函数原型

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
)

实例讲解：

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)

log info:

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 ]]

首先输入X是一个 [batch_size，step，input_size] = [4，2，3] 的tensor，注意我们这里调用的是BasicRNNCell，只有一层循环网络，outputs是最后一层每个step的输出，它的结构是[batch_size，step，n_neurons] = [4，2，5]，states是每一层的最后那个step的输出，由于本例中，我们的循环网络只有一个隐藏层，所以它就代表这一层的最后那个step的输出，因此它和step的大小是没有关系的，我们的X有4个样本组成，输出神经元大小n_neurons是5，因此states的结构就是[batch_size，n_neurons] = [4，5]，最后我们观察数据，states的每条数据正好就是outputs的最后一个step的输出。

下面我们继续讲解多个隐藏层的情况，这里是三个隐藏层，注意我们这里仍然是调用BasicRNNCell

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)

log info:

outputs_val.shape: 
Tensor("rnn/transpose_1:0", shape=(?, 2, 5), dtype=float32) 

states_val.shape: 
(<tf.Tensor 'rnn/while/Exit_3:0' shape=(?, 5) dtype=float32>, 
 <tf.Tensor 'rnn/while/Exit_4:0' shape=(?, 5) dtype=float32>, 
 <tf.Tensor 'rnn/while/Exit_5:0' shape=(?, 5) dtype=float32>)

outputs_val:
 [[[0.     0.     0.     0.     0.    ]
 [0.     0.18740742 0.     0.2997518 0.    ]]

 [[0.     0.07222144 0.     0.11551574 0.    ]
 [0.     0.     0.     0.     0.    ]]

 [[0.     0.13463384 0.     0.21534224 0.    ]
 [0.03702604 0.18443246 0.     0.34539366 0.    ]]

 [[0.     0.54511094 0.     0.8718864 0.    ]
 [0.5382122 0.     0.04396425 0.4040263 0.    ]]] 

states_val:
 (array([[0.    , 0.83723307, 0.    , 0.    , 2.8518028 ],
    [0.    , 0.1996038 , 0.    , 0.    , 1.5456247 ],
    [0.    , 1.1372368 , 0.    , 0.    , 0.832613 ],
    [0.    , 0.7904129 , 2.4675028 , 0.    , 0.36980057]],
   dtype=float32), 
 array([[0.6524607 , 0.    , 0.    , 0.    , 0.    ],
    [0.25143963, 0.    , 0.    , 0.    , 0.    ],
    [0.5010576 , 0.    , 0.    , 0.    , 0.    ],
    [0.    , 0.3166597 , 0.4545995 , 0.    , 0.    ]],
   dtype=float32), 
 array([[0.    , 0.18740742, 0.    , 0.2997518 , 0.    ],
    [0.    , 0.07222144, 0.    , 0.11551574, 0.    ],
    [0.03702604, 0.18443246, 0.    , 0.34539366, 0.    ],
    [0.5382122 , 0.    , 0.04396425, 0.4040263 , 0.    ]],
   dtype=float32))

我们说过，outputs是最后一层的输出，即 [batch_size，step，n_neurons] = [4，2，5]

states是每一层的最后一个step的输出，即三个结构为 [batch_size，n_neurons] = [4，5] 的tensor

继续观察数据，states中的最后一个array，正好是outputs的最后那个step的输出

下面我们继续讲当由BasicLSTMCell构造单元工厂的时候，只讲多层的情况，我们只需要将上面的BasicRNNCell替换成BasicLSTMCell就行了，打印信息如下：

outputs_val.shape: 
Tensor("rnn/transpose_1:0", shape=(?, 2, 5), dtype=float32) 

states_val.shape:
(LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_3:0' shape=(?, 5) dtype=float32>, 
        h=<tf.Tensor 'rnn/while/Exit_4:0' shape=(?, 5) dtype=float32>), 
LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_5:0' shape=(?, 5) dtype=float32>, 
        h=<tf.Tensor 'rnn/while/Exit_6:0' shape=(?, 5) dtype=float32>), 
LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 5) dtype=float32>, 
        h=<tf.Tensor 'rnn/while/Exit_8:0' shape=(?, 5) dtype=float32>))

outputs_val: 
[[[1.2949290e-04 0.0000000e+00 2.7623639e-04 0.0000000e+00 0.0000000e+00]
 [9.4675866e-05 0.0000000e+00 2.0214770e-04 0.0000000e+00 0.0000000e+00]]

 [[4.3100454e-06 4.2123037e-07 1.4312843e-06 0.0000000e+00 0.0000000e+00]
 [0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]]

 [[0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]
 [0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]]

 [[0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]
 [0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]]] 

states_val: 
(LSTMStateTuple(
c=array([[0.    , 0.    , 0.04676079, 0.04284539, 0.    ],
    [0.    , 0.    , 0.0115245 , 0.    , 0.    ],
    [0.    , 0.    , 0.    , 0.    , 0.    ],
    [0.    , 0.    , 0.    , 0.    , 0.    ]],
   dtype=float32), 
h=array([[0.    , 0.    , 0.00035096, 0.04284406, 0.    ],
    [0.    , 0.    , 0.00142574, 0.    , 0.    ],
    [0.    , 0.    , 0.    , 0.    , 0.    ],
    [0.    , 0.    , 0.    , 0.    , 0.    ]],
   dtype=float32)), 
LSTMStateTuple(
c=array([[0.0000000e+00, 1.0477135e-02, 4.9871090e-03, 8.2785974e-04,
    0.0000000e+00],
    [0.0000000e+00, 2.3306280e-04, 0.0000000e+00, 9.9445322e-05,
    5.9535629e-05],
    [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
    0.0000000e+00],
    [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
    0.0000000e+00]], dtype=float32), 
h=array([[0.00000000e+00, 5.23016974e-03, 2.47756205e-03, 4.11730434e-04,
    0.00000000e+00],
    [0.00000000e+00, 1.16522635e-04, 0.00000000e+00, 4.97301044e-05,
    2.97713632e-05],
    [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
    0.00000000e+00],
    [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
    0.00000000e+00]], dtype=float32)), 
LSTMStateTuple(
c=array([[1.8937115e-04, 0.0000000e+00, 4.0442235e-04, 0.0000000e+00,
    0.0000000e+00],
    [8.6200516e-06, 8.4243663e-07, 2.8625946e-06, 0.0000000e+00,
    0.0000000e+00],
    [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
    0.0000000e+00],
    [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
    0.0000000e+00]], dtype=float32), 
h=array([[9.4675866e-05, 0.0000000e+00, 2.0214770e-04, 0.0000000e+00,
    0.0000000e+00],
    [4.3100454e-06, 4.2123037e-07, 1.4312843e-06, 0.0000000e+00,
    0.0000000e+00],
    [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
    0.0000000e+00],
    [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
    0.0000000e+00]], dtype=float32)))

我们先看看LSTM单元的结构

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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