目录

Create Tensor 

Oprations on Tensor

Create Tensor 

#create tensors with specific dimension
a = torch.tensor(5, device = "cpu")
c = torch.empty(2, 3)
k = torch.ones(2, 2, dtype = torch.int). # torch.float16
t = torch.randn(2, 2)

#check the size:
k.size()

#create a tensor from list 
d = torch.tensor([1, 2, 3])

Oprations on Tensor

1. Plus, Minus, Multiplication, Diviation

   z = x + y
   z = torch.add(x, y)
   z = y.add_(x)     # dash line means inplace operation 
   
   z = x - y 
   z = torch.sub(x, y)
   
   z = x*y
   z = torch.mul(x, y)
   
   z = x/y
   z = torch.div(x, y) 

2. slicing & Reshape        

   #slicing:
   x = torch.randn(5, 3)
   print(x[:, 0]) # select all the rows and the first column 
   
   
   #When you have only one element in your tensor, you can use the .item() to get specific value 
   value = x[1,1].item()
   
   # reshape a tensor: the total number of elements must still be the same 
   x = torch.randn(4, 4)
   y = x.view(16)
   y = x.view(-1, 8) # specify the number of column but leave the number of rows caculated by python self

3. Numpy — Tensor (can only operate on cpu)

   # a and b share cpu memory now, if we modify one of them, the other one will change accordingly
   
   a = torch.ones(5)
   b = a.numpy()
   
   a = np.ones(5)
   b = torch.from_numpy(a)

4. Create a Tensor on GPU

   # How to create a Tensor on GPU 
   if torch.cuda.is_available():
   	device = torch.device("cuda")
   x = torch.randn(5, device = device)
   y = torch.randn(5)
   y.to(device)
   
   # numpy can't handle gpu tensor 
   print(y.numpy())    ----- Erro