PyTorch Cross Entropy Loss

In PyTorch, the Cross Entropy Loss function is often used in classification problems where the output is a probability distribution over multiple classes. It calculates the loss between the predicted probabilities and the actual target labels.

To use the Cross Entropy Loss in PyTorch, you can create an instance of the nn.CrossEntropyLoss class and pass in the predicted outputs and target labels as arguments. Here’s an example:

import torch
import torch.nn as nn

# Define some sample data
predicted_outputs = torch.tensor([[0.2, 0.3, 0.5], [0.8, 0.1, 0.1]])
target_labels = torch.tensor([2, 0])

# Create an instance of the CrossEntropyLoss class
criterion = nn.CrossEntropyLoss()

# Calculate the loss
loss = criterion(predicted_outputs, target_labels)

print(loss.item())

The nn.CrossEntropyLoss class automatically performs the softmax function on the predicted outputs before calculating the loss. It then computes the negative log likelihood of the predicted class probabilities with respect to the target labels.

Using the Cross Entropy Loss function in PyTorch is simple and efficient, making it a popular choice for training classification models. It helps to improve the model’s accuracy by penalizing incorrect predictions and encouraging the model to correctly classify the input data.

Overall, the PyTorch Cross Entropy Loss function is a powerful tool for optimizing classification models and achieving better performance in machine learning tasks.