Performing element-wise addition in PyTorch involves adding corresponding elements of two tensors together. Here's how you can do it:

**Create two tensors**: Start by creating two PyTorch tensors of the same shape, each containing the elements you want to add.**Use the "+" operator**: Simply use the "+" operator between the two tensors to perform element-wise addition. The resulting tensor will have the same shape as the input tensors, with each element being the sum of the corresponding elements in the input tensors.

Example:

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import torch # Create two tensors tensor1 = torch.tensor([1, 2, 3]) tensor2 = torch.tensor([4, 5, 6]) # Perform element-wise addition result = tensor1 + tensor2 # Print the result print(result) |

Output:

```
1
``` |
```
tensor([5, 7, 9])
``` |

In the example above, the element-wise addition of `tensor1`

and `tensor2`

gives us a new tensor `[5, 7, 9]`

, where each element is the sum of the corresponding elements from the input tensors.

## How to perform element-wise addition in PyTorch?

In PyTorch, element-wise addition between two tensors can be performed using the `+`

operator or the `torch.add()`

function. Here are the steps to perform element-wise addition in PyTorch:

- Import the required libraries:

```
1
``` |
```
import torch
``` |

- Create two tensors of the same shape with the elements you want to add:

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a = torch.tensor([1, 2, 3]) b = torch.tensor([4, 5, 6]) |

**Use either the + operator or the torch.add() function to perform element-wise addition**: Using + operator:

```
1
``` |
```
c = a + b
``` |

Using `torch.add()`

function:

```
1
``` |
```
c = torch.add(a, b)
``` |

- Print the result:

```
1
``` |
```
print(c)
``` |

The output will be:

```
1
``` |
```
tensor([5, 7, 9])
``` |

Note: The tensors `a`

and `b`

must have the same shape for element-wise addition to work. If they have different shapes, you need to reshape or broadcast them to the same shape before performing element-wise addition.

## What is the criteria for tensors to be eligible for element-wise addition in PyTorch?

In PyTorch, the criteria for tensors to be eligible for element-wise addition are:

- The tensors must have the same shape, i.e., the same number of dimensions and the same size along each dimension.
- The tensors must have the same data type. PyTorch doesn't allow adding tensors of different data types directly.

If both these criteria are met, element-wise addition can be performed by simply using the "+" operator on the tensors. For example:

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import torch # Creating two tensors of the same shape and data type a = torch.tensor([1, 2, 3]) b = torch.tensor([4, 5, 6]) # Element-wise addition result = a + b print(result) |

This will output:

```
1
``` |
```
tensor([5, 7, 9])
``` |

## How to handle broadcasting while performing element-wise addition in PyTorch?

To handle broadcasting while performing element-wise addition in PyTorch, you can use the broadcasting feature provided by PyTorch. Here's how you can do it:

- Make sure that the shapes of the tensors you want to add are compatible for broadcasting. The sizes of corresponding dimensions should either be equal, 1, or one of them should be empty.
- If the shapes are not already compatible, you can use the unsqueeze() or view() functions to reshape tensors by adding dimensions of size 1 as required.
- Once the shapes are compatible, simply perform the element-wise addition using the + operator.

Here's an example that demonstrates broadcasting while performing element-wise addition in PyTorch:

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import torch # Create tensors a = torch.tensor([[1, 2, 3], [4, 5, 6]]) # Shape: (2, 3) b = torch.tensor([10, 20, 30]) # Shape: (3,) # Perform element-wise addition with broadcasting result = a + b print(result) |

In this example, the tensor `a`

has shape `(2, 3)`

and the tensor `b`

has shape `(3,)`

. The shape of `b`

is compatible for broadcasting with `a`

, so PyTorch automatically broadcasts `b`

along the first dimension to match the shape of `a`

, resulting in a shape of `(2, 3)`

. The element-wise addition is then performed, resulting in the desired output:

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tensor([[11, 22, 33], [14, 25, 36]]) |