losses.py 文件源码

def calculate_loss(self, predictions, labels, b=1.0, **unused_params):
    with tf.name_scope("loss_hinge"):
      float_labels = tf.cast(labels, tf.float32)
      all_zeros = tf.zeros(tf.shape(float_labels), dtype=tf.float32)
      all_ones = tf.ones(tf.shape(float_labels), dtype=tf.float32)
      sign_labels = tf.subtract(tf.scalar_mul(2, float_labels), all_ones)
      hinge_loss = tf.maximum(
          all_zeros, tf.scalar_mul(b, all_ones) - sign_labels * predictions)
      return tf.reduce_mean(tf.reduce_sum(hinge_loss, 1))