Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Towards Data Science.
between setting the discriminator to be trainable and untrainable (we did this implicitly in part 2). def train(sample_size=100, epochs=3, batch_size=128, eval_size=16, smooth=0.1): batchCount=len(train_path)//batch_size y_train_real, y_train_fake = make_labels(batch_size) y_eval_real, y_eval_fake