optimizers.py 文件源码

def _adaptive_max_norm(norm, std_factor, decay, global_step, epsilon, name):
  """Find max_norm given norm and previous average."""
  with vs.variable_scope(name, "AdaptiveMaxNorm", [norm]):
    log_norm = math_ops.log(norm + epsilon)

    def moving_average(name, value, decay):
      moving_average_variable = vs.get_variable(
          name,
          shape=value.get_shape(),
          dtype=value.dtype,
          initializer=init_ops.zeros_initializer(),
          trainable=False)
      return moving_averages.assign_moving_average(
          moving_average_variable, value, decay, zero_debias=False)

    # quicker adaptation at the beginning
    if global_step is not None:
      n = math_ops.to_float(global_step)
      decay = math_ops.minimum(decay, n / (n + 1.))

    # update averages
    mean = moving_average("mean", log_norm, decay)
    sq_mean = moving_average("sq_mean", math_ops.square(log_norm), decay)

    variance = sq_mean - math_ops.square(mean)
    std = math_ops.sqrt(math_ops.maximum(epsilon, variance))
    max_norms = math_ops.exp(mean + std_factor * std)
    return max_norms, mean