infogan.py 文件源码

 复制代码def build_generator(self):

        model = Sequential()

        model.add(Dense(1024, activation='relu', input_dim=self.latent_dim))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(128 * 7 * 7, activation="relu"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Reshape((7, 7, 128)))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=4, padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(UpSampling2D())
        model.add(Conv2D(self.channels, kernel_size=4, padding='same'))
        model.add(Activation("tanh"))

        model.summary()

        gen_input = Input(shape=(self.latent_dim,))
        img = model(gen_input)

        return Model(gen_input, img)