PyTorch 如何检查模型梯度是否可导

from torch.autograd import gradcheck
import torch
import torch.nn as nn
 
inputs = torch.randn((10, 5), requires_grad=True, dtype=torch.double)
linear = nn.Linear(5, 3)
linear = linear.double()
test = gradcheck(lambda x: linear(x), inputs)
print("Are the gradients correct: ", test)

from torch.autograd import gradcheck
import torch
import torch.nn as nn 
# 定义神经网络模型
class Net(nn.Module):
 
    def __init__(self):
        super(Net, self).__init__()
        self.net = nn.Sequential(
            nn.Linear(15, 30),
            nn.ReLU(),
            nn.Linear(30, 15),
            nn.ReLU(),
            nn.Linear(15, 1),
            nn.Sigmoid()
        )
 
    def forward(self, x):
        y = self.net(x)
        return y
 
net = Net()
net = net.double()
inputs = torch.randn((10, 15), requires_grad=True, dtype=torch.double)
test = gradcheck(net, inputs)
print("Are the gradients correct: ", test)

>>>import torch
>>>a = torch.tensor([[1],[2],[3.],[4]])    # 4*1列向量
>>>X = torch.tensor([[1,2,3],[5,6,7],[8,9,10],[5,4,3.]],requires_grad=True)  #4*3矩阵，注意，值必须要是float类型
>>>b = torch.tensor([[2],[3],[4.]]) #3*1列向量
>>>f = a.view(1,-1).mm(X).mm(b)  # f = a^T.dot(X).dot(b)
>>>f.backward()
>>>X.grad   #df/dX = a.dot(b^T)
tensor([[ 2.,  3.,  4.],
    [ 4.,  6.,  8.],
    [ 6.,  9., 12.],
    [ 8., 12., 16.]])
>>>a.grad b.grad   # a和b的requires_grad都为默认(默认为False)，所以求导时，没有梯度
(None, None)
>>>a.mm(b.view(1,-1))  # a.dot(b^T)
    tensor([[ 2.,  3.,  4.],
    [ 4.,  6.,  8.],
    [ 6.,  9., 12.],
    [ 8., 12., 16.]])

>>>A = torch.tensor([[1,2],[3,4.]])  #2*2矩阵
>>>X =  torch.tensor([[1,2,3],[4,5.,6]],requires_grad=True)  # 2*3矩阵
>>>F = A.mm(X)
>>>F
tensor([[ 9., 12., 15.],
    [19., 26., 33.]], grad_fn=<MmBackward>)
>>>F.backgrad(torch.ones_like(F)) # 注意括号里要加上这句
>>>X.grad
tensor([[4., 4., 4.],
    [6., 6., 6.]])