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@@ -233,7 +233,7 @@ The algorithm is defined in Riemannian metrics for neural networks I: feedforwar To use this module, simply replace `nn.Linear(ninput,noutput)` with `nnx.QDRiemaNNLinear(ninput,noutput)`. As always, the step-size must be chosen accordingly. Two additional arguments are also possible: -* gamma (default=0.01): determine the update rate of the metric for a minibatch setting, i.e., (1-gamma) * oldMetric + gamma newMetric. Smaller minibatches require a smaller gamma. +* gamma (default=0.01): determine the update rate of the metric for a minibatch setting, i.e., (1-gamma) * oldMetric + gamma newMetric. Smaller minibatches require a smaller gamma. A default value depending on the size of the minibatches is `gamma = 1. - torch.pow(1.-1./nTraining,miniBatchSize)` where `nTraining` is the number of training examples of the dataset and `miniBatchSize` is the number of training examples per minibatch. * qdFlag (default=true): Whether to use the quasi-diagonal reduction (true) or only the diagonal (false). The former should be better. To implement a natural gradient descent, one should also use a module for generating the pseudo-labels. |