Understanding hyperparameter tuning in neural network(ANN Hyperparameter tuning) is a crucial step in training Artificial Neural Networks (ANNs). That is because ANN involves selecting the best set of hyperparameters to optimize the model. We can also understand hyperparameters as these are the set of tuning methods that the model cannot learn from the data set. So we can say everything necessary for the model training other than the things that can be learned from the data are called hyperparameters. There are several hyperparameters such as learning rate, epoch, loss, batch size, activation function, and even the number of layers and the number of neurons in each layer can also come under the category of hyperparameters.
- Model Performance: It is very important for the best performance of the model, for example: if the learning rate that is one of the hyperparameters is set very high then it will be impossible for the model to converge. That is just because the learning steps at which the model is learning will be very high that it will miss out on most of the data points due to which divergence will occur.
- Training Efficiency: With experience, we will come to realize which hyperparameters are the best for what type of data. That will determine the best training of the model because the selection of the best hyperparameters will decide how well our model will train on the given dataset.
- Optimal Learning: Hyperparameter tuning will define how fast and how well the model converges to the lowest possible loss during training. By hyperparameter tuning, we can enable our model to get the lowest possible loss which is the point where the model performs the best Commonly Tuned Hyperparameters
- Learning Rate: It is the size of the steps that the model uses to come from the highest point to the lowest point (converge). Ideally or by default the value for the learning rate is kept as 0.001 but this is not the ideal value for all types of training data which is why it needs to be tuned as the data changes. On the other hand, a low or too high learning rate is not good for the training that is why we must go for an optimal rate of learning rate to get the optimal performance of our model.
- Batch Size: Another hyperparameter is batch size and this can be understood by the example: a whole book is divided into chapters so that students can feel it easy to learn the entire book chunk by chunk. This chunk of data in the form of a chapter is filled in batch size. So in deep learning before feeding our data into the ANN we first divide our whole data into batches of some size called batch size. This helps our model to learn in chunks and more effectively. These methods also cater to the storage and memory issues of our device.
- Activation Functions: It is the function that determines the function that is applied to the outputs of each neuron to introduce nonlinearity to the function. This hyperparameter is the most important one and it makes the ANN distinct and more efficient than that of simple linear models. This nonlinearity helps the ANN to understand complex data and solve complex problems based on the data.
Some of the common examples of activation functions are:
- Relu: It is specifically used in hidden layers; it is also called the max function and is the most simple activation function.
- Sigmoid: in the case of binary classification, it is used in the output layer, and in the case of multiclass classification, it can be used in hidden layers too.
- Softmax: in multiclass classification, it is used in the output layer of our Neural network
Hyperparameter tuning is more about the experience than about coding, it is essential for optimizing the performance of ANNs. It can be done by carefully selecting and tuning hyperparameters by looking at the loss and accuracy changes with the change in them. If we want our model to get the best performance out of it, then we must do the best hyperparameter tuning as well.
