ops.py 文件源码

def GANLoss(logits, is_real=True, smoothing=0.9, name=None):
  """Computes standard GAN loss between `logits` and `labels`.

  Args:
    logits: logits
    is_real: boolean, True means `1` labeling, False means `0` labeling
    smoothing: one side label smoothing

  Returns:
    A scalar Tensor representing the loss value.
  """
  if is_real:
    # one side label smoothing
    labels = tf.fill(logits.get_shape(), smoothing)
  else:
    labels = tf.zeros_like(logits)

  with ops.name_scope(name, 'GAN_loss', [logits, labels]) as name:
    loss = tf.reduce_mean(
                tf.nn.sigmoid_cross_entropy_with_logits(
                                labels=labels,
                                logits=logits))

    return loss