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# How to find element-wise remainder in PyTorch?

Element-wise remainder when a tensor is divided by other tensor is computed using the **torch.remainder()** method. We can also apply **torch.fmod()** to find the remainder.

The difference between these two methods is that in **torch.remainder()**, when the sign of result is different than the sign of divisor, then the divisor is added to the result; whereas in **torch.fmod()**, it is not added.

## Syntax

torch.remainder(input, other) torch.fmod(input, other)

## Parameters

**Input**– It is a PyTorch tensor or scalar, the**dividend**.**Other**– It is also a PyTorch tensor or scalar, the**divisor**.

## Output

It returns a tensor of element-wise remainder values.

## Steps

Import the torch library.

Define tensors, the dividend and the divisor.

Compute

**torch.remainder(input, other)**or**torch.fmod(input, other)**. It gives a tensor of remainder values.Display the computed tensor of remainders.

## Example 1

In the following Python program, we will see how to find the remainder when a tensor is divided by a scalar quantity.

# Python program to find remainder using torch.remainder() # import the library import torch # define a tensor tensor1 = torch.tensor([10,-22,31,-47]) # print the created tensors print("Tensor 1:", tensor1) print("Divisor:", 5) # compute the element-wise remainder tensor/scalar rem = torch.remainder(tensor1, 5) print("Remainder:", rem)

## Output

Tensor 1: tensor([ 10, -22, 31, -47]) Divisor: 5 Remainder: tensor([0, 3, 1, 3])

## Example 2

# Python program to find remainder using torch.fmod() # import necessary libraries import torch # define a tensor tensor1 = torch.tensor([10,-22,31,-47]) # print the created tensors print("Tensor 1:", tensor1) print("Divisor:", 5) # compute the element-wise remainder of tensor/scalar rem = torch.fmod(tensor1, 5) print("Remainder:", rem)

## Output

Tensor 1: tensor([ 10, -22, 31, -47]) Divisor: 5 Remainder: tensor([ 0, -2, 1, -2])

Notice the difference between the output of the above two examples. In
both the examples, we have the same inputs, but we use different methods
to compute the remainder. In Example 1, we use **torch.remainder()**,
whereas in Example 2, we use **torch.fmod()**.

## Example 3

# import necessary libraries import torch # define two tensors tensor1 = torch.tensor([10,22,31,47]) tensor2 = torch.tensor([2,3,4,5]) # print the created tensors print("Tensor 1:", tensor1) print("Tensor 2:", tensor2) # compute the element-wise remainder of tensor1/tensor2 rem = torch.remainder(tensor1, tensor2) print("Remainder:", rem)

## Output

Tensor 1: tensor([10, 22, 31, 47]) Tensor 2: tensor([2, 3, 4, 5]) Remainder: tensor([0, 1, 3, 2])

## Example 4

# import necessary libraries import torch # define two tensors tensor1 = torch.tensor([10.,22.,31.,47.]) tensor2 = torch.tensor([0,3,0,5]) # print the created tensors print("Tensor 1:", tensor1) print("Tensor 2:", tensor2) # compute the element-wise remainder of tensor1/tensor2 rem = torch.remainder(tensor1, tensor2) print("Remainder:", rem)

## Output

Tensor 1: tensor([10., 22., 31., 47.]) Tensor 2: tensor([0, 3, 0, 5]) Remainder: tensor([nan, 1., nan, 2.])

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