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The output layer in an artificial neural network is the last layer of neurons that produces given outputs for the program. Though they are made much like other artificial neurons in the neural network, output layer neurons may be built or observed in a different way, given that they are the last “actor” nodes on the network.
A typical traditional neural network has three types of layers: one or more input layers, one or more hidden layers, and one or more output layers. Simple feedforward neural networks with three individual layers provide basic easy-to-understand models. More sophisticated, innovative neural networks may have more than one of any type of layer – and as mentioned, each type of layer may be built differently. A traditional artificial neuron is composed of some weighted inputs, a transformation function and activation function corresponding to the biological neuron’s axon. However, output layer neurons may be designed differently in order to streamline and improve the end results of the iterative process.
In a sense, the output layer coalesces and concretely produces the end result. However, to understand the neural network better, it is important to look at the input layer, hidden layers and output layer together as a whole.