SuperNN
1.0.0
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Neuron by Neuron algorithm. More...
Classes | |
struct | Hist |
Public Member Functions | |
NBN () | |
virtual | ~NBN () |
void | prepare (Network &net) |
Prepares the trainer and a neural network for training. More... | |
unsigned | train (Network &net, const Data &data, double dmse=0, unsigned max_epochs=100) |
Adjusts the synaptic weight of an artificial neural network in order to minimize the error (MSE for standard l2 training or MAE for specific l1 training). More... | |
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virtual | ~TrainingAlgorithm () |
virtual void | clear_derror_acc (Network &net) |
Clears the accumulated error partial derivatives. More... | |
virtual double | delta (Network &net, unsigned l, unsigned n) |
Calculates the local error gradient for each neuron. More... | |
virtual void | derror_acc (Network &net) |
Accumulates the error partial derivative in respect to the weights, for each connection of the neural network. More... | |
Additional Inherited Members | |
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virtual void | check (const Network &net, const Data &data) const |
Checks if the dimensions match and if the training algorithm can be used with a given network and data. More... | |
Consists in the application of the Levenberg–Marquardt optimization algorithm, but building the Quasi-Hessian matrix directly, without the need for building the Jacobian matrix, thus saving memory. For more information, see [Wilamowski, Yu, 2010].
Definition at line 234 of file training.hpp.
SuperNN::NBN::NBN | ( | ) |
Definition at line 404 of file training.cpp.
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virtual |
Definition at line 409 of file training.cpp.
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virtual |
Prepares the trainer and a neural network for training.
Usually this will initialize the learning rates, previous error informations and prepare the internal data structures for the training. Between a prepare() and a train(), the neural network and the trainer shouldn't be modified.
net | Neural network to be prepared |
Reimplemented from SuperNN::TrainingAlgorithm.
Definition at line 414 of file training.cpp.
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virtual |
Adjusts the synaptic weight of an artificial neural network in order to minimize the error (MSE for standard l2 training or MAE for specific l1 training).
net | Artificial neural network to be trained |
data | Training data |
dmse | Desired mean squared error (or MAE when applied). Stopping condition |
max_epochs | Maximum number of epochs to train the network. Stopping condition |
Implements SuperNN::TrainingAlgorithm.
Definition at line 557 of file training.cpp.