forked from animatedread/Warrior_EA
6a687cda41
Replace hardcoded lr and momentum with new input variables for Adam and SGD+momentum. Add OpenCL kernel LSTM_UpdateWeightsMomentum alongside the existing Adam kernel. Update comments and revert beta1 to book default 0.9.
87 lines
4.4 KiB
MQL5
87 lines
4.4 KiB
MQL5
//+------------------------------------------------------------------+
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//| Warrior_EA |
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//| AnimateDread |
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//| |
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//+------------------------------------------------------------------+
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#include "..\Expert\ExpertSignalAIBase.mqh"
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// wizard description start
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//+------------------------------------------------------------------+
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//| Description of the class |
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//| Title=Signals of indicator 'Convolutional AI' |
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//| Type=SignalAdvanced |
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//| Name=Convolutional AI |
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//| ShortName=CONV |
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//| Class=CSignalCONV |
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//| Page=signal_conv |
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//+------------------------------------------------------------------+
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// wizard description end
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//+------------------------------------------------------------------+
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//| Class CSignalCONV. |
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//| Purpose: Class of generator of trade signals based on |
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//| the 'Convolutional AI Neural Network. |
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//| Is derived from the CExpertSignalAIBase class. |
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//| Only the network topology differs from the other AI signals: an |
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//| input layer feeds a Conv+Pool stage before the common tapering |
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//| hidden-layer stack (see AddCustomLayers). |
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//+------------------------------------------------------------------+
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class CSignalCONV : public CExpertSignalAIBase
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{
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protected:
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virtual bool AddCustomLayers(CArrayObj *topology) override;
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public:
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CSignalCONV(void);
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//--- method of creating the indicator and timeseries
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virtual bool InitIndicators(CIndicators *indicators) override;
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};
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//+------------------------------------------------------------------+
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//| Constructor |
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//+------------------------------------------------------------------+
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CSignalCONV::CSignalCONV(void)
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{
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SetIdentity("Convolutional", "CONV");
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}
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//+------------------------------------------------------------------+
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//| Create indicators and bootstrap/load the network. |
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//+------------------------------------------------------------------+
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bool CSignalCONV::InitIndicators(CIndicators *indicators)
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{
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return InitNeuralNetwork(indicators);
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}
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//+------------------------------------------------------------------+
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//| Conv + Pool layers inserted between the input layer and the |
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//| common tapering hidden-layer stack. |
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//+------------------------------------------------------------------+
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bool CSignalCONV::AddCustomLayers(CArrayObj *topology)
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{
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//--- Convolutional Layer
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CLayerDescription *desc = new CLayerDescription();
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if(CheckPointer(desc) == POINTER_INVALID)
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return false;
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desc.count = m_hiddenLayersCount;
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desc.type = defNeuronConv;
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// PRELU, not TANH: matches what CNeuronConv's CPU path (Network.mqh) has always hardcoded
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// regardless of this setting (its activationFunction() override ignores `activation` entirely) -
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// this used to silently diverge from the GPU/DirectML tier, which DOES honor this field and was
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// therefore actually running tanh instead of the intended PReLU whenever hardware accel was active.
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desc.activation = PRELU;
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desc.optimization = (ENUM_OPTIMIZATION)m_optimizationAlgo;
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desc.window = m_neuronsCount;
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desc.step = m_neuronsCount;
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if(!topology.Add(desc))
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return false;
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//--- Pooling Layer
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desc = new CLayerDescription();
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if(CheckPointer(desc) == POINTER_INVALID)
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return false;
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desc.count = m_hiddenLayersCount;
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desc.type = defNeuronPool;
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// Unused either way - CNeuronPool::feedForward (CPU) does plain average pooling and never calls
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// activationFunction() at all, and the GPU FeedForwardProof kernel (max pooling) doesn't even take
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// an activation argument. NONE documents that accurately instead of implying an activation applies.
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desc.activation = NONE;
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desc.optimization = (ENUM_OPTIMIZATION)m_optimizationAlgo;
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desc.window = 3;
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desc.step = 2;
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return topology.Add(desc);
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}
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//+------------------------------------------------------------------+
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