src/core/network/EarlyStopping.java
public void evaluateValidationCondition(int iteration) throws MatrixException, DynamicParamException {
        if (!validationStopCondition && iteration >= validationAverageSize) {
            double lastAverage = getAverageError(validationAverages, validationAverageSize, validationMetric.getLastError());
            if (previousValidationAverage <= lastAverage && previousValidationAverage != Double.NEGATIVE_INFINITY) validationStopCount++;
            else {
                previousValidationAverage = lastAverage;
                validationStopCount = 0;
            }
            if (validationStopCount >= validationStopThreshold) validationStopCondition = true;
        }
    }
/**
     * Function that evaluates validation condition.<br>
     * It first checks that iteration count is in minimum bigger than validation average size and validation condition threshold is not reached yet.<br>
     * It then compares if current rolling validation average is less than previous rolling validation average.<br>
     * If yes validation condition count is increased otherwise it is reset to zero.<br>
     *
     * @param iteration current neural network training iteration.
     * @throws MatrixException throws exception if matrix operation fails.
     * @throws DynamicParamException throws exception if parameter (params) setting fails.
     */
Function that evaluates validation condition.
It first checks that iteration count is in minimum bigger than validation average size and validation condition threshold is not reached yet.
It then compares if current rolling validation average is less than previous rolling validation average.
If yes validation condition count is increased otherwise it is reset to zero.

@param iteration current neural network training iteration.
@throws MatrixException throws exception if matrix operation fails.
@throws DynamicParamException throws exception if parameter (params) setting fails.
Function that evaluates validation condition . It first checks that iteration count is in minimum bigger than validation average size and validation condition threshold is not reached yet . It then compares if current rolling validation average is less than previous rolling validation average . If yes validation condition count is increased otherwise it is reset to zero . @param iteration current neural network training iteration . @throws MatrixException throws exception if matrix operation fails . @throws DynamicParamException throws exception if parameter ( params ) setting fails .
public void evaluateValidationCondition(int iteration) throws MatrixException, DynamicParamException {
        if (!validationStopCondition && iteration >= validationAverageSize) {
            double lastAverage = getAverageError(validationAverages, validationAverageSize, validationMetric.getLastError());
            if (previousValidationAverage <= lastAverage && previousValidationAverage != Double.NEGATIVE_INFINITY) validationStopCount++;
            else {
                previousValidationAverage = lastAverage;
                validationStopCount = 0;
            }
            if (validationStopCount >= validationStopThreshold) validationStopCondition = true;
        }
    }
public void evaluateValidationCondition ( int iteration ) throws MatrixException , DynamicParamException { if ( ! validationStopCondition && iteration >= validationAverageSize ) { double lastAverage = getAverageError ( validationAverages , validationAverageSize , validationMetric . getLastError ( ) ) ; if ( previousValidationAverage <= lastAverage && previousValidationAverage != Double . NEGATIVE_INFINITY ) validationStopCount ++ ; else { previousValidationAverage = lastAverage ; validationStopCount = 0 ; } if ( validationStopCount >= validationStopThreshold ) validationStopCondition = true ; } }
Function that evaluates validation condition.<br>
 It first checks that iteration count is in minimum bigger than validation average size and validation condition threshold is not reached yet.<br>
 It then compares if current rolling validation average is less than previous rolling validation average.<br>
 If yes validation condition count is increased otherwise it is reset to zero.<br>
Function that evaluates validation condition . <br > It first checks that iteration count is in minimum bigger than validation average size and validation condition threshold is not reached yet . <br > It then compares if current rolling validation average is less than previous rolling validation average . <br > If yes validation condition count is increased otherwise it is reset to zero . <br >