【Pytorch深度学习开发实践学习】B站刘二大人课程笔记整理lecture11 Advanced_CNN 实现GoogleNet和ResNet

【Pytorch深度学习开发实践学习】B站刘二大人课程笔记整理lecture11 Advanced_CNN
代码：

Pytorch实现GoogleNet

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

batch_size = 64
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) #把原始图像转为tensor  这是均值和方差

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

test_set = datasets.MNIST(root='./data/mnist', train=False, download=True, transform=transform)
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True)
class Inception(torch.nn.Module):
    def __init__(self,in_channels):
        super(Inception, self).__init__()
        self.branchpool = nn.Conv2d(in_channels, 24, kernel_size=1)

        self.branch1x1 = nn.Conv2d(in_channels, 16, kernel_size=1)

        self.branch5x5_1 = nn.Conv2d(in_channels, 16, kernel_size=1)
        self.branch5x5_2 = nn.Conv2d(16, 24, kernel_size=5,padding=2)

        self.branch3x3_1 = nn.Conv2d(in_channels, 16, kernel_size=1)
        self.branch3x3_2 = nn.Conv2d(16, 24,kernel_size=3,padding=1)
        self.branch3x3_3 = nn.Conv2d(24, 24, kernel_size=3,padding=1)


    def forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch5x5 = self.branch5x5_1(x)
        branch5x5 = self.branch5x5_2(branch5x5)

        branch3x3 = self.branch3x3_1(x)
        branch3x3 = self.branch3x3_2(branch3x3)
        branch3x3 = self.branch3x3_3(branch3x3)

        branchpool = F.avg_pool2d(x, kernel_size=3,stride=1,padding=1)
        branchpool = self.branchpool(branchpool)

        outputs = torch.cat((branch1x1,branch5x5,branch3x3,branchpool),dim=1)
        return outputs

class Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(88, 20, kernel_size=5)

        self.incep1 = Inception(10)
        self.incep2 = Inception(20)

        self.fc = nn.Linear(1408, 10)
        self.maxpool = nn.MaxPool2d(kernel_size=2)

    def forward(self, x):
        in_size = x.size(0)
        x = F.relu(self.maxpool(self.conv1(x)))
        x = self.incep1(x)
        x =F.relu(self.maxpool(self.conv2(x)))
        x = self.incep2(x)
        x = x.view(in_size, -1)
        x = self.fc(x)
        return x

model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  #把模型迁移到GPU
model = model.to(device)   #把模型迁移到GPU

def train(epoch):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs,labels = inputs.to(device), labels.to(device)  #训练内容迁移到GPU上
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 300 == 299:    # print every 300 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 300))
            running_loss = 0.0

def test(epoch):
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            images,labels = images.to(device), labels.to(device)  #测试内容迁移到GPU上
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: %d %%' % (
        100 * correct / total))

if __name__ == '__main__':
    for epoch in range(100):
        train(epoch)
        if epoch % 10 == 0:
            test(epoch)
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Pytorch实现ResNet

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

batch_size = 64
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) #把原始图像转为tensor  这是均值和方差

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

test_set = datasets.MNIST(root='./data/mnist', train=False, download=True, transform=transform)
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True)

class ResidualBlock(torch.nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.channels = channels
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3,padding=1)

    def forward(self, x):
        y = F.relu(self.conv1(x))
        y = self.conv2(y)
        return F.relu(x + y)


class Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=5)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5)

        self.rblock1 = ResidualBlock(16)
        self.rblock2 = ResidualBlock(32)

        self.maxpool = nn.MaxPool2d(kernel_size=2)
        self.fc = nn.Linear(512, 10)

    def forward(self, x):
        in_size = x.size(0)
        x = self.maxpool(F.relu(self.conv1(x)))
        x = self.rblock1(x)
        x = self.maxpool(F.relu(self.conv2(x)))
        x = self.rblock2(x)
        x = x.view(in_size, -1)
        x = self.fc(x)
        return x

model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  #把模型迁移到GPU
model = model.to(device)   #把模型迁移到GPU

def train(epoch):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs,labels = inputs.to(device), labels.to(device)  #训练内容迁移到GPU上
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 300 == 299:    # print every 300 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 300))
            running_loss = 0.0

def test(epoch):
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            images,labels = images.to(device), labels.to(device)  #测试内容迁移到GPU上
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: %d %%' % (
        100 * correct / total))

if __name__ == '__main__':
    for epoch in range(100):
        train(epoch)
        if epoch % 10 == 0:
            test(epoch)
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