neural_cryptography_train.py

image_shape = (100, 100, 3)
sentence_len = 100
dict_len = 200
gen = data_generator(image_shape, sentence_len, dict_len, 32)
model, encoder, decoder = get_model(image_shape, sentence_len, dict_len)
model.fit_generator(gen, 512, 348, callbacks=[
    ModelCheckpoint("best_weights.h5",monitor="loss",
                    verbose=1,
                    save_weights_only=True,
                    save_best_only=True)
])