model.py 文件源码

def loss_graph(logits, batch_size, num_unroll_steps):
    with tf.variable_scope('Loss'):
        targets = tf.placeholder(tf.int64, [batch_size, num_unroll_steps], name='targets')
        # target_list = [tf.squeeze(x, [1]) for x in tf.split(1, num_unroll_steps, targets)]
        target_list=tf.unpack(targets, axis=1)#hjq

        loss= tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits, target_list), name='loss')
        # reduce_mean to reduce_sum,hjq

    return adict(
        targets=targets,
        loss=loss
    )