Binary cross entropy is a logarithmic loss function utilized in machine learning. It computes the difference between the estimated probabilities of a model and actual labels of data. Sometimes, users may need to find/compute the binary cross entropy between two (input and target) tensors. PyTorch provides the “**BCELoss()**” method to perform this operation.

This blog will demonstrate the method to compute/find binary cross entropy in PyTorch.

**How to Find Binary Cross Entropy in PyTorch?**

To find the binary cross entropy in PyTorch, follow the below-listed steps:

- Import required libraries
- Define and print input tensor
- Define and print target tensor
- Define a criterion to compute binary cross-entropy
- Find/compute binary cross-entropy
- Print calculated binary cross-entropy

**Step 1: Install Required Libraries **

First, install the “**torch**” library and “**torch.nn**” module that will be utilized to measure binary cross entropy:

```
import torch
import torch.nn as nn
```

**Step 2: Define Input Tensor **

Then, define the input tensor using the “**torch.tensor()**” function and display its elements. For example, we are making simple “**input_tens**” tensor:

```
input_tens = torch.tensor([[0.123, 0.984, 0.452],
[0.977, 0.491, 0.830],
[0.421, 0.382, 0.628]],
requires_grad=True)
print('input: \n', input_tens)
```

This has created an input tensor:

**Step 3: Define Target Tensor **

Next, define the target tensor and display its elements. Here, we are defining a “**target_tens**” tensor:

```
target_tens = torch.tensor([[0.289, 0.999, 0.693],
[0.899, 0.231, 0.384],
[0.452, 0.071, 0.049], ])
print('target: \n', target_tens)
```

This has created the target tensor:

** Note: **The value of target tensor elements must be between 0 and 1.

**Step 4: Define Criterion**

Now, use the “**BCELoss()**” method to create a criterion for computing binary cross entropy:

`bce_loss_cr = nn.BCELoss()`

**Step 5: Find/Compute Binary Cross Entropy**

Next, measure the binary cross entropy by passing the “**input_tens**” and “**target_tens**”:

```
output = bce_loss_cr(input_tens, target_tens)
output.backward()
```

**Step 6: Print Binary Cross Entropy**

Finally, display computed binary cross-entropy loss:

`print('Binary Cross Entropy Loss: ', output)`

The below output shows the binary cross-entropy loss:

**Note**: Click on the provided link to access our Google Colab Notebook.

**Conclusion**

To find the binary cross entropy in PyTorch, first, import the necessary torch libraries. Then, create the input and target tensors, and print their elements. After that, use the “**BCELoss()**” method to create a criterion for computing binary cross entropy. Next, measure the binary cross entropy loss and display it. This blog has demonstrated the method to compute/find binary cross entropy in PyTorch.