static void Real()
{
var inputLayer = new InputLayer3D(1, 1, 1);
var outputLayer = new OutputLayer(1)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var dataProvider = new FunctionProvider();
InitializeTrainingData(dataProvider.TrainData);
var perceptron1 = new PerceptronLayer(10, 2)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron2 = new PerceptronLayer(10, 10)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron3 = new PerceptronLayer(8, 10)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron4 = new PerceptronLayer(6, 8)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron5 = new PerceptronLayer(2, 6)
{
ActivationFunction = new SigmoidFunction()
};
MultiLayerPerceptron network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = dataProvider
};
network.HiddenLayers.Add(perceptron1);
network.HiddenLayers.Add(perceptron2);
network.HiddenLayers.Add(perceptron3);
network.HiddenLayers.Add(perceptron4);
network.HiddenLayers.Add(perceptron5);
var trainer = new FCTrainer(network, 10, 1, dataProvider);
trainer.Train(1);
var error = network.Test(1);
}
static void OneTrainingData()
{
var inputLayer = new InputLayer3D(1, 1, 3);
var outputLayer = new OutputLayer(2)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var weight1 = new List <Array3D> {
new Array3D(0.1, 0.3), new Array3D(0.3, 0.1)
};
var weight2 = new List <Array3D> {
new Array3D(0.4, 0.5), new Array3D(0.3, 0.5)
};
var perceptron1 = new PerceptronLayer(weight1)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron2 = new PerceptronLayer(weight2)
{
ActivationFunction = new SigmoidFunction()
};
var dataProvider = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D> {
Input = new Array3D(0.3, 0.4, 0.5), Expected = new Array3D(0.2, 0.6)
},
new TrainingData <Array3D, Array3D> {
Input = new Array3D(0.2, 0.4, 0.7), Expected = new Array3D(0.1, 0.8)
}
}
};
var network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = dataProvider
};
network.HiddenLayers.Add(perceptron1);
network.HiddenLayers.Add(perceptron2);
var trainer = new FCTrainer(network, 2, 1, dataProvider);
trainer.Train(100);
}
private static void OneData()
{
var trainImage = @"C:\Programming\ConvolutionNeuralNetwork\Images\Train\One\One_0.jpg";
var expected = new Array3D(0, 1, 0, 0, 0, 0, 0, 0, 0, 0);
var network = new ConvolutionNN
{
InputLayer = new InputLayer3D(28, 28),
OutputLayer = new OutputLayer(10),
DataProvider = new OneIP(trainImage, expected)
};
network.HiddenLayers.Add(new ConvolutionalLayer(3, 2, 2, 3));
network.HiddenLayers.Add(new PoolingLayer());
network.HiddenLayers.Add(new ConvolutionalLayer(3, 2, 2, 3));
network.HiddenLayers.Add(new PoolingLayer());
network.HiddenLayers.Add(new NarrowLayer());
network.HiddenLayers.Add(new PerceptronLayer(100, 147));
network.HiddenLayers.Add(new PerceptronLayer(50, 100));
network.HiddenLayers.Add(new PerceptronLayer(10, 50));
var trainer = new FCTrainer(network, 10, 1, network.DataProvider);
trainer.Train(1000);
}
static void Xor()
{
const int batchSize = 4;
const int epochSize = 16;
var inputLayer = new InputLayer3D(1, 1, 1);
var outputLayer = new OutputLayer(1)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var dataProvider = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
},
TestData = { new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0)
} },
IsQueue = false
};
var oneData = new FunctionProvider {
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
}, IsQueue = false
};
var function = new FunctionProvider(x => Math.Pow(x, 2));
var weight1 = new List <Array3D> {
new Array3D(0.1, 0.3), new Array3D(0.3, 0.1)
};
var weight2 = new List <Array3D> {
new Array3D(0.4, 0.5)
};
var perceptron1 = new PerceptronLayer(5, 2)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron1.Trainer = new MiniBatchPerceptronTrainer(perceptron1.Neurals, false)
{
BatchSize = batchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
var perceptron2 = new PerceptronLayer(1, 5)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron2.Trainer = new MiniBatchPerceptronTrainer(perceptron2.Neurals, true)
{
BatchSize = batchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
var network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = dataProvider
};
network.HiddenLayers.Add(perceptron1);
network.HiddenLayers.Add(perceptron2);
var trainer = new FCTrainer(network, epochSize, batchSize, dataProvider);
trainer.Train(200);
}
private void InitializeNetwork()
{
var inputLayer = new InputLayer3D(1, 1, 1);
var outputLayer = new OutputLayer(1)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var dataProvider = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
},
TestData = { new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0)
} },
IsQueue = false
};
var oneData = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
},
IsQueue = false
};
var function = new FunctionProvider(x => Math.Pow(x, 2));
var perceptron1 = new PerceptronLayer(10, 1)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron1.Trainer = new MiniBatchPerceptronTrainer(perceptron1.Neurals, false)
{
BatchSize = BatchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
var perceptron2 = new PerceptronLayer(1, 10)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron2.Trainer = new MiniBatchPerceptronTrainer(perceptron2.Neurals, true)
{
BatchSize = BatchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
Network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = function
};
Network.HiddenLayers.Add(perceptron1);
Network.HiddenLayers.Add(perceptron2);
Trainer = new FCTrainer(Network, EpochSize, BatchSize, function);
}
