284 lines
19 KiB
MQL5
284 lines
19 KiB
MQL5
//+------------------------------------------------------------------+
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//| Convolution_test3.mq5 |
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//| Copyright 2021, MetaQuotes Ltd. |
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//| https://www.mql5.com |
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//+------------------------------------------------------------------+
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#property copyright "Copyright 2021, MetaQuotes Ltd."
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#property link "https://www.mql5.com"
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#property version "1.00"
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#property script_show_inputs
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//+------------------------------------------------------------------+
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//| Внешние параметры для работы скрипта |
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//+------------------------------------------------------------------+
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input string StudyFileName = "study_data.csv";// Имя файла с обучающей выборкой
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input string OutputFileName = "loss_study_conv.csv";// Имя файла для записи динамики ошибки
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input int BarsToLine = 40; // Количество исторических баров в одном паттерне
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input int NeuronsToBar = 4; // Количество нейронов входного слоя на 1 бар
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input bool UseOpenCL = false; // Использовать OpenCL
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input int BatchSize = 10000; // Размер пакета для обновления матрицы весов
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input double LearningRate = 0.0001; // Коэффициент обучения
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input int HiddenLayers = 3; // Количество скрытых слоёв
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input int HiddenLayer = 40; // Количество нейронов в одном скрытом слое
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input int Epochs = 1000;
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//+------------------------------------------------------------------+
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//| Подключаем библиотеку нейронной сети |
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//+------------------------------------------------------------------+
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#include <NeuroNetworksBook\realization\neuronnet.mqh>
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CNet *net;
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//+------------------------------------------------------------------+
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//| Начало программы скрипта |
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//+------------------------------------------------------------------+
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void OnStart()
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{
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//---
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CArrayObj *layers = new CArrayObj();
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if(CheckPointer(layers) == POINTER_INVALID)
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{
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PrintFormat("Error of create CArrayObj: %d", GetLastError());
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return;
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}
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//--- Input Layer
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CLayerDescription *descr = new CLayerDescription();
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if(CheckPointer(descr) == POINTER_INVALID)
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{
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PrintFormat("Error of create CLayerDescription: %d", GetLastError());
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return;
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}
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descr.type = defNeuronBase;
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descr.count = NeuronsToBar * BarsToLine;
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descr.window = 0;
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descr.activation = ACT_None;
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descr.optimization = None;
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if(!layers.Add(descr))
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{
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PrintFormat("Error of add layer: %d", GetLastError());
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return;
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}
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//--- Свёрточный слой
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descr = new CLayerDescription();
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if(CheckPointer(descr) == POINTER_INVALID)
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{
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PrintFormat("Error of create CLayerDescription: %d", GetLastError());
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delete layers;
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return;
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}
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descr.type = defNeuronConv;
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descr.count = BarsToLine - 2;
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descr.window = 3 * NeuronsToBar;
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descr.window_out = 8;
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descr.step = NeuronsToBar;
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descr.activation = ACT_SWISH;
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descr.optimization = Adam;
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descr.activation_params[0] = 1;
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if(!layers.Add(descr))
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{
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PrintFormat("Error of add layer: %d", GetLastError());
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delete layers;
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delete descr;
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return;
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}
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//--- Подвыборочный слой
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descr = new CLayerDescription();
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if(CheckPointer(descr) == POINTER_INVALID)
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{
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PrintFormat("Error of create CLayerDescription: %d", GetLastError());
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delete layers;
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return;
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}
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descr.type = defNeuronProof;
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int prev_count = descr.count = BarsToLine - 3;
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descr.window = 2;
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descr.window_out = 8;
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descr.step = 1;
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descr.activation = ACT_MAX_POOLING;
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descr.optimization = None;
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descr.activation_params[0] = 0;
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if(!layers.Add(descr))
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{
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PrintFormat("Error of add layer: %d", GetLastError());
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delete layers;
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delete descr;
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return;
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}
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//--- Hidden Layers
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for(int i = 0; i < HiddenLayers; i++)
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{
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descr = new CLayerDescription();
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if(CheckPointer(descr) == POINTER_INVALID)
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{
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PrintFormat("Error of create CLayerDescription: %d", GetLastError());
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return;
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}
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descr.type = defNeuronBase;
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descr.count = HiddenLayer;
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descr.activation = ACT_SWISH;
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descr.optimization = Adam;
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descr.activation_params[0] = 1;
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if(!layers.Add(descr))
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{
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PrintFormat("Error of add layer: %d", GetLastError());
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return;
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}
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}
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//--- Output Layer
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descr = new CLayerDescription();
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if(CheckPointer(descr) == POINTER_INVALID)
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{
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PrintFormat("Error of create CLayerDescription: %d", GetLastError());
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return;
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}
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descr.type = defNeuronBase;
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descr.count = 2;
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descr.activation = ACT_LINE;
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descr.optimization = Adam;
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descr.activation_params[0] = 1;
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if(!layers.Add(descr))
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{
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PrintFormat("Error of add layer: %d", GetLastError());
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return;
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}
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//---
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net = new CNet();
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if(CheckPointer(net) == POINTER_INVALID)
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{
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PrintFormat("Error of create Net: %d", GetLastError());
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return;
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}
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//---
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if(!net.Create(layers, LearningRate, 0.9, 0.999, ENUM_LOSS_MSE, 0, 0))
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{
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PrintFormat("Error of init Net: %d", GetLastError());
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return;
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}
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delete layers;
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net.UseOpenCL(UseOpenCL);
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net.LossSmoothFactor(BatchSize);
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//---
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CArrayObj *data = new CArrayObj();
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if(CheckPointer(data) == POINTER_INVALID)
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{
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PrintFormat("Error of create Historical data array: %d", GetLastError());
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return;
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}
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//---
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CArrayObj *result = new CArrayObj();
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if(CheckPointer(result) == POINTER_INVALID)
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{
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PrintFormat("Error of create Target data array: %d", GetLastError());
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return;
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}
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CBufferDouble *pattern;
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//---
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CBufferDouble *target;
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//--- Открываем файл и считываем исходных данных
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int handle = FileOpen(StudyFileName, FILE_READ | FILE_CSV | FILE_ANSI | FILE_SHARE_READ, ",", CP_UTF8);
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if(handle == INVALID_HANDLE)
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{
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PrintFormat("Error of open study data file: %d", GetLastError());
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return;
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}
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while(!FileIsEnding(handle) && !IsStopped())
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{
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pattern = new CBufferDouble();
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if(CheckPointer(pattern) == POINTER_INVALID)
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{
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PrintFormat("Error of create Pattern data array: %d", GetLastError());
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return;
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}
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target = new CBufferDouble();
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if(CheckPointer(target) == POINTER_INVALID)
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{
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PrintFormat("Error of create Pattern Target array: %d", GetLastError());
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return;
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}
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for(int i = 0; i < NeuronsToBar * BarsToLine; i++)
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{
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if(!pattern.Add(FileReadNumber(handle)))
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{
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PrintFormat("Error of read study data from file: %d", GetLastError());
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return;
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}
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}
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for(int i = 0; i < 2; i++)
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if(!target.Add(FileReadNumber(handle)))
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{
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PrintFormat("Error of read study data from file: %d", GetLastError());
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return;
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}
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if(!data.Add(pattern))
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{
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PrintFormat("Error of add study data to array: %d", GetLastError());
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return;
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}
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if(!result.Add(target))
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{
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PrintFormat("Error of add study data to array: %d", GetLastError());
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return;
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}
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int total = data.Total();
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Comment(StringFormat("Read %d patterns", total));
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}
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FileClose(handle);
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//---
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int patterns = data.Total();
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CArrayDouble *loss = new CArrayDouble();
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if(CheckPointer(loss) == POINTER_INVALID)
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{
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PrintFormat("Error of create loss array: %d", GetLastError());
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return;
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}
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for(int epoch = 0; epoch < Epochs && !IsStopped(); epoch++)
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{
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for(int i = 0; i < BatchSize && !IsStopped(); i++)
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{
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int k = (int)((double)(MathRand() * MathRand()) / MathPow(32767.0, 2) * patterns);
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pattern = data.At(k);
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target = result.At(k);
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if(CheckPointer(pattern) == POINTER_INVALID || CheckPointer(target) == POINTER_INVALID)
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{
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i--;
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continue;
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}
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if(!net.FeedForward(pattern))
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{
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PrintFormat("Error in FeedForward: %d", GetLastError());
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return;
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}
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if(!net.Backpropagation(target))
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{
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PrintFormat("Error in Backpropagation: %d", GetLastError());
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return;
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}
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}
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net.UpdateWeights(BatchSize);
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if(!loss.Add(net.GetRecentAverageLoss()))
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{
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PrintFormat("Error of add loss to array: %d", GetLastError());
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return;
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}
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string s;
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Comment(s = StringFormat("Studied %d epoch, error %.5f", epoch, net.GetRecentAverageLoss()));
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}
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handle = FileOpen(OutputFileName, FILE_WRITE | FILE_CSV | FILE_ANSI, ",", CP_UTF8);
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if(handle == INVALID_HANDLE)
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{
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PrintFormat("Error of open loss file: %d", GetLastError());
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return;
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}
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for(int i = 0; i < loss.Total(); i++)
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{
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FileWrite(handle, loss.At(i));
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}
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FileClose(handle);
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PrintFormat("The dynamics of the error change is saved to a file %s\\%s",
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TerminalInfoString(TERMINAL_DATA_PATH), OutputFileName);
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net.Save("Study.net");
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delete net;
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delete loss;
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delete data;
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delete result;
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delete pattern;
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delete target;
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Comment("");
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}
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//+------------------------------------------------------------------+
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