Pytorch深度学习(九):卷积神经网络跑Minst数据集

卷积神经网络(Minst数据集)

    • 一、代码实现(CPU版)
    • 二、代码实现(GPU版)

用一下例子来表示:

Pytorch深度学习(九):卷积神经网络跑Minst数据集_第1张图片
Pytorch深度学习(九):卷积神经网络跑Minst数据集_第2张图片

一、代码实现(CPU版)

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

#1.准备数据集
batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),   #先将图像变成一个张量tensor
    transforms.Normalize((0.1307,),(0.3081,))  #其中的0.1307是MNIST数据集的均值,0.3081是MNIST数据集的标准差
])

train_dataset = datasets.MNIST(root='./dataset/mnist/',
                               train=True,
                               download=True,
                               transform=transform)
train_loader = DataLoader(train_dataset,
                          shuffle=True,
                          batch_size=batch_size)
test_dataset = datasets.MNIST(root='./dataset/mnist/',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(test_dataset,
                         shuffle=False,
                         batch_size=batch_size)
#2.构建卷积神经网络模型
class Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.conv1 = torch.nn.Conv2d(in_channels=1,out_channels=10,kernel_size=5)
        self.conv2 = torch.nn.Conv2d(in_channels=10,out_channels=20,kernel_size=5)
        self.pooling = torch.nn.MaxPool2d(kernel_size=2,stride=2)
        self.fc = torch.nn.Linear(320,10)
        
    def forward(self,x):
        # Flatten data from (n, 1, 28, 28) to (n, 784)
        batch_size = x.size(0)
        x = F.relu(self.pooling(self.conv1(x)))
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size,-1)
        x = self.fc(x)
        return x
        
model = Net()

#3.选择损失函数和构化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(),lr=0.01,momentum=0.5) #momentum 是带有优化的一个训练过程参数

#4.训练和测试
acc_list = []
epoch_list = []
def train(epoch):
    epoch_list.append(epoch)
    running_loss = 0.0
    for batch_idx,data in enumerate(train_loader,0):
        inputs,target = data
        optimizer.zero_grad()
        
        #forward
        outputs = model(inputs)
        loss = criterion(outputs,target)
        
        #backward
        loss.backward()
        
        #update
        optimizer.step()
        
        running_loss += loss.item()
        if batch_idx % 300 ==299 :  #每训练300次就打印一次结果
            print('[%d,%5d] loss: %.3f' % (epoch+1,batch_idx+1,running_loss/300))
            running_loss = 0.0
'''
在分类问题中,通常需要使用max()函数对softmax函数的输出值进行操作,求出预测值索引。下面讲解一下torch.max()函数的输入及输出值都是什么。
1. torch.max(input, dim) 函数
output = torch.max(input, dim)
输入:input是softmax函数输出的一个tensor,dim是max函数索引的维度0/1,0是每列的最大值,1是每行的最大值
输出:会返回两个tensor,第一个tensor是每行的最大值,softmax的输出中最大的是1,所以第一个tensor是全1的tensor;第二个tensor是每行最大值的索引。
'''            
def test():
    correct = 0
    total = 0 
    with torch.no_grad():   #不需要计算梯度
        for data in test_loader:
            images,labels = data
            outputs = model(images)
            _,predicted = torch.max(outputs.data,dim=1)  #用max 来找最大值的下标
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print("Accuracy on test set:%d %%" % (100*correct/total))
    acc_list.append(100*correct/total)

if __name__=='__main__':
    for epoch in range(10):
        train(epoch)
        test()
plt.plot(epoch_list,acc_list)
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.show()            

运行结果:
Pytorch深度学习(九):卷积神经网络跑Minst数据集_第3张图片
Pytorch深度学习(九):卷积神经网络跑Minst数据集_第4张图片

二、代码实现(GPU版)

# 1.准备数据集
import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),   #先将图像变成一个张量tensor
    transforms.Normalize((0.1307,),(0.3081,))  #其中的0.1307是MNIST数据集的均值,0.3081是MNIST数据集的标准差
])

train_dataset = datasets.MNIST(root='./dataset/mnist/',
                               train=True,
                               download=True,
                               transform=transform)
train_loader = DataLoader(train_dataset,
                          shuffle=True,
                          batch_size=batch_size)

test_dataset = datasets.MNIST(root='./dataset/mnist/',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(test_dataset,
                         shuffle=False,
                         batch_size=batch_size)
          
# 2.构建卷积网络模型
class Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.conv1 = torch.nn.Conv2d(in_channels=1,out_channels=10,kernel_size=5)
        self.conv2 = torch.nn.Conv2d(in_channels=10,out_channels=20,kernel_size=5)
        self.pooling = torch.nn.MaxPool2d(kernel_size=2,stride=2)
        self.fc = torch.nn.Linear(320,10)
        
    def forward(self,x):
        # Flatten data from (n, 1, 28, 28) to (n, 784)
        batch_size = x.size(0)
        x = F.relu(self.pooling(self.conv1(x)))
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size,-1)
        x = self.fc(x)
        return x
        
model = Net()

#使用GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

# 3.构造损失函数和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

#4.训练和测试
acc_list = []
epoch_list = []
def train(epoch):
    epoch_list.append(epoch)
    running_loss = 0.0
    for batch_idx,data in enumerate(train_loader,0):
        inputs,target = data
        inputs,target = inputs.to(device),target.to(device)  # 使用GPU
        optimizer.zero_grad()  # 优化器清零
        
        #forward
        outputs = model(inputs)  # 获得预测值
        loss = criterion(outputs,target)  # 获得损失值
          
        #backward
        loss.backward()
        
        #update
        optimizer.step()  
          
        running_loss += loss.item()   # 注意加上item:不构成计算图
        if batch_idx % 300 == 299:   #每训练300次就打印一次平均的loss结果
            print('[%d,%5d] loss: %.3f' % (epoch+1,batch_idx+1,running_loss/300))
            running_loss = 0.0

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images,labels = data
            images,labels = images.to(device),labels.to(device)  # 使用GPU
            
            outputs = model(images)
            _,predicted = torch.max(outputs.data,dim=1) # 每一行的最大值下标
            total += labels.size(0)   # 测试了多少个数据
            correct += (predicted == labels).sum().item()  # 计算有多少个预测正确
    print('Accuracy on test set:%d %%'%(100*correct/total))   # 输出正确率
    acc_list.append(100*correct/total)

if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()
plt.plot(epoch_list,acc_list)
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.show()

运行结果:
Pytorch深度学习(九):卷积神经网络跑Minst数据集_第5张图片
Pytorch深度学习(九):卷积神经网络跑Minst数据集_第6张图片

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