Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Towards Data Science.
DCGAN(sample_size=Noise_dim): # generator g = generator_model(sample_size, 0.2) # discriminator d = discriminator_model(0.2) d.trainable = False # GAN gan = Sequential([g, d]) sgd=SGD() gan.compile(optimizer=Adam(lr=0.0001, beta_1=0.5), loss='binary_crossentropy') return gan, g, d def