{"version":"1.0","type":"rich","title":"stroke + keras augmentation","description":"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.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-17c68bb776002a91ebdea9b3f4eae9ed?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}