forked from animatedread/Warrior_EA
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
1113 lines
63 KiB
MQL5
1113 lines
63 KiB
MQL5
//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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#include <Arrays\ArrayDouble.mqh>
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#include <Arrays\ArrayInt.mqh>
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#include <Arrays\ArrayObj.mqh>
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#include <OpenCL\OpenCL.mqh>
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#include "..\System\AtomicFile.mqh"
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//--- 3rd-tier CPU fallback (used when neither OpenCL nor DirectML/D3D12 GPU accel are available,
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//--- e.g. a VM with no GPU passthrough, or this machine's OpenCL/DirectML init failed). Sizes
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//--- WarriorCPU.dll's worker thread pool - no effect at all when a GPU tier (OpenCL or DirectML) is
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//--- active, since neither one calls into WarriorCPU.dll.
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//---
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//--- A FIXED SMALL THREAD COUNT PER NETWORK, not a share of the machine. This replaced a TargetCPULoad
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//--- input divided by the live chart count, which was wrong twice over:
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//--- - The count is a SNAPSHOT taken when each net's pool is built, and charts are attached one at a
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//--- time. Measured 2026-07-29 with five charts: they took 10/6/5/4/4% of the same budget, because
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//--- the first chart only ever saw itself and the last saw all five. So the earliest chart got
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//--- several times the threads of the latest - which silently skews any cross-topology comparison
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//--- run on those charts, the exact thing the setting existed to make fair.
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//--- - Nothing rebalances when a chart is added or removed, and rebalancing would mean tearing down a
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//--- DLL context underneath a running trainer.
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//--- Neither problem exists once the answer stops depending on how many charts are running.
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//---
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//--- Why 2 threads is not a compromise: since the topology became data-derived the widest dense layer
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//--- is 64 units (ExpertSignalAIBase.mqh's ComputeFirstLayerWidth), so each ParallelFor has almost
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//--- nothing to split and per-dispatch overhead dominates. The measurements agree - an MLP era cost
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//--- ~66s at a wildly oversubscribed 12 threads and ~80s at 1 thread, a 20% spread across a 12x
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//--- difference in thread count. Two per net also lands six concurrent charts exactly on a 12-core box.
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//---
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//--- Removing the input costs nothing on the product side: a Market build has no DLL tier at all (MQL5
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//--- Market rule IV strips the #import blocks - see AI\NeuronDirectML.mqh), so it was already compiled
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//--- out to a constant there and no buyer could ever reach it.
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#define CPU_THREADS_PER_NETWORK 2
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//+------------------------------------------------------------------+
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//| The percentage CDirectMLMy needs in order to land on |
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//| CPU_THREADS_PER_NETWORK workers, given the detected core count. |
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//| (WarriorCPU.dll takes a percentage of cores, not a thread count.) |
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//+------------------------------------------------------------------+
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int EffectiveCpuLoadPercent()
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{
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int cores = (int)TerminalInfoInteger(TERMINAL_CPU_CORES);
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//--- Unknown core count: assume a small machine rather than a large one. Guessing high here would
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//--- reintroduce exactly the oversubscription this function exists to prevent.
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if(cores <= 0)
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cores = 4;
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//--- Fewer cores than we would ask for: take the machine as it is. 100 also means "all cores" to the
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//--- DLL, so this is the one value that needs no arithmetic.
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if(cores <= CPU_THREADS_PER_NETWORK)
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return 100;
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//--- Round UP: the DLL truncates when turning this back into a thread count, and landing one thread
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//--- short of the target is a worse error than landing one over.
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int pct = (int)MathCeil(100.0 * (double)CPU_THREADS_PER_NETWORK / (double)cores);
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return (int)MathMax(1, MathMin(100, pct));
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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//--- Adam (Kingma & Ba, 2014) hyperparameters. Defaults are neuronetworksbook.pdf's own reference
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//--- library defaults (defLearningRate/defBeta1/defBeta2 = 3.0e-4/0.9/0.999) - not the paper's
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//--- abstract "0.001" mention, which the book's own worked examples don't actually use either.
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//--- 3.0e-4 also happens to sit inside the noisy/non-stationary-trading-data range (0.0003-0.0005)
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//--- this project had separately tuned lr to before this input existed, so no behavior conflict.
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//--- Beta1 was previously hand-lowered to 0.8 as an experiment to fight a multi-era same-class-streak
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//--- bug - that symptom's likely root cause (independent-sigmoid+BCE output gradient, since fixed to
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//--- a joint softmax+CCE gradient in backProp()/backPropOCL()) is addressed elsewhere now, so this
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//--- reverts to the literature/book default.
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input double AdamLearningRate = 0.0003; // Adam learning rate
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input double AdamBeta1 = 0.9; // Adam beta1
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input double AdamBeta2 = 0.999; // Adam beta2
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//--- SGD+momentum hyperparameters. The book states no distinct default learning rate for this method
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//--- (its own reference library reuses the same defLearningRate for every optimizer), so this reuses
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//--- Adam's book-default rate as its starting point too. The book also states no numeric default for
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//--- the momentum decay coefficient itself (just "in the range 0 to 1, exclusive") - 0.9 reuses
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//--- Adam's beta1, the only concrete "momentum decay" value the book ever commits to a number for.
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input double SgdLearningRate = 0.0003; // SGD learning rate
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input double SgdMomentum = 0.9; // SGD momentum
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#define lr AdamLearningRate
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#define b1 AdamBeta1
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#define b2 AdamBeta2
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#define momentum SgdMomentum
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double eta = lr;
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#define defConnect 0x7781
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#define defArrayConnects 0x7782
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#define defNeuronBase 0x7783
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#define defNeuron 0x7784
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#define defNeuronConv 0x7785
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#define defNeuronPool 0x7786
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#define defLayer 0x7787
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#define defArrayLayer 0x7788
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#define defNet 0x7789
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#define defNeuronLSTM 0x7791
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//--- Topology-descriptor form of the batch-normalization layer (CLayerDescription::type). Like
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//--- defNeuronConv/defNeuronPool it has no scalar-CPU neuron class behind it - CNet's constructor maps
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//--- it onto CNeuronBatchNormOCL, which is the only implementation. See AI\NeuronBatchNorm.mqh.
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#define defNeuronBatchNorm 0x7792
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//---
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#define defBufferDouble 0x7882
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#define defNeuronBaseOCL 0x7883
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#define defNeuronLSTMOCL 0x7884
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#define defNeuronConvOCL 0x7885
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#define defNeuronPoolOCL 0x7886
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#define defNeuronBatchNormOCL 0x7887
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//---
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#define def_k_FeedForward 0
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#define def_k_ff_matrix_w 0
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#define def_k_ff_matrix_i 1
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#define def_k_ff_matrix_o 2
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#define def_k_ff_inputs 3
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#define def_k_ff_activation 4
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//---
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#define def_k_CaclOutputGradient 1
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#define def_k_cog_matrix_t 0
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#define def_k_cog_matrix_o 1
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#define def_k_cog_matrix_ig 2
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#define def_k_cog_activation 3
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//---
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#define def_k_CaclHiddenGradient 2
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#define def_k_chg_matrix_w 0
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#define def_k_chg_matrix_g 1
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#define def_k_chg_matrix_o 2
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#define def_k_chg_matrix_ig 3
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#define def_k_chg_outputs 4
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#define def_k_chg_activation 5
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//---
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#define def_k_UpdateWeightsMomentum 3
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#define def_k_uwm_matrix_w 0
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#define def_k_uwm_matrix_g 1
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#define def_k_uwm_matrix_i 2
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#define def_k_uwm_matrix_dw 3
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#define def_k_uwm_inputs 4
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#define def_k_uwm_learning_rates 5
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#define def_k_uwm_momentum 6
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#define def_k_uwm_optimizer 7
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//---
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#define def_k_UpdateWeightsAdam 4
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#define def_k_uwa_matrix_w 0
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#define def_k_uwa_matrix_g 1
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#define def_k_uwa_matrix_i 2
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#define def_k_uwa_matrix_m 3
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#define def_k_uwa_matrix_v 4
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#define def_k_uwa_inputs 5
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#define def_k_uwa_l 6
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#define def_k_uwa_b1 7
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#define def_k_uwa_b2 8
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//---
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#define def_k_FeedForwardProof 15
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#define def_k_ffp_matrix_i 0
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#define def_k_ffp_matrix_o 1
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#define def_k_ffp_inputs 2
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#define def_k_ffp_window 3
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#define def_k_ffp_step 4
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//---
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#define def_k_CalcInputGradientProof 16
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#define def_k_cigp_matrix_i 0
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#define def_k_cigp_matrix_g 1
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#define def_k_cigp_matrix_o 2
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#define def_k_cigp_matrix_ig 3
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#define def_k_cigp_outputs 4
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#define def_k_cigp_window 5
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#define def_k_cigp_step 6
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//---
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#define def_k_FeedForwardConv 5
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#define def_k_ffc_matrix_w 0
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#define def_k_ffc_matrix_i 1
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#define def_k_ffc_matrix_o 2
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#define def_k_ffc_inputs 3
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#define def_k_ffc_step 4
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#define def_k_ffc_window_in 5
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#define def_k_ffc_window_out 6
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#define def_k_ffc_activation 7
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//---
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#define def_k_CalcHiddenGradientConv 6
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#define def_k_chgc_matrix_w 0
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#define def_k_chgc_matrix_g 1
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#define def_k_chgc_matrix_o 2
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#define def_k_chgc_matrix_ig 3
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#define def_k_chgc_outputs 4
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#define def_k_chgc_step 5
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#define def_k_chgc_window_in 6
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#define def_k_chgc_window_out 7
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#define def_k_chgc_activation 8
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//---
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#define def_k_UpdateWeightsConvMomentum 7
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#define def_k_uwcm_matrix_w 0
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#define def_k_uwcm_matrix_g 1
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#define def_k_uwcm_matrix_i 2
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#define def_k_uwcm_matrix_dw 3
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#define def_k_uwcm_inputs 4
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#define def_k_uwcm_learning_rates 5
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#define def_k_uwcm_momentum 6
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#define def_k_uwcm_window_in 7
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#define def_k_uwcm_window_out 8
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#define def_k_uwcm_step 9
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#define def_k_uwcm_optimizer 10
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//---
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#define def_k_UpdateWeightsConvAdam 8
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#define def_k_uwca_matrix_w 0
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#define def_k_uwca_matrix_g 1
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#define def_k_uwca_matrix_i 2
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#define def_k_uwca_matrix_m 3
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#define def_k_uwca_matrix_v 4
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#define def_k_uwca_inputs 5
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#define def_k_uwca_l 6
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#define def_k_uwca_b1 7
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#define def_k_uwca_b2 8
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#define def_k_uwca_window_in 9
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#define def_k_uwca_window_out 10
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#define def_k_uwca_step 11
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//---
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// LSTM (CNeuronLSTMOCL) - single-timestep-truncated BPTT (no gradient
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// flows back into h_prev/c_prev from a prior step). Supports both Adam and
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// SGD+momentum (LSTM_UpdateWeightsAdam/LSTM_UpdateWeightsMomentum below) -
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// see CNeuronLSTMOCL::updateInputWeights for the optimizer dispatch.
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// Derived from scratch from the standard LSTM equations - NOT ported from
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// the NeuroNet_DNG reference, whose LSTM_HiddenGradient kernel overwrites
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// the live weights buffer instead of writing to weights_gradient.
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#define def_k_LSTM_Gates 9
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#define def_k_lstmg_matrix_w 0
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#define def_k_lstmg_hidden_prev 1
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#define def_k_lstmg_inputs 2
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#define def_k_lstmg_concatenated 3
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#define def_k_lstmg_hidden_size 4
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#define def_k_lstmg_input_size 5
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//---
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#define def_k_LSTM_State 10
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#define def_k_lstms_concatenated 0
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#define def_k_lstms_memory 1
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#define def_k_lstms_hidden_prev 2
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#define def_k_lstms_hidden_cache 3
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#define def_k_lstms_output 4
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#define def_k_lstms_hidden_size 5
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//---
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#define def_k_LSTM_GateGradient 11
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#define def_k_lstmgg_gradient 0
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#define def_k_lstmgg_memory 1
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#define def_k_lstmgg_concatenated 2
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#define def_k_lstmgg_concatenated_gradient 3
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#define def_k_lstmgg_hidden_size 4
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//---
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#define def_k_LSTM_WeightsGradient 12
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#define def_k_lstmwg_concatenated_gradient 0
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#define def_k_lstmwg_hidden_cache 1
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#define def_k_lstmwg_inputs 2
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#define def_k_lstmwg_weights_gradient 3
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#define def_k_lstmwg_hidden_size 4
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#define def_k_lstmwg_input_size 5
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//---
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#define def_k_LSTM_InputsGradient 13
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#define def_k_lstmig_concatenated_gradient 0
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#define def_k_lstmig_matrix_w 1
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#define def_k_lstmig_inputs_gradient 2
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#define def_k_lstmig_hidden_size 3
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#define def_k_lstmig_input_size 4
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//---
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#define def_k_LSTM_UpdateWeightsAdam 14
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#define def_k_lstmuwa_matrix_w 0
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#define def_k_lstmuwa_weights_gradient 1
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#define def_k_lstmuwa_matrix_m 2
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#define def_k_lstmuwa_matrix_v 3
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#define def_k_lstmuwa_l 4
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#define def_k_lstmuwa_b1 5
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#define def_k_lstmuwa_b2 6
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//---
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// SGD+momentum counterpart to LSTM_UpdateWeightsAdam above - see
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// AI\Network.cl's LSTM_UpdateWeightsMomentum for the kernel body.
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#define def_k_LSTM_UpdateWeightsMomentum 17
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#define def_k_lstmuwm_matrix_w 0
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#define def_k_lstmuwm_weights_gradient 1
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#define def_k_lstmuwm_matrix_dw 2
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#define def_k_lstmuwm_learning_rates 3
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#define def_k_lstmuwm_momentum 4
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#define def_k_lstmuwm_optimizer 5
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//---
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// Sequence LSTM. One launch PER TIMESTEP - the recurrence is sequential and OpenCL
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// barriers only span a work-group, so the host loop is what provides the global
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// ordering. See the block comment above LSTM_SeqStepForward in AI\Network.cl.
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#define def_k_LSTM_SeqStepForward 18
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#define def_k_lsf_matrix_w 0
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#define def_k_lsf_inputs 1
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#define def_k_lsf_cache_gates 2
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#define def_k_lsf_cache_cell 3
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#define def_k_lsf_cache_hidden 4
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#define def_k_lsf_output 5
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#define def_k_lsf_hidden_size 6
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#define def_k_lsf_step_inputs 7
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#define def_k_lsf_steps 8
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#define def_k_lsf_t 9
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//---
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#define def_k_LSTM_SeqStepGateGrad 19
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#define def_k_lsgg_out_gradient 0
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#define def_k_lsgg_dh_buf 1
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#define def_k_lsgg_dc_buf 2
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#define def_k_lsgg_cache_gates 3
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#define def_k_lsgg_cache_cell 4
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#define def_k_lsgg_gate_grad 5
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#define def_k_lsgg_hidden_size 6
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#define def_k_lsgg_steps 7
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#define def_k_lsgg_t 8
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//---
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#define def_k_LSTM_SeqStepWeightGrad 20
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#define def_k_lswg_gate_grad 0
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#define def_k_lswg_cache_hidden 1
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#define def_k_lswg_inputs 2
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#define def_k_lswg_weights_gradient 3
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#define def_k_lswg_hidden_size 4
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#define def_k_lswg_step_inputs 5
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#define def_k_lswg_t 6
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//---
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#define def_k_LSTM_SeqStepInputGrad 21
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#define def_k_lsig_gate_grad 0
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#define def_k_lsig_matrix_w 1
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#define def_k_lsig_inputs_gradient 2
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#define def_k_lsig_dh_buf 3
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#define def_k_lsig_hidden_size 4
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#define def_k_lsig_step_inputs 5
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#define def_k_lsig_t 6
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//---
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// b1/b2 are now the AdamBeta1/AdamBeta2 inputs declared above (book defaults 0.9/0.999) - see
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// AdamLearningRate's declaration comment for why the earlier 0.8 experiment (fighting a multi-era
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// same-class-streak bug via a shorter momentum window) was reverted: that symptom's likely root
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// cause was the independent-sigmoid+BCE output gradient, since replaced with a joint softmax+CCE
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// gradient in backProp()/backPropOCL(), which addresses it more directly than shortening b1 ever
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// could. b1/b2 are passed as runtime parameters into every backend (not baked into compiled
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// kernels - see DirectML\WarriorCPU.cpp/WarriorDML.cpp/AI\Network.cl's UpdateWeightsAdam
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// signatures), so they're safe to expose as ordinary inputs.
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// Tightened from 1.0e6 - that ceiling was so loose it never actually engaged before training had
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// already gone unstable (real collapses were happening at weight magnitudes several orders of
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// magnitude below it). 100.0 matches the equivalent clamp in Dmitriy Gizlyk's reference NeuroNet.mqh
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// engine (references\MQL5\Experts\NeuroNet_DNG\NeuroNet.mqh) and gives a hard ceiling that's actually
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// reachable-and-meaningful given MAX_WEIGHT_DELTA=0.1 per step below.
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#define MAX_WEIGHT 100.0
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// Decoupled (AdamW-style) weight decay applied inside every Adam weight update below and in
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// DirectML\WarriorCPU.cpp/WarriorDML.cpp/AI\Network.cl (all four backends kept in sync) - see
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// WarriorCPU.cpp's WEIGHT_DECAY comment for the full rationale: MAX_WEIGHT only stops outright
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// +-Infinity blowups, it does nothing to stop weights slowly, unboundedly growing over hundreds of
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// training eras on a fixed, heavily class-balance-oversampled dataset, which was producing multi-
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// hour climb-to-90%+-then-collapse-to-single-digits OOS accuracy cycles.
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// 0.001, NOT the 0.01 Loshchilov & Hutter default: decay here is applied per SAMPLE (online updates,
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// ~20k+ steps per era), and AdamW's data term is invariant to gradient scale, so a weight's
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// sustainable magnitude is roughly (its gradient stream's signal-to-noise ratio)/WEIGHT_DECAY. For a
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// weak-signal domain like this one, 0.01 was observed (2026-07-19, SP500 H4) to grind the
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// discriminative weights down until the per-bar logit spread (avg 0.19 at era 1) fell BELOW the
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// calibration-capped class-prior offsets (~0.008): recall stayed healthy for ~30 eras while the
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// spread decayed monotonically, then argmax degenerated to constant-Neutral once the evidence tilt
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// dropped under the prior tilt. The prior offsets are capped by calibration regardless of decay
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// strength; the evidence tilts scale with 1/WEIGHT_DECAY - so decay strength decides which one wins
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// argmax. 0.001 lifts the evidence ceiling 10x while still bounding long-run weight growth.
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#define WEIGHT_DECAY 0.001
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// Per-step update clip - see WarriorCPU.cpp's matching MAX_WEIGHT_DELTA comment for the full
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// rationale: weight decay alone didn't stop the collapse cycles, since they turned out to be sudden
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// Adam overshoot events (OOS accuracy falling below the 3-class random-guess floor within ~20 eras),
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// most likely from 5x back-to-back oversampling replay building artificially correlated momentum.
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// Applied to the raw delta BEFORE it's added to the weight, unlike MAX_WEIGHT which only clamps the
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// post-update weight value and is far too loose (1e6) to prevent this.
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#define MAX_WEIGHT_DELTA 0.1
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// Floor on |activationFunctionDerivative()| for saturated tanh/sigmoid units (see
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// SigmoidFunctionDerivative/TanhFunctionDerivative below) - without this, a neuron pinned near its
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// activation extremes (output near -1/0/1) produces a near-zero derivative, which zeroes that
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// neuron's entire backprop gradient contribution regardless of how wrong its output is. A saturated
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// unit can then never receive a corrective signal to unstick it. 1e-4 matches the equivalent floor in
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// Dmitriy Gizlyk's reference NeuroNet.mqh/NeuroNet.cl engine.
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// 2026-07-27: Increased from 1.0e-4 to 1.0e-3 — must stay in sync with AI\Network.cl's matching
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// constant. See that file's comment for the full rationale (fp32 OpenCL saturation floor fix).
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#define MIN_ACTIVATION_DERIVATIVE 1.0e-3
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// Logit temperature for the 3-class softmax head (training gradient in backProp/backPropOCL AND
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// read-time ApplyClassificationSoftmax - the two MUST stay in sync or the model is scored against a
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// different distribution than it was trained on). The classification outputs are SIGMOID-bounded to
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// [0,1], so the raw logit spread can never exceed 1 and the softmax winner caps at e/(e+2)=0.576 -
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// the one-hot 1.0 target is unreachable, per-sample gradients never decay below ~0.42, and training
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// can only orbit, never converge (observed as IS error frozen at sqrt(1/3)=0.58 with all three
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// outputs saturated at 0). Scaling the logits by 6 stretches the spread to [0,6], raising the
|
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// ceiling to e^6/(e^6+2)=0.995: targets effectively reachable, gradients can vanish, and the focal
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// modulation's pt finally spans (0,1) instead of (0.21,0.58). The gradient deliberately stays
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// (target - softmax) WITHOUT the extra 6x chain-rule factor - the scale is defined as part of the
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// loss, keeping gradient magnitudes (and thus eta tuning) unchanged.
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#define CLASS_LOGIT_SCALE 6.0
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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#resource "Network.cl" as string cl_program
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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enum ENUM_ACTIVATION
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|
{
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NONE,
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|
TANH,
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SIGMOID,
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PRELU // fixed param=0.01, matches CNeuronConv's CPU activationFunction
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};
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|
//+------------------------------------------------------------------+
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|
//| Translates ENUM_ACTIVATION to the "activation" int code every |
|
|
//| native compute backend actually understands (Network.cl's |
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|
//| kernels, and the mirrored Activation()/inline switches in |
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|
//| WarriorCPU.cpp / WarriorDML.cpp): 0=TANH, 1=SIGMOID, 2=PRELU, and |
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|
//| deliberately anything else (incl. NONE) falls through every one |
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|
//| of those switches unmatched, which is exactly linear passthrough -|
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|
//| there's no case 3 anywhere on the native side, so PRELU relies on |
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|
//| the FeedForwardConv-family kernels specifically, and NONE never |
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|
//| needs a case at all. This is NOT the same numbering as |
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|
//| ENUM_ACTIVATION itself (NONE=0, TANH=1, SIGMOID=2, PRELU=3) - a |
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|
//| raw (int)activation cast at a kernel call site silently sends the |
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|
//| WRONG activation to the GPU/DLL tier (e.g. MQL5 TANH -> native |
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|
//| SIGMOID). Only use this at actual kernel-dispatch call sites - |
|
|
//| CNeuronBase::Save()/CNeuronBaseOCL::Save() persist the raw |
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|
//| ENUM_ACTIVATION value instead, and must keep using (int)activation|
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|
//| directly so saved topology files round-trip through Load() as-is. |
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|
//+------------------------------------------------------------------+
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|
int NativeActivationCode(ENUM_ACTIVATION value)
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|
{
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|
switch(value)
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|
{
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|
case TANH: return 0;
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|
case SIGMOID: return 1;
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|
case PRELU: return 2;
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|
default: return -1; // NONE (and anything unrecognized) - no kernel/DLL case matches
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|
}
|
|
}
|
|
//+------------------------------------------------------------------+
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|
//| Human-readable ENUM_ACTIVATION, for diagnostics only. Used by the |
|
|
//| load-time architecture repair (CNet::EnforceOutputActivation) so |
|
|
//| the log names the stale value it found instead of printing a bare |
|
|
//| integer nobody can decode months later. |
|
|
//+------------------------------------------------------------------+
|
|
string ActivationName(ENUM_ACTIVATION value)
|
|
{
|
|
switch(value)
|
|
{
|
|
case NONE: return "NONE";
|
|
case TANH: return "TANH";
|
|
case SIGMOID: return "SIGMOID";
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|
case PRELU: return "PRELU";
|
|
}
|
|
return "UNKNOWN(" + IntegerToString((int)value) + ")";
|
|
}
|
|
//---
|
|
//--- Guarded so an identical copy can live in Enumerations\InputEnums.mqh too: that lets Variables\
|
|
//--- Inputs.mqh (which needs this type for the TrainingOptimizer input) be included FIRST - ahead of
|
|
//--- this AI header - without a duplicate-definition error. Whichever file is parsed first defines it;
|
|
//--- the other's copy is skipped. Keep the two definitions in sync.
|
|
#ifndef WARRIOR_ENUM_OPTIMIZATION_DEFINED
|
|
#define WARRIOR_ENUM_OPTIMIZATION_DEFINED
|
|
//--- 2026-07-28: a third DFA entry was removed. It was never Direct Feedback Alignment: its feedback
|
|
//--- signal multiplied dL/dw by a DETERMINISTIC sign pattern (connectionIndex % 2), which makes half of
|
|
//--- every weight tensor perform gradient ASCENT permanently - it diverges by construction, with no
|
|
//--- hyperparameter able to rescue it. Real DFA (Nokland 2016) works because a FIXED RANDOM matrix gives
|
|
//--- a consistent feedback direction the forward weights can align to; an index-parity sign flip has no
|
|
//--- such alignment property. Its backward pass was also structurally incompatible with the OpenCL/
|
|
//--- DirectML neuron model this project actually runs on (one CNeuronBaseOCL object holds a whole layer
|
|
//--- in a device buffer, so the per-neuron host-scalar loops it used saw layer.Total()==1 and updated
|
|
//--- nothing). SGD/ADAM keep their ordinal values 0/1 - m_optimizationAlgo feeds the weights-filename
|
|
//--- fingerprint, so these must never be renumbered.
|
|
enum ENUM_OPTIMIZATION
|
|
{
|
|
SGD, // SGD + Momentum (heavy-ball, simpler, needs more eras)
|
|
ADAM // Adam (adaptive step, faster convergence, can overfit)
|
|
};
|
|
#endif
|
|
//---
|
|
enum ENUM_BUFFERS
|
|
{
|
|
WEIGHTS,
|
|
DELTA_WEIGHTS,
|
|
OUTPUT,
|
|
GRADIENT,
|
|
FIRST_MOMENTUM,
|
|
SECOND_MOMENTUM
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
#include "NeuronPrimitives.mqh"
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|
class CLayer;
|
|
//---
|
|
class CNeuronBase : public CObject
|
|
{
|
|
protected:
|
|
double outputVal;
|
|
double prevVal;
|
|
uint m_myIndex;
|
|
double gradient;
|
|
CArrayCon *Connections;
|
|
ENUM_ACTIVATION activation;
|
|
ENUM_OPTIMIZATION optimization;
|
|
int t;
|
|
//---
|
|
virtual bool feedForward(CLayer *prevLayer) { return false; }
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|
virtual bool calcHiddenGradients(CLayer *&nextLayer) { return false; }
|
|
virtual double activationFunction(double x);
|
|
virtual double SigmoidFunction(double x) { return MathPow(1 + exp(-x), -1); }
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|
virtual double TanhFunction(double x) { return tanh(x); }
|
|
virtual CLayer *getOutputLayer(void) { return NULL; }
|
|
public:
|
|
CNeuronBase(void);
|
|
~CNeuronBase(void);
|
|
virtual bool Init(uint numOutputs, uint myIndex, ENUM_OPTIMIZATION optimization_type, double weighScale = -1.0);
|
|
virtual void SetActivationFunction(ENUM_ACTIVATION value) { activation = value; }
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|
//--- Mirrors CNeuronBaseOCL::Activation(). Needed so CNet::EnforceOutputActivation() can read back
|
|
//--- what a Load() restored from disk without caring which neuron model the net was built from.
|
|
virtual ENUM_ACTIVATION Activation(void) { return activation; }
|
|
//---
|
|
static double alpha;
|
|
//---
|
|
virtual void setOutputVal(double val) { prevVal = outputVal; outputVal = val; }
|
|
virtual double getOutputVal() { return outputVal; }
|
|
virtual double getPrevVal() { return prevVal; }
|
|
virtual void setGradient(double val) { gradient = val; }
|
|
virtual double getGradient() { return gradient; }
|
|
virtual CArrayCon *getConnections() { return Connections;}
|
|
virtual double activationFunctionDerivative(double x);
|
|
virtual double SigmoidFunctionDerivative(double x) { return MathMax(MIN_ACTIVATION_DERIVATIVE, x * (1 - x)); }
|
|
virtual double TanhFunctionDerivative(double x) { return MathMax(MIN_ACTIVATION_DERIVATIVE, (1 + x) * (1 - x)); }
|
|
//---
|
|
virtual bool feedForward(CObject *&SourceObject);
|
|
virtual bool calcHiddenGradients(CObject *&TargetObject);
|
|
virtual bool updateInputWeights(CLayer *prevLayer) { return false; }
|
|
virtual bool updateInputWeights(CObject *SourceObject);
|
|
//---
|
|
virtual bool Save(int const file_handle);
|
|
virtual bool Load(int const file_handle)
|
|
{
|
|
activation = (ENUM_ACTIVATION)FileReadInteger(file_handle, INT_VALUE);
|
|
optimization = (ENUM_OPTIMIZATION)FileReadInteger(file_handle, INT_VALUE);
|
|
t = (ENUM_OPTIMIZATION)FileReadInteger(file_handle, INT_VALUE);
|
|
return(Connections.Load(file_handle));
|
|
}
|
|
//---
|
|
virtual int Type(void) const { return defNeuronBase; }
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
#include "NeuronCPU.mqh"
|
|
class COpenCLMy : public COpenCL
|
|
{
|
|
public:
|
|
COpenCLMy(void) {};
|
|
~COpenCLMy(void) {};
|
|
template<typename T>
|
|
int AddBufferFromArray(T &data[], const uint data_array_offset, const uint data_array_count, const uint flags);
|
|
};
|
|
#include "NeuronDirectML.mqh"
|
|
class CLayer: public CArrayObj
|
|
{
|
|
private:
|
|
uint iOutputs;
|
|
int iFileHandle;
|
|
COpenCLMy *OpenCL;
|
|
CDirectMLMy *DirectML;
|
|
|
|
public:
|
|
CLayer(uint outputs = 0, int handle = INVALID_HANDLE, COpenCLMy *OpenCL = NULL, CDirectMLMy *DirectML = NULL);
|
|
~CLayer(void) {};
|
|
//--- Fan-in-scaled element factory. Deliberately NOT named CreateElement: see the override below.
|
|
bool CreateElementScaled(int const index, double weighScale);
|
|
//--- CRITICAL: this MUST keep CArrayObj::CreateElement's EXACT signature - `virtual bool
|
|
//--- CreateElement(const int index)` - because it is the real virtual override that CArrayObj::Load()
|
|
//--- dispatches through, and CArrayObj::Load() is how EVERY saved layer is read back (CLayer::Load ->
|
|
//--- CNet::Load). In MQL5 an override must match the base parameter list
|
|
//--- exactly; adding even a DEFAULTED parameter makes it a separate method that merely hides the base
|
|
//--- one, silently leaving the base's `return(false)` stub in the vtable slot. That is exactly what a
|
|
//--- `double weighScale = -1.0` parameter added here did: from then on every single model load failed
|
|
//--- at the first layer ("REJECTED: only loaded 0 of N layers (failed at layer 0)") no matter how
|
|
//--- healthy the .nnw was, so every restart retrained from era 0 and every best-checkpoint restore
|
|
//--- silently kept the current weights. Never add parameters to this method - extend
|
|
//--- CreateElementScaled() and call it explicitly instead.
|
|
virtual bool CreateElement(const int index) { return CreateElementScaled(index, -1.0); }
|
|
virtual void IncreaseTotal() { m_data_total++; }
|
|
virtual int Type(void) const { return defLayer; }
|
|
virtual bool Load(const int file_handle);
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
#include "ArrayLayer.mqh"
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
class CNeuronPool : public CNeuronBase
|
|
{
|
|
protected:
|
|
CLayer *OutputLayer;
|
|
int iWindow;
|
|
int iStep;
|
|
|
|
virtual bool feedForward(CLayer *prevLayer);
|
|
virtual bool calcHiddenGradients(CLayer *&nextLayer);
|
|
|
|
public:
|
|
CNeuronPool(void) {};
|
|
~CNeuronPool(void);
|
|
virtual bool Init(uint numOutputs, uint myIndex, int window, int step, int units_count, ENUM_OPTIMIZATION optimization_type);
|
|
//---
|
|
virtual CLayer *getOutputLayer(void) { return OutputLayer; }
|
|
virtual bool calcInputGradients(CLayer *prevLayer) ;
|
|
virtual bool calcInputGradients(CNeuronBase *prevNeuron, uint index) ;
|
|
//--- methods for working with files
|
|
virtual bool Save(int const file_handle);
|
|
virtual bool Load(int const file_handle);
|
|
virtual int Type(void) const { return defNeuronPool; }
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
class CNeuronConv : public CNeuronPool
|
|
{
|
|
protected:
|
|
double param; //PReLU param
|
|
virtual bool feedForward(CLayer *prevLayer);
|
|
virtual bool calcHiddenGradients(CLayer *&nextLayer);
|
|
virtual double activationFunction(double x);
|
|
virtual bool updateInputWeights(CLayer *prevLayer);
|
|
public:
|
|
CNeuronConv() : param(0.01) { };
|
|
~CNeuronConv(void) { };
|
|
//---
|
|
virtual bool calcInputGradients(CLayer *prevLayer) ;
|
|
virtual bool calcInputGradients(CNeuronBase *prevNeuron, uint index) ;
|
|
virtual double activationFunctionDerivative(double x);
|
|
virtual int Type(void) const { return defNeuronConv; }
|
|
//--- methods for working with files
|
|
virtual bool Save(int const file_handle);
|
|
virtual bool Load(int const file_handle);
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
#include "LayerDescription.mqh"
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
class CNet
|
|
{
|
|
protected:
|
|
double dLogitAdjust[3];
|
|
bool bLogitAdjust;
|
|
void backPropOCL(CArrayDouble *targetVals, double sampleWeight = 1.0);
|
|
bool InitOpenCL(void);
|
|
bool InitDirectML(void);
|
|
//--- Pure-MQL5 forward pass over OCL-format layers loaded host-only (no backend) - see SetCpuInference.
|
|
bool feedForwardCPU(CArrayDouble *inputVals);
|
|
public:
|
|
CNet(CArrayObj *Description);
|
|
~CNet(void);
|
|
bool feedForward(CArrayDouble *inputVals);
|
|
//--- sampleWeight scales this example's output-layer gradient before it propagates back through the
|
|
//--- hidden layers - see the matching declaration comment on ExpertSignalAIBase.mqh's oversampling
|
|
//--- replacement for why (inverse-class-frequency loss weighting instead of replaying the same
|
|
//--- example multiple times).
|
|
void backProp(CArrayDouble *targetVals, double sampleWeight = 1.0);
|
|
//--- LOGIT ADJUSTMENT (Menon et al. 2021, "Long-tail learning via logit adjustment"). Per-class
|
|
//--- additive offsets tau*log(prior_c) folded into the 3-class softmax during the BACKWARD pass
|
|
//--- only. Minimizing softmax CE on adjusted logits is consistent for BALANCED error - which is
|
|
//--- exactly the metric checkpoint selection already ranks on (macro-recall), so this is the first
|
|
//--- time the loss and the selection criterion optimize the same thing.
|
|
//--- It replaces minority REPLAY (which duplicated rare bars up to 28x and made Buy and Sell
|
|
//--- compete for the same replicated capacity - the measured failure was each model taking one
|
|
//--- direction to ~50% recall and abandoning the other, with the direction chosen arbitrarily) and
|
|
//--- the post-hoc inference prior, which becomes double-counting once the offsets are trained in.
|
|
//--- Offsets are NEGATIVE (log of a probability), so a rare class gets its logit pushed DOWN during
|
|
//--- training, forcing the weights to produce a larger raw logit to compensate. At inference the
|
|
//--- offsets are absent, so that surplus becomes the calibrated boost the rare class needs.
|
|
void SetLogitAdjustment(const double &offsets[]);
|
|
void ClearLogitAdjustment(void) { bLogitAdjust = false; }
|
|
void getResults(CArrayDouble *&resultVals) ;
|
|
double getRecentAverageError() { return recentAverageError; }
|
|
//--- indicatorParams: flattened AutoTuneIndicators "winning" AD indicator param values (see
|
|
//--- CExpertSignalAIBase::FlattenIndicatorParams/UnflattenIndicatorParams); pass an empty array
|
|
//--- when there is nothing to persist/restore.
|
|
bool Save(string file_name, double error, double undefine, double forecast, datetime time, bool common, long era, bool trainingComplete, const double &indicatorParams[]);
|
|
//--- `quiet` suppresses the on-reject diagnostic Prints for callers that EXPECT a miss and handle it
|
|
//--- gracefully (the EMA shadow-net bootstrap on a CPU-DLL box, which can't hold a 2nd full net - see
|
|
//--- EnsureShadowNet). The main-model load leaves it false so a real failure is still loud.
|
|
bool Load(string file_name, double &error, double &undefine, double &forecast, datetime &time, bool common, long &era, bool &trainingComplete, double &indicatorParams[], bool quiet=false);
|
|
//--- In-MEMORY weight checkpoint (host-only, zero extra device tensors). CaptureWeights() snapshots
|
|
//--- every neuron's weights (base/conv/LSTM) into host arrays; RestoreWeights() writes them back IN
|
|
//--- PLACE via setWeights - reusing the existing neuron objects and their already-allocated device
|
|
//--- buffers, exactly like BlendWeightsFrom(). This REPLACED an earlier file-based checkpoint pair
|
|
//--- (since removed - it had no callers left) for the mid-run stability restore, because a file path RE-CREATES every neuron on
|
|
//--- load (fresh CLayer + Init), and the multithreaded CPU-DLL backend (CDirectMLMy/WarriorCPU.dll)
|
|
//--- cannot allocate a second full set of neuron tensors while the live set still exists - so the
|
|
//--- file restore failed ("read 0 layers"), the model could never roll back a regressed era, and it
|
|
//--- drifted into a Neutral collapse. In-place weight copy uses only getWeights/setWeights, which the
|
|
//--- per-era shadow blend already exercises successfully on that backend. Snapshots WEIGHTS only (not
|
|
//--- Adam moments); the regression handler decays eta on restore and per-step deltas are clipped, so
|
|
//--- stale moments can't overshoot. In-memory => valid only within a single Train() run (same as the
|
|
//--- ephemeral _ckpt.tmp was), which is exactly its scope.
|
|
//--- Per-layer weight-norm change since the previous call - the direct test for "is this stage
|
|
//--- receiving gradient at all". See the definition for why a loss curve cannot answer that.
|
|
string LayerLearningReport(void);
|
|
bool CaptureWeights(void);
|
|
bool RestoreWeights(void);
|
|
//--- EMA shadow-weight deployment: blends this net's weights a small step (tau) toward another
|
|
//--- net's weights, layer by layer, neuron by neuron - this.weight = (1-tau)*this.weight +
|
|
//--- tau*live.weight. Intended usage: `this` is a persistent "shadow" net that live trading/OOS
|
|
//--- checkpointing reads from, and `live` is the net Train()'s era loop actually backprops
|
|
//--- against. A single bad era's raw weights (e.g. an Adam overshoot) can only ever nudge the
|
|
//--- shadow by `tau`, so the deployed model can no longer whipsaw between 90%+ and single-digit
|
|
//--- OOS accuracy the way a directly-deployed live net can - the shadow is a running average over
|
|
//--- many eras, not a snapshot of whichever one happened to look best (or worst) in isolation.
|
|
//--- Requires `this` and `live` to share identical topology (same layer/neuron/window counts) -
|
|
//--- true whenever the shadow was cloned from live via Save()/Load() and never independently
|
|
//--- rebuilt. Silently skips (rather than fails) any layer/neuron pair that doesn't line up, so a
|
|
//--- topology mismatch degrades to a partial blend instead of corrupting unrelated layers.
|
|
bool BlendWeightsFrom(CNet &live, double tau);
|
|
//--- Cold-start fix: overwrites just the bias term (not the per-input weights, which stay randomly
|
|
//--- initialized and carry the real learning signal) of each output neuron's incoming weight block,
|
|
//--- on the layer immediately before the output layer - see ExpertSignalAIBase.mqh's call site
|
|
//--- (AdvanceLabelCachePrebuild()) for why: a freshly-initialized network's argmax is close to
|
|
//--- uniform noise across classes, so on a heavily imbalanced label distribution it fires far more
|
|
//--- non-majority classes than the true base rate warrants until backProp corrects it over many
|
|
//--- steps. biasValues.Size() must equal the output layer's neuron count. Only supports the
|
|
//--- OpenCL/DirectML batched neuron model (CNeuronBaseOCL) this project actually runs on - returns
|
|
//--- false (no-op) rather than corrupt anything if that assumption doesn't hold.
|
|
bool SeedOutputLayerBias(const double &biasValues[]);
|
|
//--- Pure-MQL5 (no OpenCL/DirectML/DLL) inference mode. Set BEFORE Load() in an inference-only
|
|
//--- backtest: it makes InitOpenCL()/InitDirectML() no-op (both backends stay NULL), so the OCL
|
|
//--- neurons load their weights host-side only and feedForward() runs the double-precision MQL5
|
|
//--- path (feedForwardCPU) reading those same host buffers. Training/optimization never set this
|
|
//--- (they always want a backend), so their behaviour is unchanged. See ExpertSignalAIBase.mqh's
|
|
//--- inference-only wiring and the deploy-time validation that gates it.
|
|
void SetCpuInference(bool v) { m_cpuInference = v; }
|
|
bool CpuInference(void) const { return m_cpuInference; }
|
|
//--- Re-assert the output layer's activation after a Load(), and report what it used to be.
|
|
//--- WHY THIS EXISTS: a .nnw persists the ARCHITECTURE, not just the weights. CNeuronBase::Save/
|
|
//--- CNeuronBaseOCL::Save write (int)activation per neuron and the matching Load() reads it straight
|
|
//--- back into the live object, so the activation chosen in BuildFreshTopology() only ever applies to
|
|
//--- a genuinely NEW topology. Every reload restores whatever is on disk and the next Save() writes it
|
|
//--- back out - a wrong value can never heal on its own, while the source file reads as though it were
|
|
//--- already fixed. That is exactly how models kept training with an unbounded NONE classification head
|
|
//--- for a full day after BuildFreshTopology() had been reverted to SIGMOID (2026-07-29): confirmed by
|
|
//--- parsing the binaries, `layer N: BaseOCL act=NONE out=3`, while a freshly reset model of the same
|
|
//--- config read act=SIGMOID. Symptom was negative "OOS raw out" values (impossible under sigmoid)
|
|
//--- escalating to a 4.1e13 logit spread with all three classes numerically identical.
|
|
//--- Only the output layer is repaired here: it is always a plain dense layer whose activation is a
|
|
//--- single unambiguous expression in BuildFreshTopology(). Hidden layers are deliberately left alone -
|
|
//--- they legitimately differ per stage (PRELU dense, PRELU conv, NONE pool, TANH LSTM), so blanket
|
|
//--- re-assertion there would corrupt exactly the topologies it was meant to protect.
|
|
//--- Returns true when a repair was actually made, and reports the stale value through `previous`.
|
|
bool EnforceOutputActivation(ENUM_ACTIVATION intended, ENUM_ACTIVATION &previous);
|
|
//--- Receptive field of the first conv layer as LOADED, for the stale-architecture check in
|
|
//--- CExpertSignalAIBase::EnforceTopologyContract. 0 when the net has no conv layer.
|
|
uint FirstConvWindow(void);
|
|
//--- Freeze/unfreeze every batch-normalization layer's running statistics (AI\NeuronBatchNorm.mqh).
|
|
//--- Frozen, a forward pass is a pure function of its input; unfrozen (the default) it also advances
|
|
//--- the statistics. Anything that COMPARES two forward passes must freeze first or it measures its
|
|
//--- own side effect. No-op on a net with no normalization layers.
|
|
void SetBatchNormFrozen(bool frozen);
|
|
//---
|
|
static double recentAverageSmoothingFactor;
|
|
|
|
private:
|
|
CArrayLayer *layers;
|
|
COpenCLMy *opencl;
|
|
CDirectMLMy *directml;
|
|
double recentAverageError;
|
|
bool m_cpuInference;
|
|
//--- In-memory best-weights checkpoint (see CaptureWeights/RestoreWeights). One CArrayDouble per
|
|
//--- neuron in layer-major order; host-only, no device tensors. NULL/false until the first capture.
|
|
CArrayObj *m_weightSnapshot;
|
|
bool m_haveWeightSnapshot;
|
|
//--- Previous call's per-layer weight L2 norms, for LayerLearningReport(). Sized lazily to the layer
|
|
//--- count; -1 marks "no baseline yet" so the first report says (init) instead of a bogus 0% change.
|
|
double m_prevLayerNorm[];
|
|
//--- Previous call's per-layer weight VECTORS, for the |dW| term of LayerLearningReport(). One
|
|
//--- CArrayDouble per layer index (empty for layers that own no weights), host-only.
|
|
//--- WHY BOTH: |d|W||/|W| - the change in NORM - cannot distinguish "this layer only shrank under
|
|
//--- weight decay" from "this layer moved somewhere useful". Pure decay and a genuine rotation of a
|
|
//--- constant-norm weight vector can print the same number, and on 2026-07-31 the LSTM layers printed
|
|
//--- a suspiciously constant ~1.05%/era while a sibling conv oscillated - a difference the norm-change
|
|
//--- statistic could only hint at. |dW|/|W| - the norm of the CHANGE - separates them outright: under
|
|
//--- decay alone it equals the decay rate exactly, while any gradient component adds in quadrature
|
|
//--- (sqrt(decay^2 + (g/|W|)^2)). Reading them side by side is the whole diagnostic: |dW| >> |d|W||
|
|
//--- means the layer is rotating (learning); |dW| ~= |d|W|| with the norm falling means it is only
|
|
//--- being decayed away. See [[feedback_verify_in_situ_not_offline]] - this is the in-situ check.
|
|
CArrayObj *m_prevLayerWeights;
|
|
//--- One-shot latch for BlendWeightsFrom's skip warning - it runs every era, and the condition it
|
|
//--- reports is permanent, so the second print would only be noise.
|
|
bool m_blendSkipLogged;
|
|
//--- PROCESS-WIDE compute-probe latches (shared by every CNet in this terminal process).
|
|
//--- A run legitimately builds SEVERAL CNet objects - the main Net, the EMA shadow net, the OOS-sim
|
|
//--- clone, the deploy-time MQL5-inference self-check clone - and each one probed the backends
|
|
//--- independently. On a host without OpenCL that reprinted the same 3-line banner per net
|
|
//--- ("OpenCL not found, error code=5100" comes from the STDLIB COpenCL::Initialize, so it can't be
|
|
//--- silenced at our call site), which read in the log like the EA was initializing twice.
|
|
//--- s_openclUnavailable: latched only on FAILURE, and only ever skips a probe that is already known
|
|
//--- to fail - OpenCL availability cannot change inside a process. A host that HAS OpenCL never
|
|
//--- latches, so every CNet still gets its own COpenCLMy. Skipping also keeps GetLastError() free of
|
|
//--- the harmless 5100 for later callers.
|
|
//--- s_computeTierLogged: suppresses only the repeat of the informational "tier active" line (the
|
|
//--- tier is a property of the HOST, identical for every net). Failure messages stay loud every time.
|
|
static bool s_openclUnavailable;
|
|
static bool s_computeTierLogged;
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
class CNeuronLSTM : public CNeuronPool
|
|
{
|
|
protected:
|
|
CLayer *ForgetGate;
|
|
CLayer *InputGate;
|
|
CLayer *OutputGate;
|
|
CLayer *NewContent;
|
|
CArrayDouble *Memory;
|
|
CArrayDouble *PrevMemory;
|
|
CArrayDouble *Input;
|
|
CArrayDouble *InputGradient;
|
|
//---
|
|
virtual bool feedForward(CLayer *prevLayer);
|
|
virtual bool calcHiddenGradients(CLayer *&nextLayer);
|
|
virtual bool updateInputWeights(CLayer *prevLayer);
|
|
virtual bool updateInputWeights(CLayer *gate, CArrayDouble *input_data);
|
|
virtual bool InitLayer(CLayer *layer, int numOutputs, int numUnits, ENUM_OPTIMIZATION optimization_type);
|
|
virtual CArrayDouble *CalculateGate(CLayer *gate, CArrayDouble *sequence);
|
|
|
|
public:
|
|
CNeuronLSTM(void);
|
|
~CNeuronLSTM(void);
|
|
virtual bool Init(uint numOutputs, uint myIndex, int window, int step, int units_count, ENUM_OPTIMIZATION optimization_type);
|
|
//---
|
|
virtual CLayer *getOutputLayer(void) { return OutputLayer; }
|
|
virtual bool calcInputGradients(CLayer *prevLayer) ;
|
|
virtual bool calcInputGradients(CNeuronBase *prevNeuron, uint index) ;
|
|
//--- methods for working with files
|
|
virtual bool Save(int const file_handle);
|
|
virtual bool Load(int const file_handle);
|
|
virtual int Type(void) const { return defNeuronLSTM; }
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
template<typename T>
|
|
int COpenCLMy::AddBufferFromArray(T &data[], const uint data_array_offset, const uint data_array_count, const uint flags)
|
|
{
|
|
int result = -1;
|
|
for(int i = 0; i < m_buffers_total; i++)
|
|
{
|
|
if(m_buffers[i] != INVALID_HANDLE)
|
|
continue;
|
|
result = i;
|
|
break;
|
|
}
|
|
//---
|
|
if(result < 0)
|
|
{
|
|
if(ArrayResize(m_buffers, m_buffers_total + 1) > 0)
|
|
{
|
|
m_buffers_total = ArraySize(m_buffers);
|
|
result = m_buffers_total - 1;
|
|
m_buffers[result] = INVALID_HANDLE;
|
|
}
|
|
else
|
|
return result;
|
|
}
|
|
//---
|
|
if(!BufferFromArray(result, data, data_array_offset, data_array_count, flags))
|
|
return -1;
|
|
//---
|
|
return result;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
#include "BufferDouble.mqh"
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
class CNeuronBaseOCL : public CObject
|
|
{
|
|
protected:
|
|
COpenCLMy *OpenCL;
|
|
CDirectMLMy *DirectML;
|
|
CBufferDouble *Output;
|
|
CBufferDouble *PrevOutput;
|
|
CBufferDouble *Weights;
|
|
CBufferDouble *DeltaWeights;
|
|
CBufferDouble *Gradient;
|
|
CBufferDouble *FirstMomentum;
|
|
CBufferDouble *SecondMomentum;
|
|
//---
|
|
const double alpha;
|
|
int t;
|
|
//---
|
|
ENUM_ACTIVATION activation;
|
|
ENUM_OPTIMIZATION optimization;
|
|
//---
|
|
virtual bool feedForward(CNeuronBaseOCL *NeuronOCL);
|
|
virtual bool calcHiddenGradients(CNeuronBaseOCL *NeuronOCL);
|
|
//--- Create a buffer's device-side storage on whichever backend is active, or leave it host-only
|
|
//--- (its CArrayDouble m_data already holds the values just Load()ed) when neither backend exists -
|
|
//--- the pure-MQL5 inference path (see CNet::SetCpuInference / feedForwardCPU).
|
|
bool BackendBufferCreate(CBufferDouble *buf)
|
|
{
|
|
if(CheckPointer(buf) == POINTER_INVALID)
|
|
return false;
|
|
if(CheckPointer(OpenCL) != POINTER_INVALID)
|
|
return buf.BufferCreate(OpenCL);
|
|
if(CheckPointer(DirectML) != POINTER_INVALID)
|
|
return buf.BufferCreate(DirectML);
|
|
return true; // no backend: keep host m_data, skip device allocation
|
|
}
|
|
|
|
public:
|
|
CNeuronBaseOCL(void);
|
|
~CNeuronBaseOCL(void);
|
|
virtual bool Init(uint numOutputs, uint myIndex, COpenCLMy *open_cl, uint numNeurons, ENUM_OPTIMIZATION optimization_type);
|
|
virtual bool Init(uint numOutputs, uint myIndex, CDirectMLMy *direct_ml, uint numNeurons, ENUM_OPTIMIZATION optimization_type);
|
|
virtual void SetActivationFunction(ENUM_ACTIVATION value) { activation = value; }
|
|
//---
|
|
virtual int getOutputIndex(void) { return Output.GetIndex(); }
|
|
virtual int getPrevOutIndex(void) { return PrevOutput.GetIndex(); }
|
|
virtual int getGradientIndex(void) { return Gradient.GetIndex(); }
|
|
virtual int getWeightsIndex(void) { return Weights.GetIndex(); }
|
|
virtual int getDeltaWeightsIndex(void) { return DeltaWeights.GetIndex(); }
|
|
virtual int getFirstMomentumIndex(void) { return FirstMomentum.GetIndex(); }
|
|
virtual int getSecondMomentumIndex(void) { return SecondMomentum.GetIndex();}
|
|
//---
|
|
virtual int getOutputVal(double &values[]) { return Output.GetData(values); }
|
|
virtual int getOutputVal(CArrayDouble *values) { return Output.GetData(values); }
|
|
virtual int getPrevVal(double &values[]) { return PrevOutput.GetData(values); }
|
|
virtual int getGradient(double &values[]) { return Gradient.GetData(values); }
|
|
//--- pushes locally-modified gradient values back to this buffer's GPU/CPU-DLL-side copy - used by
|
|
//--- CNet::backPropOCL() to apply per-sample loss weighting after the native CalcOutputGradient call
|
|
//--- (which only computes the raw, unweighted delta) and before the backward pass reads this same
|
|
//--- buffer to propagate into the hidden layers.
|
|
virtual bool setGradient(const double &values[])
|
|
{
|
|
int count = ArraySize(values);
|
|
for(int i = 0; i < count; i++)
|
|
if(!Gradient.Update(i, values[i]))
|
|
return false;
|
|
return Gradient.BufferWrite();
|
|
}
|
|
// Guarded: output-layer neurons (numOutputs==0) have Weights deleted in Init() but not re-created,
|
|
// so BlendWeightsFrom() walking every neuron would otherwise dereference a dead pointer here.
|
|
virtual int getWeights(double &values[]) { return (CheckPointer(Weights) == POINTER_INVALID ? 0 : Weights.GetData(values)); }
|
|
// Paired with getWeights() above for CNet::BlendWeightsFrom()'s EMA shadow-weight deployment
|
|
// (see that method's declaration comment) - writes a full replacement weight array back to this
|
|
// buffer's device-side (DLL/OpenCL/DirectML) storage. Unlike Weights.Update(i,...) (per-element,
|
|
// used by the Adam kernels' own writes), this replaces the whole buffer in one bulk assignment
|
|
// then pushes it to the device - the shape callers use when blending two already-read-out arrays.
|
|
virtual bool setWeights(double &values[])
|
|
{
|
|
if(CheckPointer(Weights) == POINTER_INVALID)
|
|
return false;
|
|
if(!Weights.AssignArray(values))
|
|
return false;
|
|
return Weights.BufferWrite();
|
|
}
|
|
virtual int Neurons(void) { return Output.Total(); }
|
|
virtual ENUM_ACTIVATION Activation(void) { return activation; }
|
|
virtual int getConnections(void) { return (CheckPointer(Weights) != POINTER_INVALID && CheckPointer(Gradient) != POINTER_INVALID && Gradient.Total() > 0 ? Weights.Total() / Gradient.Total() : 0); }
|
|
//--- Host-side (no device round-trip) buffer element access for the pure-MQL5 inference path. Safe to
|
|
//--- call after Load() with no backend: the CArrayDouble m_data holds the values, unlike getWeights()/
|
|
//--- getOutputVal() which route through BufferRead() (a device read that fails without a backend).
|
|
double OutputHost(int i) { return (CheckPointer(Output) != POINTER_INVALID && i >= 0 && i < Output.Total()) ? Output.At(i) : 0.0; }
|
|
double WeightHost(int i) { return (CheckPointer(Weights) != POINTER_INVALID && i >= 0 && i < Weights.Total()) ? Weights.At(i) : 0.0; }
|
|
int WeightsCount(void) { return (CheckPointer(Weights) != POINTER_INVALID) ? Weights.Total() : 0; }
|
|
//--- Pure-MQL5, double-precision forward pass mirroring Network.cl's FeedForward kernel, reading only
|
|
//--- host buffers. Used exclusively when no compute backend exists (CNet::SetCpuInference). Dense
|
|
//--- (fully-connected) here; conv/pool/LSTM subclasses override with their own kernel math.
|
|
virtual bool feedForwardCPU(CNeuronBaseOCL *NeuronOCL);
|
|
//--- Host-side input write for the CPU inference path's layer 0, and host-side output read for
|
|
//--- getResults() - both bypass the device buffer that the CPU path deliberately never allocates.
|
|
bool SetInputsCPU(CArrayDouble *inputVals);
|
|
int GetOutputsCPU(CArrayDouble *values);
|
|
//---
|
|
virtual bool feedForward(CObject *SourceObject);
|
|
virtual bool calcHiddenGradients(CObject *TargetObject);
|
|
virtual bool calcOutputGradients(CArrayDouble *Target);
|
|
virtual bool updateInputWeights(CNeuronBaseOCL *NeuronOCL);
|
|
virtual bool updateInputWeights(CObject *SourceObject);
|
|
//---
|
|
virtual bool Save(int const file_handle);
|
|
virtual bool Load(int const file_handle);
|
|
//---
|
|
virtual int Type(void) const { return defNeuronBaseOCL; }
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
#include "NeuronOCLConvPool.mqh"
|
|
#include "NeuronBatchNorm.mqh"
|
|
//+------------------------------------------------------------------+
|
|
//--- Marks a .nnw LSTM record as the SEQUENCE format. Chosen so it cannot collide with any value the
|
|
//--- pre-sequence format could have written in that slot (an input width, i.e. -1 or a positive count).
|
|
#define LSTM_SEQ_SAVE_TAG (-424242)
|
|
//--- Initial bias of the FORGET gate (gate 0). Every other weight starts near zero; this one must not.
|
|
//--- See the long rationale and the measured numbers at its use in CNeuronLSTMOCL::SetInputs - in
|
|
//--- short, a zero bias means sigmoid(0)=0.5, which halves the cell state every bar and leaves a
|
|
//--- 20-bar window with the memory and the gradient reach of a single bar.
|
|
#define LSTM_FORGET_BIAS_INIT (1.0)
|
|
//+------------------------------------------------------------------+
|
|
//| GPU-accelerated LSTM layer (OpenCL + DirectML). Derived from |
|
|
//| scratch from the standard LSTM equations - NOT ported from the |
|
|
//| NeuroNet_DNG reference (see the note above the LSTM kernels in |
|
|
//| Network.cl for why). Single-timestep-truncated BPTT: gradient |
|
|
//| does not flow back into h_prev/c_prev from an earlier step. |
|
|
//| Adam-only - Init fails for any other optimization type. |
|
|
//+------------------------------------------------------------------+
|
|
class CNeuronLSTMOCL : public CNeuronBaseOCL
|
|
{
|
|
protected:
|
|
int m_iInputs;
|
|
//--- Sequence shape. m_iStepInputs is the width of ONE timestep (the per-bar feature count reaching
|
|
//--- this layer), set from the layer descriptor by SetStepWidth() before the first feedForward;
|
|
//--- m_iSteps is then m_iInputs / m_iStepInputs. When m_iStepInputs <= 0 the layer falls back to the
|
|
//--- LEGACY single-timestep behaviour (whole input as one step), which is what every .nnw written
|
|
//--- before the sequence rewrite contains.
|
|
int m_iStepInputs;
|
|
int m_iSteps;
|
|
//--- Per-timestep caches, required by backpropagation-through-time: the backward pass needs each
|
|
//--- step's gate activations, cell state and hidden state, which the single-buffer Concatenated/
|
|
//--- Memory/HiddenCache trio below cannot hold because every step overwrites the last.
|
|
CBufferDouble *CacheGates; // T * 4H, gate order [f,i,o,g]
|
|
CBufferDouble *CacheCell; // T * H, c_t
|
|
CBufferDouble *CacheHidden; // T * H, h_t
|
|
CBufferDouble *WeightsLSTM;
|
|
CBufferDouble *FirstMomentumLSTM;
|
|
CBufferDouble *SecondMomentumLSTM;
|
|
CBufferDouble *DeltaWeightsLSTM;
|
|
CBufferDouble *WeightsGradient;
|
|
CBufferDouble *Concatenated;
|
|
CBufferDouble *ConcatenatedGradient;
|
|
CBufferDouble *Memory;
|
|
CBufferDouble *HiddenCache;
|
|
//---
|
|
virtual bool feedForward(CNeuronBaseOCL *NeuronOCL);
|
|
virtual bool feedForwardCPU(CNeuronBaseOCL *NeuronOCL); // pure-MQL5 mirror of LSTM_Gates + LSTM_State
|
|
virtual bool updateInputWeights(CNeuronBaseOCL *NeuronOCL);
|
|
virtual bool SetInputs(int count);
|
|
bool AllocateSequenceCaches(void);
|
|
|
|
public:
|
|
CNeuronLSTMOCL(void) : m_iInputs(-1), m_iStepInputs(-1), m_iSteps(-1)
|
|
{
|
|
CacheGates = NULL;
|
|
CacheCell = NULL;
|
|
CacheHidden = NULL;
|
|
WeightsLSTM = NULL;
|
|
FirstMomentumLSTM = NULL;
|
|
SecondMomentumLSTM = NULL;
|
|
DeltaWeightsLSTM = NULL;
|
|
WeightsGradient = NULL;
|
|
Concatenated = NULL;
|
|
ConcatenatedGradient = NULL;
|
|
Memory = NULL;
|
|
HiddenCache = NULL;
|
|
}
|
|
~CNeuronLSTMOCL(void);
|
|
virtual bool Init(uint numOutputs, uint myIndex, COpenCLMy *open_cl, uint numNeurons, ENUM_OPTIMIZATION optimization_type);
|
|
virtual bool Init(uint numOutputs, uint myIndex, CDirectMLMy *direct_ml, uint numNeurons, ENUM_OPTIMIZATION optimization_type);
|
|
//--- Per-timestep input width, from CLayerDescription::window (see AddLstmStage). Must be called
|
|
//--- between Init() and the first feedForward; <= 0 keeps the legacy single-timestep behaviour.
|
|
//--- Not persisted from here - Save/Load carry it, so a loaded model does not depend on call order.
|
|
void SetStepWidth(int stepInputs) { m_iStepInputs = (stepInputs > 0 ? stepInputs : -1); }
|
|
bool IsSequenceMode(void) const { return (m_iStepInputs > 0 && m_iSteps > 1); }
|
|
virtual bool calcInputGradients(CNeuronBaseOCL *NeuronOCL);
|
|
virtual bool Save(int const file_handle);
|
|
virtual bool Load(int const file_handle);
|
|
virtual int Type(void) const { return defNeuronLSTMOCL; }
|
|
// See CNeuronBaseOCL::getWeights/setWeights - same pair, targeting WeightsLSTM instead of the
|
|
// base class's Weights, for CNet::BlendWeightsFrom()'s EMA shadow-weight deployment.
|
|
virtual int getWeightsLSTM(double &values[]) { return (CheckPointer(WeightsLSTM) == POINTER_INVALID ? 0 : WeightsLSTM.GetData(values)); }
|
|
//--- Build this layer's weight block to match `src`, for a net that was cloned from one whose LSTM
|
|
//--- had not yet run a forward pass. Save() writes m_iInputs = -1 and omits EVERY LSTM buffer in that
|
|
//--- state, so the clone comes back with WeightsLSTM == NULL - and if that clone is an EMA shadow,
|
|
//--- which only ever receives BlendWeightsFrom and never runs forward itself, nothing would ever
|
|
//--- allocate it. m_iStepInputs must be copied FIRST: SetInputs reads it to decide whether the block
|
|
//--- is 4H(H + stepInputs + 1) (sequence) or 4H(H + inputs + 1) (single timestep).
|
|
virtual bool AdoptShapeFrom(CNeuronLSTMOCL &src)
|
|
{
|
|
if(src.m_iInputs <= 0 || Neurons() != src.Neurons())
|
|
return false;
|
|
m_iStepInputs = src.m_iStepInputs;
|
|
return SetInputs(src.m_iInputs);
|
|
}
|
|
virtual bool setWeightsLSTM(double &values[])
|
|
{
|
|
if(CheckPointer(WeightsLSTM) == POINTER_INVALID)
|
|
return false;
|
|
if(!WeightsLSTM.AssignArray(values))
|
|
return false;
|
|
return WeightsLSTM.BufferWrite();
|
|
}
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
//+------------------------------------------------------------------+
|
|
//| Implementation bodies. |
|
|
//| |
|
|
//| Everything above this line is DECLARATIONS - the nine classes |
|
|
//| plus the include chain that orders them (each nested include |
|
|
//| sits exactly where its base class becomes visible, so the order |
|
|
//| here is a dependency graph, not a preference). |
|
|
//| |
|
|
//| Everything below is method BODIES, grouped by the class they |
|
|
//| belong to. They were interleaved with the declarations in one |
|
|
//| 6266-line file; splitting them out is behaviour-neutral by |
|
|
//| construction, since a body cannot run during compilation and |
|
|
//| every declaration it could need is already visible above. |
|
|
//+------------------------------------------------------------------+
|
|
#include "Impl\NeuronBase.mqh"
|
|
#include "Impl\NeuronConvPool.mqh"
|
|
#include "Impl\NeuronLSTM.mqh"
|
|
#include "Impl\Layer.mqh"
|
|
#include "Impl\NetBuild.mqh"
|
|
#include "Impl\NetForward.mqh"
|
|
#include "Impl\NetPersistence.mqh"
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#include "Impl\NetWeights.mqh"
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#include "Impl\NeuronOCLBase.mqh"
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#include "Impl\NeuronOCLLSTM.mqh"
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//+------------------------------------------------------------------+
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