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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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hstack and column_stack in PyTorch

#python #pytorch #hstack #columnstack

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*Memos:

hstack() can get the 1D or more D horizontally(column-wisely) stacked tensor of zero or more elements from the one or more 0D or more D tensors of zero or more elements as shown below:

*Memos:

  • hstack() can be used with torch but not with a tensor.
  • The 1st argument with torch is tensors(Required-Type:tuple or list of tensor of int, float, complex or bool). *Basically, the size of tensors must be the same.
  • There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument. 
import torch tensor1 = torch.tensor(2) tensor2 = torch.tensor(7) tensor3 = torch.tensor(4) torch.hstack(tensors=(tensor1, tensor2, tensor3)) # tensor([2, 7, 4])  tensor1 = torch.tensor([2, 7, 4]) tensor2 = torch.tensor([8, 3, 2]) tensor3 = torch.tensor([5, 0, 8]) torch.hstack(tensors=(tensor1, tensor2, tensor3)) # tensor([2, 7, 4, 8, 3, 2, 5, 0, 8])  tensor1 = torch.tensor([[2, 7, 4], [8, 3, 2]]) tensor2 = torch.tensor([[5, 0, 8], [3, 6, 1]]) tensor3 = torch.tensor([[9, 4, 7], [1, 0, 5]]) torch.hstack(tensors=(tensor1, tensor2, tensor3)) # tensor([[2, 7, 4, 5, 0, 8, 9, 4, 7], # [8, 3, 2, 3, 6, 1, 1, 0, 5]])  tensor1 = torch.tensor([[2., 7., 4.], [8., 3., 2.]]) tensor2 = torch.tensor([[5., 0., 8.], [3., 6., 1.]]) tensor3 = torch.tensor([[9., 4., 7.], [1., 0., 5.]]) torch.hstack(tensors=(tensor1, tensor2, tensor3)) # tensor([[2., 7., 4., 5., 0., 8., 9., 4., 7.], # [8., 3., 2., 3., 6., 1., 1., 0., 5.]])  tensor1 = torch.tensor([[2.+0.j, 7.+0.j, 4.+0.j], [8.+0.j, 3.+0.j, 2.+0.j]]) tensor2 = torch.tensor([[5.+0.j, 0.+0.j, 8.+0.j], [3.+0.j, 6.+0.j, 1.+0.j]]) tensor3 = torch.tensor([[9.+0.j, 4.+0.j, 7.+0.j], [1.+0.j, 0.+0.j, 5.+0.j]]) torch.hstack(tensors=(tensor1, tensor2, tensor3)) # tensor([[2.+0.j, 7.+0.j, 4.+0.j, 5.+0.j, 0.+0.j, # 8.+0.j, 9.+0.j, 4.+0.j, 7.+0.j], # [8.+0.j, 3.+0.j, 2.+0.j, 3.+0.j, 6.+0.j, # 1.+0.j, 1.+0.j, 0.+0.j, 5.+0.j]])  tensor1 = torch.tensor([[True, False, True], [False, True, False]]) tensor2 = torch.tensor([[False, True, False], [True, False, True]]) tensor3 = torch.tensor([[True, False, True], [False, True, False]]) torch.hstack(tensors=(tensor1, tensor2, tensor3)) # tensor([[True, False, True, False, True, False, True, False, True], # [False, True, False, True, False, True, False, True, False]])  tensor1 = torch.tensor([[[2, 7, 4]]]) tensor2 = torch.tensor([]) tensor3 = torch.tensor([[[5, 0, 8]]]) torch.hstack(tensors=(tensor1, tensor2, tensor3)) # tensor([[[2., 7., 4.], # [5., 0., 8.]]])
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column_stack() can get the 2D or more D horizontally stacked tensor of zero or more elements from the one or more 0D or more D tensors of zero or more elements as shown below:

*Memos:

  • column_stack() can be used with torch but not with a tensor.
  • The 1st argument with torch is tensors(Required-Type:tuple or list of tensor of int, float, complex or bool). *Basically, the size of tensors must be the same.
  • There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument. 
import torch tensor1 = torch.tensor(2) tensor2 = torch.tensor(7) tensor3 = torch.tensor(4) torch.column_stack(tensors=(tensor1, tensor2, tensor3)) # tensor([[2, 7, 4]])  tensor1 = torch.tensor([2, 7, 4]) tensor2 = torch.tensor([8, 3, 2]) tensor3 = torch.tensor([5, 0, 8]) torch.column_stack(tensors=(tensor1, tensor2, tensor3)) # tensor([[2, 8, 5], [7, 3, 0], [4, 2, 8]])  tensor1 = torch.tensor([[2, 7, 4], [8, 3, 2]]) tensor2 = torch.tensor([[5, 0, 8], [3, 6, 1]]) tensor3 = torch.tensor([[9, 4, 7], [1, 0, 5]]) torch.column_stack(tensors=(tensor1, tensor2, tensor3)) # tensor([[2, 7, 4, 5, 0, 8, 9, 4, 7], # [8, 3, 2, 3, 6, 1, 1, 0, 5]])  tensor1 = torch.tensor([[2., 7., 4.], [8., 3., 2.]]) tensor2 = torch.tensor([[5., 0., 8.], [3., 6., 1.]]) tensor3 = torch.tensor([[9., 4., 7.], [1., 0., 5.]]) torch.column_stack(tensors=(tensor1, tensor2, tensor3)) # tensor([[2., 7., 4., 5., 0., 8., 9., 4., 7.], # [8., 3., 2., 3., 6., 1., 1., 0., 5.]])  tensor1 = torch.tensor([[2.+0.j, 7.+0.j, 4.+0.j], [8.+0.j, 3.+0.j, 2.+0.j]]) tensor2 = torch.tensor([[5.+0.j, 0.+0.j, 8.+0.j], [3.+0.j, 6.+0.j, 1.+0.j]]) tensor3 = torch.tensor([[9.+0.j, 4.+0.j, 7.+0.j], [1.+0.j, 0.+0.j, 5.+0.j]]) torch.column_stack(tensors=(tensor1, tensor2, tensor3)) # tensor([[2.+0.j, 7.+0.j, 4.+0.j, 5.+0.j, 0.+0.j, # 8.+0.j, 9.+0.j, 4.+0.j, 7.+0.j], # [8.+0.j, 3.+0.j, 2.+0.j, 3.+0.j, 6.+0.j, # 1.+0.j, 1.+0.j, 0.+0.j, 5.+0.j]])  tensor1 = torch.tensor([[True, False, True], [False, True, False]]) tensor2 = torch.tensor([[False, True, False], [True, False, True]]) tensor3 = torch.tensor([[True, False, True], [False, True, False]]) torch.column_stack(tensors=(tensor1, tensor2, tensor3)) # tensor([[True, False, True, False, True, False, True, False, True], # [False, True, False, True, False, True, False, True, False]])  tensor1 = torch.tensor([[]]) tensor2 = torch.tensor([8]) tensor3 = torch.tensor([[]]) torch.column_stack(tensors=(tensor1, tensor2, tensor3)) # tensor([[8.]])
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