Table 1.

Hyperparameters related to the training of a neural network [47].

Hyperparameters	Description
Learning Rate	The learning rate defines how quickly a network updates its parameters. For the classification problem, it is important to choose the optimal learning rate to minimize the loss function. A low learning rate slows down the learning process but converges smoothly. A larger learning rate speeds up the learning but may not converge.
Momentum	Momentum helps to know the direction of the next step with the knowledge of the previous steps. It helps to prevent oscillations.
Number of Epochs	The number of epochs is the number of times the whole training data are introduced to the network. It is important to determine an ideal epoch number to prevent overfitting.
MiniBatch Size	The larger minibatch size causes running of the model for a long period of time with constant weights that causes overall performance loses and increases the memory requirements. Carrying out the experiments with small minibatch sizes can be more beneficial.