public void CanAddTrainable()
{
WeightedCombiner wc = new WeightedCombiner(new NeuralNet.WeightsMatrix(1, 1));
NetComponentChain layerlist = new NetComponentChain();
layerlist.AddTrainable(wc);
List <NetComponent> allComponents = new List <NetComponent>(layerlist.ForwardEnumeration);
List <NetComponent> trainableComponents = new List <NetComponent>(layerlist.ForwardTrainableComponentsEnumeration);
Assert.AreEqual(1, layerlist.NumberOfComponents);
Assert.IsTrue(allComponents.Contains(wc));
Assert.IsTrue(trainableComponents.Contains(wc));
}
public void CannotAddLayerOfWrongSize()
{
Layer layer1 = Layer.CreateLinearLayer(new NeuralNet.WeightsMatrix(new double[, ] {
{ 1 }
}));
Layer layer2 = Layer.CreateLogisticLayer(new NeuralNet.WeightsMatrix(new double[, ] {
{ 1, 2 }
}));
NetComponentChain layerlist = new NetComponentChain(layer1);
try
{
layerlist.AddTrainable(layer2);
Assert.Fail("Add should throw and ArgumentException if when trying to add a layer of the wrong size, but did not.");
}
catch (ArgumentException)
{ }
}
public void CanRunTwoLayerNetWithOneInput()
{
Layer inputlayer = new Layer(new NeuralNet.WeightsMatrix(new double[, ] {
{ 1, 1, 1 }, { 1, 1, 1 }
}));
Layer outputlayer = Layer.CreateLinearLayer(new NeuralNet.WeightsMatrix(new double[, ] {
{ 1, 1 }
}));
NetComponentChain network = new NetComponentChain();
network.AddFixed(inputlayer);
network.AddTrainable(outputlayer);
NeuralNet.NetworkVector inputvector = new NeuralNet.NetworkVector(new double[] { 1, 0, 0 });
NetworkVector result = network.Run(inputvector);
NeuralNet.NetworkVector outputCheck = new NeuralNet.NetworkVector(new double[] { 2 });
Assert.AreEqual(outputCheck, result);
}
public void CanBackPropagateTwoLayerNetGradient1()
{
Layer inputlayer = new Layer(new NeuralNet.WeightsMatrix(new double[, ] {
{ 1, 1, 1 }, { 1, 1, 1 }
}));
Layer outputlayer = Layer.CreateLinearLayer(new NeuralNet.WeightsMatrix(new double[, ] {
{ 1, 1 }
}));
NetComponentChain network = new NetComponentChain();
network.AddFixed(inputlayer);
network.AddTrainable(outputlayer);
NeuralNet.NetworkVector inputvector = new NeuralNet.NetworkVector(new double[] { 1, 0, 0 });
NeuralNet.NetworkVector outputgradient = new NeuralNet.NetworkVector(new double[] { 1 });
network.Run(inputvector);
NeuralNet.NetworkVector inputGradientCheck = new NeuralNet.NetworkVector(new double[] { 2, 2, 2 });
Assert.AreEqual(inputGradientCheck, network.InputGradient(outputgradient));
}
public void TrainBatch_SmallChain_CorrectOnePass()
{
int inputs = 3;
int inputneurons = 2;
int outputneurons = 1;
double[,] inputWeights = new double[inputneurons, inputs];
double[,] outputWeights = new double[outputneurons, inputneurons];
for (int i = 0; i < inputneurons; i++)
{
for (int j = 0; j < inputs; j++)
{
inputWeights[i, j] = 1;
}
}
for (int i = 0; i < outputneurons; i++)
{
for (int j = 0; j < inputneurons; j++)
{
outputWeights[i, j] = 1;
}
}
Layer InputLayer = Layer.CreateLinearLayer(new WeightsMatrix(inputWeights), new NetworkVector(inputneurons));
Layer OutputLayer = Layer.CreateLinearLayer(new WeightsMatrix(outputWeights), new NetworkVector(outputneurons));
NetComponentChain network = new NetComponentChain();
network.AddTrainable(InputLayer);
network.AddTrainable(OutputLayer);
TrainingCollection trainingVectors = new TrainingCollection
{
new VectorPair(
new NetworkVector(new double[] { 0, 0, 0 }),
new NetworkVector(new double[] { 1 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 0, 0 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 0, 1, 0 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 1, 0 }),
new NetworkVector(new double[] { 1 })
)
};
Trainer trainer = new Trainer(network, new SquaredError(), new GradientDescent());
trainer.Train(trainingVectors);
WeightsMatrix inputWeightsCheck = new WeightsMatrix(new double[, ] {
{ -4, -4, 1 }, { -4, -4, 1 }
});
NetworkVector inputBiasesCheck = new NetworkVector(new double[] { -6, -6 });
WeightsMatrix outputWeightsCheck = new WeightsMatrix(new double[, ] {
{ -9, -9 }
});
NetworkVector outputBiasesCheck = new NetworkVector(new double[] { -6 });
Assert.AreEqual(inputWeightsCheck, InputLayer.Weights);
Assert.AreEqual(inputBiasesCheck, InputLayer.Biases);
Assert.AreEqual(outputWeightsCheck, OutputLayer.Weights);
Assert.AreEqual(outputBiasesCheck, OutputLayer.Biases);
}