Values meaning :
value |
In/Out |
level(s) |
action |
ipNInput |
IO |
3/0 |
Model input number. |
ipNOutput |
O |
0 |
Model output number |
ipNOrder |
O |
2 |
Model order |
ipCanLearn |
O |
1 |
Training capacity (0: no; 1: yes) |
ipDimension |
O |
0 |
Parameter vector size |
ipNbLayer |
O |
0 |
Model layer number |
ipClass |
O |
3 |
Model pilot class index(note 1) |
ipNHidden |
O |
0 |
Hidden nodes number |
ipBootStrap |
IO |
3 |
Bootstrap number |
ipStatus |
O |
0 |
Model status (0 ou error code) |
ipNNeuron |
O |
0 |
Model node number |
ipNModelInput |
O |
0 |
Model input number before eventual size reduction |
ipNSynapse |
O |
0 |
Model parameter number. |
ipNData |
O |
0 |
Training data number de |
ipTrainCost |
IO |
2/0 |
Cost function index (note 2) |
ipEndTrain |
O |
0 |
Training stop reason (note 3) |
ipDataLoaded |
O |
0 |
Training data are loaded (0: no; 1: yes) |
ipIsLin |
O |
0 |
Parameters linear model (0: no; 1: yes) |
ipCommentCount |
O |
0 |
Model stored comments number |
ipModelCount |
O |
3 |
Model count in multi models |
ipNFuncAct |
O |
0 |
Available activation function number. |
ipNFuncCost |
O |
0 |
Available cost function number. |
ipNFuncProx |
O |
3 |
Available proximity function number (Kohonen models)(note 4) |
ipNFuncDecay |
O |
3 |
Available decreasing function number (Kohonen models)(note 5) |
ipValidHandle |
O |
0 |
Valid handle (0: no; 1: yes) |
ipBin |
IO |
2/2 |
Binary storing forcing. |
ipNTrainModel |
O |
2 |
Stored models stored number for multi training. |
ipBootStrapType |
IO |
2/2 |
Bootstrap type (note 6) |
ipTrainingAlgorithm |
I |
0/0 |
Training algorithm (note 7) |
ipSelectedOutput |
IO |
0/0 |
Selected output (default 0) |
ipProximity |
IO |
0/0 |
Proximity function index (Kohonen models) |
ipDecay |
IO |
0/0 |
Decay function index (Kohonen models) |
ipProximityDecay |
IO |
0/0 |
Distance decay function index (Kohonen models) |
ipReverseMode |
IO |
0/0 |
Reverse mode (note 8). |
ipBestIndex |
O |
0 |
Best training index, following the criterium |
ipCriterium |
IO |
0/0 |
Model selection criterium. (note 9) |
ipMultiTrainCount |
O |
2 |
Train count for multi train procedure |
ipNTest |
IO |
0/0 |
Test set size |
ipTestGroup |
IO |
0/0 |
Grouped test data |
ipSaveTemp |
IO |
1/1 |
Do save on disk the temporary results |
ipLoadTrainedModel |
O |
1 |
Load the trained model of index. |
Notes :
1. Model pilot class index :
1 Usage
2 Static training
3 Dynamic training
4 Reverse mode
11 Linear symetric matrix
12 Linear square matrix
13 Linear matrix
14 Linear vector
2. Available cost functions index :
0 Null cost
1 Error square
2 Delta values square
3 Lessed delta values square
4 Weighted squared error
5 Badly classified
6 bascule
7 Relative error square
8 Gaussian error square
9 Crossed entropy
10 Exponential square error
3. Training stop reason :
0 Current training
1 Reached objective
2 Epoch number reached
3 Computation accuracy
4 Strong parameter
5 Computation error
6 User ask
7 Best validation
4. Available proximity function index :
0 1/(1 + Distance)
1 1/sqrt(1 + Distance)
2 Exp(-Distance)
5. Available decreasing function index :
0 (Ratio < 1)
1 Exp(-Ratio/2)
6. Bootstrap type :
0: nothing (default)
1: Standard
2: Residual.
7: Training algorithm :
0: Gradient; (default)
1: BFGS;
2: Levenberg-Marquardt.
8. Reverse modes :
0: icNone nothing
1: icMinimisation output minimization
2: icMaximisation output maximization
3: icReverse target-output delta minimization
4: icReverseSqrMin target-output delta minimization, while minimizing the squared inputs.
5: icReverseSqrDeltaMin target-output delta minimization, while minimizing the inputs modifications.
Value |
Name |
Meaning |
0 |
Tcr_Cost |
Training cost |
1 |
Tcr_Test |
Test cost |
2 |
Tcr_Press |
PRESS |
3 |
Tcr_Mu |
Homogeneity |
4 |
Tcr_StdDevBiasLess |
Biasless standard deviation |
5 |
Tcr_R2 |
R2 |
6 |
Tcr_R2Adjust |
Adjusted R2 |
7 |
Tcr_Correl |
Correlation |
8 |
Tcr_DeltaRank |
Jacobien rank defect |
9 |
Tcr_Determinant |
Determinant |
10 |
Tcr_EigenMin |
Higher Dispersion Matrix Eigen value |
11 |
Tcr_EigenMax |
Lower Dispersion Matrix Eigen value |
12 |
Tcr_Conditionning |
Ratio of the two upper |
13 |
Tcr_Trace |
Dispersion Matrix Trace |
14 |
Tcr_CorrelMax |
Maximum of parameters absolute correlation |
15 |
Tcr_CorrelMean |
Mean of parameters absolute correlation |