losses.py 文件源码

def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      if FLAGS.label_smoothing:
        float_labels = smoothing(labels)
      else:
        float_labels = tf.cast(labels, tf.float32)
      mse_loss = tf.square(float_labels - predictions)
      return tf.reduce_mean(tf.reduce_sum(mse_loss, 1))