Posted on Jun 30, 2024 • Edited on May 21

Create and access a tensor in PyTorch

#python #pytorch #tensor #access

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*Memos:

My post explains type conversion with type(), to() and a tensor.
My post explains type promotion, result_type(), promote_types() and can_cast().
My post explains device conversion with to(), from_numpy() and numpy().
My post explains how to check PyTorch version, CPU and GPU(CUDA).
My post explains dim(), size() , item() and tolist().
My post explains is_tensor(), numel() and device().
My post explains set_default_dtype(), set_default_device() and set_printoptions().
My post explains manual_seed(), initial_seed() and seed().

tensor() can create the 0D or more D tensor of zero or more elements as shown below:

*Memos:

tensor() can be used with torch but not with a tensor.
The 1st argument with torch is data(Required-Type:int, float, complex or bool or tuple of int, float, complex or bool or list of int, float, complex or bool). *The default type is float32.
There is dtype argument with torch(Optional-Default:None-Type:dtype): *Memos:
- If it's None, it's inferred from data, then for floating-point numbers, get_default_dtype() is used. *My post explains get_default_dtype() and set_default_dtype().
- dtype= must be used.
- My post explains dtype argument.
There is device argument with torch (Optional-Default:None-Type:str, int or device()): *Memos:
- If it's None, get_default_device() is used. *My post explains get_default_device() and set_default_device().
- device= must be used.
- My post explains device argument.
There is requires_grad argument with torch (Optional-Default:False-Type:bool): *Memos:
- requires_grad= must be used.
- My post explains requires_grad.
The one or more floating-point numbers or complex numbers of a tensor are rounded to 4 decimal places by default.

import torch """ 0D tensor """ my_tensor = torch.tensor(data=-3) my_tensor # tensor(-3)  """ 1D tensor """ torch.tensor(data=[3, 7, -5]) # tensor([3, 7, -5])  torch.tensor(data=[3.635251, 7.270649, -5.164872]) # tensor([3.6353, 7.2706, -5.1649])  torch.tensor(data=[3.635251+4.634852, 7.27+2.586449j, -5.164872-3.45]) # tensor([0.9996+0.0000j, 7.2700+2.5864j, -8.6149+0.0000j])  torch.tensor(data=[True, False, True]) # tensor([True, False, True])  """ 2D tensor """ torch.tensor(data=[[3, 7, -5], [-9, 6, 2]]) # tensor([[3, 7, -5], [-9, 6, 2]])  """ 3D tensor """ torch.tensor(data=[[[3, 7, -5], [-9, 6, 2]], [[8, 0, -1], [4, 9, -6]]]) # tensor([[[3, 7, -5], [-9, 6, 2]], # [[8, 0, -1], [4, 9, -6]]])

In addition, Tensor() can create the 1D or more D tensor of zero or more floating-point numbers as shown below:

*Memos:

Tensor() can be used with torch but not with a tensor.
The 1st argument with torch is data(Required-Type:tuple of int, float or bool or list of int, float or bool).
The one or more floating-point numbers or complex numbers of a tensor are rounded to 4 decimal places by default.

import torch torch.Tensor(data=[3., 7., -5.]) # 1D tensor # tensor([3., 7., -5.])  torch.Tensor(data=[[3., 7., -5.], [-9., 6., 2.]]) # 2D tensor # tensor([[-3., 7., -5.], [-9., 6., 2.]])  torch.Tensor(data=[[[-3., 7., -5.], [-9., 6., 2.]], # 3D tensor  [[8., 0., -1.], [4., 9., -6.]]]) # tensor([[[-3., 7., -5.], [-9., 6., 2.]], # [[8., 0., 1.], [4., 9., -6.]]])  torch.Tensor(data=[[[-3., 7., -5.], [-9., 6., 2.]], # 3D tensor  [[8., 0., -1], [4., 9., -6.]]]) # tensor([[[-3., 7., -5.], [-9., 6., 2.]], # [[8., 0., -1.], [4., 9., -6.]]])  torch.Tensor(data=[[[-3, 7, -5], [-9, 6, 2]], # 3D tensor  [[8, 0, -1], [4, 9, -6]]]) # tensor([[[-3., 7., -5.], [-9., 6., 2.]], # [[8., 0., -1.], [4., 9., -6.]]])  torch.Tensor(data=[[[True, False, True], [True, False, True]], # 3D tensor  [[False, True, False], [False, True, False]]]) # tensor([[[1., 0., 1.], [1., 0., 1.]], # [[0., 1., 0.], [0., 1., 0.]]])

You can access a 0D or more D tensor with these ways as shown below. *I give much more ways to access a 1D tensor than a 0D, 2D and 3D tensor:

import torch my_tensor = torch.tensor(3) # 0D tensor  my_tensor # tensor(3)  my_tensor = torch.tensor([3]) # 1D tensor  my_tensor # tensor([3])  my_tensor = torch.tensor([3, 7, -5, -9, 6, 2, 8, 0, -1, 4, 9, -6]) # 1D tensor my_tensor[4] my_tensor[4,] my_tensor[-8] my_tensor[-8,] my_tensor[4:5] my_tensor[4:5,] my_tensor[-8:5] my_tensor[-8:5,] my_tensor[4:-7] my_tensor[4:-7,] my_tensor[-8:-7] my_tensor[-8:-7,] my_tensor[-8:-7:1] my_tensor[-8:-7:1,] my_tensor[-8:-7:10] my_tensor[-8:-7:10,] # tensor(6)  my_tensor[4:8] my_tensor[4:8,] my_tensor[-8:8] my_tensor[-8:8,] my_tensor[4:-4] my_tensor[4:-4,] my_tensor[-8:-4] my_tensor[-8:-4,] # tensor([6, 2, 8, 0])  my_tensor[-8:-4:2] my_tensor[-8:-4:2,] # tensor([6, 8])  my_tensor[:7] my_tensor[:7,] my_tensor[:-5] my_tensor[:-5,] my_tensor[0:7] my_tensor[0:7,] my_tensor[-12:7] my_tensor[-12:7,] my_tensor[0:-5] my_tensor[0:-5,] my_tensor[-12:-5] my_tensor[-12:-5,] # tensor([3, 7, -5, -9, 6, 2, 8])  my_tensor[-12:-5:3] my_tensor[-12:-5:3,] # tensor([3, -9, 8])  my_tensor[5:] my_tensor[5:,] my_tensor[-7:] my_tensor[-7:,] my_tensor[5:12] my_tensor[5:12,] my_tensor[-7:12] my_tensor[-7:12,] # tensor([2, 8, 0, -1, 4, 9, -6])  my_tensor[-7:12:4] my_tensor[-7:12:4,] # tensor([2, 4])  my_tensor[:] my_tensor[:,] my_tensor[0:] my_tensor[0:,] my_tensor[:12] my_tensor[:12,] my_tensor[0:12] my_tensor[0:12,] # tensor([3, 7, -5, -9, 6, 2, 8, 0, -1, 4, 9, -6])  my_tensor[0:12:5] my_tensor[0:12:5,] # tensor([3, 2, 9])  my_tensor = torch.tensor([[3, 7, -5, -9, 6, 2], [8, 0, -1, 4, 9, -6]]) my_tensor[1] # 2D tensor my_tensor[:][1] my_tensor[1, :] # tensor([8, 0, -1, 4, 9, -6])  my_tensor[1, ::2] # tensor([8, -1, 9])  my_tensor[1][3] my_tensor[1, 3] # tensor(4)  my_tensor[1][:4] my_tensor[1, :4] # tensor([8, 0, -1, 4])  my_tensor[1][2:] my_tensor[1, 2:] # tensor([-1, 4, 9, -6])  my_tensor[:, 3] # tensor([-9, 4])  my_tensor[:] my_tensor[::] my_tensor[::1] # tensor([[3, 7, -5, -9, 6, 2], # [8, 0, -1, 4, 9, -6]])  my_tensor[::2] my_tensor[::5] # tensor([[3, 7, -5, -9, 6, 2]])  my_tensor = torch.tensor([[[-3, 7, -5], [-9, 6, 2]], [[8, 0, -1], [4, 9, -6]]]) my_tensor[1] # 3D tensor my_tensor[:][1] my_tensor[1, :] my_tensor[1][:2] my_tensor[1, :2] my_tensor[1][0:] my_tensor[1, 0:] # tensor([[8, 0, -1], [4, 9, -6]])  my_tensor[1][0] # tensor([8, 0, -1])  my_tensor[1][0][2] my_tensor[1, 0, 2] # tensor(-1)  my_tensor[1][0][:2] my_tensor[1, 0, :2] # tensor([8, 0])  my_tensor[1][0][1:] my_tensor[1, 0, 1:] # tensor([0, -1])  my_tensor[:, :, 1] # tensor([[7, 6], [0, 9]])  my_tensor[:] # tensor([[[-3, 7, -5], [-9, 6, 2]], # [[8, 0, -1], [4, 9, -6]]])  my_tensor[:, ::2] # tensor([[[-3, 7, -5]], # [[8, 0, -1]]])  my_tensor[:, :, ::2] # tensor([[[-3, -5], [-9, 2]], # [[8, -1], [4, -6]]])

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Create and access a tensor in PyTorch

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