losses.py 文件源码

def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      vocab_size = predictions.get_shape().as_list()[1]
      float_labels = tf.cast(labels, tf.float32)
      cross_entropy_loss = float_labels * tf.log(predictions + epsilon) + (
          1 - float_labels) * tf.log(1 - predictions + epsilon)
      cross_entropy_loss = tf.negative(cross_entropy_loss)
      neg_labels = 1 - float_labels
      predictions_pos = predictions*float_labels+10*neg_labels
      predictions_minpos = tf.reduce_min(predictions_pos,axis=1,keep_dims=True)
      predictions_neg = predictions*neg_labels-10*float_labels
      predictions_maxneg = tf.reduce_max(predictions_neg,axis=1,keep_dims=True)
      mask_1 = tf.cast(tf.greater_equal(predictions_neg, predictions_minpos),dtype=tf.float32)
      mask_2 = tf.cast(tf.less_equal(predictions_pos, predictions_maxneg),dtype=tf.float32)
      cross_entropy_loss = cross_entropy_loss*(mask_1+mask_2)*10 + cross_entropy_loss
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))