Question

我有一个简单的seq2seq模型来预测股价。我创建了一个lstm单元的编码器和一个解码器，它们可以预测接下来的5个时间步长值，但是会引发错误：

ValueError: Dimensions must be equal, but are 517 and 562 for 'rnn/while/rnn/multi_rnn_cell/cell_0/lstm_cell/MatMul_1' (op: 'MatMul') with input shapes: [10,517], [562,2048].

数据样本

               t1     t2     t3     t4    t5 ...
19/10/2018   0.005  0.100 -0.021 0.030 -0.025
20/10/2018   0.023  0.020  0.020 0.130  0.125
21/10/2018  -0.205  0.140 -0.011 0.020 -0.305

代码

import tensorflow as tf
import numpy as np

seq_len = 1
n_inputs = 50
n_outputs = 5
n_layers = 3
n_neurons = 512
batch_size = 10

g = tf.Graph()

with g.as_default():
  X = tf.placeholder(tf.float32,shape=(None,seq_len,n_inputs),name="X")
  y = tf.placeholder(tf.float32,shape=(None,seq_len,n_outputs),name="y")

  cells = tf.nn.rnn_cell.MultiRNNCell([ tf.nn.rnn_cell.LSTMCell(n_neurons) for _ in range(n_layers) ])

  init_state = cells.zero_state(batch_size, tf.float32)
  enc_outputs, enc_states = tf.nn.dynamic_rnn(cells, X,initial_state=init_state)

  dec_outputs,dec_states = tf.nn.dynamic_rnn(cells, y, initial_state=enc_states)

  loss = tf.reduce_mean(tf.square(dec_outputs - y))
  train_op = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)

  init = tf.global_variables_initializer()

sess = tf.Session(graph=g)
sess.run(init)

欢迎任何帮助。

Answer 1

首先，我无法将您的问题标记为重复，因为它有很多悬赏。之所以会出现错误，是因为您必须不要在第一层以及更深的层中重复使用相同的单元格。这是因为提供给它们的输入不同，这使得内核矩阵不同。根据{{3}}的帖子，这应该可以修复错误：

# Extra function is for readability. No problem to inline it.
def make_cell(lstm_size):
  return tf.nn.rnn_cell.BasicLSTMCell(lstm_size, state_is_tuple=True)

network = rnn_cell.MultiRNNCell([make_cell(num_units) for _ in range(num_layers)], 
                                state_is_tuple=True)

this在此问题上提供了更多帮助。

Tensorflow seq2seq回归模型

1 个答案: