public void CanRunWithZeroInput()
{
Layer layer = Layer.CreateLinearLayer(new NeuralNet.WeightsMatrix(new double[, ] {
{ 1 }
}));
NetComponentChain layerlist = new NetComponentChain(layer);
NeuralNet.NetworkVector vector = new NeuralNet.NetworkVector(new double[] { 0 });
NetworkVector result = layerlist.Run(vector);
Assert.AreEqual(vector, result);
}
public void CannotRunWithInputOfWrongSize()
{
Layer layer = Layer.CreateLinearLayer(new NeuralNet.WeightsMatrix(new double[, ] {
{ 1 }
}));
NetComponentChain layerlist = new NetComponentChain(layer);
NeuralNet.NetworkVector input = new NeuralNet.NetworkVector(new double[] { 0, 0 });
try
{
layerlist.Run(input);
Assert.Fail("Run should throw an ArgumentException for input of the wrong size, but did not.");
}
catch (ArgumentException)
{ }
}
public void CanRunTwoLayersWithZeroInput()
{
Layer layer1 = new Layer(new NeuralNet.WeightsMatrix(new double[, ] {
{ 1, 1 }, { 1, 1 }
}));
Layer layer2 = Layer.CreateLinearLayer(new NeuralNet.WeightsMatrix(new double[, ] {
{ 1, 1 }
}));
NetComponentChain layerlist = new NetComponentChain(layer1);
layerlist.AddFixed(layer2);
NeuralNet.NetworkVector vector = new NeuralNet.NetworkVector(new double[] { 0, 0 });
NetworkVector result = layerlist.Run(vector);
NeuralNet.NetworkVector outputCheck = new NeuralNet.NetworkVector(new double[] { 0 });
Assert.AreEqual(outputCheck, result);
}
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));
}