A tensor is a multidimensional array that is used to store data. So to use 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.count_nonzero()
torch.count_nonzero() is used to return the total number of non-zero elements present in the tensor. It takes two parameters.
Syntax:
torch.count_nonzero(tensor_object,dim)
Parameters:
- The tensor is the input tensor.
- dim is to reduce the dimension. dim=0 specifies column comparison, which will get the total sum of non-zeros along a column, and dim=1 specifies row comparison, which will get the total sum of non-zeros along the row.
Example 1:
In this example, we will create a tensor with two dimensions that has two rows and two columns and apply count_nonzero() on the rows.
import torch
#create a tensor with 2 dimensions (3 * 5)
#with random elements using randn() function
data = torch.tensor([[0,0],[1,0]])
#display
print(data)
print()
#get count of non zeros along rows
print(“Total number of Non zeros across rows:”)
print(torch.count_nonzero(data, dim=1))
Output:
[1, 0]])
Total number of Non zeros across rows:
tensor([0, 1])
We can see that the total number of nonzeros in the first row is 0 and in the second row is 1.
Example 2:
In this example, we will create a tensor with two dimensions that has two rows and two columns and apply count_nonzero() on the columns.
import torch
#create a tensor with 2 dimensions (3 * 5)
#with random elements using randn() function
data = torch.tensor([[0,0],[1,0]])
#display
print(data)
print()
#get count of non zeros along columns
print(“Total number of Non zeros across columns:”)
print(torch.count_nonzero(data, dim=0))
Output:
[1, 0]])
Total number of Non zeros across columns:
tensor([1, 0])
We can see that the total number of nonzeros in the first column is 1 and in the second column is 0.
Work with CPU
If you want to run the count_nonzero() function on the CPU, then we have to create a tensor with a cpu() function. This will run on a CPU machine.
At this time, when we are creating a tensor, we can use the cpu() function.
Syntax:
torch.tensor(data).cpu()
Example 1:
In this example, we will create a tensor with two dimensions on the CPU that has two rows and two columns and apply count_nonzero() on rows.
import torch
#create a tensor with 2 dimensions (3 * 5)
#with random elements using randn() function
data = torch.tensor([[0,0],[1,0]]).cpu()
#display
print(data)
print()
#get count of non zeros along rows
print("Total number of Non zeros across rows:")
print(torch.count_nonzero(data, dim=1))
Output:
[1, 0]])
Total number of Non zeros across rows:
tensor([0, 1])
We can see that the total number of nonzeros in the first row is 0 and in the second row is 1.
Example 2:
In this example, we will create a tensor with 2 dimensions on the CPU that has two rows and two columns and apply count_nonzero() on the columns.
import torch
#create a tensor with 2 dimensions (3 * 5)
#with random elements using randn() function
data = torch.tensor([[0,0],[1,0]]).cpu()
#display
print(data)
print()
#get count of non zeros along columns
print("Total number of Non zeros across columns:")
print(torch.count_nonzero(data, dim=0))
Output:
[1, 0]])
Total number of Non zeros across columns:
tensor([1, 0])
We can see that the total number of nonzeros in the first column is 1 and in the second column is 0.
Conclusion
In this PyTorch lesson, we discussed the count_nonzero() function. It returns the total number of non-zero elements present in the tensor. We saw different examples and worked these examples on a CPU machine.
