Utilities

General utilities for models, training, and visualization.

Models

ConvNet([num_classes, dropout_rate, ...])	Simple CNN for MNIST-sized inputs (28x28).
ResNet18([num_classes, input_channels])	ResNet-18 for CIFAR-10 sized inputs (32x32).
MLP(input_dim[, hidden_dims, num_classes, ...])	Multi-layer perceptron for tabular data.

Training

train_epoch(model, train_loader, criterion, ...)	Train model for one epoch.
evaluate(model, data_loader, criterion, device)	Evaluate model on dataset.
seed_everything([seed])	Set random seeds for reproducibility.

Visualization

plot_training_curves(train_losses[, ...])	Plot training and validation curves.
plot_uncertainty_distribution(uncertainties, ...)	Plot uncertainty distribution for correct vs incorrect predictions.
plot_2d_classification(X, y[, model, ...])	Plot 2D classification data and decision boundary.

Logging

get_logger(name)	Get a logger instance for a module.
setup_logging([level, format_str, handlers])	Configure logging for incerto.
disable_logging()	Disable all logging from incerto.