Jeff Heaton, the author of Introduction to Neural Networks in Java, offers a few more. So what about the size of the hidden layer(s)-how many neurons? There are some empirically derived rules of thumb of these, the most commonly relied on is ' the optimal size of the hidden layer is usually between the size of the input and size of the output layers'. One hidden layer is sufficient for the large majority of problems. One issue within this subject on which there is a consensus is the performance difference from adding additional hidden layers: the situations in which performance improves with a second (or third, etc.) hidden layer are very few. Of course, you don't need an NN to resolve your data either, but it will still do the job.īeyond that, as you probably know, there's a mountain of commentary on the question of hidden layer configuration in NNs (see the insanely thorough and insightful NN FAQ for an excellent summary of that commentary). How many hidden layers? Well, if your data is linearly separable (which you often know by the time you begin coding a NN), then you don't need any hidden layers at all. So those few rules set the number of layers and size (neurons/layer) for both the input and output layers. In which case the output layer has one node per class label in your model. If the NN is a classifier, then it also has a single node unless softmax is used If the NN is a regressor, then the output layer has a single node. Regression Mode returns a value (e.g., price). Is your NN going to run in Machine Mode or Regression Mode (the ML convention of using a term that is also used in statistics but assigning a different meaning to it is very confusing)? Machine mode: returns a class label (e.g., "Premium Account"/"Basic Account"). Determining its size (number of neurons) is simple it is completely determined by the chosen model configuration. Like the Input layer, every NN has exactly one output layer. Some NN configurations add one additional node for a bias term. Specifically, the number of neurons comprising that layer is equal to the number of features (columns) in your data. With respect to the number of neurons comprising this layer, this parameter is completely and uniquely determined once you know the shape of your training data. Simple-every NN has exactly one of them-no exceptions that I'm aware of. So every NN has three types of layers: input, hidden, and output.Ĭreating the NN architecture, therefore, means coming up with values for the number of layers of each type and the number of nodes in each of these layers. Following this schema will give you a competent architecture but probably not an optimal one.īut once this network is initialized, you can iteratively tune the configuration during training using a number of ancillary algorithms one family of these works by pruning nodes based on (small) values of the weight vector after a certain number of training epochs-in other words, eliminating unnecessary/redundant nodes (more on this below). In particular, the link describes one technique for programmatic network configuration, but that is not a " standard and accepted method" for network configuration.īy following a small set of clear rules, one can programmatically set a competent network architecture (i.e., the number and type of neuronal layers and the number of neurons comprising each layer). I realize this question has been answered, but I don't think the extant answer really engages the question beyond pointing to a link generally related to the question's subject matter.
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