epoch=train_data_generator.samples/64,  \n",
        "      validation_steps=test_data_generator.samples/64,      \n",
        "      epochs=10,     # <--------------------------------------\n",
        "      callbacks=[plot_losses]     # <--------------------------------------\n",
        ")\n",
        "\n",
        "loss, acc = model.evaluate(\n",
        "      test_data_generator,\n",
        "      steps=test_data_generator.samples/64\n",
        ")\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "\n",
        "y_ = model.predict(\n",
        "      test_data_generator,\n",
        "      steps=test_data_generator.samples/64\n",
        ")\n",
        "\n",
        "custom_labels = list(test_data_generator.class_indices.keys())\n",
        "predicted = np.argmax(y_, axis=1)\n",
        "print(predicted[0], custom_labels[predicted[0]])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q1M8LR7-T21z"
      },
      "source": [
        "# by normal CNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "6c-553FyRbyw",
        "outputId": "187964a1-705b-4ed1-9d10-17d279ae7936"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loss =  0.631060004234314 , val_loss =  0.8357013463973999\n",
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/12 [================================] - 11s 863ms/step - loss: 0.6311 - acc: 0.7354 - val_loss: 0.8357 - val_acc: 0.5731\n",
            "loss= 0.8357014060020447\n",
            "acc= 0.5730918645858765\n",
            "0 advanced\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dropout, Flatten, Dense\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "\n",
        "# from tensorflow.keras.applications import vgg16\n",
        "# from tensorflow.keras.applications import VGG16\n",
        "# from tensorflow.keras.applications import resnet50\n",
        "# from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.applications import densenet\n",
        "from tensorflow.keras.applications import DenseNet121\n",
        "\n",
        "import keras    # <--------------------------------------\n",
        "from tensorflow.keras.layers import *     # <--------------------------------------\n",
        "\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Input((224,224,3)))    # <--------------------------------------\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(3, activation='softmax'))    # <--------------------------------------\n",
        "\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=\"RMSprop\", metrics=['acc'])\n",
        "\n",
        "def my_preprocess_input(image):    # <--------------------------------------\n",
        "  return image/255.0               # <--------------------------------------\n",
        "\n",
        "train_data_generator = ImageDataGenerator(\n",
        "      rotation_range=10,\n",
        "      width_shift_range=0.1,\n",
        "      height_shift_range=0.1,\n",
        "      horizontal_flip=True,\n",
        "      vertical_flip=False,\n",
        "#       preprocessing_function=vgg16.preprocess_input\n",
        "      preprocessing_function=my_preprocess_input    # <--------------------------------------\n",
        ").flow_from_directory(\n",
        "      \"data/train\",   \n",
        "      target_size=(224,224),\n",
        "      batch_size=64,\n",
        "      class_mode='sparse'\n",
        ")\n",
        "\n",
        "test_data_generator = ImageDataGenerator(\n",
        "#       preprocessing_function=vgg16.preprocess_input\n",
        "      preprocessing_function=my_preprocess_input   # <--------------------------------------\n",
        ").flow_from_directory(\n",
        "      \"data/test\",\n",
        "      target_size=(224,224),\n",
        "      batch_size=64,\n",
        "      class_mode='sparse'\n",
        ")\n",
        "\n",
        "plot_losses = PlotLosses()\n",
        "\n",
        "model.fit(\n",
        "      train_data_generator,\n",
        "      validation_data=test_data_generator,\n",
        "      steps_per_epoch=train_data_generator.samples/64,  \n",
        "      validation_steps=test_data_generator.samples/64,      \n",
        "      epochs=10,\n",
        "      callbacks=[plot_losses]\n",
        ")\n",
        "\n",
        "loss, acc = model.evaluate(\n",
        "      test_data_generator,\n",
        "      steps=test_data_generator.samples/64\n",
        ")\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "\n",
        "y_ = model.predict(\n",
        "      test_data_generator,\n",
        "      steps=test_data_generator.samples/64\n",
        ")\n",
        "\n",
        "custom_labels = list(test_data_generator.class_indices.keys())\n",
        "predicted = np.argmax(y_, axis=1)\n",
        "print(predicted[0], custom_labels[predicted[0]])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xJLz_ZwSUGbO"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "real_practice-glaucoma_classification.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}