How to save trained models in Python
Once you’ve trained a machine learning model, it’s important to be able to save it so that you can use it later without having to retrain it. This can be especially useful when you’ve trained a model that takes a long time to run or when you want to share your model with others. In this blog post, we’ll take a look at how you can use Python to save a trained model.
The most common way to save a trained model in Python is by using the pickle library. Pickle allows you to save a Python object, such as a trained model, to a file on disk. Once the model is saved, you can load it back into your Python script and use it to make predictions.
To save a trained model using pickle, you first need to import the library, then you can use the dump function to save the model to a file. You can specify the name of the file and the model to be saved. To load the model back, you can use the load function to read the model from the file and load it into your script.
Another way to save trained models is using joblib library which is an alternative to pickle but it is more efficient on big data and large numpy arrays. Saving the model in joblib is similar to pickle, instead of dump we use dump method.
Another library that can be used to save and load trained models is Tensorflow’s SavedModel. The SavedModel is a standalone serialization format for TensorFlow objects, supported by TensorFlow serving as well as TensorFlow implementations other than Python.
It’s important to note that saving a trained model only saves the model’s architecture and parameters, it doesn’t save any information about the dataset or preprocessing steps used to train the model. To be able to use the model for predictions, you need to have that information as well.
In conclusion, saving a trained model is an important step after training it, it allows you to use it later without having to retrain it. Python provides several libraries such as pickle, joblib and Tensorflow’s SavedModel to save a trained model. Each library has its own advantage and disadvantage, but the process of saving a model is generally similar, it saves only the model’s architecture and parameters, and not the dataset or preprocessing information.
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