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( ...
