model_cifar.py 文件源码

def loss(logits, labels,n_class, scope='loss'):
  with tf.variable_scope(scope):
    # entropy loss
    targets = one_hot_embedding(labels, n_class)
    entropy_loss = tf.reduce_mean(
      tf.nn.softmax_cross_entropy_with_logits(logits, targets),
      name='entropy_loss')
    tf.add_to_collection('losses', entropy_loss)
    # weight l2 decay loss
    weight_l2_losses = [tf.nn.l2_loss(o) for o in tf.get_collection('weights')]
    weight_decay_loss = tf.mul(FLAGS.weight_decay, tf.add_n(weight_l2_losses),
      name='weight_decay_loss')
    tf.add_to_collection('losses', weight_decay_loss)
  for var in tf.get_collection('losses'):
    tf.scalar_summary('losses/' + var.op.name, var)
  # total loss
  return tf.add_n(tf.get_collection('losses'), name='total_loss')