![]() By default, this class implements StratifiedKFold split in the data with a ratio of 80% for training and 20% for validation. You may be wondering, “ what about the split into train and validation?”, well the NeuralNetClassifier class takes care of this as well. Finally, on line 9 we execute the “ fit” method, which will be in charge of performing the entire training phase. Obviously, they are not the only parameters we can define in this class, however for practicality, we will only show those already mentioned in this example. This class receives a series of important parameters (line 7) which are: the PyTorch model, the number of epochs, learning rate, batch size and optimizer. In line 4 we are importing the class that will serve as a wrapper for our PyTorch model. In line 2 we are importing the PyTorch model (which was defined in the previous section). If you want to access the full implementation, take a look at: So, first we are going to create a simple model for the classification of wines with respect to the aforementioned dataset, then we have: In order to know how SKORCH works when training a PyTorch model, we are going to create a neural network to predict the well-known wines dataset. Great, so far we already know what SKORCH is, what are its components and the advantages of using it, it is time to see an example to better understand how it works, let’s go for it! PyTorch Model In this case, SKORCH would serve as the prototype tool for the training, tuning and optimization phase of PyTorch models. On the other hand, we can see SKORCH as the “ equivalent” to the Keras API, which extends from Tensorflow to accelerate, simplify and speed up the prototyping of neural network models. Benefits from PyTorch and scikit-learn into SKORCH | Image by Author | Logos taken from original sources On the other hand, we observe that scikit-learn already known functions are extended to be able to train, evaluate, tune and optimize machine learning models, this combination makes SKORCH a powerful tool.įigure 2. As we can see, from the PyTorch side, the capabilities to prototype a model and handle datasets are used. ![]() In figure 2 we can see the capabilities of PyTorch and scikit-learn that compound SKORCH. SKORCH professes the philosophy : “Be a scikit-learn API, hackable, do not hide PyTorch, do not reinvent the wheel” All this process is simplified by SKORCH, since it is a wrap based on scikit-learn, hence extends the functions that already carry out these processes. It is common for the training module of a PyTorch model to be developed in one or more functions, however, when it is necessary to evaluate the model or optimize to find the optimal parameters, additional functions need to be developed. SKORCH tries to simplify and streamline various processes in the training phase of a PyTorch model. PyTorch is one of the most used frameworks for the development of neural network models, however, some phases take development time and sometimes it becomes a somewhat impractical part. PyTorch + SciKit-Learn = SKORCH | Image by Author | Logos taken from original sources
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