基于pytorch对stml网络优化的代码

以下是一个简单的基于PyTorch实现的LSTM网络优化代码，包括使用GPU加速：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义LSTM网络
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        # 初始化隐藏状态和单元状态
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).cuda()
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).cuda()

        # 前向传播，获取LSTM输出
        out, _ = self.lstm(x, (h0, c0))

        # 取出最后一个时间步的输出作为预测值
        out = self.fc(out[:, -1, :])
        return out

# 超参数设置
input_size = 28
hidden_size = 128
num_layers = 2
output_size = 10
lr = 0.001
batch_size = 100
num_epochs = 5

# 加载MNIST数据集
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())

# 创建数据加载器
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)

# 创建LSTM网络
lstm = LSTM(input_size, hidden_size, num_layers, output_size).cuda()

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

# 训练LSTM网络
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 将数据移到GPU上
        images = images.reshape(-1, 28, 28).cuda()
        labels = labels.cuda()

        # 前向传播，计算损失函数，反向传播，更新参数
        optimizer.zero_grad()
        outputs = lstm(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 每隔100步输出一次训练结果
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 测试LSTM网络
lstm.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        # 将数据移到GPU上
        images = images.reshape(-1, 28, 28).cuda()
        labels = labels.cuda()

        # 前向传播，计算预测值
        outputs = lstm(images)
        _, predicted = torch.max(outputs.data, 1)

        # 统计正确预测的数量和总数量
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

这个代码首先定义了一个LSTM类，包含了LSTM网络的构造函数和前向传播函数。然后设置了超参数，加载了MNIST数据集，并创建了数据加载器。接着创建了LSTM网络，定义了损失函数和优化器。在训练过程中，将数据移到GPU上，并进行前向传播、计算损失函数、反向传播和参数更新。在测试过程中，同样将数据移到GPU上，并进行前向传播和预测值计算，统计正确预测的数量和总数量，输出测试准确率。