head.py 文件源码

def _centered_bias_step(centered_bias, logits_dimension, labels, loss_fn):
  """Creates and returns training op for centered bias."""
  if (logits_dimension is None) or (logits_dimension < 1):
    raise ValueError("Invalid logits_dimension %s." % logits_dimension)
  with ops.name_scope(None, "centered_bias_step", (labels,)) as name:
    batch_size = array_ops.shape(labels)[0]
    logits = array_ops.reshape(
        array_ops.tile(centered_bias, (batch_size,)),
        (batch_size, logits_dimension))
    with ops.name_scope(None, "centered_bias", (labels, logits)):
      centered_bias_loss = math_ops.reduce_mean(
          loss_fn(logits, labels), name="training_loss")
  # Learn central bias by an optimizer. 0.1 is a convervative lr for a
  # single variable.
  return training.AdagradOptimizer(0.1).minimize(
      centered_bias_loss, var_list=(centered_bias,), name=name)