dcgan.py 文件源码

def generator_model(noise_dim=100, aux_dim=47, model_name="generator"):
    # Merge noise and auxilary inputs
    gen_input = Input(shape=(noise_dim,), name="noise_input")
    aux_input = Input(shape=(aux_dim,), name="auxilary_input")
    x = concatenate([gen_input, aux_input], axis=-1)

    # Dense Layer 1
    x = Dense(10 * 100)(x) 
    x = BatchNormalization()(x)
    x = LeakyReLU(0.2)(x) # output shape is 10*100

    # Reshape the tensors to support CNNs
    x = Reshape((100, 10))(x) # shape is 100 x 10

    # Conv Layer 1
    x = Conv1D(filters=250, kernel_size=13, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(0.2)(x) # output shape is 100 x 250
    x = UpSampling1D(size=2)(x) # output shape is 200 x 250

    # Conv Layer 2
    x = Conv1D(filters=100, kernel_size=13, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(0.2)(x) # output shape is 200 x 100
    x = UpSampling1D(size=2)(x) # output shape is 400 x 100

    # Conv Layer 3
    x = Conv1D(filters=1, kernel_size=13, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('tanh')(x) # final output shape is 400 x 1

    generator_model = Model(
        outputs=[x], inputs=[gen_input, aux_input], name=model_name)

    return generator_model