Method 2 : Using flatten() method
flatten() is used to flatten an N-Dimensional tensor to a 1D Tensor.
Syntax: torch.flatten(tensor)
Where, tensor is the input tensor
Example 1: Python code to create a tensor with 2 D elements and flatten this vector
Python3
# import torch moduleimport torch# create an 2 D tensor with 8 elements eacha = torch.tensor([[1,2,3,4,5,6,7,8], [1,2,3,4,5,6,7,8]])# display actual tensorprint(a)# flatten a tensor with flatten() functionprint(torch.flatten(a)) |
Output:
tensor([[1, 2, 3, 4, 5, 6, 7, 8],
[1, 2, 3, 4, 5, 6, 7, 8]])
tensor([1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8])
Example 2: Python code to create a tensor with 3 D elements and flatten this vector
Python3
# import torch moduleimport torch# create an 3 D tensor with 8 elements eacha = torch.tensor([[[1,2,3,4,5,6,7,8], [1,2,3,4,5,6,7,8]], [[1,2,3,4,5,6,7,8], [1,2,3,4,5,6,7,8]]])# display actual tensorprint(a)# flatten a tensor with flatten() functionprint(torch.flatten(a)) |
Output:
tensor([[[1, 2, 3, 4, 5, 6, 7, 8],
[1, 2, 3, 4, 5, 6, 7, 8]],
[[1, 2, 3, 4, 5, 6, 7, 8],
[1, 2, 3, 4, 5, 6, 7, 8]]])
tensor([1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8,
1, 2, 3, 4, 5, 6, 7, 8])
Reshaping a Tensor in Pytorch
In this article, we will discuss how to reshape a Tensor in Pytorch. Reshaping allows us to change the shape with the same data and number of elements as self but with the specified shape, which means it returns the same data as the specified array, but with different specified dimension sizes.
Creating Tensor for demonstration:
Python code to create a 1D Tensor and display it.
Python3
# import torch moduleimport torch# create an 1 D etnsor with 8 elementsa = torch.tensor([1,2,3,4,5,6,7,8])# display tensor shapeprint(a.shape)# display tensora |
Output:
torch.Size([8]) tensor([1, 2, 3, 4, 5, 6, 7, 8])
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