Propagation avant


Révision datée du 15 décembre 2021 à 10:48 par JBM (discussion | contributions) (JBM a déplacé la page Forward pass in backpropagation vers Propagation avant)

en construction

Définition

In the forward pass in backpropagation, each training pattern is presented to the input units of the network.

The hidden unit activations are computed from the inputs and input-to-hidden unit weights, and then (in the case of a 3-layer network, with only a single layer of hidden units) the outputs are computed using the hidden layer activations and the current hidden-to-output weights.

Certain statistics are kept from this computation, and used in the backward pass. The target outputs from each training pattern are compared with the actual activation levels of the output units - the difference between the two is termed the error. Training may be pattern-by-pattern or epoch-by-epoch. With pattern-by-pattern training, the pattern error is provided directly to the backward pass. With epoch-by-epoch training, the pattern errors are summed across all training patterns, and the total error is provided to the backward pass.

Français

propagation avant

propagation directe

propagation vers l'avant

Anglais

forward pass



Source : INWS machine learning dictionary

Contributeurs: Jean Benoît Morel, wiki