详细解释代码import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms from torch.utils.data import DataLoader # 图像预处理 transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) # 加载数据集 trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) trainloader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=0) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) testloader = DataLoader(testset, batch_size=128, shuffle=False, num_workers=0) # 构建模型 class RNNModel(nn.Module): def init(self): super(RNNModel, self).init() self.rnn = nn.RNN(input_size=3072, hidden_size=512, num_layers=2, batch_first=True) self.fc = nn.Linear(512, 10) def forward(self, x): # 将输入数据reshape成(batch_size, seq_len, feature_dim) x = x.view(-1, 3072, 1).transpose(1, 2) x, _ = self.rnn(x) x = x[:, -1, :] x = self.fc(x) return x net = RNNModel() # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.001) # 训练模型 loss_list = [] acc_list = [] for epoch in range(30): # 多批次循环 running_loss = 0.0 correct = 0 total = 0 for i, data in enumerate(trainloader, 0): # 获取输入 inputs, labels = data # 梯度清零 optimizer.zero_grad() # 前向传播,反向传播,优化 outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # 打印统计信息 running_loss += loss.item() _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() acc = 100 * correct / total acc_list.append(acc) loss_list.append(running_loss / len(trainloader)) print('[%d] loss: %.3f, acc: %.3f' % (epoch + 1, running_loss / len(trainloader), acc)) print('Finished Training') torch.save(net.state_dict(), 'rnn1.pt') # 绘制loss变化曲线和准确率变化曲线 import matplotlib.pyplot as plt fig, axs = plt.subplots(2, 1, figsize=(10, 10)) axs[0].plot(loss_list) axs[0].set_title("Training Loss") axs[0].set_xlabel("Epoch") axs[0].set_ylabel("Loss") axs[1].plot(acc_list) axs[1].set_title("Training Accuracy") axs[1].set_xlabel("Epoch") axs[1].set_ylabel("Accuracy") plt.show() # 测试模型 correct = 0 total = 0 with torch.no_grad(): for data in testloader: images, labels = data outputs = net(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))
时间: 2023-12-25 16:04:43 浏览: 201
torchvision.zip
这段代码是一个基于RNN(循环神经网络)的模型来对CIFAR-10数据集进行分类。以下是详细的解释:
1. 导入需要的库:
```
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
```
2. 图像预处理:
```
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
```
这里使用了transforms.Compose()函数来将多个预处理操作组合在一起。首先将图像转换成张量,然后进行归一化,使其值在[-1,1]之间。
3. 加载数据集:
```
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=0)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = DataLoader(testset, batch_size=128, shuffle=False, num_workers=0)
```
这里使用了torchvision.datasets.CIFAR10()函数来加载CIFAR-10数据集,train=True表示加载训练集,train=False表示加载测试集。通过DataLoader()函数将数据集转换成一个可迭代的对象,可以方便地进行批处理、数据增强等操作。
4. 构建模型:
```
class RNNModel(nn.Module):
def __init__(self):
super(RNNModel, self).__init__()
self.rnn = nn.RNN(input_size=3072, hidden_size=512, num_layers=2, batch_first=True)
self.fc = nn.Linear(512, 10)
def forward(self, x):
x = x.view(-1, 3072, 1).transpose(1, 2)
x, _ = self.rnn(x)
x = x[:, -1, :]
x = self.fc(x)
return x
net = RNNModel()
```
这里定义了一个RNNModel类,它继承了nn.Module类。在__init__()方法中,我们定义了一个RNN层和一个全连接层来构建模型。在forward()方法中,我们首先将输入数据reshape成(batch_size, seq_len, feature_dim)的形状,然后经过RNN层得到输出,最后经过全连接层得到最终的分类结果。
5. 定义损失函数和优化器:
```
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
```
这里使用交叉熵损失函数和Adam优化器来训练模型。
6. 训练模型:
```
loss_list = []
acc_list = []
for epoch in range(30):
running_loss = 0.0
correct = 0
total =
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