今天我们利用pytorch对二维数据进行求导并输出显示，主要用到pytorch中的Linear()、MSELoss（）等函数，具体的求导过程详见下面代码：

import torch

#create tensors of shape(10,3)and (10,2)
x=torch.randn(10,3)
y=torch.randn(10,2)

#build a fully connected layer
linear=nn.Linear(3,2)
print('w:',linear.weight)
print('b:',linear.bias)

#build loss function and optimizer
criterion=nn.MSELoss()
optimizer=torch.optim.SGD(linear.parameters(),lr=0.01)

#forward pass
pred=linear(x)

#compute loss
loss=criterion(pred,y)
print('loss:',loss.item())

#backward pass
loss.backward()

#print out the gradients
print('dl/dw:',linear.weight.grad)
print('dl/db',linear.bias.grad)

#1-step gradient descent
optimizer.step()

输出结果显示：

另外一种方案：在1-step之后输出loss

import torch

#create tensors of shape(10,3)and (10,2)
x=torch.randn(10,3)
y=torch.randn(10,2)

#build a fully connected layer
linear=nn.Linear(3,2)
print('w:',linear.weight)
print('b:',linear.bias)

#build loss function and optimizer
criterion=nn.MSELoss()
optimizer=torch.optim.SGD(linear.parameters(),lr=0.01)

# #forward pass
# pred=linear(x)
#
# #compute loss
# loss=criterion(pred,y)
# print('loss:',loss.item())
#
# #backward pass
# loss.backward()
#
# #print out the gradients
# print('dl/dw:',linear.weight.grad)
# print('dl/db',linear.bias.grad)

#1-step gradient descent
optimizer.step()

pred=linear(x)
loss=criterion(pred,y)
print('loss after 1 step optimization:',loss.item())

输出结果显示：

对比：