Python

PyTorch – all()

“In this PyTorch tutorial, we will check if the elements in the tensor evaluate to True using the all() method.

PyTorch is an open-source framework available with a Python programming language.

A tensor is a multidimensional array that is used to store the data. So for using a Tensor, we have to import the torch module.

To create a tensor, the method used is tensor()”

Syntax:

torch.tensor(data)

Where data is a multi-dimensional array.

torch.all()

torch.all() in PyTorch returns True if the values in a tensor are not equal to 0 or False. If any of the values in a tensor are equal to 0 or False, it will return False.

It takes one parameter.

Syntax:

torch.all(tensor_object)

Parameter:

tensor_object is a tensor.

Example 1

In this example, we will create a tensor with one dimension that has 5 elements and apply all() function to check the functionality.

#first import the torch module

import torch

 

#create a 1D tensor

data1 = torch.tensor([2,3,0,3,4])

 

#display

print("Actual elements in the Tensor: ")

print(data1)

print("Do all elements in a tensor are not equal to 0? ")

#all() in pytorch

print(torch.all(data1))

Output:

Actual elements in the Tensor:

tensor([2, 3, 0, 3, 4])

Do all elements in a tensor are not equal to 0?

tensor(False)

Here, we can find 0 in the 4th position. So all() returned False.

Example 2

In this example, we will create a tensor with one dimension that has 5 elements and apply all() function to check the functionality.

#first import the torch module

import torch

 

#create a 1D tensor

data1 = torch.tensor([2,3,4,3,4])

 

#display

print("Actual elements in the Tensor: ")

print(data1)

print("Do all elements in a tensor are not equal to 0? ")

#all() in pytorch

print(torch.all(data1))

Output:

Actual elements in the Tensor:

tensor([2, 3, 4, 3, 4])

Do all elements in a tensor are not equal to 0?

tensor(True)

Here, we can’t find 0 in the tensor. So all() returned True.

Example 3

In this example, we will create a tensor with one dimension that has 5 boolean elements and apply all() function to check the functionality.

#first import the torch module

import torch

 

#create a 1D tensor

data1 = torch.tensor([True,False,True,True,True])

 

#display

print("Actual elements in the Tensor: ")

print(data1)

print("Do all elements in a tensor are not equal to False? ")

#all() in pytorch

print(torch.all(data1))

Output:

Actual elements in the Tensor:

tensor([ True, False, True, True, True])

Do all elements in a tensor are not equal to False?

tensor(False)

Here, we can find False in the tensor. So all() returned False.

Work With CPU

If you want to run an all() function on the CPU, then we have to create a tensor with a cpu() function. This will run on a CPU machine.

When we are creating a tensor, at this time, we can use the cpu() function.

Syntax:

torch.tensor(data).cpu()

Example 1

In this example, we will create a tensor with one dimension that has 5 elements on the cpu and apply all() function to check the functionality.

#first import the torch module

import torch

 

#create a 1D tensor

data1 = torch.tensor([2,3,0,3,4]).cpu()

 

#display

print("Actual elements in the Tensor: ")

print(data1)

print("Do all elements in a tensor are not equal to 0? ")

#all() in pytorch

print(torch.all(data1))

Output:

Actual elements in the Tensor:

tensor([2, 3, 0, 3, 4])

Do all elements in a tensor are not equal to 0?

tensor(False)

Here, we can find 0 in the 4th position. So all() returned False.

Example 2

In this example, we will create a tensor with one dimension that has 5 elements on the cpu and apply all() function to check the functionality.

#first import the torch module

import torch

 

#create a 1D tensor

data1 = torch.tensor([2,3,4,3,4]).cpu()

 

#display

print("Actual elements in the Tensor: ")

print(data1)

print("Do all elements in a tensor are not equal to 0? ")

#all() in pytorch

print(torch.all(data1))

Output:

Actual elements in the Tensor:

tensor([2, 3, 4, 3, 4])

Do all elements in a tensor are not equal to 0?

tensor(True)

Here, we can’t find 0 in the tensor. So all() returned True.

Example 3

In this example, we will create a tensor with one dimension that has 5 boolean elements on the cpu and apply all() function to check the functionality.

#first import the torch module

import torch

 

#create a 1D tensor

data1 = torch.tensor([True,False,True,True,True]).cpu()

 

#display

print("Actual elements in the Tensor: ")

print(data1)

print("Do all elements in a tensor are not equal to False? ")

#all() in pytorch

print(torch.all(data1))

Output:

Actual elements in the Tensor:

tensor([ True, False, True, True, True])

Do all elements in a tensor are not equal to False?

tensor(False)

Here, we can find False in the tensor. So all() returned False.

Conclusion

In this PyTorch lesson, we discussed the all() function. It returns True if the values in a tensor are not equal to 0 or False. If any of the values in a tensor are equal to 0 or False, it will return False. We saw 3 different examples and also worked on these examples on a cpu machine.

About the author

Gottumukkala Sravan Kumar

B tech-hon's in Information Technology; Known programming languages - Python, R , PHP MySQL; Published 500+ articles on computer science domain