public void ZScoreTest()
{
var data = new TensorOld(new double[] { 1, 2, 3 });
var norm = new ZScoreNorm(data);
var test = new TensorOld(new double[] { 1.5, 2.5 });
var result = norm.Normalize(test);
Assert.Equal(2, norm.Mean);
Assert.Equal(Math.Sqrt(2d / 3d), norm.Delta);
Assert.True(result[0] < 0);
Assert.True(result[1] > 0);
}
public void Play()
{
var trainX = MNISTReader.ReadImagesToTensor4("Data\\train-images.idx3-ubyte", 60000);
var trainY = MNISTReader.ReadLabelsToMatrix("Data\\train-labels.idx1-ubyte", 60000);
var testX = MNISTReader.ReadImagesToTensor4("Data\\t10k-images.idx3-ubyte", 10000);
var testY = MNISTReader.ReadLabelsToMatrix("Data\\t10k-labels.idx1-ubyte", 10000);
var nn = new NeuralNetwork()
.AddLayer(new ConvLayer(10, 5, 1))
.AddReLU()
.AddLayer(new MaxPooling(2))
.AddLayer(new ConvLayer(20, 5, 1))
.AddReLU()
.AddLayer(new MaxPooling(2))
.AddLayer(new FlattenLayer())
.AddFullLayer(100)
.AddSigmoid()
.AddFullLayer(50)
.AddSigmoid()
.AddFullLayer(10)
.AddSoftmaxWithCrossEntropyLoss()
.UseAdam();
var cate = trainY
.GetRawValues()
.Distinct()
.OrderBy(a => a)
.Select(a => a.ToString())
.ToList();
var codec = new OneHotCodec(cate);
var norm = new ZScoreNorm(trainX);
var trainer = new Trainer(nn, 64, 10, true)
{
LabelCodec = codec,
Normalizer = norm,
};
trainer.StartTrain(trainX, trainY, testX, testY);
}
