The efficacy of any machine learning model can primarily be attributed to the validity of the data used for training and the model’s ability to generalize well from this information. Therefore, training a model with the lowest validation loss is critical. By training a model effectively, we are ensuring that the model is neither underfitting nor overfitting.
The validation loss corresponds to the error rate on a hold-out sample of the training set which is not used in training phase and helps us measure the model’s ability to generalize. The primary goal in any machine learning model is to achieve the lowest validation loss, thereby indicating that our model is learning and generalizing well.
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A Method to Save Models with Best Validation Loss
Training a model involves several iterations, also known as epochs, and the validation loss differs for each epoch. Python provides several libraries like Keras that stores these models at each epoch. We can use a feature called ModelCheckpoint to save the model whenever the validation loss improves from the previous one.
from keras.callbacks import ModelCheckpoint # specify the path to save the model filepath="weights.best.hdf5" # initiate the ModelCheckpoint function checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min') # define the list of callbacks callbacks_list = [checkpoint] # fit the model model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, callbacks=callbacks_list, verbose=0)
Understanding the Code: Step By Step
Let’s go through the code snippet step by step to understand each part:
1. Import the ModelCheckpoint function from Keras.
2. Define the filepath where you want to save the model using the .hdf5 format. This format is designed to store and organize large amounts of data.
3. Initiate the ModelCheckpoint function. Here, we are monitoring ‘val_loss’ with a ‘min’ mode indicating that we aim at minimizing this value. With ‘save_best_only=True’, the latest best model according to the quantity monitored will not be overwritten.
4. The checkpoint is then added to the callbacks list. Other training parameters like EarlyStopping can also be included in this list.
5. The model is then trained on the data using model.fit(). The callbacks argument takes in the callbacks_list.
The code saves the model as ‘weights.best.hdf5’ at each epoch where the validation loss is at its minimum.
Key Python Libraries for Saving Models
Python provides a rich ecosystem of libraries for saving machine learning models. The ones primarily used are:
- Keras: This high-level neural networks API capable of running on top of other lower level APIs like TensorFlow allows the easy and fast prototyping of deep learning models. ModelCheckpoint function in Keras provides the flexibility to monitor various parameters during the training process, and save the model or weights at various stages.
- TensorFlow: Python’s open-source machine learning framework allows developers to create complex ML models with ease. It provides a SavedModel module which is a universal serialization format for TensorFlow models.
- Scikit-learn: This popular Python library for machine learning provides utilities for saving and loading models. Joblib module is typically used for serializing Python objects with large numpy arrays – a common scenario in machine learning workflows.
The choice of library largely depends on the requirements of your project, your familiarity with the library, and the complexity of your model. Nonetheless, Python provides ample resources to train, assess, save and load models with ease.
