tensorflow的堆叠多层RNN tensorflow的堆叠多层RNN

使用静态堆叠和动态堆叠通过静态生成的RNN网络，生成过程所需的时间会更长，网络所占有的内存会更多，导出的模型会更大，模型中会带有第个序列中间态的信息，利于调试。在使用时必须与训练的样本序列个数相同。通过动态生成的RNN网络，所占用内存较少。模型中只会有最后的状态，在使用时还能支持不同的序列个数。
怎么堆叠rnn 【tensorflow的堆叠多层RNN】把多个rnn部件添加到lsit中，通过tf.contrib.rnn.MultiRNNCell函数可以把rnn按顺序链接，堆叠rnn就是多个rnn进行堆叠,每个lstm的单元个数可以不一样。

gru = tf.contrib.rnn.GRUCell(n_hidden*2) lstm_cell = tf.contrib.rnn.LSTMCell(n_hidden) mcell = tf.contrib.rnn.MultiRNNCell([lstm_cell,gru])

堆叠静态rnn

stacked_rnn = [] for i in range(3): stacked_rnn.append(tf.contrib.rnn.LSTMCell(n_hidden)) mcell = tf.contrib.rnn.MultiRNNCell(stacked_rnn) x1 = tf.unstack(x, n_steps, 1) outputs, states = tf.contrib.rnn.static_rnn(mcell, x1, dtype=tf.float32)

堆叠动态rnn

stacked_rnn = [] for i in range(3): stacked_rnn.append(tf.contrib.rnn.LSTMCell(n_hidden)) mcell = tf.contrib.rnn.MultiRNNCell(stacked_rnn) outputs,states= tf.nn.dynamic_rnn(mcell,x,dtype=tf.float32)#(?, 28, 256) outputs = tf.transpose(outputs, [1, 0, 2])

比较以下代码：
堆叠动态RNN：

# -*- coding: utf-8 -*- import tensorflow as tf # 导入 MINST 数据集 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 输入 (img shape: 28*28) n_steps = 28 # timesteps n_hidden = 128 # hidden layer num of features n_classes = 10# MNIST 列别 (0-9 ，一共10类) batch_size = 128tf.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 = 100000display_step = 10# 启动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: # 计算批次数据的准确率 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!")# 计算准确率 for 128 mnist test images test_len = 100 test_data = https://www.it610.com/article/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}))

堆叠静态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 输入 (img shape: 28*28) n_steps = 28 # timesteps n_hidden = 128 # hidden layer num of features n_classes = 10# MNIST 列别 (0-9 ，一共10类) batch_size = 128tf.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个时序，取最后一个时序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 = 100000display_step = 10# 启动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: # 计算批次数据的准确率 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!")# 计算准确率 for 128 mnist test images test_len = 100 test_data = https://www.it610.com/article/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}))