TF之RNN：基于顺序的RNN分类案例对手写数字图片mnist数据集实现高精度预测—Jason niu

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

lr=0.001                   
training_iters=100000       
batch_size=128                

n_inputs=28     
n_steps=28        
n_hidden_units=128  
n_classes=10         

x=tf.placeholder(tf.float32, [None,n_steps,n_inputs])
y=tf.placeholder(tf.float32, [None,n_classes])

weights ={
    'in':tf.Variable(tf.random_normal([n_inputs,n_hidden_units])),
    'out':tf.Variable(tf.random_normal([n_hidden_units,n_classes])),
    }
biases ={
    'in':tf.Variable(tf.constant(0.1,shape=[n_hidden_units,])),
    'out':tf.Variable(tf.constant(0.1,shape=[n_classes,])),
    }


def RNN(X,weights,biases):  

    X=tf.reshape(X,[-1,n_inputs])
    X_in=tf.matmul(X,weights['in'])+biases['in']    
    X_in=tf.reshape(X_in,[-1,n_steps,n_hidden_units])
    lstm_cell=tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units,forget_bias=1.0,state_is_tuple=True)
    __init__state=lstm_cell.zero_state(batch_size, dtype=tf.float32)
    outputs,states=tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=__init__state,time_major=False)
          
    outputs=tf.unpack(tf.transpose(outputs, [1,0,2])) 
    results=tf.matmul(outputs[-1],weights['out'])+biases['out'] 
    return results


pred =RNN(x,weights,biases) 
cost =tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))  
train_op=tf.train.AdamOptimizer(lr).minimize(cost)                 

correct_pred=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))                
accuracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32))              

 
with tf.Session() as sess:  
    sess.run(init)
    step=0
    while step*batch_size < training_iters:                 
        batch_xs,batch_ys=mnist.train.next_batch(batch_size)
        batch_xs=batch_xs.reshape([batch_size,n_steps,n_inputs])
        sess.run([train_op],feed_dict={
            x:batch_xs,
            y:batch_ys,})
        if step%20==0:                                         
            print(sess.run(accuracy,feed_dict={
                x:batch_xs,
                y:batch_ys,}))
        step+=1