使用PyTorch处理多维特征输入数据的完美实现

class Model(torch.nn.Module):
	def __init__(self):
		super(Model, self).__init__()
		self.linear = torch.nn.Linear(8, 1) 
		self.sigmoid = torch.nn.Sigmoid()
	def forward(self, x):
		x = self.sigmoid(self.linear(x)) 
		return x
model = Model()

class Model(torch.nn.Module):
	def __init__(self):
		super(Model, self).__init__()
		self.linear1 = torch.nn.Linear(8, 6) 
		self.linear2 = torch.nn.Linear(6, 4) 
		self.linear3 = torch.nn.Linear(4, 1) 
		self.sigmoid = torch.nn.Sigmoid()
	def forward(self, x):
		x = self.sigmoid(self.linear1(x)) 
		x = self.sigmoid(self.linear2(x)) 
		x = self.sigmoid(self.linear3(x)) 
		return x
model = Model()

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn import datasets
from sklearn.model_selection import train_test_split
import numpy as np
# 载入Diabetes数据集
diabetes = datasets.load_diabetes()
# 将数据集拆分为特征和目标
X = diabetes.data  # 特征
y = diabetes.target  # 目标
# 数据预处理
X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)  # 特征标准化
# 拆分数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 转换为PyTorch张量
X_train = torch.FloatTensor(X_train)
y_train = torch.FloatTensor(y_train).view(-1, 1)  # 将目标变量转换为列向量
X_test = torch.FloatTensor(X_test)
y_test = torch.FloatTensor(y_test).view(-1, 1)
# 构建包含多个线性层的神经网络模型
class DiabetesModel(nn.Module):
    def __init__(self, input_size):
        super(DiabetesModel, self).__init__()
        self.fc1 = nn.Linear(input_size, 64)  # 第一个线性层
        self.fc2 = nn.Linear(64, 32)  # 第二个线性层
        self.fc3 = nn.Linear(32, 1)  # 最终输出线性层
    def forward(self, x):
        x = torch.relu(self.fc1(x))  # ReLU激活函数
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x
# 初始化模型
input_size = X_train.shape[1]
model = DiabetesModel(input_size)
# 定义损失函数和优化器
criterion = nn.MSELoss()  # 均方误差损失
optimizer = optim.SGD(model.parameters(), lr=0.01)
# 训练模型
num_epochs = 1000
for epoch in range(num_epochs):
    # 前向传播
    outputs = model(X_train)
    loss = criterion(outputs, y_train)
    # 反向传播和优化
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if (epoch + 1) % 100 == 0:
        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
# 在测试集上进行预测
model.eval()
with torch.no_grad():
    y_pred = model(X_test)
# 计算性能指标
mse = nn.MSELoss()(y_pred, y_test)
print(f"均方误差 (MSE): {mse.item():.4f}")