utils.py 文件源码

def calculate_softmax_and_summaries(logits, one_hot_labels, name):
  """Calculate the softmax cross entropy loss and associated summaries.

  Args:
    logits: Tensor of logits, first dimension is batch size.
    one_hot_labels: Tensor of one hot encoded categorical labels. First
      dimension is batch size.
    name: Name to use as prefix for summaries.

  Returns:
    loss: Dimensionless tensor representing the mean negative
      log-probability of the true class.
  """
  loss = tf.nn.softmax_cross_entropy_with_logits(
      logits=logits, labels=one_hot_labels)
  loss = tf.reduce_mean(loss)
  softmax_summaries(loss, logits, one_hot_labels, name)
  return loss