Understanding Diversity in Deep Neural Networks and Model Selection
Deep Learning, which revolves around the deployment of deep neural networks (DNNs), has revolutionized numerous fields, from computer vision to natural language processing. One intriguing aspect of DNNs that often leaves newcomers puzzled is that different DNN models can be generated, even when all the hyperparameters are identical. In this blog post, we'll demystify this phenomenon and provide guidance on how to select the most suitable model for your task.
Why are different DNNs created with identical hyperparameters?
The key to understanding the reason behind this lies in how DNNs are trained. The training process typically starts with the initialization of the weights (parameters of the model) with random values. These initial values influence the model's learning trajectory during training. Since the weights are initialized randomly, even if you use the same hyperparameters (like learning rate, batch size, number of layers, etc.), you'll end up with different models each time you train.
Another contributing factor is the non-deterministic nature of many operations in deep learning frameworks and the use of stochastic optimization algorithms, such as mini-batch gradient descent.
Stochasticity in the training process helps to avoid local minima in the loss landscape and thus improves the generalization of the models, but it also means that you might get different models with different performances even with the same hyperparameters.
How to Select the Right Model?
Choosing the best model from a set of trained models isn't always straightforward, but here are some generally accepted strategies:
Validation Set Performance: The most common approach is to use a separate validation set, distinct from the training and test sets, to compare models. The model that performs best on the validation set is chosen.
Cross-validation: If you have limited data, you may choose to use cross-validation. This involves dividing your dataset into 'k' subsets, training the model on k-1 subsets, and validating on the remaining subset. This is repeated until each subset has been used for validation, and the model's average performance across the iterations is used to compare models.
Occam’s Razor: When two models perform similarly on the validation set, it is often better to choose the simpler model. A simpler model is less likely to overfit and often generalizes better to unseen data.
Ensemble Methods: Instead of choosing a single model, you can create an ensemble of models. Ensembles can provide better performance by averaging the predictions from multiple models, which can help to mitigate the individual weaknesses of each model.
Remember, the ultimate goal is to have a model that generalizes well to unseen data. Therefore, the best model isn't necessarily the one that performs the best on the training set, but rather the one that performs the best on new, unseen data.
Conclusion
While it might initially be surprising to find that different DNN models can be created even when using identical hyperparameters, understanding the stochastic nature of the training process and the role of random weight initialization can help to clear up the confusion. Selecting the right model can often involve a balance between complexity and performance, and leveraging strategies such as validation sets, cross-validation, Occam's Razor, and ensemble methods can be crucial to achieving the best performance on unseen data.
As always in machine learning, a keen understanding of your data, your model, and your tools, paired with thoughtful experimentation, will generally yield the best results.