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Contains significant improvements, bug fixes, and additional support. Get it from the releases, or pull the master branch. This package provides a few things. A high-level module for Keras-like training with callbacks, constraints, and regularizers. Comprehensive data augmentation, transforms, sampling, and loading. Utility tensor and variable functions so you don't need numpy as often. Have any feature requests? Submit an issue! I'll make it happen. Specifically, any data augmentation, data loading, or sampling functions. ModuleTrainer. The ModuleTrainer class provides a high-level training interface that abstracts away the training loop while providing callbacks, constraints, initializers, regularizers, and more. You also have access to the standard evaluation and prediction functions. Torchsample provides a wide range of callbacks, generally mimicking the interface found in Keras.
Features
- Both regularizers and constraints can be selectively applied on layers using regular expressions
- Torchsample provides a few utility functions not commonly found
- Data augmentation and datasets
- Datasets and sampling
- Access to the standard evaluation and prediction functions
- Torchsample provides a wide range of callbacks
Project Samples
