During the learning phase, the connections with the next layer can be limited to a subset of neurons to reduce the weights to be updated, this learning optimization technique is called dropout. The dropout is therefore a technique used to decrease the overfitting within a network with many layers and/or neurons. In general, the dropout layers are positioned after the layers that possess a large amount of trainable neurons.
This technique allows setting to 0, and then excluding the activation of a certain percentage of the neurons of the preceding layer. The probability that the neuron's activation is set to 0 is indicated by the dropout ratio parameter within the layer, via a number between 0 and 1: in practice the activation of a neuron is held with probability equal to the dropout ratio, otherwise it is discarded, that is, set to 0.
The neurons by this transaction do not affect, therefore...
