stroke + keras augmentation

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stroke + keras augmentation

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Last edited Jan 5, 2018

Created on Jan 5, 2018

This example demonstrates data augmentation for a convolutional neural network that classifies stroke-related images into two categories. It trains a Keras Sequential model with Conv2D, MaxPooling2D, Dropout, and LeakyReLU layers, using an ImageDataGenerator to apply shear, zoom, and horizontal flip transformations to grayscale images. The code also uses a ModelCheckpoint callback to save the best model based on validation accuracy, processing batches of 256x256 images from training and validation directories.

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