我已经修改了我在 Pytorch github 上找到的代码帽以适合我的数据,但是我的损失结果很大,每次迭代它们都会变大,后来变成 nan。代码没有给我任何错误,也没有损失结果,也没有预测。我有另一个处理简单线性回归的代码,一切正常。我想我在这里遗漏了一些简单的东西,但我看不到它。任何帮助,将不胜感激。
代码:
import sklearn.linear_model as lm
from sklearn.preprocessing import PolynomialFeatures
import torch
import torch.autograd
import torch.nn.functional as F
from torch.autograd import Variable
train_data = torch.Tensor([
[40, 6, 4],
[44, 10, 4],
[46, 12, 5],
[48, 14, 7],
[52, 16, 9],
[58, 18, 12],
[60, 22, 14],
[68, 24, 20],
[74, 26, 21],
[80, 32, 24]])
test_data = torch.Tensor([
[6, 4],
[10, 5],
[4, 8]])
x_train = train_data[:,1:3]
y_train = train_data[:,0]
POLY_DEGREE = 3
input_size = 2
output_size = 1
poly = PolynomialFeatures(input_size * POLY_DEGREE, include_bias=False)
x_train_poly = poly.fit_transform(x_train.numpy())
class Model(torch.nn.Module):
def __init__(self):
super(Model, self).__init__()
self.fc = torch.nn.Linear(poly.n_output_features_, output_size)
def forward(self, x):
return self.fc(x)
model = Model()
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
losses = []
for i in range(10):
optimizer.zero_grad()
outputs = model(Variable(torch.Tensor(x_train_poly)))
print(outputs)
loss = criterion(outputs, Variable(y_train))
print(loss.data[0])
losses.append(loss.data[0])
loss.backward()
optimizer.step()
if loss.data[0] < 1e-4:
break
print('n_iter', i)
print(loss.data[0])
plt.plot(losses)
plt.show()
输出:
[393494300459008.0,inf,inf,inf,南,南,南,南,南,南]
硝
9楠