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The input layer of a neural network is composed of artificial input neurons, and brings the initial data into the system for further processing by subsequent layers of artificial neurons. The input layer is the very beginning of the workflow for the artificial neural network.
Artificial neural networks are typically composed of input layers, hidden layers and output layers. Other components may include convolutional layers and encoding or decoding layers.
One of the distinct characteristics of the input layer is that artificial neurons in the input layer have a different role to play – experts explain this as the input layer being constituted of “passive” neurons that do not take in information from previous layers because they are the very first layer of the network. In general, artificial neurons are likely to have a set of weighted inputs and function on the basis of those weighted inputs – however, in theory, an input layer can be composed of artificial neurons that do not have weighted inputs, or where weights are calculated differently, for example, randomly, because the information is coming into the system for the first time. What is common in the neural network model is that the input layer sends the data to subsequent layers, in which the neurons do have weighted inputs.