Multi-class Neural Network Classification with Softmax and Cross-Entropy
Implement end-to-end multi-class classification using neural networks with softmax output and cross-entropy loss.
Multi-class classification assigns each input to one of k mutually exclusive classes (k>2). The neural network's output layer has k neurons, one per class. Softmax activation converts these outputs to a probability distribution over classes. Cross-entropy loss compares predicted probabilities to the one-hot encoded true labels, measuring disagreement. Training updates weights to increase predicted probability for the correct class and decrease it for others. The model's prediction is the class with highest probability (argmax). PyTorch's nn.CrossEntropyLoss combines LogSoftmax and negative log-likelihood, providing a numerically stable single loss function. This combination is standard for multi-class problems.
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