MainLoop.m and MainNNToolbox.m

 Module to control the whole training process. Refer to Section 3.1 for general functioning explanation. Called by the network definition file (see Section 4.1.1).

Calls to modules DefaultUndefs.m, LoadIO.m and RndWtsBs.m are issued. Then the following variables are initialized,

msErrDenom 

The denominator to normalize the mean square error calculation (see Base/ConvChk.m in Section 4.1.10). Calculated by taking the product of the dimensions of the desired output matrix, desiredOutsL2. The product is taken by the function prod  and the dimensions of the matrix is taken by the function size , refer to Section 5.

sampleOnes 

An array consisting of 1's of length nOfSamples , used in training calculations to expand a column vector to be a matrix containing identical columns. The function ones  is used in order to create it (refer to Section 5).

queue 

Array consisting of 1's of length queueSize , used in running queue mean error calculation (see ConvChk.m in Section 4.1.10).

qIndex 

Variable used in running queue mean error calculation (see ConvChk.m in Section 4.1.10), initialized to 1.

twopi 

Constant variable whose value is initialized to .

msErr 

Mean square error value (see ConvChk.m in Section 4.1.10), initialized to 0.

actFunc , actFuncD , actFuncD2 

The variables holding the names of the functions to use for the activation function, its first and second derivatives , respectively. They are set to the name of one of the functions given in Section 4.1.9.3, by looking at the value of the user defined variable unitType (see Section 4.1.1).

trainFunc 

Holds the name of the function to call to do training for one epoch. It is set to the name of one of the functions given in Section 4.1.9, by looking at the value of the user defined variable trainType (see Section 4.1.1).

MainLoop.m proceeds into a loop of training, until the maximum number of epochs are reached or the network has converged. Inside the loop, an optional call to function PatRandom.m (see Section 4.1.6) is issued, then the value of the variable trainFunc is evaluated (see command eval  in Section 5), thus resulting in a call to the training function for one epoch. Finally, ConvChk.m (see Section 4.1.10) is called to check for convergence and return true if so.

Once outside the loop, information about the success of the training and the accompanying details are displayed. The training is elapsed by the calls to the functions tic and toc (see Section 5), and average time per epoch is displayed. DrawResults.m (see Sections 3.1.3.1 and 4.1.11) is called to draw a graph of the error profile during training.