Cross-Entropy Loss

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Article
Topic area	Machine Learning
Difficulty	Intermediate
Prerequisites	Loss Functions, Softmax Function

Cross-entropy loss (also called log loss) is the most widely used loss function for classification tasks in machine learning. Rooted in information theory, it measures the dissimilarity between the true label distribution and the model's predicted probability distribution, providing a smooth, differentiable objective that drives probabilistic classifiers toward confident, correct predictions.

Information-Theoretic Foundations

Entropy

The entropy of a discrete probability distribution $ p $ quantifies its uncertainty:

$ H(p) = -\sum_{k=1}^{K} p_k \log p_k $

For a deterministic distribution (one-hot label), $ H(p) = 0 $. Entropy is maximized when all outcomes are equally likely.

KL Divergence

The Kullback-Leibler divergence measures how one distribution $ q $ differs from a reference distribution $ p $:

$ D_{\mathrm{KL}}(p \,\|\, q) = \sum_{k=1}^{K} p_k \log \frac{p_k}{q_k} $

KL divergence is non-negative and equals zero if and only if $ p = q $.

Cross-Entropy

The cross-entropy between distributions $ p $ (true) and $ q $ (predicted) is:

$ H(p, q) = -\sum_{k=1}^{K} p_k \log q_k = H(p) + D_{\mathrm{KL}}(p \,\|\, q) $

Since $ H(p) $ is constant with respect to model parameters, minimizing cross-entropy is equivalent to minimizing KL divergence — i.e., making the predicted distribution $ q $ as close to the true distribution $ p $ as possible.

Binary Cross-Entropy

For binary classification with true label $ y \in \{0, 1\} $ and predicted probability $ \hat{y} = \sigma(z) $ (where $ \sigma $ is the sigmoid function):

$ \mathcal{L}_{\mathrm{BCE}} = -\bigl[y \log \hat{y} + (1 - y) \log(1 - \hat{y})\bigr] $

Over a dataset of $ N $ samples:

$ \mathcal{L} = -\frac{1}{N} \sum_{i=1}^{N} \bigl[y_i \log \hat{y}_i + (1 - y_i) \log(1 - \hat{y}_i)\bigr] $

The gradient with respect to the logit $ z $ takes the elegantly simple form $ \hat{y} - y $, which is both intuitive and computationally efficient.

Categorical Cross-Entropy

For multi-class classification with $ K $ classes, the true label is typically a one-hot vector $ \mathbf{y} $ with $ y_c = 1 $ for the correct class $ c $. The predicted probabilities $ \hat{\mathbf{y}} $ are obtained via the Softmax Function:

$ \mathcal{L}_{\mathrm{CE}} = -\sum_{k=1}^{K} y_k \log \hat{y}_k = -\log \hat{y}_c $

This reduces to the negative log-probability of the correct class, which is why categorical cross-entropy is also called negative log-likelihood in this context.

Numerical Stability

The Log-Sum-Exp Trick

Naively computing $ \log(\mathrm{softmax}(z_k)) $ involves exponentiating potentially large logits, causing overflow. The log-sum-exp trick avoids this:

$ \log \hat{y}_k = z_k - \log \sum_{j=1}^{K} e^{z_j} = z_k - \left(m + \log \sum_{j=1}^{K} e^{z_j - m}\right) $

where $ m = \max_j z_j $. Subtracting the maximum logit ensures the largest exponent is zero, preventing overflow. All major deep learning frameworks implement this fused operation (e.g., PyTorch's CrossEntropyLoss accepts raw logits).

Clamping

Predicted probabilities should be clamped away from exactly 0 and 1 to avoid $ \log(0) = -\infty $. A small epsilon (e.g., $ 10^{-7} $) is typically used.

Label Smoothing

Label smoothing (Szegedy et al., 2016) replaces the hard one-hot target with a soft distribution:

$ y_k^{\mathrm{smooth}} = (1 - \alpha)\, y_k + \frac{\alpha}{K} $

where $ \alpha $ is a small constant (commonly 0.1). This prevents the model from becoming overconfident, improves calibration, and often yields better generalization. It is standard practice in training large image classifiers and Transformer models.

Comparison with Other Losses

Loss	Formula	Typical use
Cross-entropy	$ -\sum y_k \log \hat{y}_k $	Classification
Mean squared error	$ \frac{1}{K}\sum(y_k - \hat{y}_k)^2 $	Regression (poor for classification)
Hinge loss	$ \max(0, 1 - y \cdot z) $	SVM-style classification
Focal loss	$ -(1-\hat{y}_c)^\gamma \log \hat{y}_c $	Imbalanced classification

Cross-entropy has steeper gradients than MSE when the prediction is confidently wrong, leading to faster correction of large errors.

See also

• Loss Functions
• Softmax Function
• Logistic regression
• Information theory
• Neural Networks

References

• Shannon, C. E. (1948). "A Mathematical Theory of Communication". Bell System Technical Journal.
• Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning. MIT Press, Chapter 6.
• Szegedy, C. et al. (2016). "Rethinking the Inception Architecture for Computer Vision". CVPR.
• Lin, T.-Y. et al. (2017). "Focal Loss for Dense Object Detection". ICCV.