Tensorflow。 ValueError：2つの構造に同じ数の要素がありません

Question

raw_rnnを使用してエンコーダlstmを実装するための私の現在のコードです。この質問は私が以前に尋ねた別の質問（Tensorflow raw_rnn retrieve tensor of shape BATCH x DIM from embedding matrix）にも関連しています。 私は、次のコードを実行すると、私は次のエラーを取得する：

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)

エラーがライン上でoccures：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 

Answer 1

は、初期状態と入力を変更することで問題を解決しました：

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)