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1. torch.cat
ㆍ This function is that concatenates the given sequence of tensors along the given dimension
2. torch.unbind
ㆍ This function removes the tensor dimension specified by the argument 'dim(default dim=0)' and returns a tuple of slices of the tensor along the specified dim
3. torch.movedim
ㆍ This function moves the dimensions of input at the position in source to the positions specified in destination
source and destination can be either int ( single dimension ) or tuple of dimensions to be moved ; Other dimensions of input that are not explicitly moved to remain in their original order and appear at the positions not specified in the destination
4. torch.squeeze
ㆍ This operation returns a tensor with all the dimensions of input of size 1 removed. When dim is specified , then squeeze operation is done only along that dimension
5. torch.unsqueeze
ㆍ This function returns a new tensor with a dimension of size on inserted at the specified position.
6. torch.chunk
ㆍ The chunk function returns,splitting a tensor into a specific number of chunks. Each chunk is a view of the input tensor. The last chunk will be smaller if the tensor size of a dimension is not divisible by chunks. It takes three arguments input, chunks, and dimension.
7. torch.dstack
ㆍThe dstack function stack tensors in sequence depthwise. This is equivalent to concatenation along the third axis after 1D and 2D tensors have been reshaped by torch.atleast_3d()
8. torch.hstack
ㆍ Like dstack function hstack function stack tensors in sequence but horizontally. This is equivalent to concatenation along the first axis for 1D tensors, and along the second axis for all other tensors.
9. torch.std
ㆍ The std function returns the standard eviation of all elements in the tensor.If unbiased is _False_, then the standard deviation will be calculated via the biased estimator. Otherwise, Bessel's correction will be used.
# Example of torch.cat
# Example of torch.unbind
# Example of torch.movedim
# Example of torch.squeeze
# Example of torch.unsqueeze
# Example of torch.chunk
# Example of torch.dstack
# Example of torch.hstack
# Example of torch.std
## above functions of examination and example are quite useful to people who starts deeplearning by pytorch framework, if you have any question this posting , don't hesitate say to me ! :)