Saving and Loading a Trained Model

Although the process of manipulating a dataset and training the right model is crucial for developing a machine learning project, the work does not end there. Knowing how to save a trained model is key as this will allow you to save the hyperparameters, as well as the values for the weights and biases of your final model, so that it remains unchanged when it is run again.

Moreover, after the model has been saved to a file, it is also important to know how to load the saved model in order to use it to make predictions on new data. By saving and loading a model, we allow for the model to be reused at any moment and through many different means.

Saving a Model

The process of saving a model is also called serialization, and it has become increasingly important due to the popularity of neural networks that use many parameters (weights and biases) that are randomly initialized every time the model is trained, as well as due to the introduction...