KRANN 2.1.4

Maintained by Kalvar Lin.

You want to add pod 'KRANN', '~> 2.1' similar to the following to your Podfile:

target 'MyApp' do
  pod 'KRANN', '~> 2.1'
end

Then run a pod install inside your terminal, or from CocoaPods.app.

Alternatively to give it a test run, run the command:

pod try KRANN

KRANN 2.1.4

By
Kalvar Lin
ilovekalvar

ios-Multi-Perceptron-NeuralNetwork

Machine Learning (マシンラーニング) in this project, it implemented multi-perceptrons neural network (ニューラルネットワーク) based on Back Propagation Neural Network (BPN) and designed unlimited-hidden-layers to do the training tasks and also prepared flexible spaces to wait for combining Fuzzy theory in network. This network can be used in products recommendation (おすすめの商品), user behavior analysis (ユーザーの行動分析), data mining (データマイニング) and data analysis (データ分析).

This project designed for mobile device perform the basic data analysis. If you need help to know how to use this network, just ask me via email.

Podfile

platform :ios, '7.0'
pod "KRANN", "~> 2.1.4"

How to use

#import "KRANN.h"

@interface ViewController ()<KRANNDelegate>

@property (nonatomic, strong) KRANN *_krANN;

@end

@implementation ViewController

@synthesize _krANN;

//Setups any detail, and 2 outputs, you could set more outputs.
-(void)useSample1
{
    _krANN.activationFunction = KRANNActivationFunctionSigmoid;
    //各輸入向量陣列值 & 每一筆輸入向量的期望值( 輸出期望 )，因使用 S 形轉換函數，故 Input 值域須為 [0, 1]，輸出目標為 [0, 1]
    //Add the patterns, the weights connect with hidden layer, the output targets
    [_krANN addPatterns:@[@1, @0.1, @0.5, @0.2] outputGoals:@[@0.7f, @0.8f]];  //Pattern 1, net 1, 2, 3, 4, the output layer is 2 nets
    [_krANN addPatterns:@[@0, @1, @0.3, @0.9] outputGoals:@[@0.1f, @0.1f]];    //Pattern 2, same as pattern 1
    [_krANN addPatterns:@[@1, @0.3, @0.1, @0.4] outputGoals:@[@0.95f, @0.9f]]; //Pattern 3, same as pattern 1

    //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 )
    //Add pattern-weights in Input layer to first Hidden Layer
    [_krANN addPatternWeights:@[@0.2, @-0.3]]; //W15, W16
    [_krANN addPatternWeights:@[@0.4, @0.1]];  //W25, W26
    [_krANN addPatternWeights:@[@-0.5, @0.2]]; //W35, W36
    [_krANN addPatternWeights:@[@-0.1, @0.3]]; //W45, W46

    //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
    //Add Hidden Layers the biases of nets and the output weights in connect with output layer.
    [_krANN addHiddenLayerAtIndex:0 netBias:-0.4f netWeights:@[@-0.3f, @0.2f, @0.1f]]; //Net 5
    [_krANN addHiddenLayerAtIndex:0 netBias:0.2f netWeights:@[@-0.2f, @0.5f, @-0.1f]];  //Net 6

    [_krANN addHiddenLayerAtIndex:1 netBias:0.1f netWeights:@[@0.1f, @0.3f]];   //Net 7
    [_krANN addHiddenLayerAtIndex:1 netBias:0.25f netWeights:@[@0.2f, @0.1f]];  //Net 8
    [_krANN addHiddenLayerAtIndex:1 netBias:0.3f netWeights:@[@0.3f, @-0.4f]];  //Net 9

    [_krANN addHiddenLayerAtIndex:2 netBias:-0.25f netWeights:@[@0.4f, @0.3f]];  //Net 10
    [_krANN addHiddenLayerAtIndex:2 netBias:0.15f netWeights:@[@0.1f, @-0.2f]];  //Net 11

    //輸出層神經元偏權值, Net 12, Net 13
    //Add the output layer biases
    [_krANN addOutputBiases:@[@0.0f, @0.1f]];

    __block typeof(_krANN) _weakKRANN = _krANN;
    //訓練完成時( Training complete )
    [_krANN 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 ========");
            [_weakKRANN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
                NSLog(@"Training done with total times : %i", totalTimes);
                NSLog(@"TrainedInfo 2 : %@", trainedInfo);
            }];

            [_weakKRANN recoverNetwork];
            [_weakKRANN directOutputAtInputs:@[@1, @0.1, @0.5, @0.2]];
        }
    }];

    [_krANN training];
}

//Only setups patterns and output goals, and 1 output.
-(void)useSample2
{
    //Use f(x)=tanh(x)
    _krANN.activationFunction = KRANNActivationFunctionTanh;
    //How many hidden layers
    _krANN.hiddenLayerCount   = 0; //0 means let system decide the hidden layers number.

    [_krANN addPatterns:@[@1, @0.1, @0.5, @0.2] outputGoals:@[@0.7f]];    //Pattern 1, net 1, 2, 3, 4, and 1 output
    [_krANN addPatterns:@[@0, @-0.8, @0.3, @-0.9] outputGoals:@[@-0.1f]]; //Pattern 2, same as pattern 1
    [_krANN addPatterns:@[@1, @0.3, @0.1, @0.4] outputGoals:@[@0.9f]];    //Pattern 3, same as pattern 1

    __block typeof(_krANN) _weakKRANN = _krANN;
    //訓練完成時( Training complete )
    [_krANN 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 ========");
            [_weakKRANN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
                NSLog(@"Training done with total times : %i", totalTimes);
                NSLog(@"TrainedInfo 2 : %@", trainedInfo);
            }];

            [_weakKRANN recoverNetwork];
            [_weakKRANN directOutputAtInputs:@[@0, @-0.8, @0.3, @-0.9]];
        }
    }];

    [_krANN trainingByRandomWithSave];
}

//To learn and verify numbers 0 to 9. And only setups patterns and output goals, and 10 outputs.
-(void)useSample3
{
    _krANN.activationFunction = KRANNActivationFunctionSigmoid;
    //How many hidden layers, if you set 0 that means let system decide the hidden layers number.
    _krANN.hiddenLayerCount = 1;

    //1
    [_krANN addPatterns:@[@0, @0, @0, @0,
                          @0, @0, @0, @0,
                          @0, @0, @0, @0,
                          @0, @0, @0, @0,
                          @0, @0, @0, @0,
                          @0, @0, @0, @0,
                          @0, @0, @0, @1,
                          @1, @1, @1, @1,
                          @1, @1, @1, @1]
            outputGoals:@[@1, @0, @0, @0, @0, @0, @0, @0, @0, @0]];
    //2
    [_krANN addPatterns:@[@1, @0, @0, @0,
                          @1, @1, @1, @1,
                          @1, @1, @0, @0,
                          @0, @1, @0, @0,
                          @0, @1, @1, @0,
                          @0, @0, @1, @0,
                          @0, @0, @1, @1,
                          @1, @1, @1, @1,
                          @0, @0, @0, @1]
            outputGoals:@[@0, @1, @0, @0, @0, @0, @0, @0, @0, @0]];
    //3
    [_krANN addPatterns:@[@1, @0, @0, @0,
                          @1, @0, @0, @0,
                          @1, @1, @0, @0,
                          @0, @1, @0, @0,
                          @0, @1, @1, @0,
                          @0, @0, @1, @0,
                          @0, @0, @1, @1,
                          @1, @1, @1, @1,
                          @1, @1, @1, @1]
            outputGoals:@[@0, @0, @1, @0, @0, @0, @0, @0, @0, @0]];
    //4
    [_krANN addPatterns:@[@1, @1, @1, @1,
                          @1, @0, @0, @0,
                          @0, @0, @0, @0,
                          @0, @1, @0, @0,
                          @0, @0, @0, @0,
                          @0, @0, @1, @0,
                          @0, @0, @0, @1,
                          @1, @1, @1, @1,
                          @1, @1, @1, @1]
            outputGoals:@[@0, @0, @0, @1, @0, @0, @0, @0, @0, @0]];
    //5
    [_krANN addPatterns:@[@1, @1, @1, @1,
                          @1, @0, @0, @0,
                          @1, @1, @0, @0,
                          @0, @1, @0, @0,
                          @0, @1, @1, @0,
                          @0, @0, @1, @0,
                          @0, @0, @1, @1,
                          @0, @0, @0, @1,
                          @1, @1, @1, @1]
            outputGoals:@[@0, @0, @0, @0, @1, @0, @0, @0, @0, @0]];
    //6
    [_krANN addPatterns:@[@1, @1, @1, @1,
                          @1, @1, @1, @1,
                          @1, @1, @0, @0,
                          @0, @1, @0, @0,
                          @0, @1, @1, @0,
                          @0, @0, @1, @0,
                          @0, @0, @1, @1,
                          @0, @0, @0, @1,
                          @1, @1, @1, @1]
            outputGoals:@[@0, @0, @0, @0, @0, @1, @0, @0, @0, @0]];
    //7
    [_krANN addPatterns:@[@1, @0, @0, @0,
                          @0, @0, @0, @0,
                          @0, @1, @0, @0,
                          @0, @0, @0, @0,
                          @0, @0, @1, @0,
                          @0, @0, @0, @0,
                          @0, @0, @0, @1,
                          @1, @1, @1, @1,
                          @1, @1, @1, @1]
            outputGoals:@[@0, @0, @0, @0, @0, @0, @1, @0, @0, @0]];
    //8
    [_krANN addPatterns:@[@1, @1, @1, @1,
                          @1, @1, @1, @1,
                          @1, @1, @0, @0,
                          @0, @1, @0, @0,
                          @0, @1, @1, @0,
                          @0, @0, @1, @0,
                          @0, @0, @1, @1,
                          @1, @1, @1, @1,
                          @1, @1, @1, @1]
            outputGoals:@[@0, @0, @0, @0, @0, @0, @0, @1, @0, @0]];
    //9
    [_krANN addPatterns:@[@1, @1, @1, @1,
                          @1, @0, @0, @0,
                          @0, @1, @0, @0,
                          @0, @1, @0, @0,
                          @0, @0, @1, @0,
                          @0, @0, @1, @0,
                          @0, @0, @0, @1,
                          @1, @1, @1, @1,
                          @1, @1, @1, @1]
            outputGoals:@[@0, @0, @0, @0, @0, @0, @0, @0, @1, @0]];
    //0
    [_krANN addPatterns:@[@1, @1, @1, @1,
                          @1, @1, @1, @1,
                          @1, @1, @0, @0,
                          @0, @0, @0, @0,
                          @0, @1, @1, @0,
                          @0, @0, @0, @0,
                          @0, @0, @1, @1,
                          @1, @1, @1, @1,
                          @1, @1, @1, @1]
            outputGoals:@[@0, @0, @0, @0, @0, @0, @0, @0, @0, @1]];

    __block typeof(_krANN) _weakKRANN = _krANN;
    //訓練完成時( Training complete )
    [_krANN 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 ========");
            [_weakKRANN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
                NSLog(@"Training done with total times : %i", totalTimes);
                NSLog(@"TrainedInfo 2 : %@", trainedInfo);
            }];

            [_weakKRANN recoverNetwork];
            //Verified number " 7 ", and it has some defects.
            [_weakKRANN directOutputAtInputs:@[@1, @1, @1, @0,
                                               @0, @0, @0, @0,
                                               @0, @1, @0, @0,
                                               @0, @0, @0, @0,
                                               @0, @0, @1, @0,
                                               @0, @0, @0, @0,
                                               @0, @0, @0, @1,
                                               @1, @1, @1, @1,
                                               @1, @1, @1, @1]];

        }
    }];

    [_krANN trainingByRandomSettings];
}

- (void)viewDidLoad
{
    [super viewDidLoad];

    _krANN = [KRANN sharedNetwork];
    //_krANN.delegate = self;

    //學習速率
    _krANN.learningRate     = 0.8f;
    //收斂誤差值 ( Normally is 10^-3 or 10^-6 )
    _krANN.convergenceError = 0.000001f;
    //限制迭代次數
    _krANN.limitIteration   = 5000;

    //每一次的迭代( Per iteration-training )
    [_krANN setPerIteration:^(NSInteger times, NSDictionary *trainedInfo)
    {
        NSLog(@"Iteration times : %i", times);
        //NSLog(@"Iteration result : %f\n\n\n", [trainedInfo objectForKey:KRANNTrainedOutputResults]);
    }];

    [self useSample1];
    [self useSample2];
    [self useSample3];
}
@end

Version

V2.1.4

License

MIT.

Remarks

Waiting for turning performance and using EDBD (includes QuickProp) method to enhance this algorithm.
About the user guide, I have no time to write the user and technical guide in here. Maybe one day I take a long term vacations, the guide will be implemented.