build_model.py 文件源码

def cross_entropy_sequence_loss(logits, targets, sequence_length):
    with tf.name_scope('cross_entropy_sequence_loss'):
        total_length = tf.to_float(tf.reduce_sum(sequence_length))

        entropy_losses = tf.nn.sparse_softmax_cross_entropy_with_logits(
            logits=logits, labels=targets)

        # Mask out the losses we don't care about
        loss_mask = tf.sequence_mask(
            tf.to_int32(sequence_length), tf.to_int32(tf.shape(targets)[0]))
        loss_mask = tf.transpose(tf.to_float(loss_mask), [1, 0])

        losses = entropy_losses * loss_mask
        # losses.shape: T * B
        # sequence_length: B
        total_loss_avg = tf.reduce_sum(losses) / total_length

        return total_loss_avg