Pytorch简单线性Sigmoid网络不学习

Question

我正在学习pytorch，并试图将网络训练为异或门。一切运行顺利，但就是不学习。它确实改变了它的权重，但是它会收敛到超出预期结果的每一个输入的结果中。

我尝试了许多学习率和权重初始化。

因此，输入是A和B门，如果两者相等，则返回1，否则返回0，如下所示：

    [0,0] => 1
    [0,1] => 0
    [1,0] => 0
    [1,1] => 1

这是我对模型建模和训练的尝试：

    import torch as torch
    import torch.nn as nn
    
    class Network(nn.Module):
        
        def __init__(self):
            super(Network, self).__init__()
            self.x1 = nn.Linear(2,4)
            self.s1 = nn.Sigmoid()
            self.x2 = nn.Linear(4,1)
            self.s2 = nn.Sigmoid()
        
        def init(self):
            nn.init.uniform_(self.x1.weight)
            nn.init.uniform_(self.x2.weight)
    
        def forward(self, feats):
            f1 = torch.tensor(feats).float()
            xr1= self.x1(f1)
            xs1= self.s1(xr1)
            xr2= self.x2(xs1)
            out= self.s2(xr2)        
            return out  
    
        def train(self,val_expected,feats_next):
            val_expected_tensor = torch.tensor(val_expected)
            criterion = nn.MSELoss()
            optimizer = torch.optim.SGD(self.parameters(), lr=0.01)
            def closure():
                optimizer.zero_grad()
                resp = self.forward(feats_next)
                error = criterion(resp,val_expected_tensor)
                error.backward()
                return error
            optimizer.step(closure)
    
    net = Network()
    net.init()
    
    for input in ([0.,0.],[0.,1.],[1.,0.],[1.,1.]):
        response=net.forward(input)
        print(response)
    
    print ("--TRAIN START-")
    for i in range(1000):
        net.train([1.],[0.,0.])
        net.train([0.],[1.,0.])
        net.train([0.],[0.,1.])
        net.train([1.],[1.,1.])
    print ("---TRAIN END---")
    
    for input in ([0.,0.],[0.,1.],[1.,0.],[1.,1.]):
        response=net.forward(input)
        print(response)

这是一次以0.001学习率进行100000次迭代的运行：

    tensor([0.7726], grad_fn=)
    tensor([0.7954], grad_fn=)
    tensor([0.8229], grad_fn=)
    tensor([0.8410], grad_fn=)
    --TRAIN START-
    *.........*.........*.........*.........*.........*.........*.........*.........*.........*.........
    ---TRAIN END---
    tensor([0.6311], grad_fn=)
    tensor([0.6459], grad_fn=)
    tensor([0.6770], grad_fn=)
    tensor([0.6906], grad_fn=)

我真的迷路了。这不管用吗？

Answer 1

因此，在您的情况下，将训练方法保留在网络类之外。守则如下。

net = Network()
net.init()
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.1)
for input in ([0., 0.], [0., 1.], [1., 0.], [1., 1.]):
    response = net.forward(input)
    print (response)

def train(val_expected, feats_next, criterion, optimizer):
    val_expected_tensor = torch.tensor(val_expected)
    optimizer.zero_grad()
    resp = net.forward(feats_next)
    # print (resp)
    error = criterion(resp, val_expected_tensor)
    # print (error, resp, val_expected_tensor)
    error.backward()
    # print (error)
    optimizer.step()


print("--TRAIN START-")
for i in range(10000):
    train([1.], [0., 0.], criterion, optimizer)
    train([0.], [1., 0.], criterion, optimizer)
    train([0.], [0., 1.], criterion, optimizer)
    train([1.], [1., 1.], criterion, optimizer)
print("---TRAIN END---")

for input in ([0., 0.], [0., 1.], [1., 0.], [1., 1.]):
    response = net.forward(input)
    print(response)

结果如下：

tensor([0.9571], grad_fn=<SigmoidBackward>)
tensor([0.0414], grad_fn=<SigmoidBackward>)
tensor([0.0459], grad_fn=<SigmoidBackward>)
tensor([0.9621], grad_fn=<SigmoidBackward>)

我只是提高了学习率，而且在nn中有一个train方法。模块，因此在模型实例化中使用trainer方法不是一个好主意。