public void CanBPWC_trivialBatch()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1 }
});
NetworkVector biases = new NetworkVector(new double[] { 10 });
BatchWeightedCombiner wc = new BatchWeightedCombiner(weights, biases);
NetworkVector input = new NetworkVector(new double[] { 1 });
NetworkVector outputgradient = new NetworkVector(new double[] { 1 });
wc.StartBatch();
wc.Run(input);
wc.BackPropagate(outputgradient);
wc.EndBatchAndUpdate();
NetworkVector outputCheck = new NetworkVector(new double[] { 11 });
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 1 });
double[,] weightsCheck = new double[, ] {
{ 0 }
};
double[] biasesCheck = new double[] { 9 };
Assert.AreEqual(outputCheck, wc.Output);
Assert.AreEqual(inputGradientCheck, wc.InputGradient);
for (int i = 0; i < wc.NumberOfOutputs; i++)
{
Assert.AreEqual(biasesCheck[i], wc.State.Biases[i]);
for (int j = 0; j < wc.NumberOfInputs; j++)
{
Assert.AreEqual(weightsCheck[i, j], wc.State.Weights[i, j]);
}
}
}
public void CanMakeBWCWithNullWeights()
{
NetworkMatrix weights = null;
NetworkVector biases = new NetworkVector(new double[] { 1 });
try
{
BatchWeightedCombiner wc = new BatchWeightedCombiner(weights, biases);
Assert.Fail("Failure to throw ArgumentException when trying to create a WeightedCombiner with null weights.");
}
catch (ArgumentException) { }
}
public void CanRunBWC()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1 }
});
NetworkVector biases = new NetworkVector(new double[] { 3 });
BatchWeightedCombiner wc = new BatchWeightedCombiner(weights, biases);
NetworkVector input = new NetworkVector(wc.NumberOfInputs);
wc.Run(input);
Assert.AreEqual(3, wc.Output.ToArray()[0]);
}
public void CanMakeBWC()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1 }
});
BatchWeightedCombiner wc = new BatchWeightedCombiner(weights);
Assert.AreNotEqual(null, wc);
Assert.AreEqual(1, wc.NumberOfOutputs);
Assert.AreEqual(1, wc.NumberOfInputs);
Assert.AreEqual(1, wc.State.Weights[0, 0]);
Assert.AreEqual(0, wc.State.Biases[0]);
}
public void CanRunBWC_2by3()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2, 3 }, { 5, 7, 11 }
});
NetworkVector biases = new NetworkVector(new double[] { 100, 200 });
BatchWeightedCombiner wc = new BatchWeightedCombiner(weights, biases);
NetworkVector input = new NetworkVector(new double[] { 1, 2, 3 });
wc.Run(input);
NetworkVector outputcheck = new NetworkVector(new double[] { 114, 252 });
Assert.AreEqual(outputcheck, wc.Output);
}
public void CanBPWC2x3_nonTrivialBatch()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2, 3 }, { 5, 7, 11 }
});
NetworkVector biases = new NetworkVector(new double[] { 100, 200 });
BatchWeightedCombiner wc = new BatchWeightedCombiner(weights, biases);
NetworkVector input = new NetworkVector(new double[] { 1, 2, 3 });
NetworkVector outputgradient = new NetworkVector(new double[] { 1, 1 });
wc.StartBatch();
for (int i = 0; i < 2; i++)
{
wc.Run(input);
wc.BackPropagate(outputgradient);
}
wc.EndBatchAndUpdate();
NetworkVector outputCheck = new NetworkVector(new double[] { 114, 252 });
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 6, 9, 14 });
double[,] weightsCheck = new double[, ] {
{ -1, -2, -3 }, { 3, 3, 5 }
};
double[] biasesCheck = new double[] { 98, 198 };
Assert.AreEqual(outputCheck, wc.Output);
Assert.AreEqual(inputGradientCheck, wc.InputGradient);
for (int i = 0; i < wc.NumberOfOutputs; i++)
{
Assert.AreEqual(biasesCheck[i], wc.State.Biases[i]);
for (int j = 0; j < wc.NumberOfInputs; j++)
{
Assert.AreEqual(weightsCheck[i, j], wc.State.Weights[i, j]);
}
}
}
