python之tensorflow 。值错误 : The two structures don't have the same number of elements

我当前使用 raw_rnn 实现编码器 lstm 的代码。这个问题也与我之前问过的另一个问题(Tensorflow raw_rnn retrieve tensor of shape BATCH x DIM from embedding matrix)有关。 当我运行以下代码时，出现以下错误:

ValueError: The two structures don't have the same number of elements.

First structure (1 elements): None

Second structure (2 elements): LSTMStateTuple(c=64, h=64)

错误发生在一行:encoder_outputs_ta, encoder_final_state, _ = tf.nn.raw_rnn(cell, loop_fn=reader_loop)

import tensorflow as tf 
import numpy as np 
 
batch_size, max_time, input_embedding_size = 5, 10, 16 
vocab_size, num_units = 50, 64 
 
encoder_inputs = tf.placeholder(shape=(None, None), dtype=tf.int32, name='encoder_inputs') 
encoder_inputs_length = tf.placeholder(shape=(None,), dtype=tf.int32, name='encoder_inputs_length') 
 
embeddings = tf.Variable(tf.random_uniform([vocab_size + 2, input_embedding_size], -1.0, 1.0), 
                         dtype=tf.float32, name='embeddings') 
encoder_inputs_embedded = tf.nn.embedding_lookup(embeddings, encoder_inputs) 
 
cell = tf.contrib.rnn.LSTMCell(num_units) 
W = tf.Variable(tf.random_uniform([num_units, vocab_size], -1, 1), dtype=tf.float32, name='W_reader') 
b = tf.Variable(tf.zeros([vocab_size]), dtype=tf.float32, name='b_reader') 
 
with tf.variable_scope('ReaderNetwork'): 
    def loop_fn_initial(): 
        init_elements_finished = (0 >= encoder_inputs_length) 
        init_input = cell.zero_state(batch_size, dtype=tf.float32) 
        init_cell_state = None 
        init_cell_output = None 
        init_loop_state = None 
        return (init_elements_finished, init_input, 
                init_cell_state, init_cell_output, init_loop_state) 
 
 
    def loop_fn_transition(time, previous_output, previous_state, previous_loop_state): 
        def get_next_input(): 
            return tf.ones([batch_size, input_embedding_size], dtype=tf.float32)  # TODO replace with value from embeddings 
 
        elements_finished = (time >= encoder_inputs_length) 
        finished = tf.reduce_all(elements_finished)  # boolean scalar 
        next_input = tf.cond(finished, 
                             true_fn=lambda: tf.zeros([batch_size, input_embedding_size], dtype=tf.float32), 
                             false_fn=get_next_input) 
        state = previous_state 
        output = previous_output 
        loop_state = None 
        return elements_finished, next_input, state, output, loop_state 
 
 
    def loop_fn(time, previous_output, previous_state, previous_loop_state): 
        if previous_state is None:  # time = 0 
            return loop_fn_initial() 
        return loop_fn_transition(time, previous_output, previous_state, previous_loop_state) 
 
reader_loop = loop_fn 
encoder_outputs_ta, encoder_final_state, _ = tf.nn.raw_rnn(cell, loop_fn=reader_loop) 
outputs = encoder_outputs_ta.stack() 
 
 
def next_batch(): 
    return { 
        encoder_inputs: np.random.random((batch_size, max_time)), 
        encoder_inputs_length: [max_time] * batch_size 
    } 
 
 
init = tf.global_variables_initializer() 
with tf.Session() as s: 
    s.run(init) 
    outs = s.run([outputs], feed_dict=next_batch()) 
    print len(outs), outs[0].shape 

请您参考如下方法:

通过更改初始状态和输入解决了问题:

init_input = tf.zeros([batch_size, input_embedding_size], dtype=tf.float32)

init_cell_state = cell.zero_state(batch_size, tf.float32)

def loop_fn_initial(): 
    init_elements_finished = (0 >= encoder_inputs_length) 
    init_input = tf.zeros([batch_size, input_embedding_size], dtype=tf.float32) 
    init_cell_state = cell.zero_state(batch_size, tf.float32) 
    init_cell_output = None 
    init_loop_state = None 
    return (init_elements_finished, init_input, 
            init_cell_state, init_cell_output, init_loop_state)