编写pytorch代码,定义LSTMAttention模型,定义个FA_CPSO优化算法,用萤火虫随机扰动的参数和混沌映射系数调整粒子群参数,在特征训练集X_train.csv和标签训练集y_train.csv上训练模型,调用优化算法去优化模型的损失函数,将最优的参数设置给模型,在特征测试集X_test.csv和标签测试集y_test.csv上测试模型,进一步优化模型,将优化后的参数设置给模型,
时间: 2024-03-22 08:40:42 浏览: 28
以下是一个基本的PyTorch代码,用于定义LSTMAttention模型和FA_CPSO优化算法,并在训练和测试数据上训练和测试模型:
```python
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from torch.optim import Optimizer
from typing import Optional, List
# 定义LSTMAttention模型
class LSTMAttention(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.hidden_size = hidden_size
self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
self.attention = nn.Linear(hidden_size, 1)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
output, (h, c) = self.lstm(x)
attention_weights = torch.softmax(self.attention(output), dim=1)
context_vector = torch.sum(attention_weights * output, dim=1)
output = self.fc(context_vector)
return output
# 定义FA_CPSO优化算法
class FA_CPSO(Optimizer):
def __init__(self, params, lr=0.01, w=0.5, c1=1.0, c2=1.0, alpha=0.2, beta=0.2, gamma=0.2, sigma=0.1):
defaults = dict(lr=lr, w=w, c1=c1, c2=c2, alpha=alpha, beta=beta, gamma=gamma, sigma=sigma)
super(FA_CPSO, self).__init__(params, defaults)
def step(self, closure=None):
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
lr = group['lr']
w = group['w']
c1 = group['c1']
c2 = group['c2']
alpha = group['alpha']
beta = group['beta']
gamma = group['gamma']
sigma = group['sigma']
for p in group['params']:
if p.grad is None:
continue
d_p = p.grad.data
p_data = p.data
if len(p_data.shape) == 1:
# 一维情况下,粒子只有一个维度
particle = p_data.unsqueeze(0)
else:
# 多维情况下,粒子有多个维度
particle = p_data.view(1, -1)
# 计算适应度值
fitness = -1 * loss
# 更新萤火虫位置
for i in range(len(particle)):
for j in range(len(particle[i])):
r = torch.randn(1)
if r < w:
# 萤火虫随机移动
particle[i][j] += lr * (torch.rand(1) - 0.5) * sigma
else:
# 萤火虫受其他萤火虫的吸引
for q in group['params']:
if q is p:
continue
q_data = q.data
if len(q_data.shape) == 1:
# 一维情况下,萤火虫只有一个维度
field = q_data.unsqueeze(0)
else:
# 多维情况下,萤火虫有多个维度
field = q_data.view(1, -1)
# 计算吸引力
distance = torch.norm(particle[i] - field)
attraction = -1 * gamma * (particle[i][j] - field[0][j]) / (1 + alpha * distance ** 2)
# 更新萤火虫位置
particle[i][j] += lr * attraction + c1 * (torch.rand(1) - 0.5) * sigma + c2 * beta * (particle[i][j] - p_data[j])
# 更新模型参数
if len(p_data.shape) == 1:
p.data = particle[0]
else:
p.data = particle.view(p_data.shape)
return loss
# 加载数据集
X_train = pd.read_csv('X_train.csv')
y_train = pd.read_csv('y_train.csv')
X_test = pd.read_csv('X_test.csv')
y_test = pd.read_csv('y_test.csv')
# 定义模型和优化器
model = LSTMAttention(input_size=X_train.shape[2], hidden_size=64, output_size=1)
optimizer = FA_CPSO(model.parameters(), lr=0.01)
# 训练模型
for epoch in range(100):
# 随机抽取一个batch的数据
idx = np.random.choice(X_train.shape[0], size=32, replace=False)
X_batch = torch.tensor(X_train.iloc[idx].values, dtype=torch.float32)
y_batch = torch.tensor(y_train.iloc[idx].values, dtype=torch.float32)
# 前向传播和计算损失
y_pred = model(X_batch)
loss = nn.MSELoss()(y_pred, y_batch)
# 反向传播和优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 测试模型
with torch.no_grad():
X_test_tensor = torch.tensor(X_test.values, dtype=torch.float32)
y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)
y_pred = model(X_test_tensor)
loss = nn.MSELoss()(y_pred, y_test_tensor)
# 再次优化模型
for epoch in range(100):
# 随机抽取一个batch的数据
idx = np.random.choice(X_train.shape[0], size=32, replace=False)
X_batch = torch.tensor(X_train.iloc[idx].values, dtype=torch.float32)
y_batch = torch.tensor(y_train.iloc[idx].values, dtype=torch.float32)
# 前向传播和计算损失
y_pred = model(X_batch)
loss = nn.MSELoss()(y_pred, y_batch)
# 反向传播和优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 设置最优参数
best_params = {}
for name, param in model.named_parameters():
best_params[name] = param.data.numpy()
# 将优化后的参数设置给模型
with torch.no_grad():
for name, param in model.named_parameters():
param.data = torch.tensor(best_params[name])
# 再次测试模型
with torch.no_grad():
X_test_tensor = torch.tensor(X_test.values, dtype=torch.float32)
y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)
y_pred = model(X_test_tensor)
loss = nn.MSELoss()(y_pred, y_test_tensor)
```
这段代码中,我们首先定义了一个LSTMAttention模型,用于处理序列数据。然后,我们定义了一个FA_CPSO优化算法,用于优化模型的损失函数。在训练模型时,我们通过随机抽取一个batch的数据来更新模型参数。在测试模型时,我们使用测试数据集来评估模型的性能。最后,在重新训练一段时间后,我们将优化后的参数设置给模型,并再次测试模型以获得最终的性能评估。