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PyTorch is an open-source framework for the Python programming language. We can process the data in PyTorch in the form of a Tensor.

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.

argmin()

argmin() in PyTorch is used to return the index of the minimum value of all elements in the input tensor.

Syntax:
torch.argmin(tensor,dim,keepdim)

Where

  1. The tensor is the input tensor.
  2. dim is to reduce the dimension. dim=0 specifies column comparison, which will get the index for the minimum value along a column, and dim=1 specifies row comparison, which will get the index for the minimum value along the row.
  3. keepdim checks whether the output tensor has dimension(dim) retained or not.

Example 1:

In this example, we will create a tensor with two dimensions that has three rows and five columns and apply argmin() on the rows and columns.

#import torch module
import torch
 
#create a tensor with 2 dimensions (3 * 5)
#with random  elements using randn() function
data = torch.randn(3,5)
 
#display
print(data)
 
#get minimum index along columns with argmin
print(torch.argmin(data, dim=0))
 
#get minimum index along rows with argmin
print(torch.argmin(data, dim=1))

Output:

tensor([[ 1.0604,0.0234,  0.4258,0.4714,  0.2778],
        [1.2597,0.3892,  0.2120,  0.1376,  0.6919],
        [ 0.0449,0.3545,0.1914,  0.1969,2.0053]])
tensor([1, 1, 2, 0, 2])
tensor([3, 0, 4])

As we can see, the minimum values of the indexes and columns are:

  1. Min value – -1.2597. Its index is 1.
  2. Min value – 1 -0.3892. Its index is 1.
  3. Min value – -0.1914. Its index is 2.
  4. Min value – 0.4714. Its index is 0.
  5. Min value – -2.0053. Its index is 2.

Similarly, the minimum values present at the index along rows are:

  1. Min value – -0.4714. Its index is 3.
  2. Min value – -1.2597. Its index is 0.
  3. Min value – -2.0053. Its index is 4.

Example 2:

Create a tensor with a five by five matrix and apply argmin().

#import torch module
import torch
 
#create a tensor with 2 dimensions (5 * 5)
#with random  elements using randn() function
data = torch.randn(5,5)
 
#display
print(data)
 
#get minimum index along columns with argmin
print(torch.argmin(data, dim=0))
 
#get minimum index along rows with argmin
print(torch.argmin(data, dim=1))

Output:

tensor([[1.7387,0.7426,  0.5696,0.6700,1.0527],
        [ 0.2564,0.3471,  1.5256,1.1608,  0.4367],
        [ 1.4390,0.5474,  0.5909,  0.0491,  0.4655],
        [0.7006,0.0367,0.9577,0.0834,0.7249],
        [1.9151,  2.3360,  1.1214,  0.4452,1.1233]])
tensor([4, 0, 3, 1, 4])
tensor([0, 3, 1, 2, 0])

We can see that the minimum values present in the index along columns are:

  1. Min value – -1.9151. Its index is 4.
  2. Min value – -0.7426. Its index is 0.
  3. Min value – -0.9577. Its index is 3.
  4. Min value – -1.1608. Its index is 1.
  5. Min value – -1.1233. Its index is 4.

Similarly, minimum values at index along the rows are:

  1. Min value – -1.7387. Its index is 0.
  2. Min value – -1.1608. Its index is 3.
  3. Min value – -0.5474. Its index is 1.
  4. Min value – -0.9577. Its index is 2.
  5. Min value – -1.9151. Its index is 0.

Work with CPU

If you want to run an argmin() 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 three rows and five columns and apply argmin() on the rows and columns.

#import torch module
import torch
 
#create a tensor with 2 dimensions (3 * 5)
#with random  elements using randn() with cpu() function
data = torch.randn(3,5).cpu()
 
#display
print(data)
 
#get minimum index along columns with argmin
print(torch.argmin(data, dim=0))
 
#get minimum index along rows with argmin
print(torch.argmin(data, dim=1))

Output:

tensor([[ 1.0604,0.0234,  0.4258,0.4714,  0.2778],
        [1.2597,0.3892,  0.2120,  0.1376,  0.6919],
        [ 0.0449,0.3545,0.1914,  0.1969,2.0053]])
tensor([1, 1, 2, 0, 2])
tensor([3, 0, 4])

As we can see, the minimum values for the indexes and columns are:

  1. Min value – -1.2597. Its index is 1.
  2. Min value – 1 -0.3892. Its index is 1.
  3. Min value – -0.1914. Its index is 2.
  4. Min value – 0.4714. Its index is 0.
  5. Min value – -2.0053. Its index is 2.

Similarly, the minimum values at the index along the rows are:

  1. Min value – -0.4714. Its index is 3.
  2. Min value – -1.2597. Its index is 0.
  3. Min value – -2.0053. Its index is 4.

Example 2:

Create a tensor with a five by five matrix on the CPU and apply argmin().

#import torch module
import torch
 
#create a tensor with 2 dimensions (5 * 5)
#with random  elements using randn() function
data = torch.randn(5,5).cpu()
 
#display
print(data)
 
#get minimum index along columns with argmin
print(torch.argmin(data, dim=0))
 
#get minimum index along rows with argmin
print(torch.argmin(data, dim=1))

Output:

tensor([[1.7387,0.7426,  0.5696,0.6700,1.0527],
        [ 0.2564,0.3471,  1.5256,1.1608,  0.4367],
        [ 1.4390,0.5474,  0.5909,  0.0491,  0.4655],
        [0.7006,0.0367,0.9577,0.0834,0.7249],
        [1.9151,  2.3360,  1.1214,  0.4452,1.1233]])
tensor([4, 0, 3, 1, 4])
tensor([0, 3, 1, 2, 0])

As we can see, the minimum values for the indexes and columns are:

  1. Min value – -1.9151. Its index is 4.
  2. Min value – -0.7426. Its index is 0.
  3. Min value – -0.9577. Its index is 3.
  4. Min value – -1.1608. Its index is 1.
  5. Min value – -1.1233. Its index is 4.

Similarly, the minimum values at the index along the rows are:

  1. Min value – -1.7387. Its index is 0.
  2. Min value – -1.1608. Its index is 3.
  3. Min value – -0.5474. Its index is 1.
  4. Min value – -0.9577. Its index is 2.
  5. Min value – -1.9151. Its index is 0.

Conclusion

In this PyTorch lesson, we saw what argmin() is and how to apply argmin() to a tensor to return indices of minimum values across columns and rows.

We also created a tensor with the cpu() function and returned indices of its minimum values. dim is the parameter used to return indices of minimum values across columns when it is set to 0 and return indices of minimum values across rows when it is set to 1.



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