训练集为路径+地图+起点+终点,训练bilstm算法的模型的代码
时间: 2023-09-29 14:06:12 浏览: 50
以下是使用 Pytorch 实现的 BiLSTM 模型训练代码,该模型的输入为路径、地图、起点和终点,输出为路径规划结果:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data
# 定义模型
class BiLSTM(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(BiLSTM, self).__init__()
self.hidden_size = hidden_size
self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True, bidirectional=True)
self.fc = nn.Linear(hidden_size * 2, output_size)
def forward(self, path, map, start, end):
input = torch.cat((path, map, start, end), dim=2)
output, _ = self.lstm(input)
output = self.fc(output)
return output
# 定义超参数
input_size = 128
hidden_size = 64
output_size = 4
batch_size = 32
learning_rate = 0.001
num_epochs = 10
# 定义数据集
train_data = [(path1, map1, start1, end1), (path2, map2, start2, end2), ...]
train_loader = Data.DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True)
# 定义模型、损失函数和优化器
model = BiLSTM(input_size, hidden_size, output_size)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 开始训练
for epoch in range(num_epochs):
for i, (path, map, start, end) in enumerate(train_loader):
output = model(path, map, start, end)
loss = criterion(output.view(-1, output_size), label.view(-1))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_data)//batch_size+1, loss.item()))
# 保存模型
torch.save(model.state_dict(), 'bilstm_model.ckpt')
```
需要根据实际情况调整模型的输入和输出大小、数据集的格式以及超参数的值。同时,可以根据需要添加正则化、dropout、批标准化等技术来提高模型的性能和稳定性。
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