--- /dev/null
+# Logistic Regression
+
--- /dev/null
+LOCAL_PATH := $(call my-dir)
+
+include $(CLEAR_VARS)
+
+# ndk path
+ifndef ANDROID_NDK
+$(error ANDROID_NDK is not defined!)
+endif
+
+INIPARSER=../../iniparser/src/
+LOCAL_MODULE :=iniparser
+INIPARSER_SRCS := \
+ $(INIPARSER)/iniparser.c \
+ $(INIPARSER)/dictionary.c
+
+LOCAL_SRC_FILES :=$(INIPARSER_SRCS)
+LOCAL_C_INCLUDES := $(INIPARSER)
+
+LOCAL_CFLAGS += -O3 -DNDEBUG
+
+include $(BUILD_STATIC_LIBRARY)
+
+include $(CLEAR_VARS)
+NEURALNET=../../NeuralNet
+LOCAL_ARM_NEON := true
+LOCAL_CFLAGS += -std=c++11 -Ofast -mcpu=cortex-a53 -Ilz4-nougat/lib
+LOCAL_LDFLAGS += -Llz4-nougat/lib/obj/local/arm64-v8a/
+LOCAL_CXXFLAGS += -std=c++11
+LOCAL_CFLAGS += -pthread -fopenmp
+LOCAL_LDFLAGS += -fopenmp
+LOCAL_MODULE_TAGS := optional
+LOCAL_ARM_MODE := arm
+LOCAL_MODULE := LogisticRegressoin
+
+LOCAL_SRC_FILES := main.cpp $(NEURALNET)/matrix.cpp $(NEURALNET)/neuralnet.cpp \
+ $(NEURALNET)/layers.cpp
+
+LOCAL_STATIC_LIBRARIES := iniparser
+
+LOCAL_C_INCLUDES += $(INIPARSER) $(NEURALNET)
+
+include $(BUILD_EXECUTABLE)
--- /dev/null
+APP_ABI = arm64-v8a
+APP_STL = c++_shared
+APP_PLATFORM=android-24
--- /dev/null
+cmake_minimum_required(VERSION 2.8.3)
+project(LogisticRegression)
+
+set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -Werror -g -pthread")
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Werror -g -std=c++11 -pthread")
+
+set(NEURALNET ../../NeuralNet)
+set(INIPARSER ../../iniparser/src)
+
+include_directories( ${include_directories}
+ ${CMAKE_CURRENT_SOURCE_DIR}
+ ${NEURALNET}
+ ${INIPARSER}
+ )
+
+set(SRCS
+ main.cpp
+ ${NEURALNET}/neuralnet.cpp
+ ${NEURALNET}/matrix.cpp
+ ${NEURALNET}/layers.cpp
+ ${INIPARSER}/iniparser.c
+ ${INIPARSER}/dictionary.c
+ )
+
+add_executable(LogisticRegression ${SRCS})
+target_link_libraries( LogisticRegression ${CMAKE_DL_LIBS})
+
+install (TARGETS LogisticRegression
+ RUNTIME DESTINATION bin
+ )
--- /dev/null
+#include <cmath>
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include <stdlib.h>
+#include <time.h>
+
+#include "include/matrix.h"
+#include "include/neuralnet.h"
+#define training false
+
+std::string data_file;
+
+double stepFunction(double x) {
+ if (x > 0.5) {
+ return 1.0;
+ }
+
+ if (x < 0.5) {
+ return 0.0;
+ }
+
+ return x;
+}
+
+int main(int argc, char *argv[]) {
+ if (argc < 3) {
+ std::cout << "./LogisticRegression Config.ini data.txt\n";
+ exit(0);
+ }
+
+ const std::vector<std::string> args(argv + 1, argv + argc);
+ std::string config = args[0];
+ data_file = args[1];
+
+ srand(time(NULL));
+
+ std::vector<std::vector<double>> inputVector, outputVector;
+ Network::NeuralNetwork NN(config);
+
+ NN.init();
+ if (!training)
+ NN.readModel();
+
+ std::ifstream dataFile(data_file);
+ if (dataFile.is_open()) {
+ std::string temp;
+ int index = 0;
+ while (std::getline(dataFile, temp)) {
+ if (training && index % 10 == 1) {
+ std::cout << temp << std::endl;
+ index++;
+ continue;
+ }
+ std::istringstream buffer(temp);
+ std::vector<double> line;
+ std::vector<double> out;
+ double x;
+ for (int i = 0; i < 2; i++) {
+ buffer >> x;
+ line.push_back(x);
+ }
+ inputVector.push_back(line);
+ buffer >> x;
+ out.push_back(x);
+ outputVector.push_back(out);
+ index++;
+ }
+ }
+ if (training) {
+ for (unsigned int i = 0; i < NN.getEpoch(); i++) {
+ NN.backwarding(Matrix(inputVector), Matrix(outputVector), i);
+ std::cout << "#" << i + 1 << "/" << NN.getEpoch()
+ << " - Loss : " << NN.getLoss() << std::endl;
+ NN.setLoss(0.0);
+ }
+ } else {
+ std::cout << NN.forwarding(Matrix(inputVector)).applyFunction(stepFunction)
+ << std::endl;
+ }
+
+ NN.saveModel();
+ NN.finalize();
+}
--- /dev/null
+# Network Section : Network
+[Network]
+Type = Regression # Network Type : Regression, KNN, NeuralNetwork
+Layers = inputlayer \
+ outputlayer #Layers of Neuralnetwork
+Learning_rate = 0.01 # Learning Rate
+Epoch = 100 # Epoch
+Optimizer = sgd # Optimizer : sgd (stochastic gradien decent),
+ # adam (Adamtive Moment Estimation)
+Activation = sigmoid # activation : sigmoid, tanh
+Cost = logistic # Cost(loss) function : msr (mean square root error)
+ # logistic ( for logistic regression )
+Model = "model.bin" # model path to save / read
+minibatch = 1 # mini batch size
+
+# Layer Section : Name
+[inputlayer]
+Type = InputLayer
+Id = 0 # Layer Id
+Height = 10
+Width = 2 # Input Layer Dimension
+Bias_zero = true # Zero Bias
+
+[outputlayer]
+Type = OutputLayer
+Id = 1
+Height = 2 # Weight Height
+Width = 1 # Weight Width
+Bias_zero = true
--- /dev/null
+4.5192 2.6487 1.0\r
+2.4443 1.5438 1.0\r
+4.2409 1.899 1.0\r
+5.8097 2.4711 1.0\r
+6.4423 3.359 1.0\r
+5.8097 3.2406 1.0\r
+6.3917 3.8128 1.0\r
+6.8725 4.4441 1.0\r
+6.7966 3.6747 1.0\r
+8.163 4.7401 1.0\r
+7.4038 3.8917 1.0\r
+7.6316 4.602 1.0\r
+7.7581 5.7265 1.0\r
+6.5688 4.9571 1.0\r
+5.3543 3.9903 1.0\r
+4.4686 3.0236 1.0\r
+2.9757 2.0568 1.0\r
+2.4443 1.2676 1.0\r
+0.9008 1.169 1.0\r
+2.1154 1.7411 1.0\r
+3.2794 1.386 1.0\r
+4.165 1.5636 1.0\r
+4.8482 1.8793 1.0\r
+3.33 2.7868 1.0\r
+5.1518 3.5563 1.0\r
+6.2652 4.0693 1.0\r
+6.2652 4.3849 1.0\r
+7.2014 1.5438 1.0\r
+7.6569 2.412 1.0\r
+6.1387 1.7806 1.0\r
+4.4939 1.4057 1.0\r
+4.8735 2.6093 1.0\r
+5.5314 3.0828 1.0\r
+6.0121 3.9311 1.0\r
+7.1508 4.7598 1.0\r
+7.7075 5.3122 1.0\r
+8.3148 5.7068 1.0\r
+8.5172 5.1149 1.0\r
+8.7449 5.4109 1.0\r
+7.8593 3.8128 1.0\r
+6.999 3.2406 1.0\r
+5.5061 2.9052 1.0\r
+4.9241 2.6882 1.0\r
+6.6447 3.8325 1.0\r
+7.6822 4.5428 1.0\r
+8.0364 5.7857 1.0\r
+8.9221 6.5552 1.0\r
+7.8593 5.253 1.0\r
+6.5941 5.2333 1.0\r
+6.0374 4.7598 1.0\r
+2.7227 4.5822 0.0\r
+1.9383 3.6549 0.0\r
+1.6852 2.9841 0.0\r
+4.3168 4.4244 0.0\r
+3.4312 3.7536 0.0\r
+5.4808 5.2728 0.0\r
+4.1144 4.8387 0.0\r
+3.2034 4.4244 0.0\r
+4.1144 5.3911 0.0\r
+5.1012 6.0817 0.0\r
+4.8988 5.5687 0.0\r
+5.9615 6.4565 0.0\r
+5.7591 6.0028 0.0\r
+6.6953 6.7722 0.0\r
+5.7338 6.6538 0.0\r
+6.6194 7.1471 0.0\r
+7.2014 7.5219 0.0\r
+7.2014 6.8314 0.0\r
+8.5931 7.6206 0.0\r
+7.7581 7.1865 0.0\r
+7.7581 7.7784 0.0\r
+5.1012 7.6009 0.0\r
+4.2156 6.496 0.0\r
+3.4818 5.8055 0.0\r
+2.3684 5.0163 0.0\r
+1.7864 4.1876 0.0\r
+0.9008 3.4379 0.0\r
+0.9008 5.7857 0.0\r
+1.9636 6.3382 0.0\r
+1.4069 4.9571 0.0\r
+2.419 6.8511 0.0\r
+2.8745 6.0817 0.0\r
+4.0132 7.1668 0.0\r
+4.6711 7.226 0.0\r
+5.1771 8.1533 0.0\r
+6.2146 7.4825 0.0\r
+5.4555 7.0484 0.0\r
+5.9868 8.5084 0.0\r
+4.0891 7.5417 0.0\r
+2.3937 7.2063 0.0\r
+1.331 6.5355 0.0\r
+1.7358 5.4503 0.0\r
+2.4443 5.8449 0.0\r
+3.1781 4.8979 0.0\r
+4.6711 5.8055 0.0\r
+5.9868 7.3641 0.0\r
+4.6711 6.2592 0.0\r
+7.581 8.3703 0.0\r
+4.6457 8.5676 0.0\r
+4.6457 8.1676 0.0\r
--- /dev/null
+2.4443 1.5438 1.0\r
+7.6316 4.602 1.0\r
+4.165 1.5636 1.0\r
+4.8735 2.6093 1.0\r
+5.5061 2.9052 1.0\r
+1.9383 3.6549 0.0\r
+5.9615 6.4565 0.0\r
+5.1012 7.6009 0.0\r
+2.8745 6.0817 0.0\r
+1.7358 5.4503 0.0\r
InputLayer(){};
~InputLayer(){};
void read(std::ifstream &file){};
- void save(std::ofstream &file){};
+ void save(std::ofstream &file) { return; };
Matrix backwarding(Matrix input, int iteration) { return Input; };
Matrix forwarding(Matrix input);
void setOptimizer(Optimizer opt);
void setOptimizer(Optimizer opt);
void initialize(int b, int w, int h, int id, bool init_zero);
double getLoss() { return loss; }
+ void setCost(cost_type c) { this->cost = c; };
void copy(Layer *l);
private:
Matrix M;
Matrix V;
double loss;
+ cost_type cost;
};
}
void saveModel();
void readModel();
void setConfig(std::string config_path);
+ unsigned int getEpoch() { return epoch; };
NeuralNetwork ©(NeuralNetwork &from);
void finalize();
this->init_zero = init_zero;
Weight = Matrix(h, w);
Bias = Matrix(1, w);
+ this->cost = cost;
Weight = Weight.applyFunction(random);
if (init_zero) {
Matrix OutputLayer::forwarding(Matrix input) {
Input = input;
- hidden = input.dot(Weight).add(Bias).applyFunction(activation);
+ if (cost == COST_LOGISTIC)
+ hidden = input.dot(Weight).applyFunction(activation);
+ else
+ hidden = input.dot(Weight).add(Bias).applyFunction(activation);
return hidden;
}
double lossSum = 0.0;
Matrix Y2 = label;
Matrix Y = hidden;
- Matrix sub = Y2.subtract(Y);
- Matrix l = (sub.multiply(sub)).sum().multiply(0.5);
Matrix ret;
- std::vector<double> t = l.Mat2Vec();
- for (int i = 0; i < l.getBatch(); i++) {
- lossSum += t[i];
- }
+ Matrix dJdB;
+
+ if (cost == COST_LOGISTIC) {
+ dJdB = Y.subtract(Y2);
+ Matrix temp =
+ ((Y2.multiply(-1.0).transpose().dot(Y.add(1e-5).applyFunction(log)))
+ .subtract(Y2.multiply(-1.0).add(1.0).transpose().dot(
+ Y.multiply(-1.0).add(1.0).add(1e-5).applyFunction(log))));
+ loss = (1.0 / Y.Mat2Vec().size()) * temp.Mat2Vec()[0];
+ } else {
+ Matrix sub = Y2.subtract(Y);
+ Matrix l = (sub.multiply(sub)).sum().multiply(0.5);
+ std::vector<double> t = l.Mat2Vec();
+ for (int i = 0; i < l.getBatch(); i++) {
+ lossSum += t[i];
+ }
- loss = lossSum / (double)l.getBatch();
+ loss = lossSum / (double)l.getBatch();
+
+ dJdB = Y.subtract(Y2).multiply(
+ Input.dot(Weight).add(Bias).applyFunction(activationPrime));
+ }
- Matrix dJdB = Y.subtract(Y2).multiply(
- Input.dot(Weight).add(Bias).applyFunction(activationPrime));
Matrix dJdW = Input.transpose().dot(dJdB);
ret = dJdB.dot(Weight.transpose());
#include <sstream>
#include <stdio.h>
+bool compareChar(char &c1, char &c2) {
+ if (c1 == c2)
+ return true;
+ else if (std::toupper(c1) == std::toupper(c2))
+ return true;
+ return false;
+}
+
+bool caseInSensitiveCompare(std::string &str1, std::string &str2) {
+ return ((str1.size() == str2.size()) &&
+ std::equal(str1.begin(), str1.end(), str2.begin(), &compareChar));
+}
+
namespace Network {
std::vector<std::string> Optimizer_string = {"sgd", "adam"};
switch (t) {
case TOKEN_OPT:
for (i = 0; i < Optimizer_string.size(); i++) {
- if (Optimizer_string[i].compare(ll) == 0) {
+ if (caseInSensitiveCompare(Optimizer_string[i], ll)) {
return (i);
}
}
break;
case TOKEN_COST:
for (i = 0; i < Cost_string.size(); i++) {
- if (Cost_string[i].compare(ll) == 0) {
+ if (caseInSensitiveCompare(Cost_string[i], ll)) {
return (i);
}
}
break;
case TOKEN_NET:
for (i = 0; i < NetworkType_string.size(); i++) {
- if (NetworkType_string[i].compare(ll) == 0) {
+ if (caseInSensitiveCompare(NetworkType_string[i], ll)) {
return (i);
}
}
break;
case TOKEN_ACTI:
for (i = 0; i < activation_string.size(); i++) {
- if (activation_string[i].compare(ll) == 0) {
+ if (caseInSensitiveCompare(activation_string[i], ll)) {
return (i);
}
}
break;
case TOKEN_LAYER:
for (i = 0; i < layer_string.size(); i++) {
- if (layer_string[i].compare(ll) == 0) {
+ if (caseInSensitiveCompare(layer_string[i], ll)) {
return (i);
}
}
for (unsigned int i = 0; i < layers_name.size(); i++)
std::cout << layers_name[i] << std::endl;
- // std::cout << learning_rate<< " " << epoch << " " << opt.type<< " " <<
- // opt.activation<< " " << cost << " " << model << " " << batchsize<< " \n";
-
loss = 100000.0;
for (unsigned int i = 0; i < layers_name.size(); i++) {
id = iniparser_getint(ini, (layers_name[i] + ":Id").c_str(), 0);
b_zero = iniparser_getboolean(ini, (layers_name[i] + ":Bias_zero").c_str(),
true);
- std::cout << l_type << " " << t << " " << w << " " << b_zero << " " << id
- << std::endl;
+ std::cout << l_type << " " << t << " " << w << " " << h << " " << b_zero
+ << " " << id << std::endl;
switch (t) {
case Layers::LAYER_IN: {
Layers::InputLayer *inputlayer = new (Layers::InputLayer);
outputlayer->setType(t);
outputlayer->initialize(batchsize, h, w, id, b_zero);
outputlayer->setOptimizer(opt);
+ outputlayer->setCost(cost);
layers.push_back(outputlayer);
} break;
case Layers::LAYER_UNKNOWN:
projects / platform / core / ml / nntrainer.git / commitdiff