losses_embedding.py 文件源码

def calculate_loss(self, predictions, labels, **unused_params):
        with tf.name_scope("loss_xent"):
            epsilon = 10e-6
            origin_labels = tf.cast(labels, tf.float32)
            vocab_size = origin_labels.get_shape().as_list()[1]
            float_labels = tf.tile(tf.reshape(origin_labels,[-1, 1, vocab_size]),[1,FLAGS.top_k,1])
            float_labels = tf.reshape(float_labels,[-1,vocab_size])
            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)
            num_labels = tf.minimum(tf.reduce_sum(origin_labels,axis=1),tf.constant(FLAGS.top_k,dtype=tf.float32))
            mask = tf.reshape(tf.sequence_mask(num_labels,tf.constant(FLAGS.top_k,dtype=tf.float32),dtype=tf.float32),[-1])
            cross_entropy_loss = tf.reduce_sum(tf.reduce_sum(cross_entropy_loss, 1)*mask)/(tf.reduce_sum(mask)+epsilon)

            return cross_entropy_loss