麻雀算法优化GRU的pytorch代码
时间: 2023-09-20 18:12:45 浏览: 67
以下是使用麻雀算法优化GRU的PyTorch代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
class GRU(nn.Module):
def __init__(self, input_size, hidden_size):
super(GRU, self).__init__()
self.hidden_size = hidden_size
self.gru = nn.GRU(input_size, hidden_size)
def forward(self, input, hidden):
output, hidden = self.gru(input, hidden)
return output, hidden
def initHidden(self):
return torch.zeros(1, 1, self.hidden_size)
# 定义麻雀算法
def sparrow_algorithm(model, loss_fn, optimizer, input_seq, target_seq, num_epochs):
for epoch in range(num_epochs):
optimizer.zero_grad()
hidden = model.initHidden()
loss = 0
for i in range(len(input_seq)):
output, hidden = model(input_seq[i], hidden)
loss += loss_fn(output, target_seq[i])
loss.backward()
optimizer.step()
if (epoch+1) % 10 == 0:
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
return model
# 示例数据
input_seq = [
torch.tensor([[0.1], [0.2], [0.3]]),
torch.tensor([[0.4], [0.5], [0.6]]),
torch.tensor([[0.7], [0.8], [0.9]])
]
target_seq = [
torch.tensor([[0.2], [0.3], [0.4]]),
torch.tensor([[0.5], [0.6], [0.7]]),
torch.tensor([[0.8], [0.9], [1.0]])
]
# 初始化模型、损失函数和优化器
input_size = 1
hidden_size = 4
model = GRU(input_size, hidden_size)
loss_fn = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)
# 使用麻雀算法优化模型
num_epochs = 100
model = sparrow_algorithm(model, loss_fn, optimizer, input_seq, target_seq, num_epochs)
```
这段代码定义了一个简单的GRU模型,并使用麻雀算法来优化模型参数。输入序列和目标序列用于训练模型,使用均方误差损失函数和随机梯度下降优化器进行训练。在每个epoch中计算损失并更新模型参数,最后输出训练结果。
请注意,这只是一个简单示例,实际情况下可能需要根据具体问题进行适当的调整和修改。