void NeuralNetworkSimpleInternal::write(TextFileWriter const& _file) const throw()
{
const int intFieldWidth = 4;
const int size = 128;
char buf[size];
TextFileWriter file = _file;
file.write("NeuralNetworkSimple:v1\n");
snprintf(buf, size, "learnRate: %f actFuncOffset: %f\n", getLearnRate(), getActFuncOffset());
file.write(buf);
snprintf(buf, size, "Layers:%*d\n", intFieldWidth, getNumLayersIncludingInput());
file.write(buf);
snprintf(buf, size, "Layer %*d:%*d\n", intFieldWidth, 0,
intFieldWidth, numInputs);
file.write(buf);
for(int layer = 0; layer < getNumLayersExcludingInput(); layer++)
{
snprintf(buf, size, "Layer %*d:%*d\n", intFieldWidth, layer+1,
intFieldWidth, getNumNodesOnLayer(layer));
file.write(buf);
}
for(int layer = 0; layer < getNumLayersExcludingInput(); layer++)
{
const int numNodes = getNumNodesOnLayer(layer);
for(int node = 0; node < numNodes; node++)
{
NumericalArray<float> weights;
float threshold;
get(layer, node, &weights, threshold);
snprintf(buf, size, "%*d %*d %*d %.16f\n", intFieldWidth, layer,
intFieldWidth, node,
intFieldWidth, -1,
threshold);
file.write(buf);
const int numWeights = weights.size();
for(int weight = 0; weight < numWeights; weight++)
{
snprintf(buf, size, "%*d %*d %*d %.16f\n", intFieldWidth, layer,
intFieldWidth, node,
intFieldWidth, weight,
weights[weight]);
file.write(buf);
}
}
}
}
void SimpleTrainer::Train(
Network &network, vector<TrainingSample> &trainingSamples, unsigned iterations) {
random_shuffle(trainingSamples.begin(), trainingSamples.end());
curSamplesIndex = 0;
for (unsigned i = 0; i < iterations; i++) {
float lr = getLearnRate(i, iterations);
TrainingProvider samplesProvider = getStochasticSamples(trainingSamples);
pair<Tensor, float> gradientError = network.ComputeGradient(samplesProvider);
network.ApplyUpdate(gradientError.first * -lr);
}
}
