0
点赞
收藏
分享

微信扫一扫

Java项目:26 基于SpringBoot+thymeleaf实现的蓝天幼儿园管理系统

墨春 2024-02-26 阅读 11

【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)

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)

部分课件内容:
在这里插入图片描述

在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述在这里插入图片描述

在这里插入图片描述
在这里插入图片描述

在这里插入图片描述
在这里插入图片描述在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述

在这里插入图片描述

在这里插入图片描述

在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述

在这里插入图片描述在这里插入图片描述
在这里插入图片描述

在这里插入图片描述

在这里插入图片描述

在这里插入图片描述

在这里插入图片描述在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述

举报

相关推荐

0 条评论