Neural Network Tuning

Introduction to Neural Network Tuning

Deep Learning neural networks are quite simple to define due to the prevalence of available open source frameworks. However, these networks can be challenging to build and train. The proper configuration of a neural network is essential, and if the hyperparameters are not accurately chosen, it may either fail to learn efficiently or not at all. This piece aims to provide some basic guidance for the tuning of a neural network.

Hidden Layers Configuration

Firstly, we need to consider hidden layers. The total number of hidden layers is a key hyperparameter which influences the fundamental operations of a neural network. It can be categorized into three parts:

  1. Zero Hidden Layers: In a scenario where the data is linearly separable, no hidden layer is needed. Neural networks are typically leveraged to handle complex issues so, they’re not required when all that's necessary is a linear boundary.
  2. One or Two Hidden Layers: If a dataset is non-linear, then a hidden layer is needed. Generally, a single layer can suffice as the advantage gained by adding more layers is marginal when compared to the additional workload involved. So for many practical scenarios, one or two hidden layers are adequate.
  3. Multiple Hidden Layers: If a complex problem like object classification is to be tackled, multiple hidden layers are required, each layer modifying their inputs.

Neurons in Hidden Layers

The subsequent step after deciding on the hidden layers is determining the number of neurons to add to the hidden layer. Identifying the optimal number is crucial as having too few neurons may result in underfitting, while having too many can result in overfitting and longer training time. A general rule of thumb is to pick a number close to input and output sizes, varying according to the task complexity.

Training Cycle Hyperparameters

When it comes to hyperparameters attached to the training cycle and performance, considerations like learning rate, batch size, and epoch are essential. As the batch size increases, each batch becomes more representative of the entire dataset, thereby reducing noise and enabling a high learning rate for fewer training times.

Batch Size Considerations

Setting the batch size can be tricky. High batch size might lead to poor generalization, while a small batch size increases disturbance but also aids in better accuracy and quicker settling. The batch size varies depending on the sample size, problem complexity, and computational environment.

Learning Rate and Epochs

Equally important is setting the right learning rate and epoch. An empirically proven starting point for the learning rate often is 0.1, with a grid search range of 0.1 to 1e-5. A low learning rate requires more iterations; hence, more epochs are needed. The number of epochs required depends on the problem at hand and the random initialization.

Loss Function Tuning

Finally, tuning the neural network's loss function can be pivotal in both pretraining and achieving results in the output layer. For pretraining, reconstruction entropy can be selected, while for classification, multiclass cross volatility is the optimal choice. We often opt for a large number of epochs and apply early stopping to ensure that the network ceases learning when changes don't surpass a specific threshold.

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