SuperNN
1.0.0
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Batch backpropagation. More...
Public Member Functions | |
Batch () | |
virtual | ~Batch () |
virtual unsigned | train (Network &net, const Data &data, double dmse=0, unsigned max_epochs=1000) |
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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ImplBackprop () | |
virtual | ~ImplBackprop () |
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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... | |
virtual void | prepare (Network &net) |
Prepares the trainer and a neural network for training. More... | |
Additional Inherited Members | |
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double | eta |
Initial learning rate. More... | |
double | eta_df |
Learning rate decrease factor (must be <= 1) More... | |
double | eta_if |
Learning rate increase factor (must be >= 1) More... | |
double | eta_max |
Maximum learning rate. More... | |
double | eta_min |
Minimum learning rate. More... | |
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virtual void | update_eta (double mse, double last_mse) |
Calculates the new learning rate (and updates it), based on the mean squared error last change. More... | |
virtual void | update_weights (Network &net, double factor=1) |
Updates the weights using the accumulated error partial derivative calculated by derror_acc(). More... | |
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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... | |
Batch backpropagation.
The weights are changed only after all training patterns have been presented (in the end of each epoch).
Definition at line 171 of file training.hpp.
SuperNN::Batch::Batch | ( | ) |
Definition at line 202 of file training.cpp.
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virtual |
Definition at line 206 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::ImplBackprop.
Definition at line 210 of file training.cpp.