public void SparseAutoencoderComputeDeltas_ReturnsDeltas()
{
ActivationFunction sigmoidFunction = new SigmoidFunction();
Layer[] layers = new Layer[] { new Layer(2, 2, sigmoidFunction), new Layer(1, 2, sigmoidFunction) };
Network network = new Network(layers);
SparseAutoencoderLearning sparseAutoencoder = new SparseAutoencoderLearning(network);
double[][] input = new double[][] { new double[] { 0.5, 0.6 } };
double[][] target = new double[][] { new double[] { 1 } };
double[][] expected = new double[][]
{
new double[] { -0.0108698887658827, -0.0105735765912387 },
new double[] { -0.0875168367272141 }
};
layers[0][0][0] = 0.1;
layers[0][0][1] = 0.2;
layers[0][1][0] = 0.3;
layers[0][1][1] = 0.4;
layers[1][0][0] = 0.5;
layers[1][0][1] = 0.5;
layers[0][0].Bias = -0.01;
layers[0][1].Bias = -0.02;
layers[1][0].Bias = -0.05;
sparseAutoencoder.UpdateCachedActivations(input);
double[][] actual = sparseAutoencoder.ComputeDeltas(0, target[0]);
Assert.AreEqual(expected[0][0], actual[0][0], 0.0001, "Invalid deltas");
Assert.AreEqual(expected[0][1], actual[0][1], 0.0001, "Invalid deltas");
Assert.AreEqual(expected[1][0], actual[1][0], 0.0001, "Invalid deltas");
}
public void SparseAutoencoder_GradientChecking()
{
ActivationFunction sigmoidFunction = new SigmoidFunction();
Layer[] layers = new Layer[] { new Layer(2, 2, sigmoidFunction), new Layer(1, 2, sigmoidFunction) };
Network network = new Network(layers);
SparseAutoencoderLearning sparseAutoencoder = new SparseAutoencoderLearning(network);
double[][] input = new double[][] { new double[] { 0.5, 0.6 } };
double[][] target = new double[][] { new double[] { 1 } };
double[][][] gradient = new double[][][]
{
new double[][]
{
new double[] { 0, 0 },
new double[] { 0, 0 }
},
new double[][]
{
new double[] { 0, 0 }
}
};
layers[0][0][0] = 0.1;
layers[0][0][1] = 0.2;
layers[0][1][0] = 0.3;
layers[0][1][1] = 0.4;
layers[1][0][0] = 0.5;
layers[1][0][1] = 0.5;
layers[0][0].Bias = 0.01;
layers[0][1].Bias = 0.02;
layers[1][0].Bias = 0.05;
for (int i = 0; i < network.LayerCount; i++)
{
for (int j = 0; j < network[i].NeuronCount; j++)
{
for (int k = 0; k < network[i][j].InputCount; k++)
{
double tmp = network[i][j][k];
network[i][j][k] = tmp + 0.0001;
gradient[i][j][k] += Math.Pow(network.Update(input[0])[0] - target[0][0], 2);
network[i][j][k] = tmp - 0.0001;
gradient[i][j][k] -= Math.Pow(network.Update(input[0])[0] - target[0][0], 2);
network[i][j][k] = tmp;
gradient[i][j][k] = 0.5 * gradient[i][j][k] / 0.0002;
}
}
}
sparseAutoencoder.UpdateCachedActivations(input);
double[][] deltas = sparseAutoencoder.ComputeDeltas(0, target[0]);
double[][][] partialDerivatives = sparseAutoencoder.ComputePartialDerivatives(0, deltas, input[0]);
Assert.AreEqual(gradient[0][0][0], partialDerivatives[0][0][0], 0.0001, "Gradient checking failed");
Assert.AreEqual(gradient[0][0][1], partialDerivatives[0][0][1], 0.0001, "Gradient checking failed");
Assert.AreEqual(gradient[0][1][0], partialDerivatives[0][1][0], 0.0001, "Gradient checking failed");
Assert.AreEqual(gradient[0][1][1], partialDerivatives[0][1][1], 0.0001, "Gradient checking failed");
Assert.AreEqual(gradient[1][0][0], partialDerivatives[1][0][0], 0.0001, "Gradient checking failed");
Assert.AreEqual(gradient[1][0][1], partialDerivatives[1][0][1], 0.0001, "Gradient checking failed");
}
public void SparseAutoencoderUpdateCachedActivations_UpdatesCache()
{
ActivationFunction sigmoidFunction = new SigmoidFunction();
Layer[] layers = new Layer[] { new Layer(2, 2, sigmoidFunction), new Layer(1, 2, sigmoidFunction) };
Network network = new Network(layers);
SparseAutoencoderLearning sparseAutoencoder = new SparseAutoencoderLearning(network);
double[][] input = new double[][] { new double[] { 0.5, 0.6 } };
double[][][] expected = new double[][][]
{
new double[][]
{
new double[] { 0.539914884556, 0.591458978433 },
new double[] { 0.626138674824 }
},
new double[][]
{
new double[] { 0.547357618143, 0.608259030747 },
new double[] { 0.628971793540 }
}
};
layers[0][0][0] = 0.1;
layers[0][0][1] = 0.2;
layers[0][1][0] = 0.3;
layers[0][1][1] = 0.4;
layers[1][0][0] = 0.5;
layers[1][0][1] = 0.5;
layers[0][0].Bias = -0.01;
layers[0][1].Bias = -0.02;
layers[1][0].Bias = -0.05;
double[][][] cachedActivations = sparseAutoencoder.UpdateCachedActivations(input);
Assert.AreEqual(expected[0][0][0], cachedActivations[0][0][0], 0.0001, "Invalid cached activation value");
Assert.AreEqual(expected[0][0][1], cachedActivations[0][0][1], 0.0001, "Invalid cached activation value");
Assert.AreEqual(expected[0][1][0], cachedActivations[0][1][0], 0.0001, "Invalid cached activation value");
input = new double[][] { new double[] { 0.5, 0.6 }, new double[] { 0.6, 0.7 } };
sparseAutoencoder = new SparseAutoencoderLearning(network, 2);
cachedActivations = sparseAutoencoder.UpdateCachedActivations(input);
Assert.AreEqual(expected[0][0][0], cachedActivations[0][0][0], 0.0001, "Invalid cached activation value");
Assert.AreEqual(expected[0][0][1], cachedActivations[0][0][1], 0.0001, "Invalid cached activation value");
Assert.AreEqual(expected[0][1][0], cachedActivations[0][1][0], 0.0001, "Invalid cached activation value");
Assert.AreEqual(expected[1][0][0], cachedActivations[1][0][0], 0.0001, "Invalid cached activation value");
Assert.AreEqual(expected[1][0][1], cachedActivations[1][0][1], 0.0001, "Invalid cached activation value");
Assert.AreEqual(expected[1][1][0], cachedActivations[1][1][0], 0.0001, "Invalid cached activation value");
}
public void SparseAutoencoderUpdateCachedActivations_ThrowsArgument()
{
ActivationFunction sigmoidFunction = new SigmoidFunction();
Layer[] layers = new Layer[] { new Layer(2, 2, sigmoidFunction), new Layer(1, 2, sigmoidFunction) };
Network network = new Network(layers);
SparseAutoencoderLearning sparseAutoencoder = new SparseAutoencoderLearning(network, 2);
double[][] input = new double[][] { new double[] { 0.5, 0.6 } };
layers[0][0][0] = 0.1;
layers[0][0][1] = 0.2;
layers[0][1][0] = 0.3;
layers[0][1][1] = 0.4;
layers[1][0][0] = 0.5;
layers[1][0][1] = 0.5;
layers[0][0].Bias = 0.01;
layers[0][1].Bias = 0.02;
layers[1][0].Bias = 0.05;
double[][][] cachedActivations = sparseAutoencoder.UpdateCachedActivations(input);
}
public void SparseAutoencoderPartialDerivatives_ReturnsDerivatives()
{
ActivationFunction sigmoidFunction = new SigmoidFunction();
Layer[] layers = new Layer[] { new Layer(2, 2, sigmoidFunction), new Layer(1, 2, sigmoidFunction) };
Network network = new Network(layers);
SparseAutoencoderLearning sparseAutoencoder = new SparseAutoencoderLearning(network);
double[][] input = new double[][] { new double[] { 0.5, 0.6 } };
double[][] target = new double[][] { new double[] { 1 } };
double[][][] expected = new double[][][]
{
new double[][]
{
new double[] { -0.0054349443829414, -0.0065219332595296 },
new double[] { -0.0052867882956194, -0.0063441459547432 }
},
new double[][]
{
new double[] { -0.0472516427982801, -0.0517626188463657 }
}
};
layers[0][0][0] = 0.1;
layers[0][0][1] = 0.2;
layers[0][1][0] = 0.3;
layers[0][1][1] = 0.4;
layers[1][0][0] = 0.5;
layers[1][0][1] = 0.5;
layers[0][0].Bias = -0.01;
layers[0][1].Bias = -0.02;
layers[1][0].Bias = -0.05;
sparseAutoencoder.UpdateCachedActivations(input);
double[][] deltas = sparseAutoencoder.ComputeDeltas(0, target[0]);
double[][][] partialDerivatives = sparseAutoencoder.ComputePartialDerivatives(0, deltas, input[0]);
Assert.AreEqual(expected[0][0][0], partialDerivatives[0][0][0], 0.0001, "Invalid partial derivative");
Assert.AreEqual(expected[0][0][1], partialDerivatives[0][0][1], 0.0001, "Invalid partial derivative");
Assert.AreEqual(expected[0][1][0], partialDerivatives[0][1][0], 0.0001, "Invalid partial derivative");
Assert.AreEqual(expected[0][1][1], partialDerivatives[0][1][1], 0.0001, "Invalid partial derivative");
Assert.AreEqual(expected[1][0][0], partialDerivatives[1][0][0], 0.0001, "Invalid partial derivative");
Assert.AreEqual(expected[1][0][1], partialDerivatives[1][0][1], 0.0001, "Invalid partial derivative");
}