【轉載】 【TensorFlow】static_rnn 和dynamic_rnn的區別

原文地址：

https://blog.csdn.net/qq_20135597/article/details/88980975

 

 

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tensorflow中提供了rnn接口有兩種，一種是靜態的rnn，一種是動態的rnn

 

 

 

一般用法：

一、靜態接口：static_rnn

主要使用 tf.contrib.rnn

x = tf.placeholder("float", [None, n_steps, n_input])
x1 = tf.unstack(x, n_steps, 1)
lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x1, dtype=tf.float32)
pred = tf.contrib.layers.fully_connected(outputs[-1],n_classes,activation_fn = None)

 

 

靜態 rnn 的意思就是在圖中建立一個固定長度（n_steps）的網絡。這將致使

 

缺點：

1. 生成過程耗時更長，佔內存更多，導出的模型更大；
2. 沒法傳遞比最初指定的更長的序列（> n_steps）。

優勢：

模型中帶有某個序列中間臺的信息，便與調試。

 

 

 

 

 

 

 

 

二、動態接口：dynamic_rnn

主要使用 tf.nn.dynamic_rnn

x = tf.placeholder("float", [None, n_steps, n_input])
lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
outputs,_  = tf.nn.dynamic_rnn(lstm_cell ,x,dtype=tf.float32)
outputs = tf.transpose(outputs, [1, 0, 2])
pred = tf.contrib.layers.fully_connected(outputs[-1],n_classes,activation_fn = None)

 

 

    動態的tf.nn.dynamic_rnn被執行時，它使用循環來動態構建圖形。這意味着

 

優勢：

1. 圖形建立速度更快，佔用內存更少；
2. 而且能夠提供可變大小的批處理。

缺點：

1. 模型中只有最後的狀態。

 

       動態rnn的意思是隻建立樣本中的一個序列RNN，其餘序列數據會經過循環進入該RNN運算

 

 

 

 

區別：

 一、輸入輸出不一樣：

        dynamic_rnn實現的功能就是能夠讓不一樣迭代傳入的batch能夠是長度不一樣數據，但同一次迭代一個batch內部的全部數據長度仍然是固定的。例如，第一時刻傳入的數據shape=[batch_size, 10]，第二時刻傳入的數據shape=[batch_size, 12]，第三時刻傳入的數據shape=[batch_size, 8]等等。

      可是static_rnn不能這樣，它要求每一時刻傳入的batch數據的[batch_size, max_seq]，在每次迭代過程當中都保持不變。

二、訓練方式不一樣：

具體參見參考文獻1
 

 

 

 

 

 

多層LSTM的代碼實現對比：

一、靜態多層RNN

import tensorflow as tf
# 導入 MINST 數據集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("c:/user/administrator/data/", one_hot=True)
 
n_input = 28 # MNIST data 輸入 (img shape: 28*28)
n_steps = 28 # timesteps
n_hidden = 128 # hidden layer num of features
n_classes = 10  # MNIST 列別 (0-9 ，一共10類)
batch_size = 128
 
tf.reset_default_graph()
# tf Graph input
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])
 
gru = tf.contrib.rnn.GRUCell(n_hidden*2)
lstm_cell = tf.contrib.rnn.LSTMCell(n_hidden)
mcell = tf.contrib.rnn.MultiRNNCell([lstm_cell,gru])
 
x1 = tf.unstack(x, n_steps, 1)
outputs, states = tf.contrib.rnn.static_rnn(mcell, x1, dtype=tf.float32)
 
pred = tf.contrib.layers.fully_connected(outputs[-1],n_classes,activation_fn = None)
 
learning_rate = 0.001
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
 
# Evaluate model
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
 
training_iters = 100000
 
display_step = 10
 
# 啓動session
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    step = 1
    # Keep training until reach max iterations
    while step * batch_size < training_iters:
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        # Reshape data to get 28 seq of 28 elements
        batch_x = batch_x.reshape((batch_size, n_steps, n_input))
        # Run optimization op (backprop)
        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
        if step % display_step == 0:
            # 計算批次數據的準確率
            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
            # Calculate batch loss
            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
            print ("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
                  "{:.6f}".format(loss) + ", Training Accuracy= " + \
                  "{:.5f}".format(acc))
        step += 1
    print (" Finished!")
 
    # 計算準確率 for 128 mnist test images
    test_len = 100
    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))
    test_label = mnist.test.labels[:test_len]
    print ("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={x: test_data, y: test_label}))

 

 

 

 

二、動態多層RNN

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("c:/user/administrator/data/", one_hot=True)
 
n_input = 28 # MNIST data 輸入 (img shape: 28*28)
n_steps = 28 # timesteps
n_hidden = 128 # hidden layer num of features
n_classes = 10  # MNIST 列別 (0-9 ，一共10類)
batch_size = 128
 
tf.reset_default_graph()
# tf Graph input
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])
 
gru = tf.contrib.rnn.GRUCell(n_hidden*2)
lstm_cell = tf.contrib.rnn.LSTMCell(n_hidden)
mcell = tf.contrib.rnn.MultiRNNCell([lstm_cell,gru])
 
outputs,states  = tf.nn.dynamic_rnn(mcell,x,dtype=tf.float32)#(?, 28, 256)
outputs = tf.transpose(outputs, [1, 0, 2])#(28, ?, 256) 28個時序，取最後一個時序outputs[-1]=(?,256)
 
pred = tf.contrib.layers.fully_connected(outputs[-1],n_classes,activation_fn = None)
 
learning_rate = 0.001
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
 
# Evaluate model
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
 
training_iters = 100000
 
display_step = 10
 
# 啓動session
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    step = 1
    # Keep training until reach max iterations
    while step * batch_size < training_iters:
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        # Reshape data to get 28 seq of 28 elements
        batch_x = batch_x.reshape((batch_size, n_steps, n_input))
        # Run optimization op (backprop)
        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
        if step % display_step == 0:
            # 計算批次數據的準確率
            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
            # Calculate batch loss
            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
            print ("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
                  "{:.6f}".format(loss) + ", Training Accuracy= " + \
                  "{:.5f}".format(acc))
        step += 1
    print (" Finished!")
 
    # 計算準確率 for 128 mnist test images
    test_len = 100
    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))
    test_label = mnist.test.labels[:test_len]
    print ("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={x: test_data, y: test_label}))

 

 

 

 

 

【參考文獻】：

一、https://www.jianshu.com/p/1b1ea45fab47

二、What's the difference between tensorflow dynamic_rnn and rnn?

 

 

 

 

 

 

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