Skorch Notebooks Mnist Ipynb At Master Github
There was an error while loading. Please reload this page. 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. 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 💻 There was an error while loading. Please reload this page.
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- skorch/notebooks/MNIST.ipynb at master - GitHub
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- Pytorch MNIST.ipynb - Colab
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There was an error while loading. Please reload this page. 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
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 Py...
Overall, Skorch Aims At Being As Flexible As PyTorch While
Overall, skorch aims at being as flexible as PyTorch while having a clean interface as sklearn. 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
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 💻 There was an error while loading. Please reload this page.
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