lsgan.py 文件源码

def discriminator_model(model_name="discriminator"):
    disc_input = Input(shape=(400, 1), name="discriminator_input")
    aux_input = Input(shape=(47,), name="auxilary_input")

    # Conv Layer 1
    x = Convolution1D(nb_filter=100,
                      filter_length=13,
                      border_mode='same',
                      subsample_length=1)(disc_input)
    x = LeakyReLU(0.2)(x) # output shape is 100 x 400
    x = AveragePooling1D(pool_length=20)(x) # ouput shape is 100 x 20

    # Conv Layer 2
    x = Convolution1D(nb_filter=250,
                      filter_length=13,
                      border_mode='same',
                      subsample_length=1)(x)
    x = LeakyReLU(0.2)(x) # output shape is 250 x 20
    x = AveragePooling1D(pool_length=5)(x) # output shape is 250 x 4

    # Conv Layer 3
    x = Convolution1D(nb_filter=300,
                      filter_length=13,
                      border_mode='same',
                      subsample_length=1)(x)
    x = LeakyReLU(0.2)(x) # output shape is 300 x 4
    x = Flatten()(x) # output shape is 1200

    x = merge([x, aux_input], mode="concat", concat_axis=-1) # shape is 1247

    # Dense Layer 1
    x = Dense(200)(x)
    x = LeakyReLU(0.2)(x) # output shape is 200

    # Dense Layer 2
    x = Dense(1)(x)
    #x = Activation('sigmoid')(x)
    x = Activation('linear')(x) # output shape is 1

    discriminator_model = Model(
        input=[disc_input, aux_input], output=[x], name=model_name)

    return discriminator_model