关于tf.nn.dynamic_rnn返回值详解

tf.nn.dynamic_rnn(
  cell,
  inputs,
  sequence_length=None,
  initial_state=None,
  dtype=None,
  parallel_iterations=None,
  swap_memory=False,
  time_major=False,
  scope=None
)

import tensorflow as tf
import numpy as np
 
n_steps = 2
n_inputs = 3
n_neurons = 5
 
X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)
 
seq_length = tf.placeholder(tf.int32, [None])
outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32,
                  sequence_length=seq_length)
 
init = tf.global_variables_initializer()
 
X_batch = np.array([
    # step 0   step 1
    [[0, 1, 2], [9, 8, 7]], # instance 1
    [[3, 4, 5], [0, 0, 0]], # instance 2 (padded with zero vectors)
    [[6, 7, 8], [6, 5, 4]], # instance 3
    [[9, 0, 1], [3, 2, 1]], # instance 4
  ])
seq_length_batch = np.array([2, 1, 2, 2])
 
with tf.Session() as sess:
  init.run()
  outputs_val, states_val = sess.run(
    [outputs, states], feed_dict={X: X_batch, seq_length: seq_length_batch})
  print("outputs_val.shape:", outputs_val.shape, "states_val.shape:", states_val.shape)
  print("outputs_val:", outputs_val, "states_val:", states_val)

outputs_val.shape: (4, 2, 5) states_val.shape: (4, 5)
outputs_val: 
[[[ 0.53073734 -0.61281306 -0.5437517  0.7320347 -0.6109526 ]
 [ 0.99996936 0.99990636 -0.9867181  0.99726075 -0.99999976]]
 
 [[ 0.9931584  0.5877845 -0.9100412  0.988892  -0.9982337 ]
 [ 0.     0.     0.     0.     0.    ]]
 
 [[ 0.99992317 0.96815354 -0.985101  0.9995968 -0.9999936 ]
 [ 0.99948144 0.9998127 -0.57493806 0.91015154 -0.99998355]]
 
 [[ 0.99999255 0.9998929  0.26732785 0.36024097 -0.99991137]
 [ 0.98875254 0.9922327  0.6505734  0.4732064 -0.9957567 ]]] 
states_val:
 [[ 0.99996936 0.99990636 -0.9867181  0.99726075 -0.99999976]
 [ 0.9931584  0.5877845 -0.9100412  0.988892  -0.9982337 ]
 [ 0.99948144 0.9998127 -0.57493806 0.91015154 -0.99998355]
 [ 0.98875254 0.9922327  0.6505734  0.4732064 -0.9957567 ]]

import tensorflow as tf
import numpy as np
 
n_steps = 2
n_inputs = 3
n_neurons = 5
n_layers = 3
 
X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
seq_length = tf.placeholder(tf.int32, [None])
 
layers = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons,
                   activation=tf.nn.relu)
     for layer in range(n_layers)]
multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)
outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32, sequence_length=seq_length)
 
init = tf.global_variables_initializer()
 
X_batch = np.array([
    # step 0   step 1
    [[0, 1, 2], [9, 8, 7]], # instance 1
    [[3, 4, 5], [0, 0, 0]], # instance 2 (padded with zero vectors)
    [[6, 7, 8], [6, 5, 4]], # instance 3
    [[9, 0, 1], [3, 2, 1]], # instance 4
  ])
 
seq_length_batch = np.array([2, 1, 2, 2])
 
with tf.Session() as sess:
  init.run()
  outputs_val, states_val = sess.run(
    [outputs, states], feed_dict={X: X_batch, seq_length: seq_length_batch})
  print("outputs_val.shape:", outputs, "states_val.shape:", states)
  print("outputs_val:", outputs_val, "states_val:", states_val)

outputs_val.shape: 
Tensor("rnn/transpose_1:0", shape=("htmlcode">

outputs_val.shape: 
Tensor("rnn/transpose_1:0", shape=("" src="/UploadFiles/2021-04-08/20200120172149.jpg">

如果您不查看框内的内容，LSTM单元看起来与常规单元格完全相同，除了它的状态分为两个向量：h（t）和c（t）。你可以将h（t）视为短期状态，将c（t）视为长期状态。
因此我们的states包含三个LSTMStateTuple，每一个表示每一层的最后一个step的输出，这个输出有两个信息，一个是h表示短期记忆信息，一个是c表示长期记忆信息。维度都是[batch_size，n_neurons] = [4，5]，states的最后一个LSTMStateTuple中的h就是outputs的最后一个step的输出
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