RNN识别mnist的详细代码

好的，以下是使用RNN识别MNIST数据集的详细代码。我们将使用PyTorch来实现这个任务。

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义超参数
input_size = 28
sequence_length = 28
hidden_size = 128
num_layers = 2
num_classes = 10
batch_size = 100
num_epochs = 5
learning_rate = 0.001

# 加载MNIST数据集并进行预处理
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())

# 使用DataLoader加载数据
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=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):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        out, _ = self.rnn(x, h0)
        out = self.fc(out[:, -1, :])
        return out

# 将模型加载到GPU上
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):
        images = images.reshape(-1, sequence_length, input_size).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个批次打印一次日志
        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:
        images = images.reshape(-1, sequence_length, input_size).to(device)
        labels = labels.to(device)
        
        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 10000 test images: {} %'.format(100 * correct / total))

这段代码首先加载MNIST数据集并进行预处理。然后定义了一个RNN模型，将其加载到GPU上，并定义了损失函数和优化器。接下来进行训练和测试，最后输出测试结果。

需要注意的是，在训练和测试阶段，每个输入图像都被reshape为形状为(batch_size, sequence_length, input_size)的张量，其中sequence_length表示序列长度（即图像的高度），input_size表示每个时间步的输入大小（即图像的宽度）。这样做是为了适应RNN的输入要求。