RNN做cifar-10图像分类任务pytorch版本效果好且画出loss变化曲线以及准确率变化曲线的代码

RNN不太适合图像分类任务，因为图像数据的维度较高，需要大量的计算资源和时间。但是，可以通过将图像数据展平成一维向量，然后将其输入到RNN中进行分类。以下是使用PyTorch实现一个基于RNN的图像分类器来处理CIFAR-10数据集的代码示例：

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=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

# 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(10):  # 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')

# 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))

在这个例子中，我们将图像数据reshape成一维向量，然后通过一个RNN模型进行分类。模型由两层RNN和一个全连接层组成。在训练过程中，我们记录每个epoch的loss和准确率，并将它们绘制成曲线。最后，在测试集上评估模型的准确率。

可以使用Matplotlib库绘制loss和准确率的变化曲线，如上所示。