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 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"); }