losses.py 文件源码

def calculate_loss_negative(self, predictions_pos, predictions_neg, labels, **unused_params):
    with tf.name_scope("loss_negative"):
      epsilon = 10e-6
      float_labels = tf.cast(labels, tf.float32)
      weight_pos = np.loadtxt(FLAGS.autoencoder_dir+"labels_uni.out")
      weight_pos = tf.reshape(tf.cast(weight_pos,dtype=tf.float32),[1,-1])
      weight_pos = tf.log(tf.reduce_max(weight_pos)/weight_pos)+1
      cross_entropy_loss_1 = float_labels * tf.log(predictions_pos + epsilon)*weight_pos + (
          1 - float_labels) * tf.log(1 - predictions_pos + epsilon)
      cross_entropy_loss_2 = (1-float_labels) * tf.log(predictions_neg + epsilon) + \
                             float_labels * tf.log(1 - predictions_neg + epsilon)
      cross_entropy_loss = tf.negative(cross_entropy_loss_1+cross_entropy_loss_2)
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))