b43325e297498269be86b281a02e2ef79664f273,train_variational_autoencoder_tensorflow.py,,generative_network,#Any#Any#,66

Before Change

  Returns:
    bernoulli_logits: logits for the Bernoulli likelihood of the data
  
  with slim.arg_scope([slim.fully_connected], activation_fn=tf.nn.relu):
    net = slim.fully_connected(z, hidden_size)
    net = slim.fully_connected(net, hidden_size)
    bernoulli_logits = slim.fully_connected(net, 784, activation_fn=None)
    bernoulli_logits = tf.reshape(bernoulli_logits, [-1, 28, 28, 1])
  return bernoulli_logits


def train():

After Change

  Returns:
    bernoulli_logits: logits for the Bernoulli likelihood of the data
  
  generative_net = tfk.Sequential([
    tfkl.Dense(hidden_size, activation=tf.nn.relu),
    tfkl.Dense(hidden_size, activation=tf.nn.relu),
    tfkl.Dense(28 * 28, activation=None)
    ])
  bernoulli_logits = generative_net(z)
  return tf.reshape(bernoulli_logits, [-1, 28, 28, 1])

Instances