Created by: lalala805

求教育，我希望使用DynamicRNN逐个获取序列元素的embedding结果并记录在memory中（方便后续pooling），可是我concat得到的结果与预期不一样，代码如下，请问我错在哪里，或者我该如何去做，才能达到我想要的结果呢（借助DynamicRNN逐个获取序列元素的embedding并做pooling）~~ 代码：

#coding: utf-8

from pprint import pprint

import numpy as np
import paddle.fluid as fluid

a = fluid.create_lod_tensor(np.array([1,2,3,4,5,6,7,8,9,10], dtype='int64').reshape(-1, 1),
                            [[2,3,5]],
                            fluid.CPUPlace())

sentence = fluid.layers.data(name='sentence', shape=[1], dtype='int64', lod_level=1)
embedding = fluid.layers.embedding(input=sentence, size=[100, 6], is_sparse=True)

drnn = fluid.layers.DynamicRNN()
with drnn.block():
        word = drnn.step_input(embedding)
        curr_memory = drnn.memory(shape=[6], dtype='float32', value=1)
        new_memory = fluid.layers.concat(input=[curr_memory, word])
        drnn.update_memory(ex_mem=curr_memory, new_mem=new_memory)
        drnn.output(new_memory)

rnn_output = drnn()
last = fluid.layers.sequence_last_step(rnn_output)


cpu = fluid.CPUPlace()
exe = fluid.Executor(cpu)
exe.run(fluid.default_startup_program())

rslt = exe.run(feed={'sentence': a},  fetch_list = [last])
print rslt

输出结果：

[array([[1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1.]], dtype=float32)]