DL neural networks are elementary to define due to the widespread use of open source frameworks.
Nonetheless, neural networks continue to be difficult to construct and train. Neural network tuning may be complex. If the system hyperparameters are not properly selected, the network cannot learn fast enough, if at all. This glossary seeks to give some general guidelines for how to tune the neural network.
Let’s start neural network parameter tuning with hidden layers. The amount of hidden layers is an important hyperparameter that affects the underlying basis of a neural network, and it may be divided into three categories:
- 0 – If a set of data is linearly separable, no hidden layer is required. Because the neural network is designed to solve difficult issues, you don’t even need to utilize it when all you require is a linear border.
- 1 or 2 – If a data set cannot be separated linearly, a hidden layer is required. And, in most cases, one hidden layer is adequate because the degree that a system increases by extending hidden layers is minor in comparison to the additional labor required. As a result, in many practical situations, one or two concealed layers suffice.
- Many – Finally, if you’re attempting to tackle a complex issue like object categorization, you’ll need numerous hidden layers that perform adjustments to their inputs.
Now next factor you should decide is how many neurons to add to the hidden layer. Finding an optimal number is crucial since having fewer available neurons can result in underfitting while having too many can result in overfitting and prolonged training time. Factually, it’s better to pick a quantity around input and output sizes, which varies depending on the complexity of the task.
- If the problem is straightforward and the input-output relationship is evident, then a beginning point of around 2/3 of the input size might be a reasonable starting point. However, if the connection is complicated, the number might range between the input size and far less than twice the input size.
It appears ambiguous, but there is no precise method that you can follow because tuning neural networks are still an active study area and each value is unique to each situation. As a result, you should only take them as beginning points and will need to experiment to see which value works best for your scenario.
Learning rate, batch size, and epoch
Finally, we will examine hyperparameters tuning in neural networks that are linked to training duration and performance.
As batch size grows, each batch becomes more comparable to the entire data set since each batch contains more observations. This implies that every batch will be similar to the others. As a result, its noise will reduce, making it rational to employ a high learning rate for a shorter training time. When we utilize a tiny batch size, however, noise increases. As a result, we utilize a low learning rate to compensate for the noise. So, what batch size should we employ? People are still investigating, but we may learn from the experiences of others.
A high batch size may result in poor generalization, as has been demonstrated empirically. When we utilize a small batch size, the disturbance helps a network escape a local minimum, resulting in improved accuracy. It also tries to settle to a suitable solution quicker than a big batch size network. In principle, a batch size of 32 might be a decent starting point, however, this number is highly dependent on sample size, issue complexity, and processing environment. As a result, a grid search may also be useful.
We normally start with 0.1 for the learning rate, but we may also utilize a grid search between 0.1 to 1e-5. When the training rate is low, more iterations are required to locate a minimum point. As a result, more epochs are necessary, but how many?
- Neural network loss function tuning may be utilized in either pretraining to learn better weights or in classification- on the output layer to achieve a result.
The loss function that you select will be determined by the objective of your net. Choose reconstruction entropy for pretraining. Use multiclass cross volatility for classification.
Lastly, the number of epochs required for convergence varies depending on the issue and random initialization. As a result, there isn’t an ideal number of epochs that fits every circumstance. In practice, we frequently set the number of epochs to a large value and employ early stopping, so that the neural network quits learning when an improvement from changing its values does not exceed a certain threshold.