PyTorch基礎PyTorch

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Tensorflow谷歌大腦前員工：PyTorch真香，我已經(jīng)把TensorFlow代碼都搬過去啦！TensorFlow2.0和PyTorch誰更好？大牛們爭了好幾天PyTorchTensorFlow使用對比

創(chuàng)建指定大小的tensorimporttorchx=torch.empty(5,3)print(x)創(chuàng)建隨機數(shù)tensorx=torch.rand(5,3)print(x)創(chuàng)建制定數(shù)據(jù)類型的tensorx=torch.zeros(5,3,dtype=torch.long)print(x)從數(shù)據(jù)創(chuàng)建tensorx=torch.tensor([5.5,3])print(x)從已經(jīng)存在的Tensor創(chuàng)建新的Tensorx=x.new_ones(5,3,dtype=torch.double)print(x)y=torch.randn_like(x,dtype=torch.float)#改變了數(shù)據(jù)類型!

print(y)Tensor的操作加法y=torch.rand(5,3)z

=

x+y

#方式一z

=

torch.add(x,y)

#方式二z=torch.empty(5,3)

#方式三torch.add(x,y,out=z)

#方式三y.add_(x)#方式四,

inplaceprint(z)Index定位Print(x[:,

1])Resize

x=torch.randn(4,4)y=x.view(16)z=x.view(-1,8)#-1的位置的值可以按照其它緯度來計算print(x.size(),y.size(),z.size())單元素的tensor可以轉(zhuǎn)為普通python值x=torch.randn(1)print(x)print(x.item())Tensor轉(zhuǎn)為Numpy數(shù)組a=torch.ones(5)print(a)b=a.numpy()print(b)a.add_(1)print(a)print(b)Numpy數(shù)組轉(zhuǎn)為Tensorimportnumpyasnpa=np.ones(5)b=torch.from_numpy(a)np.add(a,1,out=a)print(a)print(b)使用GPU

(CUDA)iftorch.cuda.is_available():device=torch.device(“cuda”)#CUDA設備對象y=torch.ones_like(x,device=device)x=x.to(device)#或者使用x.to("cuda")

z=x+yprint(z)print(z.to("cpu",torch.double))Pytorch用GPU到底能比CPU快多少？

PyTorch的功能N維Tensor,與Numpy數(shù)組類似,但是可以在GPU上運行自動求導Tipscpunetto