losses.py 文件源码

def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_softmax"):
      epsilon = 10e-8
      float_labels = tf.cast(labels, tf.float32)
      # l1 normalization (labels are no less than 0)
      label_rowsum = tf.maximum(
          tf.reduce_sum(float_labels, 1, keep_dims=True),
          epsilon)
      norm_float_labels = tf.div(float_labels, label_rowsum)
      softmax_outputs = tf.nn.softmax(predictions)
      softmax_loss = tf.negative(tf.reduce_sum(
          tf.multiply(norm_float_labels, tf.log(softmax_outputs)), 1))
    return tf.reduce_mean(softmax_loss)