architectures.py 文件源码

def mnist_encoder_simple(data_dim, noise_dim, latent_dim=8):
    data_input = Input(shape=(data_dim,), name='enc_internal_data_input')
    noise_input = Input(shape=(noise_dim,), name='enc_internal_noise_input')
    # center the input around 0
    # centered_data = Lambda(lambda x: 2 * x - 1, name='enc_centering_data_input')(data_input)
    # concat_input = Concatenate(axis=-1, name='enc_noise_data_concat')([centered_data, noise_input])
    enc_body = repeat_dense(data_input, n_layers=2, n_units=256, activation='relu', name_prefix='enc_body')
    enc_output = Dense(100, activation='relu', name='enc_dense_before_latent')(enc_body)
    enc_output = Dense(latent_dim, name='enc_latent_features')(enc_output)
    noise_resized = Dense(latent_dim, activation=None, name='enc_noise_resizing')(noise_input)
    enc_output = Add(name='enc_add_noise_data')([enc_output, noise_resized])
    latent_factors = Model(inputs=[data_input, noise_input], outputs=enc_output, name='enc_internal_model')

    return latent_factors