1. 程式人生 > >tensorflow基本教程10:RNN迴圈神經網路對於手寫體識別預測

tensorflow基本教程10:RNN迴圈神經網路對於手寫體識別預測

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#this is data
mnist=input_data.read_data_sets("MNIST_data",one_hot=True)
n_inputs=28#輸入一行,一行有28個數據
max_time=28#一共28行
lstm_size=100#隱層單元
n_classes=10#10個分類
batch_size=50#每批次50個樣本
n_batch=mnist.train.num_examples//batch_size#計算一共有多少個批次

#這裡的none表示第一個維度可以是任意的長度
x=tf.placeholder(tf.float32,[None,784])
#正確的標籤
y=tf.placeholder(tf.float32,[None,10])

#初始化權值
weights=tf.Variable(tf.truncated_normal([lstm_size,n_classes],stddev=0.1))
#初始化偏置
biases=tf.Variable(tf.constant(0.1,shape=[n_classes]))

#定義RNN網路
def RNN(X,weights,biases):
    #inputs=[batch_size,max_time,n_inputs]
    inputs=tf.reshape(X,[-1,max_time,n_inputs])
    #定義lstm基本的cell
    lstm_cell=tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(lstm_size)
    #final_state[0]是cell state
    #final_state[1]是hidden_state
    outputs,final_state=tf.nn.dynamic_rnn(lstm_cell,inputs,dtype=tf.float32)
    result=tf.nn.softmax(tf.matmul(final_state[1],weights)+biases)
    return result
    #計算RNN的返回結果
prediction=RNN(x,weights,biases)
cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
    #使用AdamOptimizer進行優化
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    #結果存在一個布林型列表中
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一維張量中最大的值所在的位置
    #求準確率
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))#把correct_prediction變為float32型別
    #初始化
init=tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(6):
        for batch in range(n_batch):
            batch_xs,batch_ys=mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
            
        acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter"+str(epoch)+",Testing Accuracy="+str(acc))