数据挖掘 - PyTorch 中的简单线性回归 - 吾爱随笔录

我正在使用 PyTorch 执行简单的线性回归，但我的模型无法正确拟合训练数据。请查看代码以找出错误。

import torch
from torch import nn
from torch.optim import SGD, Adam
from torch.autograd import Variable

class Linear_Reg(nn.Module):
    def __init__(self):
        super(Linear_Reg, self).__init__()
        self.linear = nn.Linear(1,1)
        
    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred
    
net = Linear_Reg()

Xt = Variable(torch.Tensor(X[:,0]))
yt = Variable(torch.Tensor(y))
Xt = Xt.view(-1,1)

criterion = nn.MSELoss()
optimizer = Adam(net.parameters(), lr=0.001)

EPOCHS = 500
for epoch in range(EPOCHS):
    pred_y = net(Xt)
    loss = criterion(pred_y, yt)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    print('Eopch: {}, \t\t loss: {}'.format(epoch, loss.data.item()))

损失从~68.88减少到~30.26

结果拟合是这样的：

xxt = torch.arange(5,23)
with torch.no_grad():
    a = net(xxt.reshape(-1,1).float())
plt.scatter(X[:,1], y, s=30, c='r', marker='x', linewidths=1)
plt.plot(xxt.data.numpy(),a.data.numpy(), label='Linear regression (Gradient descent)')

这里有什么问题？

model = Sequential([ Dense(1,activation='linear',input_shape=(1,)) ]) sgd = optimizers.Adam(lr=0.001) model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae']) model.fit(test_df.x, test_df.y, validation_split = .2,epochs=500, batch_size=1) plt.scatter(test_df.x,test_df.y,s=30,c='r',marker='x') x = np.linspace(4,25,100) y = model.get_weights()[0][0][0]*x+model.get_weights()[1][0] plt.plot(x, y) plt.ylim([-5,50]) plt.xlim([4.5,23])