Choosing the Right Number of Units and Hidden Layers for your Deep Neural Network

Determining the right number of units (neurons) and hidden layers for a deep neural network is more art than science. It often requires a mix of experience, intuition, and a healthy amount of trial and error. However, some strategies can guide you in making these decisions. This article aims to shed light on this process and provide a structured approach.

Starting Point: A Small Network

In general, it's a good idea to start with a smaller network with fewer layers and units. The benefit of starting small is that your network will train faster, which allows you to iterate and experiment more quickly. This rapid iteration can help you determine a baseline performance and understand how well your initial model is learning from your data.

You can use a simple network with one or two hidden layers and a modest number of units in each layer (maybe 16-32). Train this network using your chosen optimization algorithm and see how it performs.

Hyperparameter Tuning

Once you have a baseline network, perform hyperparameter tuning. Some key hyperparameters you may want to tune include the learning rate, batch size, and the regularization parameter. There are various techniques to do this, including grid search, random search, or more advanced methods like Bayesian Optimization.

Remember that while tuning, you should use a separate validation set to avoid overfitting your model to the training data.

Expanding the Network: More Units

If your initial small network isn't giving you satisfactory results, and you suspect that your model is underfitting the data (i.e., high bias), you may want to increase its complexity. One way to do this is to increase the number of units in your existing layers.

Adding more units allows each layer to learn more complex representations. However, remember that increasing the number of units also increases the computational complexity.

After increasing the number of units, it's essential to go back and perform hyperparameter tuning again. The optimal hyperparameters for your smaller network might not be the best ones for your larger network.

Expanding the Network: More Layers

If increasing the number of units isn't helping or if you suspect that your model could benefit from learning hierarchical representations, consider adding more layers.

Deep networks are powerful because they can learn hierarchical representations. Lower layers can learn simple features (like edges in an image), while higher layers combine these simple features into more complex ones (like shapes or objects).

Again, remember that adding more layers increases the computational complexity. And similarly to before, after increasing the number of layers, you should perform hyperparameter tuning again.

Other Considerations

Other aspects can also influence the number of units and layers you should use. For example, the amount of available data, the complexity of the task, and the computational resources at your disposal can all constrain your choices.

Remember that the process outlined above is iterative. You might have to go through several rounds of expanding your network and tuning your hyperparameters to get to a satisfactory model. And, always keep in mind the risk of overfitting: if your model performs well on the training data but poorly on unseen data, it might be too complex and might be overfitting. Techniques like regularization, dropout, and data augmentation can help mitigate this.

In conclusion, there isn't a one-size-fits-all answer to choosing the number of units and hidden layers in a deep neural network. It's an iterative process that involves experimenting, tuning, and learning from each step. But, with patience and persistence, you'll find the right architecture that best suits your specific task.