deep_latent_gaussian_model.py 文件源码

def likelihood(self, z, reuse=False):
    """Build likelihood p(x | z_0). """
    cfg = self.config
    n_samples = z.get_shape().as_list()[0]
    with util.get_or_create_scope('model', reuse=reuse):
      n_out = int(np.prod(cfg['train_data/shape']))
      net = z
      with slim.arg_scope(
          [slim.fully_connected],
          activation_fn=util.get_activation(cfg['p_net/activation']),
          outputs_collections=[tf.GraphKeys.ACTIVATIONS],
          variables_collections=['model'],
          weights_initializer=layers.variance_scaling_initializer(
              factor=np.square(cfg['p_net/init_w_stddev']))):
        for i in range(cfg['p_net/n_layers']):
          net = slim.fully_connected(
              net, cfg['p_net/hidden_size'], scope='fc%d' % i)
        logits = slim.fully_connected(
            net, n_out, activation_fn=None, scope='fc_lik')
    logits = tf.reshape(
        logits, [n_samples, cfg['batch_size']] + cfg['train_data/shape'])
    return dist.Bernoulli(logits=logits, validate_args=False)