Python Deep Learning Projects

Defining the DCGAN

The following function pipes the input followed by the generator, which is then followed by the discriminator to form the DCGAN architecture:

def dcgan(discriminator, generator, input_shape):    # Set discriminator as non trainable before compiling GAN    discriminator.trainable = False    # Accepts the noised input    gan_input = Input(shape=input_shape)        # Generates image by passing the above received input to the generator    gen_img = generator(gan_input)        # Feeds the generated image to the discriminator    gan_output = discriminator(gen_img)        # Compile everything as a model with binary crossentropy loss    gan = Model(inputs=gan_input, outputs=gan_output)    return gan

If you have not seen how to use the Model function API before, please ...