Datasets
Models
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"colab": {
"name": "Training the CNN.ipynb",
"provenance": [],
"collapsed_sections": []
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "efCvb66QStgP",
"colab_type": "text"
},
"source": [
"*To run this notebook, please provide the following four file paths:*"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Iukqj1IzStyn",
"colab_type": "code",
"colab": {}
},
"source": [
"path_to_train = '/path/to/train/images'\n",
"path_to_test = '/path/to/test/images'\n",
"path_to_labels = '/path/to/labels.csv'\n",
"\n",
"path_to_save_model = '/path/to/save/model/to/cnn-checkpoint-{epoch:02d}-{val_accuracy:.2f}.hdf5'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "XqBOX3Y4MWs3",
"colab_type": "text"
},
"source": [
"## **Installing & Importing Dependencies**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "jnq7FwiHKZwY",
"colab_type": "code",
"colab": {}
},
"source": [
"!pip install keras"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OTnWqAwiKZxb",
"colab_type": "code",
"colab": {}
},
"source": [
"import tensorflow as tf\n",
"import pandas as pd\n",
"\n",
"from keras import applications\n",
"from keras import optimizers\n",
"from keras import backend as k \n",
"\n",
"from keras.models import Sequential, Model\n",
"from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D\n",
"from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping\n",
"from keras.preprocessing.image import ImageDataGenerator"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "N60M6zljKZxs",
"colab_type": "code",
"colab": {}
},
"source": [
"# Make sure that GPU is available on the machine\n",
"assert tf.test.is_gpu_available()\n",
"assert tf.test.is_built_with_cuda()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "PIErVpE6NHx6",
"colab_type": "text"
},
"source": [
"## **Reading in Data Labels**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "SGUEeB-cKZx3",
"colab_type": "code",
"colab": {}
},
"source": [
"labels_df = pd.read_csv(path_to_labels)\n",
"\n",
"# For flow_from_dataframe to function, string datatype is required\n",
"labels_df = labels_df.astype(str)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "qShGxNkPKZx7",
"colab_type": "code",
"colab": {},
"outputId": "f6cf0aa8-a383-440a-e4ed-13499e3669af"
},
"source": [
"# Inspect the DataFrame containing the labels\n",
"labels_df.head(3)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"metadata": {
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"execution_count": 19
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},
{
"cell_type": "markdown",
"metadata": {
"id": "ElzPsVvaODOk",
"colab_type": "text"
},
"source": [
"## **Building Transfer-Learning Model**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qFQ7sFZjx_ba",
"colab_type": "text"
},
"source": [
"*As our pretrained model, we choose VGG19 with ImageNet weights. Note that include_top = False (otherwise, we would be including VGG19's final 1000-node dense softmax prediction layer)*"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vh0b4EQtKZyN",
"colab_type": "code",
"colab": {}
},
"source": [
"model = applications.VGG19(weights = \"imagenet\", include_top=False, input_shape = (128, 128, 3))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "tszxYuOwyURR",
"colab_type": "text"
},
"source": [
"*Freeze the first three convolutional blocks, leave the last two unfrozen. Thereby, we transfer the model's knowledge of low-level features (like edges and angles) while allowing for it to learn new high-level features (like hemorrhages).*"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BruLVtwJKZ0g",
"colab_type": "code",
"colab": {}
},
"source": [
"for layer in model.layers[0:12]:\n",
" layer.trainable = False"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "g_Kb-J_Qylgz",
"colab_type": "text"
},
"source": [
"*We append our own custom layers to the end of VGG19. Note that a single sigmoid final prediction node is equivalent to two softmax final prediction nodes.*"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Z7uPdX-8c5eU",
"colab_type": "code",
"colab": {}
},
"source": [
"x = model.output\n",
"x = Flatten()(x)\n",
"\n",
"x = Dense(1000, activation = 'relu')(x)\n",
"x = Dropout(0.5)(x)\n",
"x = Dense(1000, activation = 'relu')(x)\n",
"output = Dense(1, activation = 'sigmoid')(x)\n",
"\n",
"custom_model = Model(inputs = model.input, outputs = output)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "X49CwEDJy7tN",
"colab_type": "text"
},
"source": [
"*Compiling the model. The primary metric we care about is recall (that is, the CNN's ability to correctly detect intracranial hemorrhages).*"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xECXyzILnWTa",
"colab_type": "code",
"colab": {}
},
"source": [
"custom_model.compile(loss = 'binary_crossentropy',\n",
" optimizer = optimizers.Adam(lr=0.0001),\n",
" metrics=['accuracy',\n",
" tf.keras.metrics.Recall(),\n",
" tf.keras.metrics.AUC(),\n",
" tf.keras.metrics.Precision()])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "u7BWA55koyNp",
"colab_type": "text"
},
"source": [
"# **Creating Train & Test Generators**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "fgTeFSx5olWu",
"colab_type": "code",
"colab": {}
},
"source": [
"# Initializing train & test generators to flow train and test images straight from the folders\n",
"# that they are stored in\n",
"train_datagen = ImageDataGenerator(rescale = 1./255,\n",
" horizontal_flip = True,\n",
" fill_mode = \"nearest\",\n",
" zoom_range = 0.3,\n",
" width_shift_range = 0.3,\n",
" height_shift_range=0.3,\n",
" rotation_range=30)\n",
"\n",
"test_datagen = ImageDataGenerator(rescale = 1./255)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7-zf69uioyiE",
"colab_type": "code",
"colab": {}
},
"source": [
"# We flow from DataFrames, that is, our images are not stored in class-specific folders---instead,\n",
"# their labels are stored in separate files (specifically, in DataFrames)\n",
"train_generator = train_datagen.flow_from_dataframe(dataframe=labels_df,\n",
" directory= path_to_train,\n",
" x_col='ID',\n",
" y_col='any',\n",
" target_size=(128, 128),\n",
" class_mode='binary')\n",
"\n",
"test_generator = test_datagen.flow_from_dataframe(dataframe=labels_df,\n",
" directory=path_to_test,\n",
" x_col='ID',\n",
" y_col='any',\n",
" target_size=(128, 128),\n",
" class_mode='binary')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "_Xh6lg1tszMo",
"colab_type": "text"
},
"source": [
"# **Fitting the Model**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "HpcKttO0qdEe",
"colab_type": "code",
"colab": {}
},
"source": [
"# Callbacks\n",
"checkpoint = ModelCheckpoint(path_to_save_model, monitor='val_acc', verbose=1, save_best_only=False, save_weights_only=False, mode='auto', period=1)\n",
"early_stopper = EarlyStopping(monitor='val_acc', min_delta=0, patience=3, verbose=1, mode='auto')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5e5xPMF2KZ1h",
"colab_type": "code",
"colab": {}
},
"source": [
"# Fitting the model \n",
"custom_model.fit_generator(train_generator,\n",
" epochs = 50,\n",
" validation_data = test_generator,\n",
" callbacks = [checkpoint, early_stopper])"
],
"execution_count": 0,
"outputs": []
}
]
}