The discriminator is really for the most part the same as any other CNN that I have previously talked about. Of course, there are a few new things that we should talk about. We will use the following code to build the discriminator:

def build_discriminator(img_shape):
    input = Input(img_shape)
    x =Conv2D(32, kernel_size=3, strides=2, padding="same")(input)
    x = LeakyReLU(alpha=0.2)(x)
    x = Dropout(0.25)(x)
    x = Conv2D(64, kernel_size=3, strides=2, padding="same")(x)
    x = ZeroPadding2D(padding=((0, 1), (0, 1)))(x)
    x = (LeakyReLU(alpha=0.2))(x)
    x = Dropout(0.25)(x)
    x = BatchNormalization(momentum=0.8)(x)
    x = Conv2D(128, kernel_size=3, strides=2, padding="same")(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Dropout(0.25)(x)
    x = BatchNormalization(momentum=0.8)(x)
    x = Conv2D(256, kernel_size=3, strides=1, padding="same")(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Dropout(0.25)(x)
    x = Flatten()(x)
    out = Dense(1, activation='sigmoid')(x)

    model = Model(input, out)
    print("-- Discriminator -- ")
    model.summary()
    return model

First you might notice the oddly shaped zeroPadding2D() layer. After the second convolution, our tensor has gone from 28 x 28 x 3 to 7 x 7 x 64. This layer just gets us back into an even number, adding zeros on one side of both the rows and columns so that our tensor is now 8 x 8 x 64.

More unusual is the use of both batch normalization and dropout. Typically, these two layers are not used together; however, in the case of GANs, they do seem to benefit the network.