An inordinately large number of neurons in the hidden layers can increase the time it takes to train the network. A second problem can occur even when the training data is sufficient. Overfitting occurs when the neural network has so much information processing capacity that the limited amount of information contained in the training set is not enough to train all of the neurons in the hidden layers.

First, too many neurons in the hidden layers may result in overfitting. Using too many neurons in the hidden layers can result in several problems.

Underfitting occurs when there are too few neurons in the hidden layers to adequately detect the signals in a complicated data set. Using too few neurons in the hidden layers will result in something called underfitting.

Both the number of hidden layers and the number of neurons in each of these hidden layers must be carefully considered. Though these layers do not directly interact with the external environment, they have a tremendous influence on the final output. The Number of Neurons in the Hidden Layers Deciding the number of neurons in the hidden layers is a very important part of deciding your overall neural network architecture.

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