Question

layer_1 = tf.layers.dense(inputs=layer_c, units=512, activation=tf.nn.tanh, name='layer1')

layer_2 = tf.layers.dense(inputs=1, units=512, activation=tf.nn.tanh, name='layer2')

在这里，我的layer_2输出为[batch_size，512]。我需要通过单个lstm单元发送此layer_2输出。但是，当我尝试 tf.nn.static_rnn 时，出现错误，提示我的输入应为序列。如何执行此任务？

Answer 1

在documentation for static_rnn中，inputs参数需要一个列表：

inputs：输入的长度T列表，每个输入为[batch_size，input_size]形状的张量或此类元素的嵌套元组。

在您的情况下为T==1，因此您可以将包含上一层的单元素列表传递给它。要以某种方式跟踪内部单元格和隐藏状态，以便跨时间步保持它们，可以添加其他占位符，然后使用static_rnn属性将其传递给initial_state。因为cell.state_size是LSTM单元（(cell_state, hidden_state)）的元组，所以我们必须为该属性传递一个元组，然后将为输出状态返回一个元组。

这是一个基于您的代码的最小工作示例，只是在每个时间步输入一个占位符作为输入，并跨时间跟踪内部状态：

import tensorflow as tf
import numpy as np


num_timesteps = 6
batch_size = 3
num_input_feats = 100
num_lstm_units = 5

lstm_cell = tf.nn.rnn_cell.LSTMCell(num_lstm_units)


input_x = tf.placeholder(tf.float32, [None, num_input_feats], name='input')

input_c_state = tf.placeholder(tf.float32, [None, lstm_cell.state_size.c], name='c_state')
input_h_state = tf.placeholder(tf.float32, [None, lstm_cell.state_size.h], name='h_state')

layer_1 = tf.layers.dense(inputs=input_x, units=512, activation=tf.nn.tanh, name='layer1')
layer_2 = tf.layers.dense(inputs=layer_1, units=512, activation=tf.nn.tanh, name='layer2')

layer_2_next, next_state = tf.nn.static_rnn(lstm_cell, [layer_2], dtype=tf.float32,
                                            initial_state=(input_c_state, input_h_state))

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())

  # initialize the internal cell state and hidden state to zero
  cur_c_state = np.zeros([batch_size, lstm_cell.state_size.c], dtype="float32")
  cur_h_state = np.zeros([batch_size, lstm_cell.state_size.h], dtype="float32")


  for i in range(num_timesteps):

    # here is your single timestep of input
    cur_x = np.ones([batch_size, num_input_feats], dtype="float32")

    y_out, out_state = sess.run([layer_2_next, next_state],
                                feed_dict={input_x: cur_x,
                                           input_c_state: cur_c_state,
                                           input_h_state: cur_h_state})
    cur_c_state, cur_h_state = out_state   # pass states along to the next timestep

    print (y_out)  # here is your single timestep of output

如何在tensorflw中仅创建一个时间步就创建一个lstm单元？

1 个答案: