public void BuildsANetworkWith784InputNeurons10OutputNeuronsAndTheRequestedHiddenLayerSize()
{
var net = MnistLearnerSigmoidNetBuilder.Build(15);
net.Net.InputLayer.Length.ShouldBe(Image.ByteSize);
net.Net.HiddenLayer.Length.ShouldBe(15);
net.Net.OutputLayer.Length.ShouldBe(10);
}
public void BuildsANetworkWithReverseInterpretationBeingTheInverseOfTheInterpretation()
{
var net = MnistLearnerSigmoidNetBuilder.Build(15);
//
var digitsAsOutputNeurons =
Enumerable.Range(0, 10)
.Select(i => Enumerable.Range(0, 10).Select(j => j == i ? new ZeroToOne(1) : new ZeroToOne(0))).ToArray();
for (byte i = 0; i < 10; i++)
{
net.ReverseInterpretation(i).ShouldEqualByValue(digitsAsOutputNeurons[i]);
}
}
public void IsTrainableByBackPropagation()
{
var net = MnistLearnerSigmoidNetBuilder.Build(15);
var trainingData = new MnistFilesReader(mnistRealDataDirectory).TrainingData.Take(200).Select(p => new LearningNeuralNetworks.V1.Pair <Image, byte>(p.Data, p.Label)).ToArray();
var scoreBeforeTraining = HitsScoredOnTestData(net, trainingData);
//
new BackPropagationWithGradientDescent().ApplyToBatches(net, trainingData, 10, 3, 100);
//
var scoreAfterTraining = HitsScoredOnTestData(net, trainingData);
Console.WriteLine("Scores before/after training: {0} / {1}", scoreBeforeTraining, scoreAfterTraining);
scoreAfterTraining.ShouldBeGreaterThan(scoreBeforeTraining);
}
public void BuildsANetworkWithInterpretationOfOutputLayerAsADigit()
{
var net = MnistLearnerSigmoidNetBuilder.Build(15);
net.OutputFor(new[] { 1d }).ShouldBeOfType <byte>();
//
var digitsAsOutputNeurons =
Enumerable.Range(0, 10)
.Select(i => Enumerable.Range(0, 10).Select(j => j == i ? new ZeroToOne(1) : new ZeroToOne(0))).ToArray();
digitsAsOutputNeurons[0].ShouldEqualByValue(new ZeroToOne[] { 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 });
digitsAsOutputNeurons[1].ShouldEqualByValue(new ZeroToOne[] { 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 });
digitsAsOutputNeurons[4].ShouldEqualByValue(new ZeroToOne[] { 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 });
digitsAsOutputNeurons[5].ShouldEqualByValue(new ZeroToOne[] { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 });
digitsAsOutputNeurons[9].ShouldEqualByValue(new ZeroToOne[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 });
}
