public void InputGradientBigNetGradient1()
{
int inputs = 100;
int inputneurons = 2000;
int outputneurons = 1;
double[] inputArray = new double[inputs];
inputArray[0] = 1;
NetworkVector inputvector = new NetworkVector(inputArray);
NetworkVector outputgradient = new NetworkVector(new double[] { 1 });
LinearTwoLayerTestNetwork network = new LinearTwoLayerTestNetwork(inputs, inputneurons, outputneurons);
network.Run(inputvector);
double[] inputGradientCheckValues = new double[inputs];
for (int i = 0; i < inputs; i++)
{
inputGradientCheckValues[i] = inputneurons;
}
NetworkVector inputGradientCheck = new NetworkVector(inputGradientCheckValues);
Assert.AreEqual(inputGradientCheck, network.InputGradient(outputgradient));
}
public void CanMakeBigNet()
{
int inputs = 100;
int inputneurons = 2000;
int outputneurons = 1;
LinearTwoLayerTestNetwork network = new LinearTwoLayerTestNetwork(inputs, inputneurons, outputneurons);
Assert.AreNotEqual(null, network);
}
public void UnrunSmallNetworkHasZeroOutput()
{
int inputs = 3;
int inputneurons = 2;
int outputneurons = 1;
LinearTwoLayerTestNetwork network = new LinearTwoLayerTestNetwork(inputs, inputneurons, outputneurons);
double output = network.Output.ToArray()[0];
Assert.AreEqual(0, output);
}
public void CanRunSmallNetWithThreeInputs()
{
int inputs = 3;
int inputneurons = 2;
int outputneurons = 1;
NetworkVector inputvector = new NetworkVector(new double[] { 1, 1, 1 });
LinearTwoLayerTestNetwork network = new LinearTwoLayerTestNetwork(inputs, inputneurons, outputneurons);
double output = network.Run(inputvector).ToArray()[0];
Assert.AreEqual(6, output);
}
public void CanRunBigNetWithZeroInput()
{
int inputs = 100;
int inputneurons = 2000;
int outputneurons = 1;
NetworkVector inputvector = new NetworkVector(100);
LinearTwoLayerTestNetwork network = new LinearTwoLayerTestNetwork(inputs, inputneurons, outputneurons);
double output = network.Run(inputvector).ToArray()[0];
Assert.AreEqual(0, output);
}
public void InputGradientSmallNetGradientThird()
{
int inputs = 3;
int inputneurons = 2;
int outputneurons = 1;
NetworkVector inputvector = new NetworkVector(new double[] { 1, 0, 0 });
NetworkVector outputgradient = new NetworkVector(new double[] { 1.0 / 3 });
LinearTwoLayerTestNetwork network = new LinearTwoLayerTestNetwork(inputs, inputneurons, outputneurons);
network.Run(inputvector);
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 2.0 / 3, 2.0 / 3, 2.0 / 3 });
Assert.AreEqual(inputGradientCheck, network.InputGradient(outputgradient));
}
public void CanRunBigNetWithTwoInputs()
{
int inputs = 100;
int inputneurons = 2000;
int outputneurons = 1;
double[] inputArray = new double[inputs];
inputArray[0] = 1;
inputArray[5] = 1;
NetworkVector inputvector = new NetworkVector(inputArray);
LinearTwoLayerTestNetwork network = new LinearTwoLayerTestNetwork(inputs, inputneurons, outputneurons);
double output = network.Run(inputvector).ToArray()[0];
Assert.AreEqual(4000, output);
}
public void TrainBatch_SmallNet_Correct()
{
int inputs = 3;
int inputneurons = 2;
int outputneurons = 1;
LinearTwoLayerTestNetwork network = new LinearTwoLayerTestNetwork(inputs, inputneurons, outputneurons);
List <VectorPair> trainingVectors = new List <VectorPair>
{
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 })
)
};
NetworkTrainer trainer = new BatchNetworkTrainer(network, trainingVectors);
trainer.Train();
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, network.InputLayer.Weights);
Assert.AreEqual(inputBiasesCheck, network.InputLayer.Biases);
Assert.AreEqual(outputWeightsCheck, network.OutputLayer.Weights);
Assert.AreEqual(outputBiasesCheck, network.OutputLayer.Biases);
}
public void TrainOnline_SmallNet_Correct()
{
int inputs = 3;
int inputneurons = 2;
int outputneurons = 1;
LinearTwoLayerTestNetwork network = new LinearTwoLayerTestNetwork(inputs, inputneurons, outputneurons);
List <VectorPair> trainingVectors = new List <VectorPair>
{
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 })
)
};
NetworkTrainer trainer = new OnlineNetworkTrainer(network, trainingVectors);
trainer.Train();
WeightsMatrix inputWeightsCheck = new WeightsMatrix(new double[, ] {
{ -35499715, -35499260, 1 }, { -35499715, -35499260, 1 }
});
NetworkVector inputBiasesCheck = new NetworkVector(new double[] { -35499265, -35499265 });
WeightsMatrix outputWeightsCheck = new WeightsMatrix(new double[, ] {
{ -224831362, -224831362 }
});
NetworkVector outputBiasesCheck = new NetworkVector(new double[] { -251825 });
Assert.AreEqual(inputWeightsCheck, network.InputLayer.Weights);
Assert.AreEqual(inputBiasesCheck, network.InputLayer.Biases);
Assert.AreEqual(outputWeightsCheck, network.OutputLayer.Weights);
Assert.AreEqual(outputBiasesCheck, network.OutputLayer.Biases);
}
