classification_task1.ipynb

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  "metadata": {
    "colab": {
      "name": "classification_task1.ipynb",
      "version": "0.3.2",
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      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMkuXGUYOkvZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.applications.resnet50 import ResNet50\n",
        "from keras.preprocessing import image\n",
        "from keras.models import Model\n",
        "from keras.layers import GlobalAveragePooling2D\n",
        "import numpy as np\n",
        "import cv2 \n",
        "import os\n",
        "import glob\n",
        "from keras.models import Sequential, Model, load_model\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Activation\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Input\n",
        "from keras.layers import BatchNormalization\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras import losses, metrics, optimizers\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
        "from keras import backend as K\n",
        "from keras import regularizers\n",
        "from keras.utils import to_categorical\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, precision_score\n",
        "from skimage.transform import resize, rescale\n",
        "from skimage.io import imread\n",
        "from matplotlib import pyplot as plt\n",
        "import pickle\n",
        "import pandas as pd\n",
        "from sklearn.utils import class_weight\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbqqHDvxOste",
        "colab_type": "code",
        "outputId": "57c727be-94db-48f9-853b-985986565b3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#loading the pre-processed dataset via mounting google drive locally\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuQyH23-XAX6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#load the dataset into the environment\n",
        "\n",
        "with open('gdrive/My Drive/dataset_isic_2/X_clf', 'rb') as f:\n",
        "    X = pickle.load(f)\n",
        "    \n",
        "with open('gdrive/My Drive/dataset_isic_2/Y_clf', 'rb') as f:\n",
        "    y = pickle.load(f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVhexYRNO1e4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#train val test split the dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsX3bB91AHwh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a2be1f45-c03e-4aab-b31a-2f01fa71e24d"
      },
      "source": [
        "type(X_train)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvM4t5gNO4nX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "b6ebbf74-d716-48c8-ee40-c3465ac1ccbe"
      },
      "source": [
        "#CNN starts here\n",
        "model = Sequential()\n",
        "CNN_input_size = [64, 64, 3]\n",
        "model.add(Conv2D(32, (3, 3), input_shape = CNN_input_size, activation = 'relu', padding = 'same', name = 'conv1'))\n",
        "model.add(MaxPooling2D((2, 2), strides = (2, 2)))\n",
        "model.add(Conv2D(32, (3, 3), input_shape = CNN_input_size, activation = 'relu', padding = 'same', name = 'conv2'))\n",
        "model.add(MaxPooling2D((2, 2), strides = (2, 2)))\n",
        "model.add(Conv2D(32, (3, 3), input_shape = CNN_input_size, activation = 'relu', padding = 'same', name = 'conv3'))\n",
        "model.add(MaxPooling2D((2, 2), strides = (2, 2)))\n",
        "model.add(Conv2D(32, (3, 3), input_shape = CNN_input_size, activation = 'relu', padding = 'same', name = 'conv4'))\n",
        "model.add(MaxPooling2D((2, 2), strides = (2, 2)))\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(128, activation = 'relu', name = 'dense_1'))\n",
        "model.add(Dense(1, name = 'dense_2'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Activation('sigmoid'))\n",
        "model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1 (Conv2D)               (None, 64, 64, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_44 (MaxPooling (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2 (Conv2D)               (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_45 (MaxPooling (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv3 (Conv2D)               (None, 16, 16, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_46 (MaxPooling (None, 8, 8, 32)          0         \n",
            "_________________________________________________________________\n",
            "conv4 (Conv2D)               (None, 8, 8, 32)          9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_47 (MaxPooling (None, 4, 4, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_22 (Flatten)         (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               65664     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 129       \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 1)                 0         \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 94,433\n",
            "Trainable params: 94,433\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CVOmCUbGB6k4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#compute class weight as imbalanced dataset\n",
        "class_weights = class_weight.compute_class_weight('balanced', np.unique(y_train), y_train)\n",
        "model_checkpoint = ModelCheckpoint('./basic_conv.hdf5',save_best_only=True, monitor='val_loss', verbose=2)\n",
        "tb = TensorBoard(log_dir='./Graph/basic_conv/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "em9wve3lgkj1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "97d13385-94d4-48c9-d551-05ba1cfecd86"
      },
      "source": [
        "class_weights"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.62, 2.64])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KI8txKf3B7ml",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1394
        },
        "outputId": "6af38fee-9483-4f36-a6b2-52cfa4e00933"
      },
      "source": [
        "#fit the model\n",
        "history = model.fit(np.array(X_train), np.array(y_train), epochs=20, batch_size=32, validation_data = (np.array(X_val), np.array(y_val)), callbacks=[model_checkpoint,tb], class_weight=class_weights)"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1280 samples, validate on 320 samples\n",
            "Epoch 1/20\n",
            "1280/1280 [==============================] - 8s 6ms/step - loss: 0.6217 - acc: 0.8117 - val_loss: 0.5230 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.52305, saving model to ./basic_conv.hdf5\n",
            "Epoch 2/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5971 - acc: 0.8109 - val_loss: 0.4779 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.52305 to 0.47790, saving model to ./basic_conv.hdf5\n",
            "Epoch 3/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5885 - acc: 0.8109 - val_loss: 0.4985 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.47790\n",
            "Epoch 4/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5789 - acc: 0.8109 - val_loss: 0.4735 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.47790 to 0.47345, saving model to ./basic_conv.hdf5\n",
            "Epoch 5/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5778 - acc: 0.8109 - val_loss: 0.4537 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.47345 to 0.45373, saving model to ./basic_conv.hdf5\n",
            "Epoch 6/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5676 - acc: 0.8109 - val_loss: 0.4983 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00006: val_loss did not improve from 0.45373\n",
            "Epoch 7/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5631 - acc: 0.8109 - val_loss: 0.4518 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.45373 to 0.45184, saving model to ./basic_conv.hdf5\n",
            "Epoch 8/20\n",
            "1280/1280 [==============================] - 8s 6ms/step - loss: 0.5470 - acc: 0.8109 - val_loss: 0.4425 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.45184 to 0.44252, saving model to ./basic_conv.hdf5\n",
            "Epoch 9/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5614 - acc: 0.8148 - val_loss: 0.4685 - val_acc: 0.8313\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.44252\n",
            "Epoch 10/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5576 - acc: 0.8156 - val_loss: 0.4649 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.44252\n",
            "Epoch 11/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5586 - acc: 0.8156 - val_loss: 0.4468 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.44252\n",
            "Epoch 12/20\n",
            "1280/1280 [==============================] - 8s 6ms/step - loss: 0.5410 - acc: 0.8133 - val_loss: 0.4414 - val_acc: 0.8281\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.44252 to 0.44142, saving model to ./basic_conv.hdf5\n",
            "Epoch 13/20\n",
            "1280/1280 [==============================] - 8s 6ms/step - loss: 0.5420 - acc: 0.8203 - val_loss: 0.4986 - val_acc: 0.8031\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.44142\n",
            "Epoch 14/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5497 - acc: 0.8258 - val_loss: 0.4791 - val_acc: 0.8031\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.44142\n",
            "Epoch 15/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5310 - acc: 0.8320 - val_loss: 0.4720 - val_acc: 0.8156\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.44142\n",
            "Epoch 16/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5278 - acc: 0.8258 - val_loss: 0.5049 - val_acc: 0.8187\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.44142\n",
            "Epoch 17/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5314 - acc: 0.8305 - val_loss: 0.4526 - val_acc: 0.8281\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.44142\n",
            "Epoch 18/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.4989 - acc: 0.8398 - val_loss: 0.4592 - val_acc: 0.8219\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.44142\n",
            "Epoch 19/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.5166 - acc: 0.8461 - val_loss: 0.4602 - val_acc: 0.8156\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.44142\n",
            "Epoch 20/20\n",
            "1280/1280 [==============================] - 7s 6ms/step - loss: 0.4925 - acc: 0.8492 - val_loss: 0.4421 - val_acc: 0.8063\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.44142\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OiWK4WZkH6pP",
        "colab_type": "code",
        "outputId": "7564729d-e652-46c3-a12b-e95a6c6de9f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "source": [
        "#predict on test set\n",
        "model = load_model('./basic_conv.hdf5')\n",
        "y_pred = model.predict(np.array(X_test),verbose=1)"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 2s 4ms/step\n"
          ],
          "name": "stdout"
        }
      ]
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      "metadata": {
        "id": "-ph-BdN-w4hx",
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          "height": 6817
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        "outputId": "54afa034-cb2b-4889-fd3a-462b537d1696"
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      "source": [
        "y_pred"
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      "execution_count": 169,
      "outputs": [
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          "output_type": "execute_result",
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              "       [0.16]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7n4L5ytIl1q",
        "colab_type": "code",
        "outputId": "d0fc73b4-6047-45a0-8f77-4407f5313419",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_pred_class = y_pred > 0.5\n",
        "print (\"Precision Score %f\",precision_score(y_test, y_pred_class, average='binary'))"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision Score %f 0.3333333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnB-hwseFURB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f676a718-d45d-452b-c6d9-69ed5f02f145"
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1 = f1_score(y_test, y_pred_class, average = None)\n",
        "f1"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.88, 0.1 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8x1udMXxcCv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import itertools\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOxx75Vhxe7g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "outputId": "062130e6-2e23-4191-ac55-8d0aa6ec7a3b"
      },
      "source": [
        "class_names = [\"Others\",\"Melanoma\"]\n",
        "# Compute confusion matrix\n",
        "cnf_matrix = confusion_matrix(y_test, y_pred_class)\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=class_names,\n",
        "                      title='Confusion matrix, without normalization')\n",
        "\n",
        "# Plot normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n",
        "                      title='Normalized confusion matrix')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[309  10]\n",
            " [ 76   5]]\n",
            "Normalized confusion matrix\n",
            "[[0.97 0.03]\n",
            " [0.94 0.06]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U1FR2agSNEYXVXFOTuO/Kk3j1trP5x31P8+LE6cx470Pmzp0HwKQpb7Pyiv0BeOzZSYzY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\nwMnVzKwGnFzNzGrAydXMrAb+H8hgh/ec5VDoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HnHXsUCZO+YAHn3mDrTdag5HH70EEPDdxGr86/65F719lpWXpvWI3np0wrYhH0XJUVlZy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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}