Skorch Documentation Skorch 1 2 0 Documentation

A scikit-learn compatible neural network library that wraps PyTorch. The goal of skorch is to make it possible to use PyTorch with sklearn. This is achieved by providing a wrapper around PyTorch that has an sklearn interface. skorch does not re-invent the wheel, instead getting as much out of your way as possible. If you are familiar with sklearn and PyTorch, you don’t have to learn any new concepts, and the syntax should be well known. (If you’re not familiar with those libraries, it is worth getting familiarized.)

Additionally, skorch abstracts away the training loop, making a lot of boilerplate code obsolete. A simple net.fit(X, y) is enough. Out of the box, skorch works with many types of data, be it PyTorch Tensors, NumPy arrays, Python dicts, and so on. However, if you have other data, extending skorch is easy to allow for that. Overall, skorch aims at being as flexible as PyTorch while having a clean interface as sklearn. pip install skorch Copy PIP instructions

scikit-learn compatible neural network library for pytorch A scikit-learn compatible neural network library that wraps PyTorch. To see more elaborate examples, look here. skorch also provides many convenient features, among others: This page provides a comprehensive introduction to skorch, a Python library that bridges PyTorch and scikit-learn by providing a scikit-learn compatible interface for neural networks implemented in PyTorch. For detailed information about specific components, please refer to their respective pages in this wiki.

skorch is a high-level neural network library that wraps PyTorch models in a scikit-learn compatible API. It allows users to build, train, and evaluate PyTorch neural networks using familiar scikit-learn patterns and workflows, including integration with GridSearchCV, Pipelines, and other scikit-learn tools. The library provides a seamless interface between PyTorch's flexibility in creating custom neural network architectures and scikit-learn's consistent API and wealth of utility functions for model selection, evaluation, and preprocessing. skorch is built around a central NeuralNet class which serves as the foundation for more specialized neural network implementations. The diagram shows the core architecture of skorch. The central NeuralNet class inherits from scikit-learn's BaseEstimator and serves as the base for specialized classes like NeuralNetClassifier, NeuralNetBinaryClassifier, and NeuralNetRegressor.

The NeuralNet class wraps PyTorch components (module, optimizer, criterion) and provides a scikit-learn compatible interface. Skorch is an open-source Python library that provides a scikit-learn compatible interface for training neural networks using PyTorch. It simplifies deep learning workflows by combining PyTorch's flexibility with the familiar scikit-learn API. To check the installed version of Skorch on Ubuntu 24.04, run these commands in your environment: Disclaimer: Skorch is released under the BSD 3-Clause License and maintained by the Skorch and PyTorch communities. Users are responsible for correct usage in their specific applications.

Always refer to the official Skorch documentation for the most accurate and up-to-date information. © Copyright 2017, Marian Tietz, Daniel Nouri, Benjamin Bossan. Concrete-ML uses Skorch to implement multi-layer, fully-connected torch neural networks in a way that is compatible with the Scikit-learn API. This wrapper implements Torch training boilerplate code, alleviating the work that needs to be done by the user. It is possible to add hooks during the training phase, for example once an epoch is finished. Skorch allows the user to easily create a classifier or regressor around a neural network (NN), implemented in Torch as a nn.Module, which is used by Concrete-ML to provide a fully-connected multi-layer NN with...

Under the hood, Concrete-ML uses a Skorch wrapper around a single torch module, SparseQuantNeuralNetImpl. More information can be found in the API guide. A linear or convolutional layer of an NN will compute a linear combination of weights and inputs (also called a 'multi-sum'). For example, a linear layer will compute: A scikit-learn compatible neural network library that wraps pytorch The following are examples and notebooks on how to use skorch.

Basic Usage - Explores the basics of the skorch API. Run in Google Colab 💻 MNIST with scikit-learn and skorch - Define and train a simple neural network with PyTorch and use it with skorch. Run in Google Colab 💻 Benchmarks skorch vs pure PyTorch - Compares the performance of skorch and using pure PyTorch on MNIST. Transfer Learning with skorch - Train a neural network using transfer learning with skorch.

Run in Google Colab 💻 Below, we define our own PyTorch Module and train it on a toy classification dataset using skorch NeuralNetClassifier: In this example, instead of using the standard softmax non-linearity with NLLLoss as criterion, no output non-linearity is used and CrossEntropyLoss as criterion. The reason is that the use of softmax can lead to numerical instability in some cases. Since NeuralNetClassifier provides an sklearn-compatible interface, it is possible to put it into an sklearn Pipeline: Another advantage of skorch is that you can perform an sklearn GridSearchCV or RandomizedSearchCV:

Please visit the Tutorials page to explore additional examples on using skorch!

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