Pytorch回归问题的图解预测和实际结果

import torch

# 定义回归模型
class RegressionModel(torch.nn.Module):
    def __init__(self):
        super(RegressionModel, self).__init__()
        self.linear = torch.nn.Linear(1, 1)  # 输入维度为1，输出维度为1

    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred

# 创建模型实例
model = RegressionModel()

# 加载训练好的模型参数
model.load_state_dict(torch.load('model.pth'))

# 输入特征数据
x = torch.tensor([[1.0]])  # 例如输入特征为1.0

# 进行图解预测
y_pred = model.forward(x)

# 输出预测结果
print("预测结果:", y_pred.item())

# 比较预测结果与实际结果
y_actual = 2.0  # 例如实际结果为2.0
mse = torch.nn.functional.mse_loss(y_pred, torch.tensor([[y_actual]]))
mae = torch.nn.functional.l1_loss(y_pred, torch.tensor([[y_actual]]))
print("均方误差:", mse.item())
print("平均绝对误差:", mae.item())