tsne_and_towards_err_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmmYUAHN89aE",
        "outputId": "ef24d99a-db9c-4252-f144-f4e32f52d5d0"
      },
      "source": [
        "import csv\n",
        "\n",
        "dictio={}\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "\n",
        "with open('/content/drive/MyDrive/223B/dataset.csv', newline='') as csvfile:\n",
        "    sreader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
        "    for row in sreader:\n",
        "        tilename = row[0].split(\",\")[1]\n",
        "        cancer = row[0].split(\",\")[2]\n",
        "        if cancer==\"1\" or cancer==\"0\":\n",
        "            dictio[tilename]=int(cancer)  \n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-0ntrUqgSSeY",
        "outputId": "bad646e0-6263-4f3b-f2ff-081882c2cfce"
      },
      "source": [
        "import csv\n",
        "\n",
        "dictio2={}\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "\n",
        "with open('/content/drive/MyDrive/223B/path_256_1_new/data/dataset.csv', newline='') as csvfile:\n",
        "    sreader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
        "    for row in sreader:\n",
        "        tilename = row[0].split(\",\")[1]\n",
        "        cancer = row[0].split(\",\")[2]\n",
        "        #print(cancer, tilename)\n",
        "        if cancer==\"0+0\":\n",
        "            dictio2[tilename]=int(0)\n",
        "        else:\n",
        "            dictio2[tilename]=int(1)  \n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aBFehw188YG2",
        "outputId": "3efd10fd-c17b-4817-a676-78e9327eee23"
      },
      "source": [
        "# clean up test data\n",
        "\n",
        "test_pics = []\n",
        "test_cancer =[]\n",
        "from imutils import paths\n",
        "from PIL import Image, ImageFile\n",
        "# from io import BytesIO\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "path_64_1_test = '/content/drive/MyDrive/223B/64_1_copy/64_1_test'\n",
        "path_128_1_test = '/content/drive/MyDrive/223B/128_1/128_1_test'\n",
        "path_256_1_test = '/content/drive/MyDrive/223B/256_1_separ/256_1_test'\n",
        "path_512_test = '/content/drive/MyDrive/223B/512/512_test'\n",
        "\n",
        "path_new = path_256_1_test\n",
        "imagePathsNew = list(paths.list_images(path_new))\n",
        "\n",
        "\n",
        "numbering = 0\n",
        "for imagePath in imagePathsNew:\n",
        "    # convert images to arrays\n",
        "    pic = Image.open(imagePath)\n",
        "    pix = np.array(pic)\n",
        "\n",
        "    # add array_images to a list\n",
        "    test_pics.append(np.asarray(pic))\n",
        "    \n",
        "    tilenm = imagePath.split(\"/\")[-1][0:-4]\n",
        "    test_cancer.append(dictio[tilenm])\n",
        "    print(numbering, \": \", tilenm , \"\\n\")\n",
        "    numbering +=1\n",
        "    \n",
        "\n",
        "# convert pics list of arrays into a 4D array with or without normalization\n",
        "# convert cancer into 1D array\n",
        "\n",
        "x_test = np.array(test_pics) / 127.5 - 1.\n",
        "#test_pics_wo_norm = np.array(test_pics) \n",
        "\n",
        "test_cancer = np.array(test_cancer)\n",
        "test_cancer = test_cancer.reshape((200,1)) \n",
        "\n",
        "y_test = test_cancer\n",
        "\n",
        "del test_pics\n",
        "\n",
        "print(np.shape(x_test), np.shape(y_test), test_cancer[0])\n",
        "\n",
        "unique, counts = np.unique(y_test, return_counts=True)\n",
        "dict(zip(unique, counts))\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
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            "\n",
            "(200, 256, 256, 3) (200, 1) [1]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 98, 1: 102}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ne_RKp4P8pw_",
        "outputId": "173a90a6-fd16-4158-f72b-68aecedbc6d7"
      },
      "source": [
        "# clean up train+val data\n",
        "\n",
        "\n",
        "training_pics = []\n",
        "training_cancer =[]\n",
        "from imutils import paths\n",
        "from PIL import Image, ImageFile\n",
        "# from io import BytesIO\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "\n",
        "path_64_1_train = '/content/drive/MyDrive/223B/64_1_copy/64_1_train'\n",
        "path_128_1_train = '/content/drive/MyDrive/223B/128_1/128_1_train'\n",
        "path_256_1_train = '/content/drive/MyDrive/223B/256_1_separ/256_1_train'\n",
        "path_512_train = '/content/drive/MyDrive/223B/512/512_train'\n",
        "\n",
        "path_train = path_256_1_train\n",
        "imagePathsNew = list(paths.list_images(path_train))\n",
        "\n",
        "\n",
        "for imagePath in imagePathsNew:\n",
        "    # convert images to arrays\n",
        "    # print(imagePath)\n",
        "    pic = Image.open(imagePath)\n",
        "    pix = np.array(pic)\n",
        "\n",
        "    # add array_images to a list\n",
        "    training_pics.append(np.asarray(pic))\n",
        "    \n",
        "    tilenm = imagePath.split(\"/\")[-1][0:-4]\n",
        "    training_cancer.append(dictio[tilenm])\n",
        "\n",
        "\n",
        "\n",
        "path_tiles_from_model = '/content/drive/MyDrive/223B/path_256_1_new/'\n",
        "imagePaths_tiles_from_model = list(paths.list_images(path_tiles_from_model))\n",
        "\n",
        "count = 0\n",
        "for imagePath in imagePaths_tiles_from_model:\n",
        "    count +=1\n",
        "    # convert images to arrays\n",
        "    # print(imagePath)\n",
        "    pic = Image.open(imagePath)\n",
        "    pix = np.array(pic)\n",
        "\n",
        "    # add array_images to a list\n",
        "    training_pics.append(np.asarray(pic))\n",
        "    \n",
        "    tilenm = imagePath.split(\"/\")[-1][0:-4]\n",
        "    training_cancer.append(dictio2[tilenm])\n",
        "    if count>2000:\n",
        "      break\n",
        "    \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# convert pics list of arrays into a 4D array with or without normalization\n",
        "# convert cancer into 1D array\n",
        "\n",
        "training_pics = np.array(training_pics) / 127.5 - 1.\n",
        "#training_pics_wo_norm = np.array(training_pics) \n",
        "\n",
        "training_cancer = np.array(training_cancer)\n",
        "training_cancer = training_cancer.reshape((-1,1)) \n",
        "\n",
        "print( np.shape(training_pics), np.shape(training_cancer), training_cancer[0] )\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2801, 256, 256, 3) (2801, 1) [0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7fzcQqN9zVW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40fead70-a71e-4fd1-943e-95fd4e8a9c2f"
      },
      "source": [
        "#create validation\n",
        "\n",
        "from __future__ import print_function\n",
        "import tensorflow.keras\n",
        "from tensorflow.keras.layers import Dense, Conv2D, Dropout\n",
        "from tensorflow.keras.layers import BatchNormalization, Activation\n",
        "from tensorflow.keras.layers import AveragePooling2D, Input, Flatten\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.models import Model\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "# split into train test sets\n",
        "x_train, x_val, y_train, y_val = train_test_split(training_pics, training_cancer, test_size=0.20,  random_state=1)\n",
        "print(np.shape(x_train),np.shape(y_train) )\n",
        "print(np.shape(x_val),np.shape(y_val) )\n",
        "\n",
        "del training_pics"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2240, 256, 256, 3) (2240, 1)\n",
            "(561, 256, 256, 3) (561, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jO_TkMBm9Goq"
      },
      "source": [
        "def lr_schedule(epoch, lr_init):\n",
        "    \"\"\"Learning Rate Schedule\n",
        "\n",
        "    Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.\n",
        "    Called automatically every epoch as part of callbacks during training.\n",
        "\n",
        "    # Arguments\n",
        "        epoch (int): The number of epochs\n",
        "\n",
        "    # Returns\n",
        "        lr (float32): learning rate\n",
        "    \"\"\"\n",
        "\n",
        "  #  lr = 1e-3\n",
        "  #  if epoch > 180:\n",
        "  #    lr *= 0.5e-3\n",
        "  #  elif epoch > 160:\n",
        "  #    lr *= 1e-3\n",
        "  #  elif epoch > 120:\n",
        "  #    lr *= 1e-2\n",
        "  #  elif epoch > 80:\n",
        "  #    lr *= 1e-1\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    lr =  lr_init                   \n",
        "    # if epoch > 180:\n",
        "    #     lr *= 0.5e-5\n",
        "    # elif epoch > 40:\n",
        "    #     lr *= 1e-4\n",
        "    # elif epoch > 30:\n",
        "    #     lr *= 1e-3\n",
        "    # elif epoch > 20:\n",
        "    #     lr *= 1e-2\n",
        "    #print('Learning rate: ', lr)\n",
        "\n",
        "    if epoch > 1800:\n",
        "        lr *= 0.5e-3\n",
        "    elif epoch > 800:\n",
        "        lr *= 1e-4\n",
        "    elif epoch > 500:\n",
        "        lr *= 1e-3\n",
        "    elif epoch > 40:\n",
        "        lr *= 1e-3\n",
        "    # #print('Learning rate: ', lr)\n",
        "    return lr"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRudVrea9ZOV"
      },
      "source": [
        "def resnet_layer(inputs,\n",
        "                 num_filters=16,\n",
        "                 kernel_size=3,\n",
        "                 strides=1,\n",
        "                 activation='relu',\n",
        "                 batch_normalization=True,\n",
        "                 conv_first=True):\n",
        "    \"\"\"2D Convolution-Batch Normalization-Activation stack builder\n",
        "\n",
        "    # Arguments\n",
        "        inputs (tensor): input tensor from input image or previous layer\n",
        "        num_filters (int): Conv2D number of filters\n",
        "        kernel_size (int): Conv2D square kernel dimensions\n",
        "        strides (int): Conv2D square stride dimensions\n",
        "        activation (string): activation name\n",
        "        batch_normalization (bool): whether to include batch normalization\n",
        "        conv_first (bool): conv-bn-activation (True) or\n",
        "            bn-activation-conv (False)\n",
        "\n",
        "    # Returns\n",
        "        x (tensor): tensor as input to the next layer\n",
        "    \"\"\"\n",
        "    conv = Conv2D(num_filters,\n",
        "                  kernel_size=kernel_size,\n",
        "                  strides=strides,\n",
        "                  padding='same',\n",
        "                  kernel_initializer='he_normal',\n",
        "                  kernel_regularizer=l2(1e-4))\n",
        "\n",
        "    x = inputs\n",
        "    if conv_first:\n",
        "        x = conv(x)\n",
        "        if batch_normalization:\n",
        "            x = BatchNormalization()(x)\n",
        "        if activation is not None:\n",
        "            x = Activation(activation)(x)\n",
        "    else:\n",
        "        if batch_normalization:\n",
        "            x = BatchNormalization()(x)\n",
        "        if activation is not None:\n",
        "            x = Activation(activation)(x)\n",
        "        x = conv(x)\n",
        "    return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mopbGLWL9gye"
      },
      "source": [
        "def resnet_v2(input_shape, depth, num_units, num_classes=2, droprate=0.33):\n",
        "    \"\"\"ResNet Version 2 Model builder [b]\n",
        "\n",
        "    Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as\n",
        "    bottleneck layer\n",
        "    First shortcut connection per layer is 1 x 1 Conv2D.\n",
        "    Second and onwards shortcut connection is identity.\n",
        "    At the beginning of each stage, the feature map size is \n",
        "    halved (downsampled) by a convolutional layer with \n",
        "    strides=2, while the number of filter maps is\n",
        "    doubled. Within each stage, the layers have the same\n",
        "    number filters and the same filter map sizes.\n",
        "    Features maps sizes:\n",
        "    conv1  : 32x32,  16\n",
        "    stage 0: 32x32,  64\n",
        "    stage 1: 16x16, 128\n",
        "    stage 2:  8x8,  256\n",
        "\n",
        "    # Arguments\n",
        "        input_shape (tensor): shape of input image tensor\n",
        "        depth (int): number of core convolutional layers\n",
        "        num_classes (int): number of classes (CIFAR10 has 10)\n",
        "\n",
        "    # Returns\n",
        "        model (Model): Keras model instance\n",
        "    \"\"\"\n",
        "\n",
        "\n",
        "    if (depth - 2) % 9 != 0:\n",
        "        raise ValueError('depth should be 9n+2 (eg 56 or 110 in [b])')\n",
        "    # Start model definition.\n",
        "    num_filters_in = 16\n",
        "    num_res_blocks = int((depth - 2) / 9)\n",
        "\n",
        "    inputs = Input(shape=input_shape)\n",
        "    # v2 performs Conv2D with BN-ReLU on input before splitting into 2 paths\n",
        "    x = resnet_layer(inputs=inputs,\n",
        "                     num_filters=num_filters_in,\n",
        "                     conv_first=True)\n",
        "    \n",
        "    c1 = x\n",
        "\n",
        "    # Instantiate the stack of residual units\n",
        "    for stage in range(3):\n",
        "        for res_block in range(num_res_blocks):\n",
        "            activation = 'relu'\n",
        "            batch_normalization = True\n",
        "            strides = 1\n",
        "            if stage == 0:\n",
        "                num_filters_out = num_filters_in * 4\n",
        "                if res_block == 0:  # first layer and first stage\n",
        "                    activation = None\n",
        "                    batch_normalization = False\n",
        "            else:\n",
        "                num_filters_out = num_filters_in * 2\n",
        "                if res_block == 0:  # first layer but not first stage\n",
        "                    strides = 2    # downsample\n",
        "\n",
        "            # bottleneck residual unit\n",
        "            y = resnet_layer(inputs=x,\n",
        "                             num_filters=num_filters_in,\n",
        "                             kernel_size=1,\n",
        "                             strides=strides,\n",
        "                             activation=activation,\n",
        "                             batch_normalization=batch_normalization,\n",
        "                             conv_first=False)\n",
        "            y = resnet_layer(inputs=y,\n",
        "                             num_filters=num_filters_in,\n",
        "                             conv_first=False)\n",
        "            y = resnet_layer(inputs=y,\n",
        "                             num_filters=num_filters_out,\n",
        "                             kernel_size=1,\n",
        "                             conv_first=False)\n",
        "            if res_block == 0:\n",
        "                # linear projection residual shortcut connection to match\n",
        "                # changed dims\n",
        "                x = resnet_layer(inputs=x,\n",
        "                                 num_filters=num_filters_out,\n",
        "                                 kernel_size=1,\n",
        "                                 strides=strides,\n",
        "                                 activation=None,\n",
        "                                 batch_normalization=False)\n",
        "            x = tensorflow.keras.layers.add([x, y])\n",
        "\n",
        "        num_filters_in = num_filters_out\n",
        "\n",
        "    # Add classifier on top.\n",
        "    # v2 has BN-ReLU before Pooling\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = AveragePooling2D(pool_size=8)(x)\n",
        "    y = Flatten()(x)\n",
        "    z = Dense(num_units,\n",
        "              activation='relu',\n",
        "              kernel_initializer='he_normal')(y)\n",
        "\n",
        "    z = Dropout(droprate)(z)\n",
        "    z = Dense(num_classes,\n",
        "                    activation='softmax',\n",
        "                    kernel_initializer='he_normal')(z)\n",
        "    outputs = z\n",
        "\n",
        "\n",
        "    # Instantiate model.\n",
        "    model = Model(inputs=inputs, outputs=outputs)\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sROAaS2AuM8V",
        "outputId": "2f4a1f05-e785-4d29-d7a0-66c8a10c3cd5"
      },
      "source": [
        "#subtract mean and to_categorical\n",
        "\n",
        "# Input image dimensions.\n",
        "input_shape = x_train.shape[1:]\n",
        "\n",
        "# Normalize data.\n",
        "x_train = x_train.astype('float32')\n",
        "print(\"before: \", np.shape(x_train) , np.shape(y_train), np.shape(x_val),  np.shape(y_val), np.shape(x_test),  np.shape(y_test)  )\n",
        "x_val = x_val.astype('float32') \n",
        "\n",
        "x_test = x_test.astype('float32') \n",
        "\n",
        "NUM_CLASSES = np.unique(y_train).shape[0] # 2\n",
        "SUBTRACT_PIXEL_MEAN = True\n",
        "\n",
        "# If subtract pixel mean is enabled\n",
        "if SUBTRACT_PIXEL_MEAN:\n",
        "    x_train_mean = np.mean(x_train, axis=0)\n",
        "    x_train -= x_train_mean\n",
        "    x_test -= x_train_mean\n",
        "    x_val = x_val - x_train_mean\n",
        "\n",
        "\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')\n",
        "print('y_train shape:', y_train.shape)\n",
        "\n",
        "# Convert class vectors to binary class matrices.\n",
        "y_train = tensorflow.keras.utils.to_categorical(y_train, NUM_CLASSES)\n",
        "y_val = tensorflow.keras.utils.to_categorical(y_val, NUM_CLASSES)\n",
        "y_test = tensorflow.keras.utils.to_categorical(y_test, NUM_CLASSES)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "before:  (2240, 256, 256, 3) (2240, 1) (561, 256, 256, 3) (561, 1) (200, 256, 256, 3) (200, 1)\n",
            "x_train shape: (2240, 256, 256, 3)\n",
            "2240 train samples\n",
            "200 test samples\n",
            "y_train shape: (2240, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGHx-Cvf9oDv"
      },
      "source": [
        "import time\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow import keras\n",
        "\n",
        "def evaluate_network( BATCH_SIZE, EPOCHS, dropout, lr_initial):\n",
        "\n",
        "  # full_evaluate_network has x_tr, y_tr, x_te, y_te, as input \n",
        "\n",
        "  x_tr = x_train\n",
        "  y_tr = y_train\n",
        "  x_te = x_val\n",
        "  y_te = y_val\n",
        "\n",
        "  USE_AUGMENTATION = True\n",
        "  NUM_CLASSES = np.unique(y_tr).shape[0] # 10\n",
        "  COLORS = x_tr.shape[3]\n",
        "  \n",
        "  depth = (COLORS+3) * 9 + 2 # this depth is meant for resnet_v2\n",
        "  num_units = 50\n",
        "  model = resnet_v2(input_shape, depth, num_units, num_classes=2, droprate=dropout)\n",
        "  model.compile(loss='categorical_crossentropy',\n",
        "               optimizer=Adam(lr=lr_initial),\n",
        "               metrics=['accuracy', 'AUC'])\n",
        "  \n",
        "  BATCH_SIZE = math.floor(BATCH_SIZE)\n",
        "  EPOCHS = math.floor(EPOCHS)\n",
        "\n",
        "\n",
        "  start_time = time.time()\n",
        "\n",
        "  # Prepare callbacks for model saving and for learning rate adjustment.\n",
        "  lr_scheduler = LearningRateScheduler(lr_schedule)\n",
        "\n",
        "\n",
        "\n",
        "  lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),\n",
        "                               cooldown=0,\n",
        "                               patience=5,\n",
        "                               min_lr=0.5e-6)\n",
        "\n",
        "  callbacks = [lr_reducer, lr_scheduler]\n",
        "\n",
        "  # Run training, with or without data augmentation.\n",
        "  if not USE_AUGMENTATION:\n",
        "    print('Not using data augmentation.')\n",
        "    model.fit(x_tr, y_tr,\n",
        "               batch_size=BATCH_SIZE,\n",
        "                epochs=EPOCHS,\n",
        "                validation_data=(x_te, y_te),\n",
        "              shuffle=True,\n",
        "              callbacks=callbacks)\n",
        "  else:\n",
        "    #print('Using real-time data augmentation.')  # **** uncomment?\n",
        "\n",
        "    # This will do preprocessing and realtime data augmentation:\n",
        "    datagen = ImageDataGenerator(\n",
        "        # set input mean to 0 over the dataset\n",
        "        featurewise_center=False,\n",
        "        # set each sample mean to 0\n",
        "        samplewise_center=False,\n",
        "        # divide inputs by std of dataset\n",
        "        featurewise_std_normalization=False,\n",
        "        # divide each input by its std\n",
        "        samplewise_std_normalization=False,\n",
        "        # apply ZCA whitening\n",
        "        zca_whitening=False,\n",
        "        # epsilon for ZCA whitening\n",
        "        zca_epsilon=1e-06,\n",
        "        # randomly rotate images in the range (deg 0 to 180)\n",
        "        rotation_range=0,\n",
        "        # randomly shift images horizontally\n",
        "        width_shift_range=0.1,\n",
        "        # randomly shift images vertically\n",
        "        height_shift_range=0.1,\n",
        "        # set range for random shear\n",
        "        shear_range=0.,\n",
        "        # set range for random zoom\n",
        "        zoom_range=0.,\n",
        "        # set range for random channel shifts\n",
        "        channel_shift_range=0.,\n",
        "        # set mode for filling points outside the input boundaries\n",
        "        fill_mode='nearest',\n",
        "        # value used for fill_mode = \"constant\"\n",
        "        cval=0.,\n",
        "        # randomly flip images\n",
        "        horizontal_flip=True,\n",
        "        # randomly flip images\n",
        "        vertical_flip=False,\n",
        "        # set rescaling factor (applied before any other transformation)\n",
        "        rescale=None,\n",
        "        # set function that will be applied on each input\n",
        "        preprocessing_function=None,\n",
        "        # image data format, either \"channels_first\" or \"channels_last\"\n",
        "        data_format=None,\n",
        "        # fraction of images reserved for validation \n",
        "        # (strictly between 0 and 1)\n",
        "        validation_split=0.0)\n",
        "\n",
        "    # Compute quantities required for featurewise normalization\n",
        "    # (std, mean, and principal components if ZCA whitening is applied).\n",
        "    datagen.fit(x_tr)\n",
        "\n",
        "\n",
        "    # Fit the model on the batches generated by datagen.flow().\n",
        "    history = model.fit_generator(datagen.flow(x_tr, y_tr, \n",
        "                        batch_size=BATCH_SIZE),\n",
        "                        validation_data=(x_te, y_te),\n",
        "                        epochs=EPOCHS, verbose=0, workers=1,\n",
        "                        callbacks=callbacks, \n",
        "                        use_multiprocessing=False)\n",
        "\n",
        "\n",
        "  # # summarize history for accuracy\n",
        "  # plt.plot(history.history['accuracy'])\n",
        "  # plt.plot(history.history['val_accuracy'])\n",
        "  # plt.title('model accuracy')\n",
        "  # plt.ylabel('accuracy')\n",
        "  # plt.xlabel('epoch')\n",
        "  # plt.legend(['train', 'val'], loc='upper left')\n",
        "  # plt.show()\n",
        "  # # summarize history for loss\n",
        "  # plt.plot(history.history['loss'])\n",
        "  # plt.plot(history.history['val_loss'])\n",
        "  # plt.title('model loss')\n",
        "  # plt.ylabel('loss')\n",
        "  # plt.ylim([0,2])\n",
        "  # plt.xlabel('epoch')\n",
        "  # plt.legend(['train', 'val'], loc='upper left')\n",
        "  # plt.show()\n",
        "  # # summarize history for loss\n",
        "  # plt.plot(history.history['loss'])\n",
        "  # plt.plot(history.history['val_loss'])\n",
        "  # plt.title('model loss')\n",
        "  # plt.ylabel('loss')\n",
        "  # plt.xlabel('epoch')\n",
        "  # plt.legend(['train', 'val'], loc='upper left')\n",
        "  # plt.show()\n",
        "\n",
        "\n",
        "\n",
        "  # print(np.shape(x_tr), np.shape(x_te), BATCH_SIZE, EPOCHS)\n",
        "  # elapsed_time = time.time() - start_time\n",
        "  # print(\"Elapsed time: {}\".format(hms_string(elapsed_time)))\n",
        "  # scores = model.evaluate(x_te, y_te, verbose=0)\n",
        "  # print('Test loss:', scores[0])\n",
        "  # print('Test accuracy:', scores[1])\n",
        "  # #print('\\n','\\n')\n",
        "\n",
        "\n",
        "\n",
        "  #print(history.history['val_accuracy'])\n",
        "\n",
        "  #print(np.shape(x_te), np.shape(y_te))\n",
        "\n",
        "\n",
        "\n",
        "  scores = model.evaluate(x_te, y_te, return_dict=False, verbose=0)\n",
        "  print(scores)\n",
        "  tensorflow.keras.backend.clear_session()\n",
        "  return (scores[1])\n",
        "  #return (- scores[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTl2Qmg-cmqI"
      },
      "source": [
        "import time\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow import keras\n",
        "\n",
        "def create_train_return_model( BATCH_SIZE, EPOCHS, dropout, lr_initial):\n",
        "\n",
        "  # full_evaluate_network has x_tr, y_tr, x_te, y_te, as input \n",
        "\n",
        "  x_tr = x_train\n",
        "  y_tr = y_train\n",
        "  x_te = x_val\n",
        "  y_te = y_val\n",
        "\n",
        "  USE_AUGMENTATION = True\n",
        "  NUM_CLASSES = np.unique(y_tr).shape[0] # 10\n",
        "  COLORS = x_tr.shape[3]\n",
        "\n",
        "\n",
        "  \n",
        "  depth = (COLORS+3) * 9 + 2 # this depth is meant for resnet_v2\n",
        "  num_units = 50\n",
        "  model = resnet_v2(input_shape, depth, num_units, num_classes=2, droprate=dropout)\n",
        "  model.compile(loss='categorical_crossentropy',\n",
        "               optimizer=Adam(lr=lr_initial),\n",
        "               metrics=['accuracy', 'AUC'])\n",
        "  \n",
        "  BATCH_SIZE = math.floor(BATCH_SIZE)\n",
        "  EPOCHS = math.floor(EPOCHS)\n",
        "\n",
        "\n",
        "  start_time = time.time()\n",
        "\n",
        "  # Prepare callbacks for model saving and for learning rate adjustment.\n",
        "  lr_scheduler = LearningRateScheduler(lr_schedule)\n",
        "\n",
        "\n",
        "\n",
        "  lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),\n",
        "                               cooldown=0,\n",
        "                               patience=5,\n",
        "                               min_lr=0.5e-6)\n",
        "\n",
        "  callbacks = [lr_reducer, lr_scheduler]\n",
        "\n",
        "  # Run training, with or without data augmentation.\n",
        "  if not USE_AUGMENTATION:\n",
        "    print('Not using data augmentation.')\n",
        "    model.fit(x_tr, y_tr,\n",
        "               batch_size=BATCH_SIZE,\n",
        "                epochs=EPOCHS,\n",
        "                validation_data=(x_te, y_te),\n",
        "              shuffle=True,\n",
        "              callbacks=callbacks)\n",
        "  else:\n",
        "    #print('Using real-time data augmentation.')  # **** uncomment?\n",
        "\n",
        "    # This will do preprocessing and realtime data augmentation:\n",
        "    datagen = ImageDataGenerator(\n",
        "        # set input mean to 0 over the dataset\n",
        "        featurewise_center=False,\n",
        "        # set each sample mean to 0\n",
        "        samplewise_center=False,\n",
        "        # divide inputs by std of dataset\n",
        "        featurewise_std_normalization=False,\n",
        "        # divide each input by its std\n",
        "        samplewise_std_normalization=False,\n",
        "        # apply ZCA whitening\n",
        "        zca_whitening=False,\n",
        "        # epsilon for ZCA whitening\n",
        "        zca_epsilon=1e-06,\n",
        "        # randomly rotate images in the range (deg 0 to 180)\n",
        "        rotation_range=0,\n",
        "        # randomly shift images horizontally\n",
        "        width_shift_range=0.1,\n",
        "        # randomly shift images vertically\n",
        "        height_shift_range=0.1,\n",
        "        # set range for random shear\n",
        "        shear_range=0.,\n",
        "        # set range for random zoom\n",
        "        zoom_range=0.,\n",
        "        # set range for random channel shifts\n",
        "        channel_shift_range=0.,\n",
        "        # set mode for filling points outside the input boundaries\n",
        "        fill_mode='nearest',\n",
        "        # value used for fill_mode = \"constant\"\n",
        "        cval=0.,\n",
        "        # randomly flip images\n",
        "        horizontal_flip=True,\n",
        "        # randomly flip images\n",
        "        vertical_flip=False,\n",
        "        # set rescaling factor (applied before any other transformation)\n",
        "        rescale=None,\n",
        "        # set function that will be applied on each input\n",
        "        preprocessing_function=None,\n",
        "        # image data format, either \"channels_first\" or \"channels_last\"\n",
        "        data_format=None,\n",
        "        # fraction of images reserved for validation \n",
        "        # (strictly between 0 and 1)\n",
        "        validation_split=0.0)\n",
        "\n",
        "    # Compute quantities required for featurewise normalization\n",
        "    # (std, mean, and principal components if ZCA whitening is applied).\n",
        "    datagen.fit(x_tr)\n",
        "\n",
        "\n",
        "    # Fit the model on the batches generated by datagen.flow().\n",
        "    history = model.fit_generator(datagen.flow(x_tr, y_tr, \n",
        "                        batch_size=BATCH_SIZE),\n",
        "                        validation_data=(x_te, y_te),\n",
        "                        epochs=EPOCHS, verbose=1, workers=1,\n",
        "                        callbacks=callbacks, \n",
        "                        use_multiprocessing=False)\n",
        "\n",
        "\n",
        "\n",
        "  # summarize history for accuracy\n",
        "  plt.plot(history.history['accuracy'])\n",
        "  plt.plot(history.history['val_accuracy'])\n",
        "  plt.title('model accuracy')\n",
        "  plt.ylabel('accuracy')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'val'], loc='upper left')\n",
        "  plt.show()\n",
        "  # summarize history for loss\n",
        "  plt.plot(history.history['loss'])\n",
        "  plt.plot(history.history['val_loss'])\n",
        "  plt.title('model loss')\n",
        "  plt.ylabel('loss')\n",
        "  plt.ylim([0,2])\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'val'], loc='upper left')\n",
        "  plt.show()\n",
        "  # summarize history for loss\n",
        "  plt.plot(history.history['loss'])\n",
        "  plt.plot(history.history['val_loss'])\n",
        "  plt.title('model loss')\n",
        "  plt.ylabel('loss')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'val'], loc='upper left')\n",
        "  plt.show()\n",
        "\n",
        "  \n",
        "\n",
        "  scores = model.evaluate(x_te, y_te, return_dict=False, verbose=0)\n",
        "  print(scores)\n",
        "  tensorflow.keras.backend.clear_session()\n",
        "  return (model, scores[0], scores[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqdcFn80cwrl",
        "outputId": "45d140c3-3945-424e-e25c-43933b1a8138"
      },
      "source": [
        "!pip install bayesian-optimization\n",
        "from bayes_opt import BayesianOptimization\n",
        "import time\n",
        "\n",
        "# Supress NaN warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\",category =UserWarning)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\",category =RuntimeWarning)\n",
        "\n",
        "# Bounded region of parameter space\n",
        "pbounds = {'dropout': (0.05, 0.3), \n",
        "            'BATCH_SIZE': (7, 12),\n",
        "            'EPOCHS': (80, 80),\n",
        "           'lr_initial': (1e-4, 1e-2)\n",
        "           }\n",
        "\n",
        "\n",
        "\n",
        "optimizer = BayesianOptimization(\n",
        "    f=evaluate_network,\n",
        "    pbounds=pbounds,\n",
        "    verbose=2,  # verbose = 1 prints only when a maximum \n",
        "    # is observed, verbose = 0 is silent\n",
        "    random_state=1,\n",
        ")\n",
        "\n",
        "start_time = time.time()\n",
        "optimizer.maximize(init_points=5, n_iter=15,)\n",
        "time_took = time.time() - start_time\n",
        "\n",
        "print(time_took)\n",
        "#print(f\"Total runtime: {hms_string(time_took)}\")\n",
        "print(optimizer.max)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting bayesian-optimization\n",
            "  Downloading https://files.pythonhosted.org/packages/bb/7a/fd8059a3881d3ab37ac8f72f56b73937a14e8bb14a9733e68cc8b17dbe3c/bayesian-optimization-1.2.0.tar.gz\n",
            "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from bayesian-optimization) (1.19.5)\n",
            "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from bayesian-optimization) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.18.0 in /usr/local/lib/python3.6/dist-packages (from bayesian-optimization) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18.0->bayesian-optimization) (1.0.0)\n",
            "Building wheels for collected packages: bayesian-optimization\n",
            "  Building wheel for bayesian-optimization (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for bayesian-optimization: filename=bayesian_optimization-1.2.0-cp36-none-any.whl size=11685 sha256=907192b5279c9c49fd6975a13c4d51fee6d666cd562c3c43064abba3fb2656fa\n",
            "  Stored in directory: /root/.cache/pip/wheels/5a/56/ae/e0e3c1fc1954dc3ec712e2df547235ed072b448094d8f94aec\n",
            "Successfully built bayesian-optimization\n",
            "Installing collected packages: bayesian-optimization\n",
            "Successfully installed bayesian-optimization-1.2.0\n",
            "|   iter    |  target   | BATCH_... |  EPOCHS   |  dropout  | lr_ini... |\n",
            "-------------------------------------------------------------------------\n",
            "[0.4612526297569275, 0.8374999761581421]\n",
            "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.8375  \u001b[0m | \u001b[0m 9.085   \u001b[0m | \u001b[0m 80.0    \u001b[0m | \u001b[0m 0.05003 \u001b[0m | \u001b[0m 0.003093\u001b[0m |\n",
            "[0.506418764591217, 0.8187500238418579]\n",
            "| \u001b[0m 2       \u001b[0m | \u001b[0m 0.8188  \u001b[0m | \u001b[0m 7.734   \u001b[0m | \u001b[0m 80.0    \u001b[0m | \u001b[0m 0.09657 \u001b[0m | \u001b[0m 0.003521\u001b[0m |\n",
            "[1.0066522359848022, 0.8187500238418579]\n",
            "| \u001b[0m 3       \u001b[0m | \u001b[0m 0.8188  \u001b[0m | \u001b[0m 8.984   \u001b[0m | \u001b[0m 80.0    \u001b[0m | \u001b[0m 0.1548  \u001b[0m | \u001b[0m 0.006884\u001b[0m |\n",
            "[0.6419292688369751, 0.831250011920929]\n",
            "| \u001b[0m 4       \u001b[0m | \u001b[0m 0.8313  \u001b[0m | \u001b[0m 8.022   \u001b[0m | \u001b[0m 80.0    \u001b[0m | \u001b[0m 0.05685 \u001b[0m | \u001b[0m 0.006738\u001b[0m |\n",
            "[0.4938718378543854, 0.831250011920929]\n",
            "| \u001b[0m 5       \u001b[0m | \u001b[0m 0.8313  \u001b[0m | \u001b[0m 9.087   \u001b[0m | \u001b[0m 80.0    \u001b[0m | \u001b[0m 0.0851  \u001b[0m | \u001b[0m 0.002061\u001b[0m |\n",
            "[0.5186153054237366, 0.84375]\n",
            "| \u001b[95m 6       \u001b[0m | \u001b[95m 0.8438  \u001b[0m | \u001b[95m 12.0    \u001b[0m | \u001b[95m 80.0    \u001b[0m | \u001b[95m 0.05    \u001b[0m | \u001b[95m 0.01    \u001b[0m |\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1kLDL9f4wrom",
        "outputId": "ee243dcf-5e9e-4991-93a7-9faec0dc459d"
      },
      "source": [
        "BATCH_SIZE = 7\n",
        "EPOCHS = 35\n",
        "dropout = 0.05\n",
        "lr_initial = 0.001\n",
        "\n",
        "model_best, loss, accuracy = create_train_return_model( BATCH_SIZE, EPOCHS, dropout, lr_initial)\n",
        "#evaluate_network( BATCH_SIZE, EPOCHS, dropout, lr_initial)\n",
        "\n",
        "scores = model_best.evaluate(x_test, y_test, verbose=1)\n",
        "print('Test loss:', scores[0])\n",
        "print('Test accuracy:', scores[1])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#model_best.summary()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/35\n",
            "320/320 [==============================] - 75s 161ms/step - loss: 2.0117 - accuracy: 0.6630 - auc: 0.7186 - val_loss: 1.1992 - val_accuracy: 0.8235 - val_auc: 0.9098\n",
            "Epoch 2/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 1.1365 - accuracy: 0.8170 - auc: 0.8884 - val_loss: 0.8526 - val_accuracy: 0.8449 - val_auc: 0.9269\n",
            "Epoch 3/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.8802 - accuracy: 0.8068 - auc: 0.8866 - val_loss: 0.8323 - val_accuracy: 0.8039 - val_auc: 0.8481\n",
            "Epoch 4/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.7416 - accuracy: 0.7940 - auc: 0.8841 - val_loss: 0.7696 - val_accuracy: 0.6667 - val_auc: 0.8124\n",
            "Epoch 5/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.6453 - accuracy: 0.8086 - auc: 0.8945 - val_loss: 0.5731 - val_accuracy: 0.8645 - val_auc: 0.9238\n",
            "Epoch 6/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.5954 - accuracy: 0.8097 - auc: 0.8933 - val_loss: 0.8197 - val_accuracy: 0.3815 - val_auc: 0.4507\n",
            "Epoch 7/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.5715 - accuracy: 0.8091 - auc: 0.8891 - val_loss: 0.6638 - val_accuracy: 0.6774 - val_auc: 0.8131\n",
            "Epoch 8/35\n",
            "320/320 [==============================] - 49s 153ms/step - loss: 0.5618 - accuracy: 0.8115 - auc: 0.8880 - val_loss: 0.4620 - val_accuracy: 0.8770 - val_auc: 0.9382\n",
            "Epoch 9/35\n",
            "320/320 [==============================] - 49s 153ms/step - loss: 0.5003 - accuracy: 0.8362 - auc: 0.9091 - val_loss: 0.4578 - val_accuracy: 0.8556 - val_auc: 0.9322\n",
            "Epoch 10/35\n",
            "320/320 [==============================] - 49s 153ms/step - loss: 0.4927 - accuracy: 0.8273 - auc: 0.9099 - val_loss: 0.4212 - val_accuracy: 0.8645 - val_auc: 0.9424\n",
            "Epoch 11/35\n",
            "320/320 [==============================] - 49s 153ms/step - loss: 0.5294 - accuracy: 0.8206 - auc: 0.8917 - val_loss: 0.4260 - val_accuracy: 0.8627 - val_auc: 0.9375\n",
            "Epoch 12/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.4627 - accuracy: 0.8266 - auc: 0.9124 - val_loss: 0.5112 - val_accuracy: 0.7736 - val_auc: 0.8987\n",
            "Epoch 13/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.4755 - accuracy: 0.8321 - auc: 0.9081 - val_loss: 0.4243 - val_accuracy: 0.8663 - val_auc: 0.9314\n",
            "Epoch 14/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.4463 - accuracy: 0.8307 - auc: 0.9154 - val_loss: 0.4341 - val_accuracy: 0.8538 - val_auc: 0.9245\n",
            "Epoch 15/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.4663 - accuracy: 0.8229 - auc: 0.9052 - val_loss: 0.4909 - val_accuracy: 0.7326 - val_auc: 0.8829\n",
            "Epoch 16/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.4358 - accuracy: 0.8393 - auc: 0.9176 - val_loss: 0.3604 - val_accuracy: 0.8895 - val_auc: 0.9502\n",
            "Epoch 17/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.4233 - accuracy: 0.8602 - auc: 0.9277 - val_loss: 0.3743 - val_accuracy: 0.8806 - val_auc: 0.9458\n",
            "Epoch 18/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3845 - accuracy: 0.8509 - auc: 0.9378 - val_loss: 0.3543 - val_accuracy: 0.8895 - val_auc: 0.9500\n",
            "Epoch 19/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.4172 - accuracy: 0.8425 - auc: 0.9233 - val_loss: 0.4795 - val_accuracy: 0.7932 - val_auc: 0.8973\n",
            "Epoch 20/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.4025 - accuracy: 0.8562 - auc: 0.9309 - val_loss: 0.3537 - val_accuracy: 0.8966 - val_auc: 0.9527\n",
            "Epoch 21/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3783 - accuracy: 0.8619 - auc: 0.9400 - val_loss: 0.3583 - val_accuracy: 0.8824 - val_auc: 0.9490\n",
            "Epoch 22/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3634 - accuracy: 0.8690 - auc: 0.9446 - val_loss: 0.3660 - val_accuracy: 0.8699 - val_auc: 0.9480\n",
            "Epoch 23/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.4064 - accuracy: 0.8576 - auc: 0.9296 - val_loss: 0.3436 - val_accuracy: 0.8966 - val_auc: 0.9542\n",
            "Epoch 24/35\n",
            "320/320 [==============================] - 49s 153ms/step - loss: 0.4071 - accuracy: 0.8601 - auc: 0.9283 - val_loss: 0.3538 - val_accuracy: 0.8770 - val_auc: 0.9500\n",
            "Epoch 25/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3883 - accuracy: 0.8624 - auc: 0.9348 - val_loss: 0.3464 - val_accuracy: 0.8948 - val_auc: 0.9551\n",
            "Epoch 26/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3743 - accuracy: 0.8514 - auc: 0.9389 - val_loss: 0.3672 - val_accuracy: 0.8717 - val_auc: 0.9460\n",
            "Epoch 27/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3741 - accuracy: 0.8667 - auc: 0.9392 - val_loss: 0.3339 - val_accuracy: 0.8948 - val_auc: 0.9572\n",
            "Epoch 28/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3456 - accuracy: 0.8757 - auc: 0.9510 - val_loss: 0.3198 - val_accuracy: 0.8966 - val_auc: 0.9593\n",
            "Epoch 29/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3738 - accuracy: 0.8633 - auc: 0.9416 - val_loss: 0.4237 - val_accuracy: 0.8895 - val_auc: 0.9340\n",
            "Epoch 30/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3789 - accuracy: 0.8720 - auc: 0.9397 - val_loss: 0.3309 - val_accuracy: 0.9020 - val_auc: 0.9590\n",
            "Epoch 31/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3581 - accuracy: 0.8738 - auc: 0.9461 - val_loss: 0.6890 - val_accuracy: 0.7041 - val_auc: 0.8219\n",
            "Epoch 32/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3358 - accuracy: 0.8761 - auc: 0.9523 - val_loss: 0.3326 - val_accuracy: 0.9055 - val_auc: 0.9573\n",
            "Epoch 33/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3787 - accuracy: 0.8609 - auc: 0.9380 - val_loss: 0.3981 - val_accuracy: 0.8503 - val_auc: 0.9319\n",
            "Epoch 34/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3817 - accuracy: 0.8542 - auc: 0.9358 - val_loss: 0.3277 - val_accuracy: 0.9055 - val_auc: 0.9588\n",
            "Epoch 35/35\n",
            "320/320 [==============================] - 49s 154ms/step - loss: 0.3212 - accuracy: 0.8932 - auc: 0.9584 - val_loss: 0.3268 - val_accuracy: 0.9055 - val_auc: 0.9577\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"
          }
        },
        {
          "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": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[0.3268224895000458, 0.9055258631706238, 0.9577404260635376]\n",
            "7/7 [==============================] - 1s 163ms/step - loss: 0.6355 - accuracy: 0.7800 - auc: 0.8620\n",
            "Test loss: 0.6354832649230957\n",
            "Test accuracy: 0.7799999713897705\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QP6IZd06glpX",
        "outputId": "b7b3b7e9-ce12-44e3-c9e3-d175b282d57f"
      },
      "source": [
        "print(np.shape(x_test), np.shape(y_test))\n",
        "scores = model_best.evaluate(x_test, y_test, verbose=1)\n",
        "print('Test loss:', scores[0])\n",
        "print('Test accuracy:', scores[1])\n",
        "\n",
        "\n",
        "\n",
        "wrong_ct = 0\n",
        "wrong_list = []\n",
        "\n",
        "for i in range(np.shape(y_test)[0]):\n",
        "  x1 = x_test[i,:,:,:]\n",
        "  y1 = y_test[i]\n",
        "  x1 = x1.reshape( (1,256,256,3))\n",
        "  if np.argmax(model_best.predict(x1)[0]) != np.argmax(y1):\n",
        "    print(i, model_best.predict(x1)[0], np.argmax(model_best.predict(x1)[0]), np.argmax(y1) )\n",
        "    #print(model_best.predict(x1)[0], y1 )\n",
        "    #print(i)\n",
        "    wrong_list.append(i)\n",
        "    wrong_ct +=1\n",
        "print(wrong_ct/200)\n",
        "print(wrong_list)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(200, 256, 256, 3) (200, 2)\n",
            "7/7 [==============================] - 1s 133ms/step - loss: 0.6355 - accuracy: 0.7800 - auc: 0.8620\n",
            "Test loss: 0.6354832649230957\n",
            "Test accuracy: 0.7799999713897705\n",
            "2 [0.24696009 0.75303996] 1 0\n",
            "7 [0.11211292 0.88788706] 1 0\n",
            "9 [0.54061073 0.45938927] 0 1\n",
            "13 [0.05925232 0.94074774] 1 0\n",
            "21 [0.25044602 0.749554  ] 1 0\n",
            "28 [0.15534985 0.8446501 ] 1 0\n",
            "29 [9.316316e-05 9.999068e-01] 1 0\n",
            "35 [0.04161225 0.95838773] 1 0\n",
            "37 [0.22820814 0.7717919 ] 1 0\n",
            "38 [0.07463402 0.9253659 ] 1 0\n",
            "47 [0.06815623 0.9318438 ] 1 0\n",
            "50 [0.1136829 0.8863171] 1 0\n",
            "54 [0.40567818 0.5943218 ] 1 0\n",
            "61 [0.21674645 0.78325355] 1 0\n",
            "65 [0.12682477 0.8731752 ] 1 0\n",
            "66 [0.2772802 0.7227197] 1 0\n",
            "67 [0.05339581 0.94660425] 1 0\n",
            "72 [0.00249998 0.9975    ] 1 0\n",
            "75 [0.13020156 0.8697985 ] 1 0\n",
            "76 [0.08584382 0.9141562 ] 1 0\n",
            "90 [0.32808492 0.6719151 ] 1 0\n",
            "101 [0.04785495 0.95214504] 1 0\n",
            "106 [0.42034578 0.5796542 ] 1 0\n",
            "114 [0.25345013 0.7465499 ] 1 0\n",
            "124 [0.1740556  0.82594436] 1 0\n",
            "133 [0.08624743 0.9137526 ] 1 0\n",
            "134 [0.18449822 0.8155018 ] 1 0\n",
            "141 [0.46266094 0.53733903] 1 0\n",
            "145 [0.16149242 0.83850753] 1 0\n",
            "146 [0.05347144 0.9465285 ] 1 0\n",
            "149 [0.12284577 0.8771542 ] 1 0\n",
            "155 [0.361794 0.638206] 1 0\n",
            "157 [0.14206603 0.85793394] 1 0\n",
            "158 [0.28226805 0.7177319 ] 1 0\n",
            "163 [0.18442266 0.8155774 ] 1 0\n",
            "166 [0.11868513 0.8813149 ] 1 0\n",
            "168 [0.32549846 0.67450154] 1 0\n",
            "172 [0.11784688 0.8821531 ] 1 0\n",
            "173 [0.00639825 0.99360174] 1 0\n",
            "184 [0.11961071 0.8803893 ] 1 0\n",
            "192 [0.10169438 0.89830565] 1 0\n",
            "193 [0.10138744 0.8986125 ] 1 0\n",
            "194 [0.09075587 0.9092441 ] 1 0\n",
            "196 [0.301143 0.698857] 1 0\n",
            "0.22\n",
            "[2, 7, 9, 13, 21, 28, 29, 35, 37, 38, 47, 50, 54, 61, 65, 66, 67, 72, 75, 76, 90, 101, 106, 114, 124, 133, 134, 141, 145, 146, 149, 155, 157, 158, 163, 166, 168, 172, 173, 184, 192, 193, 194, 196]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eV4tPGXWmPXk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "50bf5352-2388-4a85-efb5-14cdfb873adb"
      },
      "source": [
        "from matplotlib import pyplot\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "model_best.summary()\n",
        "\n",
        "print(model_best.layers[183].name)\n",
        "\n",
        "# redefine model to output right after the first hidden layer\n",
        "\n",
        "feat_extr_model = Model(inputs=model_best.inputs, outputs=model_best.layers[183].output)\n",
        "hidden_features = feat_extr_model.predict(x_test)\n",
        "hidden_features = hidden_features.reshape( (200,-1)  )\n",
        "\n",
        "print('shape of hidden_features is ', np.shape(hidden_features) )\n",
        "\n",
        "pca = PCA(n_components=20)\n",
        "pca_result = pca.fit_transform(hidden_features)\n",
        "print('Variance PCA: {}'.format(np.sum(pca.explained_variance_ratio_)))\n",
        "\n",
        "tsne = TSNE(n_components=2, verbose = 1)\n",
        "tsne_results = tsne.fit_transform(pca_result)\n",
        "\n",
        "color_map = np.argmax(y_test, axis=1)\n",
        "plt.figure(figsize=(10,10))\n",
        "for cl in range(2):\n",
        "    indices = np.where(color_map==cl)\n",
        "    indices = indices[0]\n",
        "    plt.scatter(tsne_results[indices,0], tsne_results[indices, 1], label=cl)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 256, 256, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 256, 256, 16) 448         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 256, 256, 16) 64          conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, 256, 256, 16) 0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 256, 256, 16) 272         activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 256, 256, 16) 64          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 256, 256, 16) 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 256, 256, 16) 2320        activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 256, 256, 16) 64          conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 256, 256, 16) 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 256, 256, 64) 1088        activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 256, 256, 64) 1088        activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 256, 256, 64) 0           conv2d_4[0][0]                   \n",
            "                                                                 conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 256, 256, 64) 256         add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 256, 256, 64) 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 256, 256, 16) 1040        activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 256, 256, 16) 64          conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 256, 256, 16) 0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 256, 256, 16) 2320        activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 256, 256, 16) 64          conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 256, 256, 16) 0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 256, 256, 64) 1088        activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 256, 256, 64) 0           add[0][0]                        \n",
            "                                                                 conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 256, 256, 64) 256         add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 256, 256, 64) 0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 256, 256, 16) 1040        activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 256, 256, 16) 64          conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 256, 256, 16) 0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 256, 256, 16) 2320        activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 256, 256, 16) 64          conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 256, 256, 16) 0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 256, 256, 64) 1088        activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 256, 256, 64) 0           add_1[0][0]                      \n",
            "                                                                 conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 256, 256, 64) 256         add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 256, 256, 64) 0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 256, 256, 16) 1040        activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 256, 256, 16) 64          conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 256, 256, 16) 0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 256, 256, 16) 2320        activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 256, 256, 16) 64          conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 256, 256, 16) 0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 256, 256, 64) 1088        activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 256, 256, 64) 0           add_2[0][0]                      \n",
            "                                                                 conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 256, 256, 64) 256         add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 256, 256, 64) 0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 256, 256, 16) 1040        activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 256, 256, 16) 64          conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 256, 256, 16) 0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 256, 256, 16) 2320        activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 256, 256, 16) 64          conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 256, 256, 16) 0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 256, 256, 64) 1088        activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 256, 256, 64) 0           add_3[0][0]                      \n",
            "                                                                 conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 256, 256, 64) 256         add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 256, 256, 64) 0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 256, 256, 16) 1040        activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 256, 256, 16) 64          conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 256, 256, 16) 0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 256, 256, 16) 2320        activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 256, 256, 16) 64          conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 256, 256, 16) 0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 256, 256, 64) 1088        activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 256, 256, 64) 0           add_4[0][0]                      \n",
            "                                                                 conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 256, 256, 64) 256         add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 256, 256, 64) 0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 128, 128, 64) 4160        activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 128, 128, 64) 256         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 128, 128, 64) 0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 128, 128, 64) 36928       activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 128, 128, 64) 256         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 128, 128, 64) 0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 128, 128, 128 8320        add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 128, 128, 128 8320        activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 128, 128, 128 0           conv2d_23[0][0]                  \n",
            "                                                                 conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 128, 128, 128 512         add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 128, 128, 128 0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 128, 128, 64) 8256        activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 128, 128, 64) 256         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 128, 128, 64) 0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 128, 128, 64) 36928       activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 128, 128, 64) 256         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 128, 128, 64) 0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 128, 128, 128 8320        activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 128, 128, 128 0           add_6[0][0]                      \n",
            "                                                                 conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 128, 128, 128 512         add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 128, 128, 128 0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 128, 128, 64) 8256        activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 128, 128, 64) 256         conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 128, 128, 64) 0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 128, 128, 64) 36928       activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 128, 128, 64) 256         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 128, 128, 64) 0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 128, 128, 128 8320        activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 128, 128, 128 0           add_7[0][0]                      \n",
            "                                                                 conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 128, 128, 128 512         add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 128, 128, 128 0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 128, 128, 64) 8256        activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 128, 128, 64) 256         conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 128, 128, 64) 0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 128, 128, 64) 36928       activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 128, 128, 64) 256         conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 128, 128, 64) 0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 128, 128, 128 8320        activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 128, 128, 128 0           add_8[0][0]                      \n",
            "                                                                 conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 128, 128, 128 512         add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 128, 128, 128 0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 128, 128, 64) 8256        activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 128, 128, 64) 256         conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 128, 128, 64) 0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 128, 128, 64) 36928       activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 128, 128, 64) 256         conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 128, 128, 64) 0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 128, 128, 128 8320        activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 128, 128, 128 0           add_9[0][0]                      \n",
            "                                                                 conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 128, 128, 128 512         add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 128, 128, 128 0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 128, 128, 64) 8256        activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 128, 128, 64) 256         conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 128, 128, 64) 0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 128, 128, 64) 36928       activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 128, 128, 64) 256         conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 128, 128, 64) 0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 128, 128, 128 8320        activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 128, 128, 128 0           add_10[0][0]                     \n",
            "                                                                 conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 128, 128, 128 512         add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 128, 128, 128 0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 64, 64, 128)  16512       activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 64, 64, 128)  512         conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 64, 64, 128)  0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 64, 64, 128)  147584      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 64, 64, 128)  512         conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 64, 64, 128)  0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 64, 64, 256)  33024       add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 64, 64, 256)  33024       activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 64, 64, 256)  0           conv2d_42[0][0]                  \n",
            "                                                                 conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 64, 64, 256)  1024        add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 64, 64, 256)  0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 64, 64, 128)  32896       activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 64, 64, 128)  512         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 64, 64, 128)  0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 64, 64, 128)  147584      activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 64, 64, 128)  512         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 64, 64, 128)  0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 64, 64, 256)  33024       activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, 64, 64, 256)  0           add_12[0][0]                     \n",
            "                                                                 conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 64, 64, 256)  1024        add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 64, 64, 256)  0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 64, 64, 128)  32896       activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 64, 64, 128)  512         conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 64, 64, 128)  0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 64, 64, 128)  147584      activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 64, 64, 128)  512         conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 64, 64, 128)  0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 64, 64, 256)  33024       activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, 64, 64, 256)  0           add_13[0][0]                     \n",
            "                                                                 conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 64, 64, 256)  1024        add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 64, 64, 256)  0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 64, 64, 128)  32896       activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 64, 64, 128)  512         conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 64, 64, 128)  0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 64, 64, 128)  147584      activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 64, 64, 128)  512         conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 64, 64, 128)  0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 64, 64, 256)  33024       activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, 64, 64, 256)  0           add_14[0][0]                     \n",
            "                                                                 conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 64, 64, 256)  1024        add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 64, 64, 256)  0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 64, 64, 128)  32896       activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 64, 64, 128)  512         conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 64, 64, 128)  0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 64, 64, 128)  147584      activation_49[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 64, 64, 128)  512         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, 64, 64, 128)  0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, 64, 64, 256)  33024       activation_50[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, 64, 64, 256)  0           add_15[0][0]                     \n",
            "                                                                 conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 64, 64, 256)  1024        add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, 64, 64, 256)  0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 64, 64, 128)  32896       activation_51[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 64, 64, 128)  512         conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, 64, 64, 128)  0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 64, 64, 128)  147584      activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 64, 64, 128)  512         conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, 64, 64, 128)  0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 64, 64, 256)  33024       activation_53[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_17 (Add)                    (None, 64, 64, 256)  0           add_16[0][0]                     \n",
            "                                                                 conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, 64, 64, 256)  1024        add_17[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, 64, 64, 256)  0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, 8, 8, 256)    0           activation_54[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 16384)        0           average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 50)           819250      flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 50)           0           dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 2)            102         dropout[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 2,490,520\n",
            "Trainable params: 2,480,120\n",
            "Non-trainable params: 10,400\n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57\n",
            "shape of hidden_features is  (200, 1048576)\n",
            "Variance PCA: 0.4847313165664673\n",
            "[t-SNE] Computing 91 nearest neighbors...\n",
            "[t-SNE] Indexed 200 samples in 0.000s...\n",
            "[t-SNE] Computed neighbors for 200 samples in 0.005s...\n",
            "[t-SNE] Computed conditional probabilities for sample 200 / 200\n",
            "[t-SNE] Mean sigma: 8.822044\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 58.222710\n",
            "[t-SNE] KL divergence after 1000 iterations: 0.694140\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9iMo9pY1Q6-J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f143b980-728a-41ea-916f-00d66bc0ad4a"
      },
      "source": [
        "# Display\n",
        "import tensorflow as tf\n",
        "from IPython.display import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.cm as cm\n",
        "from tensorflow import keras\n",
        "from keras.applications.densenet import preprocess_input, decode_predictions\n",
        "\n",
        "\n",
        "from imutils import paths\n",
        "#from PIL import Image, ImageFile\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#model_builder = keras.applications.xception.Xception      #(include_top=False,  input_shape=(256, 256, 3))\n",
        "img_size = (256, 256)\n",
        "#preprocess_input = keras.applications.xception.preprocess_input\n",
        "#decode_predictions = keras.applications.xception.decode_predictions\n",
        "\n",
        "last_conv_layer_name = 'activation_54'     #'conv5_block32_concat'      #\"conv5_block32_2_conv\"     'bn', 'relu',\n",
        "#classifier_layer_names = ['avg_pool', 'flatten', 'dense_post_pool', 'predictions']\n",
        "\n",
        "classifier_layer_names = [ 'average_pooling2d', 'flatten', 'dense', 'dropout', 'dense_1']\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# # The local path to our target image\n",
        "img_path = '/content/drive/MyDrive/223B/normal_256_1_new/4271_27776_23296.png'\n",
        "# img_path = '/content/drive/MyDrive/223B/normal_256_1_new/189_4032_11648.png'\n",
        "display(Image(img_path))\n",
        "print('displayed')\n",
        "\n",
        "\n",
        "\n",
        "# _45696_13440     4+5\n",
        "# 2978  :  9063_84224_24192     4+5\n",
        "# 2979  :  9063_9856_11648     4+5\n",
        "# 2980  :  398_17024_24192     0+0\n",
        "# 2981  :  398_85120_26880     0+0\n",
        "# 2982  :  398_28672_14336     0+0\n",
        "# 2983  :  398_5376_34944     0+0\n",
        "# 2984  :  398_23296_16128     0+0\n",
        "# 2985  :  398_86912_28672     0+0\n",
        "# 2986  :  398_19712_18816     0+0\n",
        "# 2987  :  398_17024_25984     0+0\n",
        "# 2988  :  398_100352_10752     0+0\n",
        "# 2989  :  398_29568_15232     0+0\n",
        "# 2990  :  4271_89600_28672     3+3\n",
        "# 2991  :  4271_90496_26880     3+3\n",
        "# 2992  :  4271_31360_25984\n",
        "# 2993  :  4271_30464_28672     3+3\n",
        "# 2994  :  4271_89600_27776     3+3\n",
        "# 2995  :  4271_26880_23296     3+3\n",
        "# 2996  :  4271_27776_23296     3+3\n",
        "# 2997  :  4271_89600_26880     3+3\n",
        "# 2998  :  4271_91392_26880     3+3\n",
        "# 2999  :  4271_31360_26880\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def get_img_array(img_path, size):\n",
        "    # `img` is a PIL image of size 299x299\n",
        "    img = keras.preprocessing.image.load_img(img_path, target_size=size)\n",
        "    # `array` is a float32 Numpy array of shape (299, 299, 3)\n",
        "    array = keras.preprocessing.image.img_to_array(img)\n",
        "    # We add a dimension to transform our array into a \"batch\"\n",
        "    # of size (1, 299, 299, 3)\n",
        "    array = np.expand_dims(array, axis=0)\n",
        "    return array\n",
        "\n",
        "\n",
        "def make_gradcam_heatmap(\n",
        "    img_array, model, last_conv_layer_name, classifier_layer_names\n",
        "):\n",
        "    # First, we create a model that maps the input image to the activations\n",
        "    # of the last conv layer\n",
        "    last_conv_layer = model.get_layer(last_conv_layer_name)\n",
        "    last_conv_layer_model = keras.Model(model.inputs, last_conv_layer.output)\n",
        "\n",
        "    # Second, we create a model that maps the activations of the last conv\n",
        "    # layer to the final class predictions\n",
        "    classifier_input = keras.Input(shape=last_conv_layer.output.shape[1:])\n",
        "    x = classifier_input\n",
        "    for layer_name in classifier_layer_names:\n",
        "        print(layer_name)\n",
        "        x = model.get_layer(layer_name)(x)\n",
        "    classifier_model = keras.Model(classifier_input, x)\n",
        "\n",
        "    # Then, we compute the gradient of the top predicted class for our input image\n",
        "    # with respect to the activations of the last conv layer\n",
        "    with tf.GradientTape() as tape:\n",
        "        # Compute activations of the last conv layer and make the tape watch it\n",
        "        last_conv_layer_output = last_conv_layer_model(img_array)\n",
        "        tape.watch(last_conv_layer_output)\n",
        "        # Compute class predictions\n",
        "        preds = classifier_model(last_conv_layer_output)\n",
        "        top_pred_index = tf.argmax(preds[0])\n",
        "        top_class_channel = preds[:, top_pred_index]\n",
        "\n",
        "    # This is the gradient of the top predicted class with regard to\n",
        "    # the output feature map of the last conv layer\n",
        "    grads = tape.gradient(top_class_channel, last_conv_layer_output)\n",
        "\n",
        "    # This is a vector where each entry is the mean intensity of the gradient\n",
        "    # over a specific feature map channel\n",
        "    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n",
        "\n",
        "    # We multiply each channel in the feature map array\n",
        "    # by \"how important this channel is\" with regard to the top predicted class\n",
        "    last_conv_layer_output = last_conv_layer_output.numpy()[0]\n",
        "    pooled_grads = pooled_grads.numpy()\n",
        "    for i in range(pooled_grads.shape[-1]):\n",
        "        last_conv_layer_output[:, :, i] *= pooled_grads[i]\n",
        "\n",
        "    # The channel-wise mean of the resulting feature map\n",
        "    # is our heatmap of class activation\n",
        "    heatmap = np.mean(last_conv_layer_output, axis=-1)\n",
        "\n",
        "    # For visualization purpose, we will also normalize the heatmap between 0 & 1\n",
        "    heatmap = np.maximum(heatmap, 0)  / np.max(heatmap) \n",
        "    return heatmap\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#######    select an image where the softmax error is very big   e.g. No91    '/content/drive/MyDrive/223B/256_1/256_1_test/5602_157696_32256.png'\n",
        "\n",
        "img_path_select_image = img_path\n",
        "\n",
        "# Prepare image\n",
        "img_array = preprocess_input(get_img_array(img_path_select_image, size=img_size))\n",
        "\n",
        "print('out of the relu  ', feat_extr_model.predict(img_array))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Print what the top predicted class is\n",
        "preds = model_best.predict(img_array)\n",
        "#print(\"Predicted:\", decode_predictions(preds, top=1)[0])\n",
        "\n",
        "# Generate class activation heatmap\n",
        "heatmap = make_gradcam_heatmap(\n",
        "    img_array, model_best, last_conv_layer_name, classifier_layer_names\n",
        ")\n",
        "\n",
        "# Display heatmap\n",
        "plt.matshow(heatmap)\n",
        "plt.show()\n",
        "\n",
        "print('Displayed Heatmap')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "######### display the grad_cam\n",
        "\n",
        "# We load the original image\n",
        "img = keras.preprocessing.image.load_img(img_path)\n",
        "img = keras.preprocessing.image.img_to_array(img)\n",
        "\n",
        "# We rescale heatmap to a range 0-255\n",
        "heatmap = np.uint8(255 * heatmap)\n",
        "\n",
        "# We use jet colormap to colorize heatmap\n",
        "jet = cm.get_cmap(\"jet\")\n",
        "\n",
        "# We use RGB values of the colormap\n",
        "jet_colors = jet(np.arange(256))[:, :3]\n",
        "jet_heatmap = jet_colors[heatmap]\n",
        "\n",
        "# We create an image with RGB colorized heatmap\n",
        "jet_heatmap = keras.preprocessing.image.array_to_img(jet_heatmap)\n",
        "jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))\n",
        "jet_heatmap = keras.preprocessing.image.img_to_array(jet_heatmap)\n",
        "\n",
        "# Superimpose the heatmap on original image\n",
        "superimposed_img = jet_heatmap * 0.4 + img\n",
        "superimposed_img = keras.preprocessing.image.array_to_img(superimposed_img)\n",
        "\n",
        "# Save the superimposed image\n",
        "save_path = \"elephant_cam.jpg\"\n",
        "superimposed_img.save(save_path)\n",
        "\n",
        "# Display Grad CAM\n",
        "display(Image(save_path))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "displayed\n",
            "out of the relu   [[[[-0.8290422   2.770359    0.2981292  ...  0.6323552  -0.6631241\n",
            "     3.385754  ]\n",
            "   [-1.4233366   4.8623176   0.51967776 ...  1.1436554  -1.1384443\n",
            "     5.9583983 ]\n",
            "   [-2.4150443   8.173258    0.90289015 ...  1.9485639  -1.9334083\n",
            "    10.024572  ]\n",
            "   ...\n",
            "   [-2.3783102   8.089106    0.88974273 ...  1.931649   -1.9027737\n",
            "     9.92385   ]\n",
            "   [-3.3538065  11.379589    1.2447145  ...  2.6985667  -2.6832452\n",
            "    13.949149  ]\n",
            "   [-2.320367    8.209747    0.862146   ...  2.0155373  -1.8560102\n",
            "    10.105144  ]]\n",
            "\n",
            "  [[-1.6499913   5.46663     0.60147506 ...  1.2521309  -1.3102723\n",
            "     6.6938844 ]\n",
            "   [-2.867894    9.737405    1.0460087  ...  2.272714   -2.2792318\n",
            "    11.943024  ]\n",
            "   [-4.3024154  14.519342    1.5983964  ...  3.4344687  -3.4202075\n",
            "    17.835543  ]\n",
            "   ...\n",
            "   [-4.635262   15.629755    1.7238094  ...  3.6916404  -3.689444\n",
            "    19.18565   ]\n",
            "   [-6.467519   21.840986    2.3810186  ...  5.1224027  -5.1430206\n",
            "    26.795408  ]\n",
            "   [-4.5694175  16.188566    1.6796889  ...  3.9284425  -3.6251714\n",
            "    19.932516  ]]\n",
            "\n",
            "  [[-2.2818305   7.4705257   0.8561351  ...  1.7319432  -1.8057065\n",
            "     9.162271  ]\n",
            "   [-4.1616387  13.983391    1.5389184  ...  3.2713664  -3.2992637\n",
            "    17.163364  ]\n",
            "   [-5.727489   19.26407     2.165698   ...  4.6058784  -4.540098\n",
            "    23.692354  ]\n",
            "   ...\n",
            "   [-6.9322443  23.193132    2.6240962  ...  5.5228834  -5.510513\n",
            "    28.496302  ]\n",
            "   [-9.2291565  30.96815     3.4657543  ...  7.328298   -7.318606\n",
            "    38.041626  ]\n",
            "   [-6.5337386  23.032389    2.42787    ...  5.610996   -5.1663404\n",
            "    28.369516  ]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[-2.55343     8.241839    0.9883166  ...  1.9487848  -2.0203192\n",
            "    10.126532  ]\n",
            "   [-4.3829656  14.607512    1.6619493  ...  3.4657106  -3.462827\n",
            "    17.955812  ]\n",
            "   [-5.809833   19.5332      2.2194626  ...  4.7017326  -4.5968513\n",
            "    24.036148  ]\n",
            "   ...\n",
            "   [-5.714557   19.285385    2.1882694  ...  4.675494   -4.523239\n",
            "    23.749557  ]\n",
            "   [-5.05789    17.137655    1.9183203  ...  4.1283474  -4.001996\n",
            "    21.092707  ]\n",
            "   [-3.0810826  10.778421    1.1638582  ...  2.6532786  -2.4390392\n",
            "    13.290609  ]]\n",
            "\n",
            "  [[-2.316328    7.4809747   0.8935613  ...  1.7645792  -1.8318692\n",
            "     9.191824  ]\n",
            "   [-3.892159   12.983458    1.4741799  ...  3.080365   -3.073421\n",
            "    15.961756  ]\n",
            "   [-5.246439   17.586079    2.0146108  ...  4.245662   -4.150244\n",
            "    21.647596  ]\n",
            "   ...\n",
            "   [-5.2254996  17.588324    2.0171187  ...  4.2850485  -4.1348763\n",
            "    21.669155  ]\n",
            "   [-4.599284   15.511214    1.7587117  ...  3.7482777  -3.6379602\n",
            "    19.095888  ]\n",
            "   [-2.7729206   9.715076    1.0447026  ...  2.3820105  -2.1895778\n",
            "    11.978909  ]]\n",
            "\n",
            "  [[-1.3523197   4.251748    0.53821295 ...  1.0293605  -1.0606945\n",
            "     5.2531123 ]\n",
            "   [-2.2193377   7.3224993   0.85097855 ...  1.7437834  -1.7290618\n",
            "     9.028635  ]\n",
            "   [-3.0656054  10.136162    1.1896971  ...  2.4568188  -2.398242\n",
            "    12.512347  ]\n",
            "   ...\n",
            "   [-2.969665    9.915628    1.1464107  ...  2.4088995  -2.3255954\n",
            "    12.241659  ]\n",
            "   [-2.649821    8.804529    1.0244576  ...  2.133013   -2.073115\n",
            "    10.867824  ]\n",
            "   [-1.595118    5.58409     0.59216976 ...  1.351665   -1.2447441\n",
            "     6.889418  ]]]]\n",
            "average_pooling2d\n",
            "flatten\n",
            "dense\n",
            "dropout\n",
            "dense_1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:139: RuntimeWarning: invalid value encountered in true_divide\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/image.py:452: UserWarning: Warning: converting a masked element to nan.\n",
            "  dv = np.float64(self.norm.vmax) - np.float64(self.norm.vmin)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/image.py:459: UserWarning: Warning: converting a masked element to nan.\n",
            "  a_min = np.float64(newmin)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/image.py:464: UserWarning: Warning: converting a masked element to nan.\n",
            "  a_max = np.float64(newmax)\n",
            "<string>:6: UserWarning: Warning: converting a masked element to nan.\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: UserWarning: Warning: converting a masked element to nan.\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Displayed Heatmap\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCs3h9Jg_XdC"
      },
      "source": [
        "# plot accuracy vs image size\n",
        "\n",
        "size = [64, 128, 256]\n",
        "accuracies_pl = [0.805, 0.880, 0.825]\n",
        "\n",
        "plt.plot(size, accuracies_pl )\n",
        "#plt.plot([0.1, 0.2, 0.3, 0.4], [1, 4, 9, 16])\n",
        "plt.xlabel(\"Time (s)\")\n",
        "plt.ylabel(\"Scale (Bananas)\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}