This article uses tensorflow to implement the image training and testing process on the mnist data set, using a simple two-layer neural network, and the content involved in the code is marked in the form of remarks.
Regarding the data set in this article, if you haven't downloaded it, you can download it from my network disk. The link is as follows:
https://pan.baidu.com/s/1KU_YZhouwk0h9MK0xVZ_QQ
After downloading it, extract it to the mnist folder of the F disk, or Choose the file storage location yourself, and then change it in the corresponding location of the code below.
Go directly to the code:
import tensorflow as tf
import numpy as np
#引入input_mnist
from 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.images
trainlabel = mnist.train.labels
testimg = mnist.test.images
testlabel = mnist.test.labels
print("MNIST LOAD READY")
#输入图片尺寸28*28
n_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 = {
'bc1':tf.Variable(tf.random_normal([64],stddev = 0.1)),
'bc2':tf.Variable(tf.random_normal([128],stddev=0.1)),
'bd1':tf.Variable(tf.random_normal([1024],stddev=0.1)),
'bd2':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['bc1']))
_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['bc2']))
_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['bd1']))
_fc_dr1 = tf.nn.dropout(_fc1,_keepratio)
#FULL CONNECTION2
_out = tf.add(tf.matmul(_fc_dr1,_w['wd2']),_b['bd2'])
#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
}
return out
print("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0
accr = 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=1
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
#训练15个epoch
training_epochs = 15
batch_size = 16
display_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")Part of the training process is as follows:
The testing process is as follows:
just modify do_train==0 during testing. If you use Anaconda's spyder, remember to restart the kennel before testing.