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]]])
