制作数据集

from sklearn.datasets import make_circles
n_samples=1000
x, y = make_circles(n_samples, noise=0.03, random_state=42)
X = torch.from_numpy(x).type(torch.float)
Y = torch.from_numpy(y).type(torch.float)
from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(X, Y, test_size=0.2, random_state=42)

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模型（预测圆）（我们知道圆s的方程（x**2 + y**2 = r**2））

class cp(nn.Module):
    def __init__(self):
        super().__init__()
        self.r = nn.Parameter(torch.randn(1, requires_grad = True, dtype=torch.float))
    def forward(self, x):
        return x[:, 0]**2 + x[:, 1]**2 - self.r**2
torch.manual_seed(42)
m = cp()
loss_fn = nn.BCEWithLogitsLoss()
opt = torch.optim.SGD(params = m.parameters(), lr=0.1)

型

训练循环

e = 1000
for i in range(e+1):
    m.train()
    p = m(xtrain).squeeze()
    f = torch.round(torch.sigmoid(p))
    l = loss_fn(p, ytrain)
    acc = accuracy_fn(f, ytrain)
    opt.zero_grad()
    l.backward()
    opt.step()
    if(i%10==0):
        with torch.inference_mode():
            tp = m(xtest).squeeze()
            tf= torch.round(torch.sigmoid(tp))
            tl = loss_fn(tp, ytest)
            tacc = accuracy_fn(tf, ytest)
            print(f"epoch: {i} | train loss: {l:.4f} | train acc: {acc} | test loss: {tl:.4f} | test acc: {tacc}")
            if(i%100==0): print(m.state_dict())

型
如果我以上述方式训练我的模型，它预测1为0，O为1。即ytrain[：10] = tensor([1., 0., 0., 0., 1., 0., 1., 1., 0., 0.])。预测值为：torch.round（torch.sigmoid（m（xtrain[：10]）））= tensor([0., 1., 1., 1., 0., 1., 0., 0., 1., 1.])但是，当我绘制圆时，它的预测正确（圆的半径预测正确）predicted circle
我试图预测一个圆.所有里面是0的和外面是1的.但经过训练，其预测在相反的顺序（即0的外面和1的里面）.请检查一次.我提供了整个代码.