losses.py 文件源码

def calculate_loss(self, predictions, labels, **unused_params):
    bound = FLAGS.softmax_bound
    vocab_size_1 = bound
    with tf.name_scope("loss_softmax"):
      epsilon = 10e-8
      float_labels = tf.cast(labels, tf.float32)
      labels_1 = float_labels[:,:vocab_size_1]
      predictions_1 = predictions[:,:vocab_size_1]
      cross_entropy_loss = CrossEntropyLoss().calculate_loss(predictions_1,labels_1)
      lables_2 = float_labels[:,vocab_size_1:]
      predictions_2 = predictions[:,vocab_size_1:]
      # l1 normalization (labels are no less than 0)
      label_rowsum = tf.maximum(
          tf.reduce_sum(lables_2, 1, keep_dims=True),
          epsilon)
      label_append = 1.0-tf.reduce_max(lables_2, 1, keep_dims=True)
      norm_float_labels = tf.concat((tf.div(lables_2, label_rowsum),label_append),axis=1)
      predictions_append = 1.0-tf.reduce_sum(predictions_2, 1, keep_dims=True)
      softmax_outputs = tf.concat((predictions_2,predictions_append),axis=1)
      softmax_loss = norm_float_labels * tf.log(softmax_outputs + epsilon) + (
          1 - norm_float_labels) * tf.log(1 - softmax_outputs + epsilon)
      softmax_loss = tf.negative(tf.reduce_sum(softmax_loss, 1))
    return tf.reduce_mean(softmax_loss) + cross_entropy_loss