(logits, labels, name)
return loss
Calculate the softmax cross entropy loss and associated summaries.

  Args:
    logits: Tensor of logits, first dimension is batch size.
    labels: Tensor of categorical labels [ints]. 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.
Calculate the softmax cross entropy loss and associated summaries.
Calculate the softmax cross entropy loss and associated summaries .
def calculate_sparse_softmax_and_summaries(logits, labels, name):
  """Calculate the softmax cross entropy loss and associated summaries.

  Args:
    logits: Tensor of logits, first dimension is batch size.
    labels: Tensor of categorical labels [ints]. 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.sparse_softmax_cross_entropy_with_logits(
      logits=logits, labels=labels)
  loss = tf.reduce_mean(loss)
  softmax_summaries(loss, logits, labels, name)
  return loss
def calculate_sparse_softmax_and_summaries ( logits , labels , name ) : loss = tf . nn . sparse_softmax_cross_entropy_with_logits ( logits = logits , labels = labels ) loss = tf . reduce_mean ( loss ) softmax_summaries ( loss , logits , labels , name ) return loss