blacklight/neuralpp
1
0
Fork 0
mirror of https://github.com/BlackLight/neuralpp.git synced 2024-12-28 04:05:12 +01:00
Code Issues Projects Releases Packages Wiki Activity

float -> double for everything, learning rate now always considered >= 0

Browse source
This commit is contained in:
blacklight 2009-08-08 18:05:02 +02:00
parent 1f65c8a26b
commit 1aa4ec7646
9 changed files with 110 additions and 110 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

BIN
examples/adder.net
View file

Binary file not shown.

14
examples/adderFromScratch.cpp
View file

@ -10,9 +10,9 @@
using namespace neuralpp; using namespace neuralpp;
int main() { int main() {
NeuralNet *net = new NeuralNet(2, 2, 1, -0.005, 10000); NeuralNet net(2, 2, 1, 0.005, 10000);
string xml; string xml;
float tmp; double tmp;
// XML initialization. Then, I say XML that 2+3=5, 3+3=6, 5+4=9 // XML initialization. Then, I say XML that 2+3=5, 3+3=6, 5+4=9
// Strings' format is "input1,input2,...,inputn;output1,output2,...,outputm // Strings' format is "input1,input2,...,inputn;output1,output2,...,outputm
@ -22,8 +22,8 @@ int main() {
xml += NeuralNet::XMLFromSet(2, "5,4;9"); xml += NeuralNet::XMLFromSet(2, "5,4;9");
NeuralNet::closeXML(xml); NeuralNet::closeXML(xml);
net->train(xml, NeuralNet::str); net.train(xml, NeuralNet::str);
vector<float> v; vector<double> v;
cout << "Network status: trained\n\n"; cout << "Network status: trained\n\n";
cout << "First number to add: "; cout << "First number to add: ";
@ -34,9 +34,9 @@ int main() {
cin >> tmp; cin >> tmp;
v.push_back(tmp); v.push_back(tmp);
net->setInput(v); net.setInput(v);
net->propagate(); net.propagate();
cout << "Output: " << net->getOutput() << endl; cout << "Output: " << net.getOutput() << endl;
return 0; return 0;
} }

14
examples/doAdd.cpp
View file

@ -12,11 +12,11 @@ using namespace neuralpp;
#define NETFILE "adder.net" #define NETFILE "adder.net"
int main() { int main() {
float a,b; double a,b;
NeuralNet *net = NULL; NeuralNet net;
try { try {
net = new NeuralNet(NETFILE); net = NeuralNet(NETFILE);
} }
catch (NetworkFileNotFoundException e) { catch (NetworkFileNotFoundException e) {
@ -30,13 +30,13 @@ int main() {
cout << "Second number to add: "; cout << "Second number to add: ";
cin >> b; cin >> b;
vector<float> v; vector<double> v;
v.push_back(a); v.push_back(a);
v.push_back(b); v.push_back(b);
net->setInput(v); net.setInput(v);
net->propagate(); net.propagate();
cout << "Neural net output: " << net->getOutput() << endl; cout << "Neural net output: " << net.getOutput() << endl;
return 0; return 0;
} }

6
examples/learnAdd.cpp
View file

@ -11,12 +11,12 @@
using namespace neuralpp; using namespace neuralpp;
int main() { int main() {
NeuralNet *net = new NeuralNet (2, 2, 1, -0.005, 10000); NeuralNet net(2, 2, 1, 0.005, 10000);
cout << "Training in progress - This may take a while...if it gets stuck, interrupt and restart the app\n"; cout << "Training in progress - This may take a while...if it gets stuck, interrupt and restart the app\n";
net->train("adder.xml", NeuralNet::file); net.train("adder.xml", NeuralNet::file);
net->save("adder.net"); net.save("adder.net");
cout << "Network trained. You can use adder.net file now to load this network\n"; cout << "Network trained. You can use adder.net file now to load this network\n";
return 0; return 0;
} }

102
include/neural++.hpp
View file

@ -30,7 +30,7 @@ using namespace std;
namespace neuralpp { namespace neuralpp {
//! Default rand value: |sin(rand)|, always >= 0 and <= 1 //! Default rand value: |sin(rand)|, always >= 0 and <= 1
#define RAND ( (float) abs( sinf((float) rand()) ) ) #define RAND ( (double) abs( sinf((double) rand()) ) )
class Synapsis; class Synapsis;
class Neuron; class Neuron;
@ -46,8 +46,8 @@ namespace neuralpp {
class NeuralNet { class NeuralNet {
int epochs; int epochs;
int ref_epochs; int ref_epochs;
float l_rate; double l_rate;
float ex; double ex;
Layer* input; Layer* input;
Layer* hidden; Layer* hidden;
@ -71,10 +71,10 @@ namespace neuralpp {
* @param Expected value * @param Expected value
* @return Mean error * @return Mean error
*/ */
float error(float); double error(double);
float (*actv_f)(float); double (*actv_f)(double);
float (*deriv)(float); double (*deriv)(double);
public: public:
/** /**
@ -94,7 +94,7 @@ namespace neuralpp {
* @param e Epochs (cycles) to execute (the most you execute, the most the network * @param e Epochs (cycles) to execute (the most you execute, the most the network
* can be accurate for its purpose) * can be accurate for its purpose)
*/ */
NeuralNet (size_t, size_t, size_t, float, int); NeuralNet (size_t, size_t, size_t, double, int);
/** /**
* @brief Constructor * @brief Constructor
@ -116,29 +116,29 @@ namespace neuralpp {
* @param e Epochs (cycles) to execute (the most you execute, the most the network * @param e Epochs (cycles) to execute (the most you execute, the most the network
* can be accurate for its purpose) * can be accurate for its purpose)
*/ */
NeuralNet (size_t, size_t, size_t, float(*)(float), float(*)(float), float, int); NeuralNet (size_t, size_t, size_t, double(*)(double), double(*)(double), double, int);
/** /**
* @brief It gets the output of the network (note: the layer output should contain * @brief It gets the output of the network (note: the layer output should contain
* an only neuron) * an only neuron)
*/ */
float getOutput(); double getOutput();
/** /**
* @brief It gets the output of the network in case the output layer contains more neurons * @brief It gets the output of the network in case the output layer contains more neurons
*/ */
vector<float> getVectorOutput(); vector<double> getVectorOutput();
/** /**
* @brief It gets the value expected. Of course you should specify this when you * @brief It gets the value expected. Of course you should specify this when you
* build your network by using setExpected. * build your network by using setExpected.
*/ */
float expected(); double expected();
/** /**
* @brief It sets the value you expect from your network * @brief It sets the value you expect from your network
*/ */
void setExpected(float); void setExpected(double);
/** /**
* @brief It updates through back-propagation the weights of the synapsis and * @brief It updates through back-propagation the weights of the synapsis and
@ -155,9 +155,9 @@ namespace neuralpp {
/** /**
* @brief It sets the input for the network * @brief It sets the input for the network
* @param v Vector of floats, containing the values to give to your network * @param v Vector of doubles, containing the values to give to your network
*/ */
void setInput (vector<float>&); void setInput (vector<double>&);
/** /**
* @brief It links the layers of the network (input, hidden, output). Don't use unless * @brief It links the layers of the network (input, hidden, output). Don't use unless
@ -186,12 +186,12 @@ namespace neuralpp {
static void initXML (string&); static void initXML (string&);
/** /**
* @brief Splits a string into a vector of floats, given a delimitator * @brief Splits a string into a vector of doubles, given a delimitator
* @param delim Delimitator * @param delim Delimitator
* @param str String to be splitted * @param str String to be splitted
* @return Vector of floats containing splitted values * @return Vector of doubles containing splitted values
*/ */
static vector<float> split (char, string); static vector<double> split (char, string);
/** /**
* @brief Get a training set from a string and copies it to an XML * @brief Get a training set from a string and copies it to an XML
@ -220,17 +220,17 @@ namespace neuralpp {
* you're doing, use NeuralNet instead * you're doing, use NeuralNet instead
*/ */
class Synapsis { class Synapsis {
float delta; double delta;
float weight; double weight;
Neuron *in; Neuron *in;
Neuron *out; Neuron *out;
float (*actv_f)(float); double (*actv_f)(double);
float (*deriv)(float); double (*deriv)(double);
public: public:
Synapsis(Neuron* i, Neuron* o, float w, float d) { Synapsis(Neuron* i, Neuron* o, double w, double d) {
in=i; out=o; in=i; out=o;
weight=w; delta=d; weight=w; delta=d;
} }
@ -242,7 +242,7 @@ namespace neuralpp {
* @param a Activation function * @param a Activation function
* @param d Derivate for activation function * @param d Derivate for activation function
*/ */
Synapsis (Neuron* i, Neuron* o, float(*)(float), float(*)(float)); Synapsis (Neuron* i, Neuron* o, double(*)(double), double(*)(double));
/** /**
* @brief Constructor * @brief Constructor
@ -252,7 +252,7 @@ namespace neuralpp {
* @param a Activation function * @param a Activation function
* @param d Derivate for activation function * @param d Derivate for activation function
*/ */
Synapsis (Neuron* i, Neuron* o, float w, float(*)(float), float(*)(float)); Synapsis (Neuron* i, Neuron* o, double w, double(*)(double), double(*)(double));
/** /**
* @return Reference to input neuron of the synapsis * @return Reference to input neuron of the synapsis
@ -267,23 +267,23 @@ namespace neuralpp {
/** /**
* @brief It sets the weight of the synapsis * @brief It sets the weight of the synapsis
*/ */
void setWeight(float); void setWeight(double);
/** /**
* @brief It sets the delta (how much to change the weight after an update) * @brief It sets the delta (how much to change the weight after an update)
* of the synapsis * of the synapsis
*/ */
void setDelta(float); void setDelta(double);
/** /**
* @return Weight of the synapsis * @return Weight of the synapsis
*/ */
float getWeight(); double getWeight();
/** /**
* @return Delta of the synapsis * @return Delta of the synapsis
*/ */
float getDelta(); double getDelta();
}; };
/** /**
@ -292,26 +292,26 @@ namespace neuralpp {
* you're doing, use NeuralNet instead * you're doing, use NeuralNet instead
*/ */
class Neuron { class Neuron {
float actv_val; double actv_val;
float prop_val; double prop_val;
vector< Synapsis > in; vector< Synapsis > in;
vector< Synapsis > out; vector< Synapsis > out;
float (*actv_f)(float); double (*actv_f)(double);
float (*deriv)(float); double (*deriv)(double);
public: public:
/** /**
* @brief Constructor * @brief Constructor
* @param a Activation function * @param a Activation function
* @param d Its derivate * @param d Its derivate
*/ */
Neuron (float (*)(float), float(*)(float)); Neuron (double (*)(double), double(*)(double));
/** /**
* @brief Alternative constructor, that gets also the synapsis linked to the neuron * @brief Alternative constructor, that gets also the synapsis linked to the neuron
*/ */
Neuron (vector< Synapsis >, vector< Synapsis >, float (*)(float), float(*)(float)); Neuron (vector< Synapsis >, vector< Synapsis >, double (*)(double), double(*)(double));
/** /**
* @brief Gets the i-th synapsis connected on the input of the neuron * @brief Gets the i-th synapsis connected on the input of the neuron
@ -336,27 +336,27 @@ namespace neuralpp {
/** /**
* @brief Change the activation value of the neuron * @brief Change the activation value of the neuron
*/ */
void setActv (float); void setActv (double);
/** /**
* @brief Change the propagation value of the neuron * @brief Change the propagation value of the neuron
*/ */
void setProp (float); void setProp (double);
/** /**
* @brief It gets the activation value of the neuron * @brief It gets the activation value of the neuron
*/ */
float getActv(); double getActv();
/** /**
* @brief It gets the propagation value of the neuron * @brief It gets the propagation value of the neuron
*/ */
float getProp(); double getProp();
/** /**
* @brief It propagates its activation value to the connected neurons * @brief It propagates its activation value to the connected neurons
*/ */
float propagate(); double propagate();
/** /**
* @return Number of input synapsis * @return Number of input synapsis
@ -383,8 +383,8 @@ namespace neuralpp {
vector< Neuron > elements; vector< Neuron > elements;
void (*update_weights)(); void (*update_weights)();
float (*actv_f)(float); double (*actv_f)(double);
float (*deriv)(float); double (*deriv)(double);
public: public:
/** /**
@ -393,13 +393,13 @@ namespace neuralpp {
* @param a Activation function * @param a Activation function
* @param d Its derivate * @param d Its derivate
*/ */
Layer (size_t sz, float (*)(float), float(*)(float)); Layer (size_t sz, double (*)(double), double(*)(double));
/** /**
* @brief Alternative constructor. It directly gets a vector of neurons to build * @brief Alternative constructor. It directly gets a vector of neurons to build
* the layer * the layer
*/ */
Layer (vector< Neuron >&, float(*)(float), float(*)(float)); Layer (vector< Neuron >&, double(*)(double), double(*)(double));
/** /**
* @brief Redefinition for operator []. It gets the neuron at <i>i</i> * @brief Redefinition for operator []. It gets the neuron at <i>i</i>
@ -416,13 +416,13 @@ namespace neuralpp {
* @brief It sets a vector of propagation values to all its neurons * @brief It sets a vector of propagation values to all its neurons
* @param v Vector of values to write as propagation values * @param v Vector of values to write as propagation values
*/ */
void setProp (vector<float>&); void setProp (vector<double>&);
/** /**
* @brief It sets a vector of activation values to all its neurons * @brief It sets a vector of activation values to all its neurons
* @param v Vector of values to write as activation values * @param v Vector of values to write as activation values
*/ */
void setActv (vector<float>&); void setActv (vector<double>&);
/** /**
* @brief It propagates its activation values to the output layers * @brief It propagates its activation values to the output layers
@ -441,18 +441,18 @@ namespace neuralpp {
int output_size; int output_size;
int epochs; int epochs;
float l_rate; double l_rate;
float ex; double ex;
}; };
struct neuronrecord { struct neuronrecord {
float prop; double prop;
float actv; double actv;
}; };
struct synrecord { struct synrecord {
float w; double w;
float d; double d;
}; };
} }

8
src/layer.cpp
View file

@ -21,7 +21,7 @@ using namespace neuralpp;
* @param a Activation function * @param a Activation function
* @param d Its derivate * @param d Its derivate
*/ */
Layer::Layer (size_t sz, float(*a)(float), float(*d)(float)) { Layer::Layer (size_t sz, double(*a)(double), double(*d)(double)) {
for (size_t i=0; i<sz; i++) { for (size_t i=0; i<sz; i++) {
Neuron n(a,d); Neuron n(a,d);
elements.push_back(n); elements.push_back(n);
@ -35,7 +35,7 @@ Layer::Layer (size_t sz, float(*a)(float), float(*d)(float)) {
* @brief Alternative constructor. It directly gets a vector of neurons to build * @brief Alternative constructor. It directly gets a vector of neurons to build
* the layer * the layer
*/ */
Layer::Layer (vector< Neuron > &el, float (*a)(float), float(*d)(float)) { Layer::Layer (vector< Neuron > &el, double (*a)(double), double(*d)(double)) {
elements=el; elements=el;
actv_f=a; actv_f=a;
deriv=d; deriv=d;
@ -75,7 +75,7 @@ void Layer::link (Layer& l) {
* @brief It sets a vector of propagation values to all its neurons * @brief It sets a vector of propagation values to all its neurons
* @param v Vector of values to write as propagation values * @param v Vector of values to write as propagation values
*/ */
void Layer::setProp (vector<float> &v) { void Layer::setProp (vector<double> &v) {
for (size_t i=0; i<size(); i++) for (size_t i=0; i<size(); i++)
elements[i].setProp(v[i]); elements[i].setProp(v[i]);
} }
@ -84,7 +84,7 @@ void Layer::setProp (vector<float> &v) {
* @brief It sets a vector of activation values to all its neurons * @brief It sets a vector of activation values to all its neurons
* @param v Vector of values to write as activation values * @param v Vector of values to write as activation values
*/ */
void Layer::setActv (vector<float> &v) { void Layer::setActv (vector<double> &v) {
for (size_t i=0; i<size(); i++) for (size_t i=0; i<size(); i++)
elements[i].setActv(v[i]); elements[i].setActv(v[i]);
} }

48
src/neuralnet.cpp
View file

@ -19,12 +19,12 @@ using namespace neuralpp;
/** /**
* @brief Built-in function. The default activation function: f(x)=x * @brief Built-in function. The default activation function: f(x)=x
*/ */
float __actv(float prop) { return prop; } double __actv(double prop) { return prop; }
/** /**
* @brief Default derivate for default activation function: f'(x)=1 * @brief Default derivate for default activation function: f'(x)=1
*/ */
float __deriv(float prop) { return 1; } double __deriv(double prop) { return 1; }
/** /**
* @brief Constructor * @brief Constructor
@ -36,7 +36,7 @@ float __deriv(float prop) { return 1; }
* @param e Epochs (cycles) to execute (the most you execute, the most the network * @param e Epochs (cycles) to execute (the most you execute, the most the network
* can be accurate for its purpose) * can be accurate for its purpose)
*/ */
NeuralNet::NeuralNet (size_t in_size, size_t hidden_size, size_t out_size, float l, int e) { NeuralNet::NeuralNet (size_t in_size, size_t hidden_size, size_t out_size, double l, int e) {
epochs=e; epochs=e;
ref_epochs=epochs; ref_epochs=epochs;
l_rate=l; l_rate=l;
@ -62,7 +62,7 @@ NeuralNet::NeuralNet (size_t in_size, size_t hidden_size, size_t out_size, float
* can be accurate for its purpose) * can be accurate for its purpose)
*/ */
NeuralNet::NeuralNet (size_t in_size, size_t hidden_size, size_t out_size, NeuralNet::NeuralNet (size_t in_size, size_t hidden_size, size_t out_size,
float(*a)(float), float(*d)(float), float l, int e) { double(*a)(double), double(*d)(double), double l, int e) {
epochs=e; epochs=e;
ref_epochs=epochs; ref_epochs=epochs;
l_rate=l; l_rate=l;
@ -80,13 +80,13 @@ NeuralNet::NeuralNet (size_t in_size, size_t hidden_size, size_t out_size,
* @brief It gets the output of the network (note: the layer output should contain * @brief It gets the output of the network (note: the layer output should contain
* an only neuron) * an only neuron)
*/ */
float NeuralNet::getOutput() { return (*output)[0].getActv(); } double NeuralNet::getOutput() { return (*output)[0].getActv(); }
/** /**
* @brief It gets the output of the network in case the output layer contains more neurons * @brief It gets the output of the network in case the output layer contains more neurons
*/ */
vector<float> NeuralNet::getVectorOutput() { vector<double> NeuralNet::getVectorOutput() {
vector<float> v; vector<double> v;
for (size_t i=0; i<output->size(); i++) for (size_t i=0; i<output->size(); i++)
v.push_back( (*output)[i].getActv() ); v.push_back( (*output)[i].getActv() );
@ -98,7 +98,7 @@ vector<float> NeuralNet::getVectorOutput() {
* @param Expected value * @param Expected value
* @return Mean error * @return Mean error
*/ */
float NeuralNet::error(float expected) { double NeuralNet::error(double expected) {
return abs( (getOutput() - expected* return abs( (getOutput() - expected*
deriv(getOutput())) / (abs(expected)) ); deriv(getOutput())) / (abs(expected)) );
} }
@ -114,9 +114,9 @@ void NeuralNet::propagate() {
/** /**
* @brief It sets the input for the network * @brief It sets the input for the network
* @param v Vector of floats, containing the values to give to your network * @param v Vector of doubles, containing the values to give to your network
*/ */
void NeuralNet::setInput(vector<float>& v) { void NeuralNet::setInput(vector<double>& v) {
input->setProp(v); input->setProp(v);
input->setActv(v); input->setActv(v);
} }
@ -133,38 +133,38 @@ void NeuralNet::link() {
/** /**
* @brief It sets the value you expect from your network * @brief It sets the value you expect from your network
*/ */
void NeuralNet::setExpected(float e) { ex=e; } void NeuralNet::setExpected(double e) { ex=e; }
/** /**
* @brief It gets the value expected. Of course you should specify this when you * @brief It gets the value expected. Of course you should specify this when you
* build your network by using setExpected. * build your network by using setExpected.
*/ */
float NeuralNet::expected() { return ex; } double NeuralNet::expected() { return ex; }
/** /**
* @brief It updates the weights of the net's synapsis through back-propagation. * @brief It updates the weights of the net's synapsis through back-propagation.
* In-class use only * In-class use only
*/ */
void NeuralNet::updateWeights() { void NeuralNet::updateWeights() {
float out_delta; double out_delta;
for (size_t i=0; i<output->size(); i++) { for (size_t i=0; i<output->size(); i++) {
Neuron *n = &(*output)[i]; Neuron *n = &(*output)[i];
for (size_t j=0; j<n->nIn(); j++) { for (size_t j=0; j<n->nIn(); j++) {
Synapsis *s = &(n->synIn(j)); Synapsis *s = &(n->synIn(j));
out_delta = s->getIn()->getActv() * error(ex) * l_rate; out_delta = s->getIn()->getActv() * error(ex) * (-l_rate);
s->setDelta(out_delta); s->setDelta(out_delta);
} }
} }
for (size_t i=0; i<hidden->size(); i++) { for (size_t i=0; i<hidden->size(); i++) {
Neuron *n = &(*hidden)[i]; Neuron *n = &(*hidden)[i];
float d = deriv(n->getProp()) * n->synOut(0).getWeight() * out_delta; double d = deriv(n->getProp()) * n->synOut(0).getWeight() * out_delta;
for (size_t j=0; j<n->nIn(); j++) { for (size_t j=0; j<n->nIn(); j++) {
Synapsis *s = &(n->synIn(j)); Synapsis *s = &(n->synIn(j));
s->setDelta(l_rate * d * s->getIn()->getActv()); s->setDelta((-l_rate) * d * s->getIn()->getActv());
} }
} }
} }
@ -420,7 +420,7 @@ NeuralNet::NeuralNet (const char *fname) throw() {
* @throw InvalidXMLException * @throw InvalidXMLException
*/ */
void NeuralNet::train (string xmlsrc, NeuralNet::source src = file) throw() { void NeuralNet::train (string xmlsrc, NeuralNet::source src = file) throw() {
float out; double out;
CMarkup xml; CMarkup xml;
if (src == file) if (src == file)
@ -435,8 +435,8 @@ void NeuralNet::train (string xmlsrc, NeuralNet::source src = file) throw() {
if (xml.FindElem("NETWORK")) { if (xml.FindElem("NETWORK")) {
while (xml.FindChildElem("TRAINING")) { while (xml.FindChildElem("TRAINING")) {
vector<float> input; vector<double> input;
float output; double output;
bool valid = false; bool valid = false;
xml.IntoElem(); xml.IntoElem();
@ -489,14 +489,14 @@ void NeuralNet::initXML (string& xml) {
} }
/** /**
* @brief Splits a string into a vector of floats, given a delimitator * @brief Splits a string into a vector of doubles, given a delimitator
* @param delim Delimitator * @param delim Delimitator
* @param str String to be splitted * @param str String to be splitted
* @return Vector of floats containing splitted values * @return Vector of doubles containing splitted values
*/ */
vector<float> NeuralNet::split (char delim, string str) { vector<double> NeuralNet::split (char delim, string str) {
char tmp[1024]; char tmp[1024];
vector<float> v; vector<double> v;
memset (tmp, 0x0, sizeof(tmp)); memset (tmp, 0x0, sizeof(tmp));
for (unsigned int i=0, j=0; i <= str.length(); i++) { for (unsigned int i=0, j=0; i <= str.length(); i++) {
@ -525,7 +525,7 @@ vector<float> NeuralNet::split (char delim, string str) {
*/ */
string NeuralNet::XMLFromSet (int id, string set) { string NeuralNet::XMLFromSet (int id, string set) {
string xml; string xml;
vector<float> in, out; vector<double> in, out;
unsigned int delimPos = -1; unsigned int delimPos = -1;
char delim=';'; char delim=';';
char tmp[1024]; char tmp[1024];

16
src/neuron.cpp
View file

@ -19,7 +19,7 @@ using namespace neuralpp;
* @param a Activation function * @param a Activation function
* @param d Its derivate * @param d Its derivate
*/ */
Neuron::Neuron (float (*a)(float), float (*d)(float)) { Neuron::Neuron (double (*a)(double), double (*d)(double)) {
actv_f=a; actv_f=a;
deriv=d; deriv=d;
} }
@ -27,7 +27,7 @@ Neuron::Neuron (float (*a)(float), float (*d)(float)) {
/** /**
* @brief Alternative constructor, that gets also the synapsis linked to the neuron * @brief Alternative constructor, that gets also the synapsis linked to the neuron
*/ */
Neuron::Neuron (vector< Synapsis > i, vector< Synapsis > o, float (*a)(float), float(*d)(float)) { Neuron::Neuron (vector< Synapsis > i, vector< Synapsis > o, double (*a)(double), double(*d)(double)) {
in=i; in=i;
out=o; out=o;
@ -58,12 +58,12 @@ void Neuron::push_out (Synapsis& s) { out.push_back(s); }
/** /**
* @brief Change the propagation value of the neuron * @brief Change the propagation value of the neuron
*/ */
void Neuron::setProp (float val) { prop_val=val; } void Neuron::setProp (double val) { prop_val=val; }
/** /**
* @brief Change the activation value of the neuron * @brief Change the activation value of the neuron
*/ */
void Neuron::setActv (float val) { actv_val=actv_f(val); } void Neuron::setActv (double val) { actv_val=actv_f(val); }
/** /**
* @return Number of input synapsis * @return Number of input synapsis
@ -78,18 +78,18 @@ size_t Neuron::nOut() { return out.size(); }
/** /**
* @brief It gets the propagation value of the neuron * @brief It gets the propagation value of the neuron
*/ */
float Neuron::getProp() { return prop_val; } double Neuron::getProp() { return prop_val; }
/** /**
* @brief It gets the activation value of the neuron * @brief It gets the activation value of the neuron
*/ */
float Neuron::getActv() { return actv_val; } double Neuron::getActv() { return actv_val; }
/** /**
* @brief Propagate a neuron's activation value to the connected neurons * @brief Propagate a neuron's activation value to the connected neurons
*/ */
float Neuron::propagate() { double Neuron::propagate() {
float aux=0; double aux=0;
for (size_t i=0; i<nIn(); i++) for (size_t i=0; i<nIn(); i++)
aux += (in[i].getWeight() * in[i].getIn()->actv_val); aux += (in[i].getWeight() * in[i].getIn()->actv_val);

12
src/synapsis.cpp
View file

@ -22,7 +22,7 @@ using namespace neuralpp;
* @param a Activation function * @param a Activation function
* @param d Derivate for activation function * @param d Derivate for activation function
*/ */
Synapsis::Synapsis (Neuron* i, Neuron* o, float(*a)(float), float(*d)(float)) { Synapsis::Synapsis (Neuron* i, Neuron* o, double(*a)(double), double(*d)(double)) {
srand((unsigned) time(NULL)); srand((unsigned) time(NULL));
delta=0; delta=0;
@ -42,7 +42,7 @@ Synapsis::Synapsis (Neuron* i, Neuron* o, float(*a)(float), float(*d)(float)) {
* @param a Activation function * @param a Activation function
* @param d Derivate for activation function * @param d Derivate for activation function
*/ */
Synapsis::Synapsis (Neuron* i, Neuron* o, float w, float(*a)(float), float(*d)(float)) { Synapsis::Synapsis (Neuron* i, Neuron* o, double w, double(*a)(double), double(*d)(double)) {
delta=0; delta=0;
weight=w; weight=w;
in=i; in=i;
@ -65,21 +65,21 @@ Neuron* Synapsis::getOut() { return out; }
/** /**
* @return Weight of the synapsis * @return Weight of the synapsis
*/ */
float Synapsis::getWeight() { return weight; } double Synapsis::getWeight() { return weight; }
/** /**
* @return Delta of the synapsis * @return Delta of the synapsis
*/ */
float Synapsis::getDelta() { return delta; } double Synapsis::getDelta() { return delta; }
/** /**
* @brief It sets the weight of the synapsis * @brief It sets the weight of the synapsis
*/ */
void Synapsis::setWeight(float w) { weight=w; } void Synapsis::setWeight(double w) { weight=w; }
/** /**
* @brief It sets the delta (how much to change the weight after an update) * @brief It sets the delta (how much to change the weight after an update)
* of the synapsis * of the synapsis
*/ */
void Synapsis::setDelta(float d) { delta=d; } void Synapsis::setDelta(double d) { delta=d; }