private static NeuralNetwork InitializeNeuralNetwork(int seed)
{
Random random = new Random(seed == 0 ? new Random().Next() : seed);
float RandomWeight() => (float)(random.NextDouble() * 2 - 1);
Layer prevLayer;
InputLayer li = new InputLayer(3, 5);
prevLayer = li;
ConvolutionalLayer l0 = new ConvolutionalLayer(8, 2, 1, 0, prevLayer, ActivationFunctions.ReLU(true));
prevLayer = l0;
prevLayer.InitializeWeights(RandomWeight);
ConvolutionalLayer l2 = new ConvolutionalLayer(16, 2, 1, 0, prevLayer, ActivationFunctions.ReLU(true));
prevLayer = l2;
prevLayer.InitializeWeights(RandomWeight);
FullyConnectedLayer l7 = new FullyConnectedLayer(16, prevLayer, ActivationFunctions.Sigmoid(1));
prevLayer = l7;
prevLayer.InitializeWeights(RandomWeight);
FullyConnectedLayer l8 = new FullyConnectedLayer(10, prevLayer, ActivationFunctions.SoftMax(1));
prevLayer = l8;
prevLayer.InitializeWeights(RandomWeight);
return(new NeuralNetwork(li, l0, l2, l7, l8));
}
private static NeuralNetwork InitializeNeuralNetwork(int seed)
{
Random random = new Random(seed == 0 ? new Random().Next() : seed);
float RandomWeight() => (float)(random.NextDouble() * 2 - 1);
Layer prevLayer;
InputLayer li = new InputLayer(28, 28);
prevLayer = li;
ConvolutionalLayer l0 = new ConvolutionalLayer(15, 5, 1, 0, prevLayer, ActivationFunctions.ReLU(true));
prevLayer = l0;
prevLayer.InitializeWeights(RandomWeight);
MaxPoolingLayer l1 = new MaxPoolingLayer(2, 2, prevLayer);
prevLayer = l1;
ConvolutionalLayer l2 = new ConvolutionalLayer(30, 4, 1, 0, prevLayer, ActivationFunctions.ReLU(true));
prevLayer = l2;
prevLayer.InitializeWeights(RandomWeight);
MaxPoolingLayer l3 = new MaxPoolingLayer(3, 2, prevLayer);
prevLayer = l3;
ConvolutionalLayer l4 = new ConvolutionalLayer(45, 2, 2, 0, prevLayer, ActivationFunctions.ReLU(true));
prevLayer = l4;
prevLayer.InitializeWeights(RandomWeight);
MaxPoolingLayer l5 = new MaxPoolingLayer(2, 1, prevLayer);
prevLayer = l5;
FullyConnectedLayer l6 = new FullyConnectedLayer(64, prevLayer, ActivationFunctions.Sigmoid(1));
prevLayer = l6;
prevLayer.InitializeWeights(RandomWeight);
FullyConnectedLayer l7 = new FullyConnectedLayer(32, prevLayer, ActivationFunctions.Sigmoid(1));
prevLayer = l7;
prevLayer.InitializeWeights(RandomWeight);
FullyConnectedLayer l8 = new FullyConnectedLayer(10, prevLayer, ActivationFunctions.SoftMax(1));
prevLayer = l8;
prevLayer.InitializeWeights(RandomWeight);
return(new NeuralNetwork(li, l0, l1, l2, l3, l4, l5, l6, l7, l8));
}
public void TestReLU()
{
Matrix matrix = new Matrix(new double[, ] {
{ -1, 0, 2, 3 }
});
Matrix matrix1 = new Matrix(new double[, ] {
{ 0, 0, 2, 3 }
});
Assert.IsTrue(ActivationFunctions.ReLU(matrix) == matrix1);
}
