parameters: (logits, labels)
return_statement: return tf.add_n(tf.get_collection('losses'), name='total_loss')
docstring: Add L2Loss to all the trainable variables.
  
    Add summary for "Loss" and "Loss/avg".
    Args:
      logits: Logits from inference().
      labels: Labels from distorted_inputs or inputs(). 1-D tensor
              of shape [batch_size]
  
    Returns:
      Loss tensor of type float.
docstring_summary: Add L2Loss to all the trainable variables.
docstring_tokens: Add L2Loss to all the trainable variables .
function: def loss(logits, labels):
    """Add L2Loss to all the trainable variables.
  
    Add summary for "Loss" and "Loss/avg".
    Args:
      logits: Logits from inference().
      labels: Labels from distorted_inputs or inputs(). 1-D tensor
              of shape [batch_size]
  
    Returns:
      Loss tensor of type float.
    """
    # Calculate the average cross entropy loss across the batch.
    labels = tf.cast(labels, tf.int64)
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits, labels, name='cross_entropy_per_example')
    cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
    tf.add_to_collection('losses', cross_entropy_mean)
  
    # The total loss is defined as the cross entropy loss plus all of the weight
    # decay terms (L2 loss).
    return tf.add_n(tf.get_collection('losses'), name='total_loss')
function_tokens: def loss ( logits , labels ) : # Calculate the average cross entropy loss across the batch. labels = tf . cast ( labels , tf . int64 ) cross_entropy = tf . nn . sparse_softmax_cross_entropy_with_logits ( logits , labels , name = 'cross_entropy_per_example' ) cross_entropy_mean = tf . reduce_mean ( cross_entropy , name = 'cross_entropy' ) tf . add_to_collection ( 'losses' , cross_entropy_mean ) # The total loss is defined as the cross entropy loss plus all of the weight # decay terms (L2 loss). return tf . add_n ( tf . get_collection ( 'losses' ) , name = 'total_loss' )