introduction to generative adversarial networks generator_input keras input(shape=(latent_dim,) layers dense( )(generator_inputx layers leakyrelu()(xx layers reshape(( ))(xtransforms the input into -channel feature map layers conv ( padding='same')(xx layers leakyrelu()(xx layers conv dtranspose( strides= padding='same')(xx layers leakyrelu()(xupsamples to layers conv ( padding='same')(xx layers leakyrelu()(xx layers conv ( padding='same')(xx layers leakyrelu()(xx layers conv (channels activation='tanh'padding='same')(xgenerator keras models model(generator_inputxgenerator summary(instantiates the generator modelwhich maps the input of shape (latent_dim,into an image of shape ( produces -channel feature map (shape of cifar imagethe discriminator nextyou'll develop discriminator model that takes as input candidate image (real or syntheticand classifies it into one of two classes"generated imageor "real image that comes from the training set listing the gan discriminat or net work discriminator_input layers input(shape=(heightwidthchannels) layers conv ( )(discriminator_inputx layers leakyrelu()(xx layers conv ( strides= )(xx layers leakyrelu()(xx layers conv ( strides= )(xone dropout layerx layers leakyrelu()(xan important trickx layers conv ( strides= )(xx layers leakyrelu()(xx layers flatten()(xclassification layer layers dropout( )(xx layers dense( activation='sigmoid')(xdiscriminator keras models model(discriminator_inputxdiscriminator summary(discriminator_optimizer keras optimizers rmsproplr= clipvalue= decay= - discriminator compile(optimizer=discriminator_optimizerloss='binary_crossentropy'licensed to instantiates the discriminator modelwhich turns ( input into binary classifi-cation decision (fake/realuses gradient clipping (by valuein the optimizer to stabilize traininguses learning-rate decay