ONNX input and output
Javier Santiago Gaston De Iriarte Cabrera:
hi
I have an ONNX model, my inputs where just the close values, and the outputs are predicted, close values.
In ONNX it has this shape:
input
output
How do I say in mql5 (for testing purpose), an unknown value? is it -1? because it doesnt work for me ...
unk__6 and unk__7 are variable names. These variables should contain first dimension values. But there are no onnx runtime api functions to define part of dimensions as variable.
Did you try to set dimensions immediately as [1,1]?
Slava #:
unk__6 and unk__7 are variable names. These variables should contain first dimension values. But there are no onnx runtime api functions to define part of dimensions as variable.
Did you try to set dimensions immediately as [1,1]?
yes ... but got many errors.
I have 1 q, in for exaple this:
//--- since not all sizes defined in the input tensor we must set them explicitly //--- first index - batch size, second index - series size, third index - number of series (OHLC) const long input_shape[] = {1,SAMPLE_SIZE,1}; if(!OnnxSetInputShape(handle,0,input_shape)) { Print("OnnxSetInputShape error ",GetLastError()); OnnxRelease(handle); return(-1); } //--- since not all sizes defined in the output tensor we must set them explicitly //--- first index - batch size, must match the batch size of the input tensor //--- second index - number of predicted prices (we only predict Close) const long output_shape[] = {1,1}; if(!OnnxSetOutputShape(handle,0,output_shape)) { Print("OnnxSetOutputShape error ",GetLastError()); OnnxRelease(handle); return(-1); }
what are the batch's sizes? for input, is it the length of the data I used to train? and for the output the length of the predictions? or am I confused? ... I don't understand why both batchs must be the same
Javier Santiago Gaston De Iriarte Cabrera #:
Can you share your ONNX-file?
yes ... but got many errors.
I have 1 q, in for exaple this:
what are the batch's sizes? for input, is it the length of the data I used to train? and for the output the length of the predictions? or am I confused? ... I don't understand why both batchs must be the same
It's working properly
const long input_shape[] = {1,1}; if(!OnnxSetInputShape(handle,0,input_shape)) { Print("OnnxSetInputShape error ",GetLastError()); OnnxRelease(handle); return(-1); }
Your input_shape is wrong. It should be {1,1}
It may be {10,1} or {100,1} or so on
I suspect output shape must be the same
Slava #:
It's working properly
Your input_shape is wrong. It should be {1,1}
It may be {10,1} or {100,1} or so on
I suspect output shape must be the same
I keep on getting errors with this. Why?
I'm using this:
//+------------------------------------------------------------------+ //| ONNX.Price.Prediction.Test.mq5 | //| Copyright 2023, MetaQuotes Ltd. | //| https://www.mql5.com | //+------------------------------------------------------------------+ #property copyright "Copyright 2023, MetaQuotes Ltd." #property link "https://www.mql5.com" #property version "1.00" #property description "Evaluation of the quality of the next Close price prediction by Python\\model.onnx.\n" "Start in strategy tester on EURUSD,H1, open prices, from 2023.01.01 to 2023.02.01" #resource "/Files/Python_2/model.onnx" as uchar ExtModel[] #define SAMPLE_SIZE 5760 // bars count in sample as defined in Python\PricePredictionTraining.py // X, y = collect_dataset(df, history_size=10) long ExtHandle=INVALID_HANDLE; double ExtPredicted=0; datetime ExtNextBar=0; long ExtTests=0; long ExtRightDirection=0; double ExtSumAbsoluteError=0.0; //+------------------------------------------------------------------+ //| Expert initialization function | //+------------------------------------------------------------------+ int OnInit() { if(Symbol()!="EURUSD" || Period()!=PERIOD_M1) { Print("model must work with EURUSD,H1"); return(INIT_FAILED); } //--- create a model from static buffer ExtHandle=OnnxCreateFromBuffer(ExtModel,ONNX_DEFAULT); if(ExtHandle==INVALID_HANDLE) { Print("OnnxCreateFromBuffer error ",GetLastError()); return(INIT_FAILED); } //--- since not all sizes defined in the input tensor we must set them explicitly //--- first index - batch size, second index - series size, third index - number of series (OHLC) const long input_shape[] = {1,SAMPLE_SIZE,4}; if(!OnnxSetInputShape(ExtHandle,0,input_shape)) { Print("OnnxSetInputShape error ",GetLastError()); return(INIT_FAILED); } //--- since not all sizes defined in the output tensor we must set them explicitly //--- first index - batch size, must match the batch size of the input tensor //--- second index - number of predicted prices (we only predict Close) const long output_shape[] = {1,1}; if(!OnnxSetOutputShape(ExtHandle,0,output_shape)) { Print("OnnxSetOutputShape error ",GetLastError()); return(INIT_FAILED); } //--- return(INIT_SUCCEEDED); } //+------------------------------------------------------------------+ //| Expert deinitialization function | //+------------------------------------------------------------------+ void OnDeinit(const int reason) { if(ExtHandle!=INVALID_HANDLE) { OnnxRelease(ExtHandle); ExtHandle=INVALID_HANDLE; } } //+------------------------------------------------------------------+ //| Expert tick function | //+------------------------------------------------------------------+ void OnTick() { //--- check new bar if(TimeCurrent()<ExtNextBar) return; ExtTests++; //--- set next bar time ExtNextBar=TimeCurrent(); ExtNextBar-=ExtNextBar%PeriodSeconds(); ExtNextBar+=PeriodSeconds(); //--- check predicted price CheckPredicted(); //--- predict next price PredictPrice(); } //+------------------------------------------------------------------+ //| Check predicted price | //+------------------------------------------------------------------+ void CheckPredicted(void) { if(ExtPredicted!=0.0) { static double highs[3]; static double lows[3]; static datetime times[3]; double hhlls[2] = {0.0}; if(CopyHigh(Symbol(),Period(),1,3,highs)==3 && CopyLow(Symbol(),Period(),1,3,lows)==3 && CopyTime(Symbol(),Period(),1,3,times)==3) { hhlls[0] = (highs[1] - highs[0]) + (lows[1] - lows[0]); hhlls[1] = (highs[2] - highs[1]) + (lows[2] - lows[1]); ExtSumAbsoluteError+=MathAbs(ExtPredicted-hhlls[1]); double delta_predict=ExtPredicted > 0.0 ? 1 : ExtPredicted < 0.0 ? -1 : 0.0; double delta_actual=hhlls[1] > 0.0 ? 1 : hhlls[1] < 0.0 ? -1 : 0.0; if(delta_predict == delta_actual) ExtRightDirection++; Print("----------------------------------"); Print("times[0]: ", times[0], "; highs[0]: ", highs[0], "; lows[0]: ", lows[0], "times[1]: ", times[1], "; highs[1]: ", highs[1], "; lows[1]: ", lows[1], "; hhlls[0]: ", hhlls[0], "; times[2]: ", times[2], "; highs[2]: ", highs[2], "; lows[2]: ", lows[2], "; hhlls[1]: ", hhlls[1]); Print("ExtPredicted: ", ExtPredicted, "; ExtSumAbsoluteError (ExtPredicted-hhlls[1]): ", ExtSumAbsoluteError, "; delta_predict: ", delta_predict, "; delta_actual: ", delta_actual, "; ExtRightDirection: ", ExtRightDirection, "; ExtTests: ", ExtTests); } } } //+------------------------------------------------------------------+ //| Predict next price | //+------------------------------------------------------------------+ void PredictPrice(void) { static matrixf input_data(SAMPLE_SIZE,4); // matrix for prepared input data static vectorf output_data(1); // vector to get result static matrix mm(SAMPLE_SIZE,4); // matrix of horizontal vectors Mean static matrix ms(SAMPLE_SIZE,4); // matrix of horizontal vectors Std static matrix x_norm(SAMPLE_SIZE,4); // matrix for prices normalize //--- prepare input data matrix rates; //--- request last bars if(!rates.CopyRates("EURUSD",PERIOD_M1,COPY_RATES_OHLC,1,SAMPLE_SIZE)) { ExtPredicted=0.0; return; } //--- get series Mean vector m=rates.Mean(1); //--- get series Std vector s=rates.Std(1); //--- prepare matrices for prices normalization for(int i=0; i<SAMPLE_SIZE; i++) { mm.Row(m,i); ms.Row(s,i); } //--- the input of the model must be a set of vertical OHLC vectors x_norm=rates.Transpose(); //--- normalize prices x_norm-=mm; x_norm/=ms; //--- run the inference input_data.Assign(x_norm); if(!OnnxRun(ExtHandle,ONNX_NO_CONVERSION,input_data,output_data)) { ExtPredicted=0.0; return; } //--- denormalize the price from the output value ExtPredicted=output_data[0] /* * ((s[1] - s[0]) - (s[0] - s[2])) + ((m[1] - m[0]) - (m[0] - m[2]))*/ ; } //+------------------------------------------------------------------+ //| Tester function | //+------------------------------------------------------------------+ double OnTester() { double mae=ExtSumAbsoluteError/ExtTests; Print("mae = ",mae); double right_directions=(ExtRightDirection*100.0)/ExtTests; PrintFormat("right_directions = %.2f%%",right_directions); //--- return(right_directions); } //+------------------------------------------------------------------+
What is wrong? or where is the error?
Files:
model.onnx
295 kb
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hi
I have an ONNX model, my inputs where just the close values, and the outputs are predicted, close values.
In ONNX it has this shape:
input
output
How do I say in mql5 (for testing purpose), an unknown value? is it -1? because it doesnt work for me ...