#import "KRBPN.h" @interface ViewController () @property (nonatomic, strong) KRBPN *_krBPN; @end @implementation ViewController @synthesize _krBPN; - (void)viewDidLoad { _krBPN = [KRBPN sharedNetwork]; //_krBPN.delegate = self; /*  * @ 各輸入向量陣列值 & 每一筆輸入向量的期望值( 輸出期望 )  */ //Input Pattern 1, Output Goal of Input Pattern 1 [_krBPN addPatterns:@[@1, @2, @0.5, @1.2] outputGoal:1.0f]; //Input Pattern 2 [_krBPN addPatterns:@[@0, @1, @0.3, @-0.9] outputGoal:0.0f]; //Input Pattern 3 [_krBPN addPatterns:@[@1, @-3, @-1, @0.4] outputGoal:1.0f]; //輸入層各向量值到隱藏層神經元的權重 ( 連結同一個 Net 的就一組一組分開，有幾個 Hidden Net 就會有幾組 ) //W15, W16 [_krBPN addPatternWeights:@[@0.2, @-0.3]]; //W25, W26 [_krBPN addPatternWeights:@[@0.4, @0.1]]; //W35, W36 [_krBPN addPatternWeights:@[@-0.5, @0.2]]; //W45, W46 [_krBPN addPatternWeights:@[@-0.1, @0.3]]; /*  * @ 設定隱藏層神經元的偏權值 & 隱藏層神經元到輸出層神經元的權重值  */ //Net 5, Net 6 //第 1 層, 隱藏層神經元 Net 4 的偏權值, 隱藏層神經元 Net 4 到下一層神經元的權重值 [_krBPN addHiddenLayerAtIndex:0 netBias:-0.4 netWeights:@[@-0.3, @0.2, @0.15]]; [_krBPN addHiddenLayerAtIndex:0 netBias:0.2 netWeights:@[@-0.2, @0.5, @0.35]]; //Net 7, Net 8 //第 2 層 [_krBPN addHiddenLayerAtIndex:1 netBias:0.3 netWeights:@[@-0.5, @0.1]]; [_krBPN addHiddenLayerAtIndex:1 netBias:0.7 netWeights:@[@0.2, @0.4]]; [_krBPN addHiddenLayerAtIndex:1 netBias:0.2 netWeights:@[@-0.2, @0.5]]; //Net 9, Net 10 //第 3 層 (單 Output，最後的 netWeights 只需設 1 組，設多組則為多 Output Results) [_krBPN addHiddenLayerAtIndex:2 netBias:-0.2 netWeights:@[@0.3]]; [_krBPN addHiddenLayerAtIndex:2 netBias:0.25 netWeights:@[@0.2]]; //輸出層神經元偏權值, Net 6 for output _krBPN.outputBias = 0.1f; //學習速率 _krBPN.learningRate = 0.8f; //收斂誤差值 ( 一般是 10^-3 或 10^-6 ) _krBPN.convergenceError = 0.000001f; //限制迭代次數 _krBPN.limitGeneration = 500; __block typeof(_krBPN) _weakKrBPN = _krBPN; //每一次的迭代( Every generation-training ) [_krBPN setEachGeneration:^(NSInteger times, NSDictionary *trainedInfo) { NSLog(@"Generation times : %i", times); //NSLog(@"Generation result : %f\n\n\n", [trainedInfo objectForKey:KRBPNTrainedInfoOutputResults]); }]; //訓練完成時( Training complete ) [_krBPN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes) { if( success ) { NSLog(@"Training done with total times : %i", totalTimes); NSLog(@"TrainedInfo 1 : %@", trainedInfo); //Start in checking the network is correctly trained. NSLog(@"======== Start in Verification ========"); [_weakKrBPN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes) { NSLog(@"Training done with total times : %i", totalTimes); NSLog(@"TrainedInfo 2 : %@", trainedInfo); }]; [_weakKrBPN recoverTrainedNetwork]; _weakKrBPN.inputs = [NSMutableArray arrayWithObjects: @[@0, @-1, @2, @0.1], nil]; [_weakKrBPN useTrainedNetworkToOutput]; } }]; //Remove your testing trained-network records. //[_krBPN removeTrainedNetwork]; //Start the training, and random the weights, biases, if you use this method that you won't need to setup any weights and biases before. //Random means let network to auto setup inputWeights, hiddenBiases, hiddenWeights values. //[_krBPN trainingWithRandom]; //As above said, then it will be saved the trained network after done. //[_krBPN trainingWithRandomAndSave]; //Start the training network, and it won't be saving the trained-network when finished. [_krBPN training]; //Start the training network, and it will auto-saving the trained-network when finished. //[_krBPN trainingDoneSave]; //If you wanna pause the training. //[_krBPN pause]; //If you wanna continue the paused training. //[_krBPN continueTraining]; //If you wanna reset the network back to initial situation. //[_krBPN reset]; //When the training finished, to save the trained-network into NSUserDefaults. //[_krBPN saveTrainedNetwork]; //If you wanna recover the trained-network data. //[_krBPN recoverTrainedNetwork]; //Or you wanna use the KRBPNTrainedNetwork object to recover the training data. /*  KRBPNTrainedNetwork *_trainedNetwork = [[KRBPNTrainedNetwork alloc] init];  _trainedNetwork.inputs = [NSMutableArray arrayWithObjects:  @[@1],  @[@0],  @[@1],  nil];  [_krBPN recoverTrainedNetwork:_trainedNetwork];  */ //To remove the saved trained-network. //[_krBPN removeTrainedNetwork]; } @end