The generator uses a few new layers that we will talk about in this section. First, take a chance to skim through the following code:
def build_generator(noise_shape=(100,)): input = Input(noise_shape) x = Dense(128 * 7 * 7, activation="relu")(input) x = Reshape((7, 7, 128))(x) x = BatchNormalization(momentum=0.8)(x) x = UpSampling2D()(x) x = Conv2D(128, kernel_size=3, padding="same")(x) x = Activation("relu")(x) x = BatchNormalization(momentum=0.8)(x) x = UpSampling2D()(x) x = Conv2D(64, kernel_size=3, padding="same")(x) x = Activation("relu")(x) x = BatchNormalization(momentum=0.8)(x) x = Conv2D(1, kernel_size=3, padding="same")(x) out = Activation("tanh")(x) model = Model(input, out) print("-- Generator -- ") model.summary() ...
