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# How to compute the Cosine Similarity between two tensors in PyTorch?

To compute the cosine similarity between two tensors, we use the **CosineSimilarity()** function provided by the **torch.nn** module. It returns the cosine similarity value computed along **dim**.

**dim** is an optional parameter to this function along which cosine similarity is computed.

For 1D tensors, we can compute the cosine similarity along

**dim=0**only.For 2D tensors, we can compute cosine similarity along

**dim=0**or**1**.The size of both tensors must be the same to compute the cosine similarity. Both tensors must be real-valued. Cosine similarity is often used to measure document similarity in text analysis.

## Syntax

torch.nn.CosineSimilarity(dim=1)

The default **dim** is set to 1. But if you measure the cosine similarity between **1D**
**tensors**, then we set **dim** to 0.

## Steps

Import the required library. In all the following examples, the required Python library is

**torch**. Make sure you have already installed it.

import torch

Create two tensors and print them. Both tensors must be real-valued.

tensor1 = torch.randn(3,4) tensor2 = torch.randn(3,4)

Define a method to measure cosine similarity along dimension

**dim.**

cos = torch.nn.CosineSimilarity(dim=0)

Compute the Cosine Similarity using the above defined method.

output = cos(tensor1, tensor2)

Print the computed tensor with cosine similarity values.

print("Cosine Similarity:",output)

## Example 1

The following Python program computes the **Cosine Similarity** between two 1D tensors.

# Import the required library import torch # define two input tensors tensor1 = torch.tensor([0.1, 0.3, 2.3, 0.45]) tensor2 = torch.tensor([0.13, 0.23, 2.33, 0.45]) # print above defined two tensors print("Tensor 1:

", tensor1) print("Tensor 2:

", tensor2) # define a method to measure cosine similarity cos = torch.nn.CosineSimilarity(dim=0) output = cos(tensor1, tensor2) # display the output tensor print("Cosine Similarity:",output)

## Output

Tensor 1: tensor([0.1000, 0.3000, 2.3000, 0.4500]) Tensor 2: tensor([0.1300, 0.2300, 2.3300, 0.4500]) Cosine Similarity: tensor(0.9995)

## Example 2

In this Python program, we compute the Cosine Similarity between two 2D tensors along different **dim**.

# Import the required library import torch # define two input tensors tensor1 = torch.randn(3,4) tensor2 = torch.randn(3,4) # print above defined two tensors print("Tensor 1:

", tensor1) print("Tensor 2:

", tensor2) # define a method to measure cosine similarity in dim 0 cos0 = torch.nn.CosineSimilarity(dim=0) output0 = cos0(tensor1, tensor2) print("Cosine Similarity in dim 0:

",output0) # define a method to measure cosine similarity in dim 1 cos1 = torch.nn.CosineSimilarity(dim=1) output1 = cos1(tensor1, tensor2) print("Cosine Similarity in dim 1:

",output1)

## Output

Tensor 1: tensor([[ 0.2714, 1.1430, 1.3997, 0.8788], [-2.2268, 1.9799, 1.5682, 0.5850], [ 1.2289, 0.5043, -0.1625, 1.1403]]) Tensor 2: tensor([[-0.3299, 0.6360, -0.2014, 0.5989], [-0.6679, 0.0793, -2.5842, -1.5123], [ 1.1110, -0.1212, 0.0324, 1.1277]]) Cosine Similarity in dim 0: tensor([ 0.8076, 0.5388, -0.7941, 0.3016]) Cosine Similarity in dim 1: tensor([ 0.4553, -0.3140, 0.9258])

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