有几个错误：
1.缺少optimizer.step()：
optimizer.step()基于反向传播的梯度和其他累积动量等更新参数。
1.softmax与CrossEntropy损耗的用法：
Pytorch CrossEntropyLoss标准将nn.LogSoftmax()和nn.NLLLoss()组合在一个类中。也就是说，它应用softmax，然后取负对数。所以在你的例子中，你需要softmax（softmax（output））。正确的方法是使用linear输出层，而training和使用softmax层或仅使用argmax进行预测。
1.小网络丢包率高：
这导致了拟合不足。
下面是更正的代码：

import torch
import torch.nn as nn

torch.manual_seed(1)

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.input = nn.Linear(2,4)
        self.hidden = nn.Linear(4,4)
        self.output = nn.Linear(4,2)
        self.relu = nn.ReLU()
        self.softmax = nn.Softmax(dim=1)
        # self.dropout = nn.Dropout(0.0)
    def forward(self,x):
        x = self.input(x)
        # x = self.dropout(x)
        x = self.relu(x)
        x = self.hidden(x)
        # x = self.dropout(x)
        x = self.relu(x)
        x = self.output(x)
        # x = self.softmax(x)
        return x

    def predict(self, x):
        with torch.no_grad():
            out = self.forward(x)
        return self.softmax(out)

X = torch.tensor([[1,0],[1,0],[0,1],[0,1]],dtype=torch.float)

net = Net()

criterion = nn.CrossEntropyLoss()

opt = torch.optim.Adam(net.parameters(), lr=0.001)

for i in range(100000):
    opt.zero_grad()
    y = net(X)
    loss = criterion(y,torch.argmax(X,dim=1))
    loss.backward()
    # This was missing before
    opt.step()
    if i%500 ==0:
        print("Epoch: ",i)
        pred = net.predict(X)
        print(f'prediction: {torch.argmax(pred, dim=1).detach().numpy().tolist()}, actual: {torch.argmax(X,dim=1)}')
        print("Loss: ", loss.item())

输出量：

Epoch:  0
prediction: [0, 0, 0, 0], actual: tensor([0, 0, 1, 1])
Loss:  0.7042869329452515
Epoch:  500
prediction: [0, 0, 1, 1], actual: tensor([0, 0, 1, 1])
Loss:  0.1166711300611496
Epoch:  1000
prediction: [0, 0, 1, 1], actual: tensor([0, 0, 1, 1])
Loss:  0.05215628445148468
Epoch:  1500
prediction: [0, 0, 1, 1], actual: tensor([0, 0, 1, 1])
Loss:  0.02993333339691162
Epoch:  2000
prediction: [0, 0, 1, 1], actual: tensor([0, 0, 1, 1])
Loss:  0.01916157826781273
Epoch:  2500
prediction: [0, 0, 1, 1], actual: tensor([0, 0, 1, 1])
Loss:  0.01306679006665945
Epoch:  3000
prediction: [0, 0, 1, 1], actual: tensor([0, 0, 1, 1])
Loss:  0.009280549362301826
.
.
.