Machine Learning, Data Science, Artificial Intelligence, Towards Data Science, Deep Learning.
discriminator.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(lr=0.0002), metrics=['mae']) # 2. Build generator. generator = build_generator(NET_CAPACITY, FILTER_SIZE, LATENT_DIM) # 3. Build full GAN setup by stacking generator and discriminator. gan = keras.Sequential()