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from numpy.random import randn

def activation(x):     return 1/(1+torch.exp(-x)) dataiter = iter(trainloader) images, labels = dataiter.next() print("images.shape[0]=",images.shape[0]) inputs= torch.tensor(images.view(images.shape[0],-1)) print("inputs.size=",inputs.size()) w1 = randn(784,256) b1 = randn(256) w2 = randn(256,10) b2 = randn(10)  inputs = torch.Tensor(inputs) w1 = torch.Tensor(w1) b1 = torch.Tensor(b1) print("b1.size=",b1.size()) h=activation(torch.mm(inputs,w1)+b1) print("h.size=",h.size()) w2 = torch.Tensor(w2)

print("w2.size=",w2.size())

h = torch.Tensor(h) b2 = torch.Tensor(b2) print("b2.size=",b2.size()) out = torch.mm(h,w2)+b2 print("out.size=",out.size())

 

结果

images.shape[0]= 64

C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).  import sys

inputs.size= torch.Size([64, 784])b1.size= torch.Size([256])h.size= torch.Size([64, 256])w2.size= torch.Size([256, 10])b2.size= torch.Size([10])out.size= torch.Size([64, 10])

 

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