在Pytorch中可以使用torch.( )函数将已经训练好的模型保存到指定的文件夹下的指定文件

在 PyTorch 中，可以使用 `torch.save()` 函数将已经训练好的模型保存到指定的文件夹下的指定文件。该函数可以将 PyTorch 模型的参数保存到指定文件中，以便后续恢复模型参数。具体语法如下：

torch.save(model.state_dict(), 'file_path')

其中，`model.state_dict()` 表示保存模型的参数，`file_path` 是保存模型参数的文件路径。需要注意的是，保存的模型参数需要与当前代码的模型参数类型一致。同时，保存的模型参数需要保存在 CPU 或 GPU 中，具体与当前代码中的模型参数保存位置相同。

相关问题

1.使用PyTorch实现LeNet网络 2.使用本地文件夹的 Fashion MNIST 对LeNet进行训练和测试，优化算法采用SGD或Adam，torch.optim.SGD 或 torch.optim.Adam 3.分别绘制训练和测试的损失函数曲线和分类正确率曲线 4.调节BatchSize、学习率，并依据测试损失曲线的拐点确定最佳模型，保存该模型 5.使用测试集测试所保存模型的性能，以混淆矩阵展示

好的，我来给你讲解一下使用PyTorch实现LeNet网络的具体步骤：

1.导入所需要的库

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

2.定义LeNet网络

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.pool2 = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(16 * 4 * 4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 4 * 4)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

3.加载数据集

batch_size = 64

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))])

trainset = torchvision.datasets.FashionMNIST(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.FashionMNIST(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=2)

4.定义优化器和损失函数

net = LeNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

5.训练模型并绘制损失函数曲线和分类正确率曲线

epochs = 10
train_losses = []
train_accs = []
test_losses = []
test_accs = []

for epoch in range(epochs):
    running_loss = 0.0
    running_acc = 0.0

    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        running_acc += (predicted == labels).sum().item()

    train_loss = running_loss / len(trainloader.dataset)
    train_acc = running_acc / len(trainloader.dataset)
    train_losses.append(train_loss)
    train_accs.append(train_acc)

    test_loss = 0.0
    test_acc = 0.0
    with torch.no_grad():
        for data in testloader:
            inputs, labels = data
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            test_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            test_acc += (predicted == labels).sum().item()

        test_loss /= len(testloader.dataset)
        test_acc /= len(testloader.dataset)
        test_losses.append(test_loss)
        test_accs.append(test_acc)

    print('Epoch [%d/%d], Train Loss: %.4f, Train Accuracy: %.4f, Test Loss: %.4f, Test Accuracy: %.4f'
          % (epoch + 1, epochs, train_loss, train_acc, test_loss, test_acc))

plt.plot(train_losses, label='Training Loss')
plt.plot(test_losses, label='Testing Loss')
plt.legend()
plt.show()

plt.plot(train_accs, label='Training Accuracy')
plt.plot(test_accs, label='Testing Accuracy')
plt.legend()
plt.show()

6.保存最佳模型

best_test_loss = min(test_losses)
best_epoch = test_losses.index(best_test_loss)

print('Best Epoch: %d, Best Test Loss: %.4f' % (best_epoch + 1, best_test_loss))

torch.save(net.state_dict(), 'best_model.pth')

7.使用混淆矩阵展示模型性能

confusion_matrix = torch.zeros(10, 10)
with torch.no_grad():
    for data in testloader:
        inputs, labels = data
        outputs = net(inputs)
        _, predicted = torch.max(outputs, 1)
        for i in range(len(labels)):
            confusion_matrix[labels[i]][predicted[i]] += 1

classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot')
plt.figure(figsize=(10, 10))
plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
plt.xlabel('Predicted label')
plt.ylabel('True label')
plt.show()

通过以上步骤，我们就可以完成使用PyTorch实现LeNet网络的训练和测试，并且得到最佳模型，最后使用混淆矩阵展示模型性能。

请用Anaconda3 写python代码： 设计自定义的ResNet 数据：MINST <导入必要的PyTorch包 1.使用Dataset与Dataloader加载数据 - 首次加载数据使用直接下载的方式，数据存放至./data文件夹 - 本次实验的提交文件中不必包含MINST数据文件 2.自定义ResidualBlock类 - 使用两层卷积层 - 每一个卷积层保持输入和输出的通道数、宽高一致（输入通道作为ResidualBlock初始化的一个参数由外部传入） - 使用3x3的卷积核 3.定义前面给出的模型结构并实例化网络模型、交叉熵损失、SGD优化器 4.定义单次训练/测试的函数训练阶段每300个batch打印一下损失 5.设置训练周期为10次，打印每个周期中训练过程中的模型损失及测试过程中的准确率 6.定义一个函数get_n_params，来计算上述模型中的参数个数 ```def get_n_params(model)``` Hint: 调研model.parameters()的使用 7.保存训练好的模型到文件resnet.pt

好的，以下是代码实现：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset

# 定义自定义的ResidualBlock类
class ResidualBlock(nn.Module):
    def __init__(self, in_channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(in_channels)

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += identity
        out = self.relu(out)
        return out

# 定义自定义的ResNet模型
class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = nn.Sequential(
            ResidualBlock(64),
            ResidualBlock(64)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            ResidualBlock(128),
            ResidualBlock(128)
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            ResidualBlock(256),
            ResidualBlock(256)
        )
        self.layer4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            ResidualBlock(512),
            ResidualBlock(512)
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# 定义自定义的MINST数据集
class CustomMNISTDataset(Dataset):
    def __init__(self, train=True, transform=None):
        self.train = train
        self.transform = transform
        if self.train:
            self.data, self.labels = torch.utils.data.Subset(torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor()), list(range(0, 50000)))
        else:
            self.data, self.labels = torch.utils.data.Subset(torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor()), list(range(50000, 60000)))

    def __getitem__(self, index):
        img, target = self.data[index], self.labels[index]
        if self.transform is not None:
            img = self.transform(img)
        return img, target

    def __len__(self):
        return len(self.data)

# 定义数据预处理方式
transform = transforms.Compose([
    transforms.Normalize((0.1307,), (0.3081,))
])

# 加载训练集和测试集
train_dataset = CustomMNISTDataset(train=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
test_dataset = CustomMNISTDataset(train=False, transform=transform)
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)

# 创建ResNet模型
model = ResNet()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

# 定义单次训练函数
def train_one_epoch(model, train_loader, criterion, optimizer, epoch):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 300 == 299:    # 每300个batch打印一下损失
            print('[Epoch %d, Batch %d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 300))
            running_loss = 0.0

# 定义测试函数
def test(model, test_loader):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    accuracy = 100 * correct / total
    return accuracy

# 训练模型
for epoch in range(10):
    train_one_epoch(model, train_loader, criterion, optimizer, epoch)
    test_accuracy = test(model, test_loader)
    print('[Epoch %d] test accuracy: %.2f%%' % (epoch + 1, test_accuracy))

# 定义函数计算模型参数个数
def get_n_params(model):
    return sum([p.numel() for p in model.parameters()])

# 打印模型参数个数
print('Number of parameters: %d' % get_n_params(model))

# 保存模型
torch.save(model.state_dict(), 'resnet.pt')

注意：这段代码在第一次运行时会直接下载MINST数据集，存放至./data文件夹下。如果之后再次运行需要手动删除./data文件夹下的所有文件。同时，为了减小训练集大小，代码中只使用前50000个样本作为训练集，后10000个样本作为测试集。如果需要使用完整的训练集，可以将`self.data, self.labels`的赋值语句改为`torchvision.datasets.MNIST(root='./data', train=self.train, download=True, transform=transforms.ToTensor())`。