Skorch: The Power of PyTorch Combined with The Elegance of Sklearn
The best of both worlds.
PyTorch has always been my go-to library for building any deep learning model.
However, one thing I particularly dislike about PyTorch is manually writing its long training loops, which go as follows:
For every epoch:
For every batch:
Run the forward pass
Calculate the loss
Compute the gradients
Run backpropagation
Compute epoch accuracy
Print the accuracy, loss, etc.
That’s too much work and code, isn’t it?
Skorch immensely simplifies training neural networks with PyTorch.
Skorch (Sklearn + PyTorch) is an open-source library that provides full Scikit-learn compatibility to PyTorch.
This means we can train PyTorch models in a way similar to Scikit-learn, using functions such as fit(), predict(), score(), etc.
Isn’t that cool?
Let’s see how to use it!
First, we define our PyTorch neural network as we usually would (no change here):
Make sure you have installed Skorch: pip install skorch.
As we are creating a classifier, we import and create an object of Skorch’s NeuralNetClassifier class.
There’s a class for regression models as well:
NeuralNetRegressor.
The first argument is the PyTorch model class (
MyClassifier).Next, we specify training hyperparameters like learning rate, batch size, etc.
We also specify the optimizer and loss function as a parameter.
Done!
Now, we can directly invoke fit() method to train the model as follows:
As shown above, Skorch automatically prints all training metrics for us.
What’s more, we can also call the predict() and score() methods to generate predictions and output accuracy, respectively.
Isn’t that simple, cool, and elegant?
👉 Over to you: Are you aware of any other utility libraries to simplify model training? Let me know :)
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