Can hidden layer be larger than input layer?

Can hidden layer be larger than input layer?

The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer.

Is the output layer a hidden layer?

The Neural Network is constructed from 3 type of layers: Input layer — initial data for the neural network. Hidden layers — intermediate layer between input and output layer and place where all the computation is done. Output layer — produce the result for given inputs.

How do you determine the number of hidden nodes?

  1. The number of hidden neurons should be between the size of the input layer and the size of the output layer.
  2. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer.
  3. The number of hidden neurons should be less than twice the size of the input layer.

What does hidden layer between input and output mean?

Adding a hidden layer between the input and output layers turns the Perceptron into a universal approximator, which essentially means that it is capable of capturing and reproducing extremely complex input–output relationships.

How to choose the number of hidden layers and nodes?

The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer.

Can a hidden layer be capable of universal approximation?

A single hidden layer neural networks is capable of [universal approximation] https://en.wikipedia.org/wiki/Universal_approximation_theorem.

What happens when there are too many hidden layers?

Both the number of hidden layers and the number of neurons in each of these hidden layers must be carefully considered. Using too few neurons in the hidden layers will result in something called underfitting. Underfitting occurs when there are too few neurons in the hidden layers to adequately detect the signals in a complicated data set.