Hi Muhammad
In general, you cannot analytically calculate the number of layers or the number of nodes to use per layer in an artificial neural network to address a specific real-world problem. The number of layers and the number of nodes in each layer are model hyperparameters that you must specify.
The simplest method to choose number of neurons is probably to apply a k-fold cross validation.
You can design an automated search to test different network configurations. Some popular search strategies include:
- Random: Try random configurations of layers and nodes per layer.
- Grid: Try a systematic search across the number of layers and nodes per layer.
- Heuristic: Try a directed search across configurations such as a genetic algorithm or Bayesian optimization.
- Exhaustive: Try all combinations of layers and the number of nodes; it might be feasible for small networks and datasets
You can also determine the topology of the neural network using principal component analysis (PCA) and K-means clustering. PCA is used to determine the hidden layers number, while clustering is used to determine the hidden neurons number for corresponding hidden layer. The selected topology is then used for prediction. Then the performance of topology determination using PCA and clustering is then compared with several other methods.
Hope it helps !
