LSTM动作识别 pytorch

LSTM是一种循环神经网络，可以用于时间序列数据的建模和预测。在动作识别中，可以使用LSTM来识别人体动作。

在PyTorch中，可以使用torch.nn.LSTM模块来构建LSTM模型。需要将输入数据转换为三维张量，分别表示序列长度、批次大小和特征维度。可以使用torch.utils.data.Dataset和torch.utils.data.DataLoader来加载和处理数据。

以下是一个简单的LSTM动作识别模型的示例代码：

import torch
import torch.nn as nn

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
        c0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

# 定义模型参数
input_size = 10
hidden_size = 20
num_classes = 5

# 初始化模型
model = LSTMModel(input_size, hidden_size, num_classes)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)

        # 前向传播
        outputs = model(inputs)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for inputs, labels in test_loader:
        inputs = inputs.to(device)
        labels = labels.to(device)
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy: {} %'.format(100 * correct / total))