双向循环神经网络tensorflow实现

双向循环神经网络不仅能捕获当前状态与之前状态的联系，也能捕获与之后的状态的联系。

#coding:utf-8
import tensorflow as tf
import numpy as np 
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('/tmp/data',one_hot = True)

learning_rate = 0.01
max_samples = 400000
batch_size = 128
display_step = 10


n_input = 28 #图像的宽度
n_steps = 28 #LSTM的展开步数，图像的高
n_hidden = 256
n_classes = 10

x = tf.placeholder('float',[None,n_steps,n_input])#none 高度 宽度
y = tf.placeholder('float',[None,n_classes])
weights = tf.Variable(tf.random_normal([2*n_hidden,n_classes]))
biases = tf.Variable(tf.random_normal([n_classes]))


def BiRNN(x,weights,biases):
    x = tf.transpose(x,[1,0,2])
    x = tf.reshape(x,[-1,n_input])
    x = tf.split(x,n_steps)

    lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden,forget_bias = 1.0)
    lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden,forget_bias = 1.0)

    outputs,_,_ = tf.contrib.rnn.static_bidirectional_rnn(lstm_fw_cell,lstm_bw_cell,x,dtype = tf.float32) 

return tf.matmul(outputs[-1],weights)+biases



pred = BiRNN(x,weights,biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = pred,labels = y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))

accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    step = 1
while step*batch_size<max_samples:
        batch_x,batch_y = mnist.train.next_batch(batch_size)
        batch_x = batch_x.reshape((batch_size,n_steps,n_input))
        sess.run(optimizer,feed_dict={x:batch_x,y:batch_y})
if step % display_step ==0:
            acc = sess.run(accuracy,feed_dict={x:batch_x,y:batch_y})
            loss = sess.run(cost,feed_dict={x:batch_x,y:batch_y})
print "step",step," acc = ",acc," lost = ",loss
        step += 1

print "finished!"