脚本专栏 发布日期：2025/10/25 浏览次数：1
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=("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
以上这篇关于tf.nn.dynamic_rnn返回值详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。