def build_cost(self, labels, logits):
    """
    Build the graph for cost from the logits if logits are provided.
    If predictions are provided, logits are extracted from the operation.
    """
    op = logits.op
    if "softmax" in str(op).lower():
      logits, = op.inputs

    with tf.variable_scope('costs'):
      xent = tf.nn.softmax_cross_entropy_with_logits(
          logits=logits, labels=labels)
      cost = tf.reduce_mean(xent, name='xent')
      cost += self._decay()
      cost = cost

    return cost
def build_cost ( self , labels , logits ) : op = logits . op if "softmax" in str ( op ) . lower ( ) : logits , = op . inputs with tf . variable_scope ( 'costs' ) : xent = tf . nn . softmax_cross_entropy_with_logits ( logits = logits , labels = labels ) cost = tf . reduce_mean ( xent , name = 'xent' ) cost += self . _decay ( ) cost = cost return cost
Build the graph for cost from the logits if logits are provided.
    If predictions are provided, logits are extracted from the operation.