RNN做cifar-10图像分类任务pytorch版本的代码

以下是使用PyTorch实现的RNN模型对CIFAR-10图像分类任务的代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义超参数
input_size = 32  # 输入图像大小
hidden_size = 128  # 隐藏层大小
num_layers = 2  # RNN层数
num_classes = 10  # 输出类别数
batch_size = 100
num_epochs = 10
learning_rate = 0.001

# 加载数据集
transform = transforms.Compose(
    [transforms.Resize((input_size, input_size)),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义RNN模型
class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)
        
    def forward(self, x):
        # 初始化隐藏状态
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        
        # 前向传播 RNN
        out, _ = self.rnn(x, h0)
        
        # 解码最后一个时间步的隐状态
        out = self.fc(out[:, -1, :])
        return out

# 实例化模型并将其移到GPU上（如果有GPU）
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 将数据移到GPU上（如果有GPU）
        images = images.reshape(-1, input_size, input_size*3).to(device)
        labels = labels.to(device)
        
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        # 每100个batch打印一次训练状态
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        # 将数据移到GPU上（如果有GPU）
        images = images.reshape(-1, input_size, input_size*3).to(device)
        labels = labels.to(device)
        
        # 前向传播
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    
    print('Test Accuracy of the model on the {} test images: {} %'.format(total, 100 * correct / total))

以上代码使用了一个2层的RNN模型对CIFAR-10数据集进行图像分类。请注意，这个模型可能无法取得最好的性能，因为对于图像分类任务，通常使用卷积神经网络（CNN）而不是循环神经网络（RNN）。