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cleaning + Network getter and setter for input / output size

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Shin
2016-05-03 22:03:03 +02:00
parent 6a17694a6b
commit 58f7f8f69b
9 changed files with 85 additions and 58 deletions
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33
include/NeuralNetwork/Cascade/Cascade.h → include/NeuralNetwork/Cascade/Network.h
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@@ -10,7 +10,7 @@ namespace NeuralNetwork {
* @brief Constructor for Network
* @param _inputSize is number of inputs to network
*/
Network(std::size_t inputSize, std::size_t outputSize) : NeuralNetwork::Network(), _inputSize(inputSize), _outputSize(outputSize) {
Network(std::size_t inputSize, std::size_t outputSize) : NeuralNetwork::Network(inputSize,outputSize) {
_neurons.push_back(std::make_shared<BiasNeuron>());
for(std::size_t i = 0; i < inputSize; i++) {
@@ -29,16 +29,16 @@ namespace NeuralNetwork {
compute[0] = 1.0;
for(std::size_t i = 1; i <= _inputSize; i++) {
for(std::size_t i = 1; i <= _inputs; i++) {
compute[i] = input[i - 1];
}
// 0 is bias, 1-_inputSize is input
for(std::size_t i = _inputSize + 1; i < _neurons.size(); i++) {
for(std::size_t i = _inputs + 1; i < _neurons.size(); i++) {
compute[i] = (*_neurons[i].get())(compute);
}
return std::vector<float>(compute.end() - _outputSize, compute.end());
return std::vector<float>(compute.end() - _outputs, compute.end());
}
std::size_t getNeuronSize() const {
@@ -52,16 +52,16 @@ namespace NeuralNetwork {
std::shared_ptr<NeuronInterface> addNeuron() {
_neurons.push_back(std::make_shared<Neuron>());
auto neuron = _neurons.back();
neuron->setInputSize(_neurons.size() - _outputSize);
neuron->setInputSize(_neurons.size() - _outputs);
// 0 is bias, 1-_inputSize is input
std::size_t maxIndexOfNeuron = _neurons.size() - 1;
// move output to right position
for(std::size_t i = 0; i < _outputSize; i++) {
for(std::size_t i = 0; i < _outputs; i++) {
std::swap(_neurons[maxIndexOfNeuron - i], _neurons[maxIndexOfNeuron - i - 1]);
}
for(std::size_t i = 0; i < _outputSize; i++) {
_neurons[maxIndexOfNeuron - i]->setInputSize(_neurons.size() - _outputSize);
for(std::size_t i = 0; i < _outputs; i++) {
_neurons[maxIndexOfNeuron - i]->setInputSize(_neurons.size() - _outputs);
}
return neuron;
}
@@ -74,8 +74,8 @@ namespace NeuralNetwork {
return {
{"class", "NeuralNetwork::Recurrent::Network"},
{"inputSize", _inputSize},
{"outputSize", _outputSize},
{"inputSize", _inputs},
{"outputSize", _outputs},
{"neurons", neuronsSerialized}
};
}
@@ -86,7 +86,7 @@ namespace NeuralNetwork {
Network *net = new Network(inputSize, outputSize);
net->_neurons.clear();
for(const auto& neuronObj: obj["neurons"].as<SimpleJSON::Type::Array>()) {
for(const auto &neuronObj: obj["neurons"].as<SimpleJSON::Type::Array>()) {
net->_neurons.push_back(Neuron::Factory::deserialize(neuronObj.as<SimpleJSON::Type::Object>()));
}
@@ -94,9 +94,16 @@ namespace NeuralNetwork {
}
//I I H H O O 6
void randomizeWeights() {
for(std::size_t neuron = _neurons.size() - _outputs; neuron < _neurons.size(); neuron++) {
for(std::size_t weight = 0; weight < _neurons.size() - _outputs; weight++) {
_neurons[neuron]->weight(weight) = 1.0 - static_cast<float>(rand() % 2001) / 1000.0;
}
}
}
protected:
std::size_t _inputSize;
std::size_t _outputSize;
std::vector<std::shared_ptr<NeuronInterface>> _neurons = {};
SIMPLEJSON_REGISTER(NeuralNetwork::Cascade::Network::Factory, NeuralNetwork::Cascade::Network, deserialize)