Python利用全连接神经网络求解MNIST问题详解

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

 x=tf.placeholder(tf.float32,[None,784],name='x') y=tf.placeholder(tf.float32,[None,10],name='y')

 h1_num=256                        #设置隐藏层神经元数量 W1=tf.Variable(tf.random_normal([784,h1_num]),name='W1') b1=tf.Variable(tf.zeros([h1_num]),name='b1') Y1=tf.nn.relu(tf.matmul(x,W1)+b1)             #激活函数

 W2=tf.Variable(tf.random_normal([h1_num,10]),name='W2') b2=tf.Variable(tf.zeros([10]),name='b2') Y2=tf.matmul(Y1,W2)+b2 pred=tf.nn.softmax(Y2)

 train_epochs=20                    #训练轮数 batch_size=50                     #每个批次的样本数 batch_num=int(mnist.train.num_examples/batch_size)  #一轮需要训练多少批 learning_rate=0.01 #定义损失函数、优化器 loss_function=tf.reduce_mean(             #softmax交叉熵损失函数 tf.nn.softmax_cross_entropy_with_logits(logits=Y2,labels=y)) optimizer=tf.train.AdamOptimizer(learning_rate).minimize(loss_function) #定义准确率 correct_prediction=tf.equal(tf.argmax(pred,1),tf.argmax(y,1)) accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

 ss=tf.Session() ss.run(tf.global_variables_initializer())           #进行全部变量的初始化 for epoch in range(train_epochs): for batch in range(batch_num):              #分批次读取数据进行训练 xs,ys=mnist.train.next_batch(batch_size) ss.run(optimizer,feed_dict={x:xs,y:ys}) loss,acc=ss.run([loss_function,accuracy],\ feed_dict={x:mnist.validation.images,y:mnist.validation.labels}) print('第%2d轮训练：损失为：%9f，准确率：%.4f'%(epoch+1,loss,acc)) ss.close()

 test_res=ss.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}) print('测试集的准确率为：%.4f'%(test_res))

 #构建输入层 x=tf.placeholder(tf.float32,[None,784],name='x') y=tf.placeholder(tf.float32,[None,10],name='y') #构建第一个隐藏层 h1_num=256                            #第一隐藏层神经元数量256 W1=tf.Variable(tf.truncated_normal([784,h1_num],stddev=0.1),name='W1') b1=tf.Variable(tf.zeros([h1_num]),name='b1') Y1=tf.nn.relu(tf.matmul(x,W1)+b1) #构建第二个隐藏层 h2_num=64                             #第二隐藏层神经元数量64 W2=tf.Variable(tf.random_normal([h1_num,h2_num],stddev=0.1),name='W2') b2=tf.Variable(tf.zeros([h2_num]),name='b2') Y2=tf.nn.relu(tf.matmul(Y1,W2)+b2) #构建输出层 W3=tf.Variable(tf.random_normal([h2_num,10],stddev=0.1),name='W3') b3=tf.Variable(tf.zeros([10]),name='b3') Y3=tf.matmul(Y2,W3)+b3 pred=tf.nn.softmax(Y3)

 #定义一个通用的全连接层函数模型 def fcn_layer(inputs,in_dim,out_dim,activation=None): W=tf.Variable(tf.truncated_normal([in_dim,out_dim],stddev=0.1)) b=tf.Variable(tf.zeros([out_dim])) Y=tf.matmul(inputs,W)+b if activation==None: output=Y else: output=activation(Y) return output #构建第一个隐藏层 Y1=fcn_layer(x,784,256,tf.nn.relu) #构建第二个隐藏层 Y2=fcn_layer(Y1,256,64,tf.nn.relu) #构建输出层 Y3=fcn_layer(Y2,64,10) pred=tf.nn.softmax(Y3)

 import os save_dir='D:/Temp/MachineLearning/ModelSaving/'    #定义模型的保存路径 if not os.path.exists(save_dir):            #如果不存在该路径则创建 os.makedirs(save_dir)

 save_step=5            #定义存储粒度 saver=tf.train.Saver()      #定义saver

 if epoch%save_step==0: saver.save(ss,os.path.join(save_dir,'mnist_fcn_{:02d}.ckpt'.format(epoch+1)))

 saver.save(ss,os.path.join(save_dir,'mnist_fcn.ckpt'))

 save_dir='D:/Temp/MachineLearning/ModelSaving/'    #定义模型的保存路径 saver=tf.train.Saver()                 #定义saver ss=tf.Session() ss.run(tf.global_variables_initializer()) ckpt=tf.train.get_checkpoint_state(save_dir)      #读取存盘点 if ckpt and ckpt.model_checkpoint_path: saver.restore(ss,ckpt.model_checkpoint_path)    #从存盘中恢复参数到当前的session print('数据恢复从',ckpt.model_checkpoint_path) test_res=accuracy.eval(session=ss,feed_dict={x:mnist.test.images,y:mnist.test.labels}) print('测试集的准确率为：%.4f'%(test_res))

 NotFoundError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a Variable name or other graph key that is missing from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint.

 import tensorflow as tf v=tf.Variable(1.0,'new_var') with tf.Session() as ss: tf.train.write_graph(ss.graph_def,'D:\Temp\MachineLearning\ModelSaving\Graph', 'test_graph.pb',as_text=False)

 with tf.Session() as ss: with tf.gfile.GFile('D:/Temp\MachineLearning/ModelSaving/Graph/test_graph.pb','rb') as pb_file: graph_def=tf.GraphDef() graph_def.ParseFromString(pb_file.read()) ss.graph.as_default() tf.import_graph_def(graph_def) print(graph_def)