A hidden layer in an artificial neural network is a layer between input layers and output layers.
Neural networks perform ideally on account of hidden layers. Hidden layers perform multiple functions –data transformation, automatic feature creation and so on.
In hidden layers, artificial neurons take a set of weighted inputs and produce an output through an activation function.
How many hidden layers are used? What is the purpose? If there are more hidden layers/neurons, does it improve results?
As we know, artificial neural networks (ANNs) are inspired by biological neural networks. They are represented as a set of layers — input, hidden and output. Input and output layers are easily grasped. The number of neurons in the input layers equals the number of input variables in the data being processed. The number of neurons in the output layer equals the number of outputs associated with each input.
How to know the number of hidden layers? It is a classification problem.
Depending on the data, draw a decision boundary to separate the classes. The decision boundaries are expressed as a set of lines. The number of selected lines represents the number of hidden neurons in the hidden layer.
To connect the lines created by previous layer, a new hidden layer is added. This happens every time new connections are to be created among the lines of the previous hidden layer. The number of hidden neurons in each new hidden layer equals the number of connections to be made.
In ANNs, hidden layers are required if (and only if) the data must be separated non-linearly. In CNNs, the hidden layers consist of convolutional layers and normalization layers.
It is the hidden layer where all processing happens. These allow you to model complex data thanks to their nodes/neurons.
If data is less complex (fewer dimensions or features), there could be 1 to 2 hidden layers. For complex data, 3-5 hidden layers could be used. A CNN has typically three layers.
The layers between input and output is a hidden layer. A single hidden layer makes the network shallow. In deep neural networks, there are two or more hidden layers. It is a hyper-parameter.
All the computation is done on hidden layers. These hidden layers break down the function of neural network into specific transformations of data.
A neural network (NN), without a hidden layer, is simply linear regression. Of course, there is activation function. But inverse function of that activation function could be used. It is essentially a linear regression.
The size of the hidden layer is generally between the size of the input and output, say 2/3rd of the size of input layer plus the size of the output layer.
At least two hidden layers are sufficient to train the network.
All hidden layers use the same activation function. The output layer uses a different activation function. It depends upon the type of prediction required by the model.