API Reference

Complete documentation of Leaf's API, including all classes, methods, and utilities.

Core API

Model Creation

Create and initialize machine learning models

import leaf as lf

model = lf.Model()
model.add(lf.layers.Dense(64))
model.add(lf.layers.Activation('relu'))

Training

Train models with various configurations

model.train(
  data=training_data,
  labels=training_labels,
  epochs=10,
  batch_size=32
)

Prediction

Make predictions with trained models

predictions = model.predict(test_data)
probabilities = model.predict_proba(test_data)

Data Processing

Data Loading

Load and preprocess datasets

data = lf.data.load_dataset('mnist')
X_train, X_test = lf.data.split_data(data)

Transformations

Apply data transformations

normalized_data = lf.preprocessing.normalize(data)
encoded_labels = lf.preprocessing.one_hot_encode(labels)

Model Evaluation

Metrics

Calculate model performance metrics

accuracy = lf.metrics.accuracy(y_true, y_pred)
precision = lf.metrics.precision(y_true, y_pred)

Visualization

Visualize model results

lf.viz.plot_training_history(history)
lf.viz.plot_confusion_matrix(y_true, y_pred)