rnn.py 文件源码

def layer_norm(x,
               num_units,
               scope='layer_norm',
               reuse=False,
               gamma_start=1.0,
               epsilon=1e-3,
               use_bias=True):
  """Calculate layer norm."""
  axes = [1]
  mean = tf.reduce_mean(x, axes, keep_dims=True)
  x_shifted = x - mean
  var = tf.reduce_mean(tf.square(x_shifted), axes, keep_dims=True)
  inv_std = tf.rsqrt(var + epsilon)
  with tf.variable_scope(scope):
    if reuse is True:
      tf.get_variable_scope().reuse_variables()
    gamma = tf.get_variable(
        'ln_gamma', [num_units],
        initializer=tf.constant_initializer(gamma_start))
    if use_bias:
      beta = tf.get_variable(
          'ln_beta', [num_units], initializer=tf.constant_initializer(0.0))
  output = gamma * (x_shifted) * inv_std
  if use_bias:
    output += beta
  return output