losses.py 文件源码

def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      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)
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))