LeNetBN.py 文件源码

def loss(self, logits, labels):
        """Add L2Loss to all the trainable variables.
        Args:
          logits: Logits from get().
          labels: Labels from train_inputs or inputs(). 1-D tensor
                  of shape [batch_size]

        Returns:
          Loss tensor of type float.
        """
        with tf.variable_scope('loss'):
            # 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=logits, labels=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).
            error = tf.add_n(tf.get_collection(LOSSES), name='total_loss')

        return error