d7eea325fb
- Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean - Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function - Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
263 lines
9.6 KiB
MQL5
263 lines
9.6 KiB
MQL5
//+------------------------------------------------------------------+
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//| Layer.mqh |
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//| |
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//| CLayer - neuron container: element construction from a topology |
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//| descriptor or a .nnw stream. |
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//| |
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//| Included from AI\Network.mqh AFTER every class declaration - |
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//| bodies only, no declarations. Relocation is behaviour-neutral by |
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//| construction: nothing here is reachable until Network.mqh ends. |
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//+------------------------------------------------------------------+
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#ifndef WARRIOR_AI_IMPL_LAYER_MQH
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#define WARRIOR_AI_IMPL_LAYER_MQH
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CLayer::CreateElementScaled(int index, double weighScale)
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{
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if(index >= m_data_max)
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return false;
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//---
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bool result = false;
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CNeuronBase *temp = NULL;
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CNeuronPool *temp_p = NULL;
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CNeuronBaseOCL *temp_ocl = NULL;
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if(iFileHandle <= 0)
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{
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temp = new CNeuron();
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if(CheckPointer(temp) == POINTER_INVALID || !temp.Init(iOutputs, index, SGD, weighScale))
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return false;
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result = true;
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}
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else
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{
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int type = FileReadInteger(iFileHandle);
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switch(type)
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{
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case defNeuron:
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temp = new CNeuron();
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if(CheckPointer(temp) == POINTER_INVALID)
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{
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result = false;
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break;
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}
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result = temp.Init(iOutputs, index, ADAM);
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break;
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case defNeuronPool:
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temp_p = new CNeuronPool();
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if(CheckPointer(temp_p) == POINTER_INVALID)
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{
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result = false;
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break;
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}
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if(temp_p.Init(iOutputs, index, 1, 1, 1, ADAM))
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{
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temp = temp_p;
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result = true;
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}
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break;
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case defNeuronConv:
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temp_p = new CNeuronConv();
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if(CheckPointer(temp_p) == POINTER_INVALID)
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{
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result = false;
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break;
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}
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if(temp_p.Init(iOutputs, index, 1, 1, 1, ADAM))
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{
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temp = temp_p;
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result = true;
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}
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break;
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case defNeuronLSTM:
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temp_p = new CNeuronLSTM();
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if(CheckPointer(temp_p) == POINTER_INVALID)
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{
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result = false;
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break;
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}
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if(temp_p.Init(iOutputs, index, 1, 1, 1, ADAM))
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{
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temp = temp_p;
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result = true;
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}
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break;
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case defNeuronBaseOCL:
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temp_ocl = new CNeuronBaseOCL();
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if(CheckPointer(temp_ocl) == POINTER_INVALID)
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{
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result = false;
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break;
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}
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//--- Pure-MQL5 inference (no backend): construct host-only. Load() restores the weights into
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//--- the host buffers and CNeuronBaseOCL::feedForwardCPU() computes on them. The device Init()
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//--- below REQUIRES a backend, so it only runs when one exists (training/optimization/GPU run).
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if(CheckPointer(OpenCL) == POINTER_INVALID && CheckPointer(DirectML) == POINTER_INVALID)
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{
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m_data[index] = temp_ocl;
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return true;
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}
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if(CheckPointer(OpenCL) != POINTER_INVALID
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? temp_ocl.Init(iOutputs, index, OpenCL, 1, ADAM)
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: temp_ocl.Init(iOutputs, index, DirectML, 1, ADAM))
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{
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m_data[index] = temp_ocl;
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return true;
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}
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break;
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case defNeuronConvOCL:
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{
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//--- placeholder dims; the real window/step/units are restored by Load() right after
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CNeuronConvOCL *temp_conv = new CNeuronConvOCL();
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if(CheckPointer(temp_conv) == POINTER_INVALID)
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{
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result = false;
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break;
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}
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//--- Pure-MQL5 inference (no backend): construct host-only, Load() fills the host buffers,
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//--- CNeuronConvOCL::feedForwardCPU() computes on them (device Init below needs a backend).
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if(CheckPointer(OpenCL) == POINTER_INVALID && CheckPointer(DirectML) == POINTER_INVALID)
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{
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m_data[index] = temp_conv;
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return true;
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}
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if(CheckPointer(OpenCL) != POINTER_INVALID
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? temp_conv.Init(iOutputs, index, OpenCL, 1, 1, 1, 1, ADAM)
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: temp_conv.Init(iOutputs, index, DirectML, 1, 1, 1, 1, ADAM))
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{
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m_data[index] = temp_conv;
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return true;
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}
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break;
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}
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case defNeuronBatchNormOCL:
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{
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//--- Placeholder width of 1; CNeuronBatchNormOCL::Load() replaces both the buffers and the
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//--- whole gamma/beta/statistics block with the real ones, then verifies they agree.
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CNeuronBatchNormOCL *temp_bn = new CNeuronBatchNormOCL();
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if(CheckPointer(temp_bn) == POINTER_INVALID)
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{
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result = false;
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break;
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}
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//--- Pure-MQL5 inference (no backend): construct host-only (see the defNeuronConvOCL note).
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if(CheckPointer(OpenCL) == POINTER_INVALID && CheckPointer(DirectML) == POINTER_INVALID)
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{
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m_data[index] = temp_bn;
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return true;
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}
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if(CheckPointer(OpenCL) != POINTER_INVALID
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? temp_bn.Init(iOutputs, index, OpenCL, 1, 1, ADAM)
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: temp_bn.Init(iOutputs, index, DirectML, 1, 1, ADAM))
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{
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m_data[index] = temp_bn;
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return true;
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}
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break;
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}
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case defNeuronPoolOCL:
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{
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CNeuronPoolOCL *temp_pool = new CNeuronPoolOCL();
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if(CheckPointer(temp_pool) == POINTER_INVALID)
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{
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result = false;
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break;
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}
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//--- Pure-MQL5 inference (no backend): construct host-only (see the defNeuronConvOCL note).
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if(CheckPointer(OpenCL) == POINTER_INVALID && CheckPointer(DirectML) == POINTER_INVALID)
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{
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m_data[index] = temp_pool;
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return true;
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}
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if(CheckPointer(OpenCL) != POINTER_INVALID
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? temp_pool.Init(iOutputs, index, OpenCL, 1, 1, 1, ADAM)
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: temp_pool.Init(iOutputs, index, DirectML, 1, 1, 1, ADAM))
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{
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m_data[index] = temp_pool;
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return true;
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}
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break;
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}
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case defNeuronLSTMOCL:
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{
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CNeuronLSTMOCL *temp_lstm = new CNeuronLSTMOCL();
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if(CheckPointer(temp_lstm) == POINTER_INVALID)
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{
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result = false;
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break;
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}
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//--- Pure-MQL5 inference (no backend): construct host-only (see the defNeuronConvOCL note).
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if(CheckPointer(OpenCL) == POINTER_INVALID && CheckPointer(DirectML) == POINTER_INVALID)
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{
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m_data[index] = temp_lstm;
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return true;
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}
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if(CheckPointer(OpenCL) != POINTER_INVALID
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? temp_lstm.Init(iOutputs, index, OpenCL, 1, ADAM)
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: temp_lstm.Init(iOutputs, index, DirectML, 1, ADAM))
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{
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m_data[index] = temp_lstm;
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return true;
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}
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break;
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}
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default:
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result = false;
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break;
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}
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}
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if(result)
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m_data[index] = temp;
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//---
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return (result);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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CLayer::CLayer(uint outputs = 0, int handle = -1, COpenCLMy *opencl = NULL, CDirectMLMy *directml = NULL)
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{
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iOutputs = outputs;
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iFileHandle = handle;
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OpenCL = opencl;
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DirectML = directml;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CLayer::Load(const int file_handle)
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{
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iFileHandle = file_handle;
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if(!CArrayObj::Load(file_handle))
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return false;
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// A corrupt/truncated save can produce a 0-element layer here - CArrayObj::Load itself would
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// still return true for it, so m_data[0] must be guarded before indexing or MQL5 raises an
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// "array out of range" runtime error that aborts the whole CNet::Load call.
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if(Total() <= 0 || CheckPointer(m_data[0]) == POINTER_INVALID)
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{
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Print(__FUNCTION__ + ": layer loaded with no elements (corrupt or truncated save file)");
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return false;
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}
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//---
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switch(m_data[0].Type())
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{
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case defNeuronBaseOCL:
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case defNeuronConvOCL:
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case defNeuronPoolOCL:
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case defNeuronLSTMOCL:
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case defNeuronBatchNormOCL:
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{
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CNeuronBaseOCL *temp = m_data[0];
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iOutputs = temp.getConnections();
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break;
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}
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default:
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{
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CNeuronBase *temp = m_data[0];
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iOutputs = temp.getConnections().Total();
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break;
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}
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}
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//---
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return true;
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}
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#endif
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