architectures.py 文件源码

def mnist_adaptive_prior_discriminator(data_dim, latent_dim):
    data_input = Input(shape=(data_dim,), name='disc_internal_data_input')
    # center the data around 0 in [-1, 1] as it is in [0, 1].
    centered_data = Lambda(lambda x: 2 * x - 1, name='disc_centering_data_input')(data_input)
    discriminator_body_data = repeat_dense(centered_data, n_layers=3, n_units=512, name_prefix='disc_body_data')
    theta = Dense(4*512, activation='relu', name='disc_theta')(discriminator_body_data)
    discriminator_body_data_t = repeat_dense(centered_data, n_layers=3, n_units=512, name_prefix='disc_body_data_t')
    discriminator_body_data_t = Dense(1, activation=None, name='disc_data_squash')(discriminator_body_data_t)

    latent_input = Input(shape=(latent_dim,), name='disc_internal_latent_input')
    discriminator_body_latent = repeat_dense(latent_input, n_layers=3, n_units=512, name_prefix='disc_body_latent')
    sigma = Dense(4*512, activation='relu', name='disc_sigma')(discriminator_body_latent)
    discriminator_body_latent_t = repeat_dense(latent_input, n_layers=3, n_units=512, name_prefix='disc_body_latent_t')
    discriminator_body_latent_t = Dense(1, activation=None, name='disc_latent_squash')(discriminator_body_latent_t)

    merged_data_latent = Multiply(name='disc_mul_sigma_theta')([theta, sigma])
    merged_data_latent = Lambda(lambda x: ker.sum(x, axis=-1), name='disc_add_activ_sig_the')(merged_data_latent)
    discriminator_output = Add(name='disc_add_data_latent_t')([discriminator_body_data_t,
                                                               discriminator_body_latent_t,
                                                               merged_data_latent])
    collapsed_noise = Lambda(lambda x:  0.5 * ker.sum(x**2, axis=-1), name='disc_noise_addition')(latent_input)
    discriminator_output = Add(name='disc_add_all_toghether')([discriminator_output, collapsed_noise])
    discriminator_model = Model(inputs=[data_input, latent_input], outputs=discriminator_output,
                                name='disc_internal_model')
    return discriminator_model