vae_conv.py 文件源码

def q_net(x, n_xl, n_z, n_particles, is_training):
    with zs.BayesianNet() as variational:
        normalizer_params = {'is_training': is_training,
                             'updates_collections': None}
        lz_x = tf.reshape(tf.to_float(x), [-1, n_xl, n_xl, 1])
        lz_x = layers.conv2d(
            lz_x, 32, kernel_size=5, stride=2,
            normalizer_fn=layers.batch_norm,
            normalizer_params=normalizer_params)
        lz_x = layers.conv2d(
            lz_x, 64, kernel_size=5, stride=2,
            normalizer_fn=layers.batch_norm,
            normalizer_params=normalizer_params)
        lz_x = layers.conv2d(
            lz_x, 128, kernel_size=5, padding='VALID',
            normalizer_fn=layers.batch_norm,
            normalizer_params=normalizer_params)
        lz_x = layers.dropout(lz_x, keep_prob=0.9, is_training=is_training)
        lz_x = tf.reshape(lz_x, [-1, 128 * 3 * 3])
        lz_mean = layers.fully_connected(lz_x, n_z, activation_fn=None)
        lz_logstd = layers.fully_connected(lz_x, n_z, activation_fn=None)
        z = zs.Normal('z', lz_mean, logstd=lz_logstd, n_samples=n_particles,
                      group_ndims=1)
    return variational