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Env

import torch

Visit
  Indexes and slices

X = torch.arange(12, dtype=torch.float32).reshape((3,4))
Y = torch.tensor([[2.0, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])
X, Y 
(tensor([[ 0.,  1.,  2.,  3.],
         [ 4.,  5.,  6.,  7.],
         [ 8.,  9., 10., 11.]]),
 tensor([[2., 1., 4., 3.],
         [1., 2., 3., 4.],
         [4., 3., 2., 1.]]))

X[1, 1] 
tensor(5.)
X[0, -1] 
tensor(3.)

X[0:2, :] 
tensor([[0., 1., 2., 3.],
        [4., 5., 6., 7.]])

  len

len(X)
Output 
3

  shape

X.shape
Output 
torch.Size([3, 4])

new assignment (save memory)
  why this is important

X = torch.arange(12, dtype=torch.float32).reshape((3,4))
Y = torch.tensor([[2.0, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])
before = id(Y)

Y = Y + X
id(Y) == before 
False

  safe way

Y[:] = X + Y
id(Y) == before 
True
Y += X
id(Y) == before 
True




New assignment (indexes and slices)

X[0:2, :] = 12
X 
tensor([[12., 12., 12., 12.],
        [12., 12., 12., 12.],
        [ 8.,  9., 10., 11.]])

Change to other objects
  array, tensor

A = X.numpy()
B = torch.tensor(A)
A,B 
(array([[12., 12., 12., 12.],
        [12., 12., 12., 12.],
        [ 8.,  9., 10., 11.]], dtype=float32),
 tensor([[12., 12., 12., 12.],
         [12., 12., 12., 12.],
         [ 8.,  9., 10., 11.]]))
type(A), type(B) 
(numpy.ndarray, torch.Tensor)

  python scalar

a = torch.tensor([3.5])
a 
tensor([3.5000])
a.item() 
3.5
float(a) 
3.5
int(a) 
3

a.item() == float(a) 
True