Skorch Doc Test 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. In that sense, skorch is the spiritual successor to nolearn, but instead of using Lasagne and Theano, it uses PyTorch. 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.
Below, we define our own PyTorch Module and train it on a toy classification dataset using skorch NeuralNetClassifier: 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! © Copyright 2017, Marian Tietz, Daniel Nouri, Benjamin Bossan. Revision e32c195a.
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 💻 We recommend to use a virtual environment for this. If you would like to use the must 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.
PyTorch is not covered by the dependencies, since the PyTorch version you need is dependent on your system. For installation instructions for PyTorch, visit the pytorch website. © Copyright 2017, Marian Tietz, Daniel Nouri, Benjamin Bossan. Revision e32c195a. © Copyright 2017, Marian Tietz, Daniel Nouri, Benjamin Bossan. Revision 68ef3133.
© Copyright 2017, Marian Tietz, Daniel Nouri, Benjamin Bossan. Revision e32c195a.
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A Scikit-learn Compatible Neural Network Library That Wraps PyTorch. The
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. In that sense, skorch is the spiritual successor to nolearn, but instead of using Lasagne and Theano, it uses PyTorch. skorch does not re-invent the wheel, instead getting as...
(If You’re Not Familiar With Those Libraries, It Is Worth
(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 ...
Below, We Define Our Own PyTorch Module And Train It
Below, we define our own PyTorch Module and train it on a toy classification dataset using skorch NeuralNetClassifier: 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 ...
The Following Are Examples And Notebooks On How To Use
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 - Train a neural network using transfer learning with skorch. Run in Google Colab 💻 We recommend to use a virtual environment for this. If you would like to use the must 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.
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