Skorch Documentation Skorch 1 2 0 Documentation Read The Docs

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. We recommend to use a virtual environment for this. If you would like to use the most recent additions to skorch or help development, you should install skorch from source. You need a working conda installation. Get the correct miniconda for your system from here. You may adjust the Python version to any of the supported Python versions, i.e.

Python 3.9 or higher. PyTorch is not covered by the dependencies, since the PyTorch version you need is dependent on your OS and device. For installation instructions for PyTorch, visit the PyTorch website. skorch officially supports the last four minor PyTorch versions, which currently are: To apply L2 regularization (aka weight decay), PyTorch supplies the weight_decay parameter, which must be supplied to the optimizer. To pass this variable in skorch, use the double-underscore notation for the optimizer:

By default, when you call fit() more than once, the training starts from zero instead of from where it was left. This is in line with sklearn's behavior but not always desired. If you would like to continue training, use partial_fit() instead of fit(). Alternatively, there is the warm_start argument, which is False by default. Set it to True instead and you should be fine. skorch uses DataLoader from PyTorch under the hood.

This class takes a couple of arguments, for instance shuffle. We therefore need to pass the shuffle argument to DataLoader, which we achieve by using the double-underscore notation (as known from sklearn): Note that we have an iterator_train for the training data and an iterator_valid for validation and test data. In general, you only want to shuffle the train data, which is what the code above does. skorch supports dicts as input but sklearn doesn’t. To get around that, try to wrap your dictionary into a SliceDict.

This is a data container that partly behaves like a dict, partly like an ndarray. For more details on how to do this, have a look at the corresponding data section in the notebook. Contains history class and helper functions. History for use in training using multiple processes When using skorch with AccelerateMixin for multi GPU training, use this class instead of the default History class. When using PyTorch torch.nn.parallel.DistributedDataParallel, the whole training process is forked and batches are processed in parallel.

That means that the standard History does not see all the batches that are being processed, which results in the different processes having histories that are out of sync. This is bad because the history is used as a reference to influence the training, e.g. to control early stopping. This class solves the problem by using a distributed store from PyTorch, e.g. torch.distributed.TCPStore, to synchronize the batch information across processes. This ensures that the information stored in the individual history copies is identical for history[:, 'batches'].

When it comes to the epoch-level information, it can still diverge between processes (e.g. the recorded duration of the epoch). © Copyright 2017, Marian Tietz, Daniel Nouri, Benjamin Bossan. 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 💻

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