本文使用tensorflow实现在mnist数据集上的图片训练和测试过程，使用了简单的两层神经网络，代码中涉及到的内容，均以备注的形式标出。
关于文中的数据集，大家如果没有下载下来，可以到我们社群下载：667950672
下载下来后解压到F盘mnist文件夹下，或者自己选择文件存储位置，然后在下面代码的相应位置改过来即可。
直接上代码：
import tensorflow as tfimport numpy as np#引入input_mnistfrom tensorflow.examples.tutorials.mnist import input_data#加载mnist信息,获得训练和测试图片以及对应标签mnist = input_data.read_data_sets(F:/mnist/data/,one_hot =True)trainimg = mnist.train.imagestrainlabel = mnist.train.labelstestimg = mnist.test.imagestestlabel = mnist.test.labelsprint("MNIST LOAD READY")#输入图片尺寸28*28n_input =784#输出类别数n_output =10#初始化权重weights ={#卷积层参数，采用高斯初始化wc1:tf.Variable(tf.random_normal([3,3,1,64],stddev =0.1)),wc2:tf.Variable(tf.random_normal([3,3,64,128],stddev=0.1)),#全连接层参数wd1:tf.Variable(tf.random_normal([7*7*128,1024],stddev=0.1)),wd2:tf.Variable(tf.random_normal([1024,n_output],stddev=0.1))}#初始化偏置biases ={c1:tf.Variable(tf.random_normal([64],stddev =0.1)),c2:tf.Variable(tf.random_normal([128],stddev=0.1)),d1:tf.Variable(tf.random_normal([1024],stddev=0.1)),d2:tf.Variable(tf.random_normal([n_output],stddev=0.1))}#定义前向传播函数def conv_basic(_input,_w,_b,_keepratio):#输入#reshape()中的-1表示不用我们指定，让函数自己计算 _input_r = tf.reshape(_input,shape =[-1,28,28,1])#CONV1 _conv1 = tf.nn.conv2d(_input_r,_w[wc1],strides=[1,1,1,1],padding=SAME) _conv1 = tf.nn.relu(tf.nn.bias_add(_conv1,_b[c1])) _pool1 = tf.nn.max_pool(_conv1,ksize =[1,2,2,1],strides =[1,2,2,1],padding=SAME)#Dropout层既可以使用在全连接层之后，也可以使用在每层之后，这里在每层之后都加了Dropout _pool_dr1 = tf.nn.dropout(_pool1,_keepratio)#CONV2#conv2d计算二维卷积 _conv2 = tf.nn.conv2d(_pool_dr1,_w[wc2],strides=[1,1,1,1],padding=SAME) _conv2 = tf.nn.relu(tf.nn.bias_add(_conv2,_b[c2])) _pool2 = tf.nn.max_pool(_conv2,ksize =[1,2,2,1],strides =[1,2,2,1],padding=SAME) _pool_dr2 = tf.nn.dropout(_pool2,_keepratio)#向量化 全连接层输入 得到wd1层的7*7*128的shape 然后转化为向量 _dense1 = tf.reshape(_pool_dr2,[-1,_w[wd1].get_shape().as_list()[0]])#FULL CONNECTION1 _fc1 = tf.nn.relu(tf.add(tf.matmul(_dense1,_w[wd1]),_b[d1])) _fc_dr1 = tf.nn.dropout(_fc1,_keepratio)#FULL CONNECTION2 _out = tf.add(tf.matmul(_fc_dr1,_w[wd2]),_b[d2])#输出字典out={input_r:_input_r,conv1:_conv1,pool1:_pool1,pool1_dr1:_pool_dr1,conv2:_conv2,pool2:_pool2,pool_dr2:_pool_dr2,dense1:_dense1,fc1:_fc1,fc_dr1:_fc_dr1,out:_out}returnoutprint("CNN READY")a = tf.Variable(tf.random_normal([3,3,1,64],stddev=0.1))print(a)a = tf.Print(a,[a],"a: ")init = tf.global_variables_initializer()sess = tf.Session()sess.run(init)#填充x = tf.placeholder(tf.float32,[None,n_input])y = tf.placeholder(tf.float32,[None,n_output])keepratio = tf.placeholder(tf.float32)#进行一次前向传播_pred = conv_basic(x,weights,biases,keepratio)[out]#计算损失cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = _pred,labels=y))#定义优化器optm = tf.train.AdamOptimizer(learning_rate =0.001).minimize(cost)#比较预测的标签和真实标签是否一致，一致返回True，不一致返回False#argmax找到给定的张量tensor中在指定轴axis上的最大值/最小值的位置，0为每一列，1为每一行_corr = tf.equal(tf.argmax(_pred,1),tf.argmax(y,1))#True转化为1 False为0accr = tf.reduce_mean(tf.cast(_corr,tf.float32))#每1个epoch保存一次save_step =1#max_to_keep最终只保留三组模型，即(12 13 14)saver = tf.train.Saver(max_to_keep=3)#控制训练还是测试do_train=1init = tf.global_variables_initializer()sess = tf.Session()sess.run(init)#训练15个epochtraining_epochs =15batch_size =16display_step =1#训练过程if do_train ==1:for epoch in range(training_epochs): avg_cost=0. total_batch =10#迭代优化for i in range(total_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size) sess.run(optm,feed_dict ={x:batch_xs,y:batch_ys,keepratio:0.7}) avg_cost += sess.run(cost,feed_dict={x:batch_xs,y:batch_ys,keepratio:1.})/total_batch#打印信息if(epoch+1)% display_step ==0:print("Epoch:%03d/%03d cost:%.9f"%(epoch,training_epochs,avg_cost)) train_acc = sess.run(accr,feed_dict ={x:batch_xs,y:batch_ys,keepratio:1.})print("Train accuracy:%.3f"%(train_acc))#保存模型if epoch % save_step ==0: saver.save(sess,"F:/mnist/data/model.ckpt-"+str(epoch))#测试（cpu版本慢的要死 电脑都快要被卡死了...)if do_train ==0:#epoch = 15 减1之后即加载第14个模型 epoch = training_epochs-1#读取模型 saver.restore(sess,"F:/mnist/data/model.ckpt-"+str(epoch))#打印测试精度 test_acc = sess.run(accr,feed_dict={x:testimg,y:testlabel,keepratio:1.})print("test accr is:%.3f"%(test_acc))print("Optimization Finished")训练的部分过程如下：
测试过程如下：
测试时只需修改do_train==0 即可。如果使用Anaconda的spyder的话，记得测试之前先restart kennel一下。
留言快来关注小编吧，我们有最有活力的人工智能深度学习社群。
昂钛客AI
