PyTorch is an open-source framework available with a Python programming language. Tensor is a multidimensional array that is used to store the data. To use a tensor, we have to import the torch module. To create a tensor, the method used is tensor().
Syntax:
Where the data is a multi-dimensional array.
Torch.rsqrt()
Sqrt() in PyTorch returns the reciprocal square root of every element in the PyTorch tensor. It takes one parameter.
Syntax:
Parameter:
tensor_object is a tensor
Example 1:
In this example, we will create a tensor with one dimension that has 5 elements and return the reciprocal square roots of these 5 elements in a tensor.
import torch
#create a tensor
data1 = torch.tensor([12,34,56,1,10])
#display
print("Actual Tensor: ")
print(data1)
print("Reciprocal square Root: ")
print(torch.rsqrt(data1))
Output:
tensor([12, 34, 56, 1, 10])
Reciprocal square Root:
tensor([0.2887, 0.1715, 0.1336, 1.0000, 0.3162])
Working:
- 1/√12 =0.2887
- 1/√34 = 0.1715
- 1/√56 =0.1336
- 1/√1 =1.0000
- 1/√10 =0.3162
Example 2:
In this example, we will create a tensor with two dimensions that has 5 elements in each row and return the reciprocal square root of elements.
import torch
#create a 2D tensor
data1=torch.tensor([[45,67,21,23,2],[2,3,4,5,6]])
#display
print("Actual Tensor: ")
print(data1)
print("Reciprocal square Root: ")
print(torch.rsqrt(data1))
Output:
tensor([[45, 67, 21, 23, 2],
[ 2, 3, 4, 5, 6]])
Reciprocal square Root:
tensor([[0.1491, 0.1222, 0.2182, 0.2085, 0.7071],
[0.7071, 0.5774, 0.5000, 0.4472, 0.4082]])
Working:
- 1/√45 = 0.1491, 1/√2 = 0.7071
- 1/√67 = 0.1222, 1/√3=0.5774
- 1/√21 = 0.2182, 1/√4=0.5000
- 1/√23 = 0.2085, 1/√5=0.4472
- 1/√2 = 0.7071, 1/√6=0.4082
Work with CPU
If you want to run a rsqrt() function on the CPU, we have to create a tensor with a cpu() function. This will run on a CPU machine.
When we create a tensor, this time, we can use the cpu() function.
Syntax:
Example 1:
In this example, we will create a tensor with one dimension that has 5 elements on the cpu and return the reciprocal square roots of these 5 elements in a tensor.
import torch
#create a tensor
data1 = torch.tensor([12,34,56,1,10]).cpu()
#display
print("Actual Tensor: ")
print(data1)
print("Reciprocal square Root: ")
print(torch.rsqrt(data1))
Output:
tensor([12, 34, 56, 1, 10])
Reciprocal square Root:
tensor([0.2887, 0.1715, 0.1336, 1.0000, 0.3162])
Working:
- 1/√12 =0.2887
- 1/√34 = 0.1715
- 1/√56 =0.1336
- 1/√1 =1.0000
- 1/√10 =0.3162
Example 2:
In this example, we will create a tensor with two dimensions that has 5 elements on the cpu in each row and return the reciprocal square root of elements.
import torch
#create a 2D tensor
data1=torch.tensor([[45,67,21,23,2],[2,3,4,5,6]]).cpu()
#display
print("Actual Tensor: ")
print(data1)
print("Reciprocal square Root: ")
print(torch.rsqrt(data1))
Output:
tensor([[45, 67, 21, 23, 2],
[ 2, 3, 4, 5, 6]])
Reciprocal square Root:
tensor([[0.1491, 0.1222, 0.2182, 0.2085, 0.7071],
[0.7071, 0.5774, 0.5000, 0.4472, 0.4082]])
Working:
- 1/√45 = 0.1491, 1/√2 = 0.7071
- 1/√67 = 0.1222, 1/√3=0.5774
- 1/√21 = 0.2182, 1/√4=0.5000
- 1/√23 = 0.2085, 1/√5=0.4472
- 1/√2 = 0.7071, 1/√6=0.4082
Conclusion
In this PyTorch lesson, we discussed about the rsqrt() function. It returns the reciprocal square root of every element in the PyTorch tensor. We discussed the two examples with the different dimensional tensors to perform the rsqrt() function.
