In this article, we will discuss tensor operations in PyTorch.
PyTorch is a scientific package used to perform operations on the given data like tensor in python. A Tensor is a collection of data like a numpy array. We can create a tensor using the tensor function:
Syntax: torch.tensor([[[element1,element2,.,element n],……,[element1,element2,.,element n]]])
where,
- torch is the module
- tensor is the function
- elements are the data
The Operations in PyTorch that are applied on tensor are:
expand()
This operation is used to expand the tensor into a number of tensors, a number of rows in tensors, and a number of columns in tensors.
Syntax: tensor.expand(n,r,c)
where,
- tensor is the input tensor
- n is to return the number of tensors
- r is the number of rows in each tensor
- c is the number of columns in each tensor
Example: In this example, we will expand the tensor into 4 tensors, 2 rows and 3 columns in each tensor
Python3
# import module import torch # create a tensor with 2 data # in 3 three elements each data = torch.tensor([[10, 20, 30], [45, 67, 89]]) # display print(data) # expand the tensor into 4 tensors , 2 # rows and 3 columns in each tensor print(data.expand(4, 2, 3)) |
Output:
tensor([[10, 20, 30],
[45, 67, 89]])
tensor([[[10, 20, 30],
[45, 67, 89]],
[[10, 20, 30],
[45, 67, 89]],
[[10, 20, 30],
[45, 67, 89]],
[[10, 20, 30],
[45, 67, 89]]])
permute()
This is used to reorder the tensor using row and column
Syntax: tensor.permute(a,b,c)
where
- tensor is the input tensor
- permute(1,2,0) is used to permute the tensor by row
- permute(2,1,0) is used to permute the tensor by column
Example: In this example, we are going to permute the tensor first by row and by column.
Python3
# import module import torch # create a tensor with 2 data # in 3 three elements each data = torch.tensor([[[10, 20, 30], [45, 67, 89]]]) # display print(data) # permute the tensor first by row print(data.permute(1, 2, 0)) # permute the tensor first by column print(data.permute(2, 1, 0)) |
Output:
tensor([[[10, 20, 30],
[45, 67, 89]]])
tensor([[[10],
[20],
[30]],
[[45],
[67],
[89]]])
tensor([[[10],
[45]],
[[20],
[67]],
[[30],
[89]]])
tolist()
This method is used to return a list or nested list from the given tensor.
Syntax: tensor.tolist()
Example: In this example, we are going to convert the given tensor into the list.
Python3
# import module import torch # create a tensor with 2 data in # 3 three elements each data = torch.tensor([[[10, 20, 30], [45, 67, 89]]]) # display print(data) # convert the tensor to list print(data.tolist()) |
Output:
tensor([[[10, 20, 30],
[45, 67, 89]]])
[[[10, 20, 30], [45, 67, 89]]]
narrow()
This function is used to narrow the tensor. in other words, it will extend the tensor based on the input dimensions.
Syntax: torch.narrow(tensor,d,i,l)
where,
- tensor is the input tensor
- d is the dimension to narrow
- i is the starting index of the vector
- l is the length of the new tensor along the dimension – d
Example: In this example, we will narrow the tensor with 1 dimension which is starting from 1 st index, and the length of each dimension is 2 and we will narrow the tensor with 1 dimension which is starting from the 0th index and the length of each dimension is 2
Python3
# import module import torch # create a tensor with 2 data in # 3 three elements each data = torch.tensor([[10, 20, 30], [45, 67, 89], [23, 45, 67]]) # display print(data) # narrow the tensor # with 1 dimension # starting from 1 st index # length of each dimension is 2 print(torch.narrow(data, 1, 1, 2)) # narrow the tensor # with 1 dimension # starting from 0 th index # length of each dimension is 2 print(torch.narrow(data, 1, 0, 2)) |
Output:
tensor([[10, 20, 30],
[45, 67, 89],
[23, 45, 67]])
tensor([[20, 30],
[67, 89],
[45, 67]])
tensor([[10, 20],
[45, 67],
[23, 45]])
where()
This function is used to return the new tensor by checking the existing tensors conditionally.
Syntax: torch.where(condition,statement1,statement2)
where,
- condition is used to check the existing tensor condition by applying conditions on the existing tensors
- statememt1 is executed when condition is true
- statememt2 is executed when condition is false
Example: We will use different relational operators to check the functionality
Python3
# import module import torch # create a tensor with 3 data in # 3 three elements each data = torch.tensor([[[10, 20, 30], [45, 67, 89], [23, 45, 67]]]) # display print(data) # set the number 100 when the # number in greater than 45 # otherwise 50 print(torch.where(data > 45, 100, 50)) # set the number 100 when the # number in less than 45 # otherwise 50 print(torch.where(data < 45, 100, 50)) # set the number 100 when the number in # equal to 23 otherwise 50 print(torch.where(data == 23, 100, 50)) |
Output:
tensor([[[10, 20, 30],
[45, 67, 89],
[23, 45, 67]]])
tensor([[[ 50, 50, 50],
[ 50, 100, 100],
[ 50, 50, 100]]])
tensor([[[100, 100, 100],
[ 50, 50, 50],
[100, 50, 50]]])
tensor([[[ 50, 50, 50],
[ 50, 50, 50],
[100, 50, 50]]])
