parameters: (logits, labels)
return_statement: return tf.reduce_sum(tf.cast(correct, tf.int32))
docstring: Evaluate the quality of the logits at predicting the label.
  
    Args:
      logits: Logits tensor, float - [batch_size, NUM_CLASSES].
      labels: Labels tensor, int32 - [batch_size], with values in the
        range [0, NUM_CLASSES).
  
    Returns:
      A scalar int32 tensor with the number of examples (out of batch_size)
      that were predicted correctly.
docstring_summary: Evaluate the quality of the logits at predicting the label.
docstring_tokens: Evaluate the quality of the logits at predicting the label .
function: def evaluation(logits, labels):
    """Evaluate the quality of the logits at predicting the label.
  
    Args:
      logits: Logits tensor, float - [batch_size, NUM_CLASSES].
      labels: Labels tensor, int32 - [batch_size], with values in the
        range [0, NUM_CLASSES).
  
    Returns:
      A scalar int32 tensor with the number of examples (out of batch_size)
      that were predicted correctly.
    """
    # For a classifier model, we can use the in_top_k Op.
    # It returns a bool tensor with shape [batch_size] that is true for
    # the examples where the label is in the top k (here k=1)
    # of all logits for that example.
    correct = tf.nn.in_top_k(logits, labels, 1)
    # Return the number of true entries.
    return tf.reduce_sum(tf.cast(correct, tf.int32))
function_tokens: def evaluation ( logits , labels ) : # For a classifier model, we can use the in_top_k Op. # It returns a bool tensor with shape [batch_size] that is true for # the examples where the label is in the top k (here k=1) # of all logits for that example. correct = tf . nn . in_top_k ( logits , labels , 1 ) # Return the number of true entries. return tf . reduce_sum ( tf . cast ( correct , tf . int32 ) )