手写体数字识别

import os
import numpy as np
from time import time
import matplotlib.pyplot as plt
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from tensorflow.examples.tutorials.mnist import input_data
#步骤1：读取数据
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
#步骤2：搭建多隐藏层神经网络
def fcn_layer(output_dim,input_dim,inputs,activation=None):
    W=tf.Variable(tf.random_normal([input_dim,output_dim]))
    b=tf.Variable(tf.random_normal([1,output_dim]))
    XWb=tf.matmul(inputs,W)+b
    if activation is None:
        outputs=XWb
    else:
        outputs=activation(XWb)
    return outputs
x = tf.placeholder("float",[None,784])
# 隐藏层h1
h1=fcn_layer(output_dim=256,input_dim=784,inputs=x ,activation=tf.nn.relu) 
# 隐藏层h2
h2=fcn_layer(output_dim=256,input_dim=256,inputs=h1,activation=tf.nn.relu)  
pred=fcn_layer(output_dim=10,input_dim=256,inputs=h2,activation=None)
y=tf.placeholder("float",[None,10])
loss_function=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))#交叉熵
train_epochs=30
batch_size=100
total_batch=int(mnist.train.num_examples/batch_size)
display_step=1
save_step=5
learning_rate=0.001#学习率
epoch_list=[];loss_list=[];accuracy_list=[]
#步骤3：训练模型
startTime=time()
optimizer=tf.train.AdamOptimizer(learning_rate).minimize(loss_function)
correct_prediction=tf.equal(tf.argmax(y, 1),tf.argmax(pred, 1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
saver=tf.train.Saver()
sess=tf.Session()#生成会话
sess.run(tf.global_variables_initializer())
for epoch in range(train_epochs):
    for batch in range(total_batch):
        xs, ys=mnist.train.next_batch(batch_size)
        sess.run(optimizer,feed_dict={x: xs,y: ys})     
    loss,acc=sess.run([loss_function,accuracy],feed_dict={x:mnist.validation.images,y:mnist.validation.labels})    
    epoch_list.append(epoch)
    loss_list.append(loss)
    accuracy_list.append(acc)        
    if (epoch+1)%display_step==0:
        print("Train Epoch:",'%02d'%(epoch+1),"Loss=","{:.9f}".format(loss)," Accuracy=","{:.4f}".format(acc))
duration=time()-startTime
print("Train Finished takes:","{:.2f}".format(duration))
sess.close()
print("Starting another session for prediction")
saver=tf.train.Saver()
sess=tf.Session() 
init=tf.global_variables_initializer() 
sess.run(init)

#步骤4：测试模型
def plot_images_labels_prediction(images,labels,prediction,idx,num=10):
    fig=plt.gcf()
    fig.set_size_inches(10,12)
    if num>25:num=25 
    for i in range(0,num):
        ax=plt.subplot(5,5,1+i)       
        ax.imshow(np.reshape(images[idx],(28, 28)),cmap='binary')           
        title="label=" +str(np.argmax(labels[idx]))
        if len(prediction)>0:
            title+=",predict="+str(prediction[idx])             
        ax.set_title(title,fontsize=10) 
        ax.set_xticks([])
        ax.set_yticks([])        
        idx+=1 
    plt.show()
prediction_result=sess.run(tf.argmax(pred,1),feed_dict={x: mnist.test.images})
plot_images_labels_prediction(mnist.test.images,mnist.test.labels,prediction_result,10)
count=0
for i in range(500):
    if prediction_result[i]!=np.argmax(mnist.test.labels[i]):
        print("i="+str(i)+"label=",np.argmax(mnist.test.labels[i]),"predict=",prediction_result[i])
        count+=1
print("count=", count)