There is no clear and fast rule for determining the exact number of neurons and hidden layers required to design a neural network architecture given a business problem. The size of the hidden layer in a neural network should be somewhere between the size of the output layers and that of the input layers. However, there are a few basic ways that might help you get a head start on constructing a neural network architecture:
The best method to approach any unique real-world predictive modelling problem is to start with some basic systematic experimentation to see what would perform best for any given dataset based on previous experience working with neural networks in similar real-world situations. The network configuration can be chosen based on one's understanding of the problem domain and previous expertise with neural networks. The number of layers and neurons employed on similar issues is always a good place to start when evaluating a neural network's configuration.
It is best to start with simple neural network architecture and gradually increase the complexity of the neural network based on predicted output and accuracy.
