train_model.py

import pathlib

sample_size = len(list(pathlib.Path('../data/images_cropped/').rglob('./*')))
batch_size = 16

hist = model.fit_generator(
    train_generator,
    steps_per_epoch=sample_size // batch_size,
    epochs=50,
    validation_data=validation_generator,
    validation_steps=round(sample_size * 0.2) // batch_size,
    callbacks=[EarlyStopping(monitor='val_loss', min_delta=0, patience=4)]
)

model.save("clf.h5")