Neural Networks Libraries

   matrix Matrix = matrix_utils.ReadCsv("Nasdaq analysis.csv"); 
   
   matrix x_train, x_test;
   vector y_train, y_test;
    
   matrix_utils.TrainTestSplitMatrices(Matrix,x_train,y_train,x_test,y_test,0.7,42);

   reg_nets = new CRegressorNets(x_train,y_train,AF_RELU_,HL, NORM_MIN_MAX_SCALER); //INitializing network
   
   reg_nets.RegressorNetsBackProp(100, 0.01, LOSS_MSE_, OPTIMIZER_NONE); //Training


//--- Testing the NN

   vector test_pred = reg_nets.RegressorNetsFF(x_test);
   
   Print("Test Acc ",metrics.r_squared(y_test, test_pred));
   

Outputs:

CS      0       19:20:31.261    Regressor Nets Test (EURUSD,H1) [ Epoch 1/100 ] Cost 0.07168254 Acc 0.11997991 | Elapsed 0.017 Seconds  
CS      0       19:20:31.280    Regressor Nets Test (EURUSD,H1) [ Epoch 2/100 ] Cost 0.02692840 Acc 0.66940996 | Elapsed 0.019 Seconds  
CS      0       19:20:31.300    Regressor Nets Test (EURUSD,H1) [ Epoch 3/100 ] Cost 0.00741691 Acc 0.90894538 | Elapsed 0.020 Seconds  
CS      0       19:20:31.330    Regressor Nets Test (EURUSD,H1) [ Epoch 4/100 ] Cost 0.00375571 Acc 0.95389260 | Elapsed 0.029 Seconds  
....
CS      0       19:20:32.410    Regressor Nets Test (EURUSD,H1) [ Epoch 100/100 ] Cost 0.00035787 Acc 0.99560655 | Elapsed 0.010 Seconds  
CS      0       19:20:32.411    Regressor Nets Test (EURUSD,H1) Test Acc 0.994581797713619

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