Beispiel #1
0
        public void testSensitivityMatrixCalculationFromSucceedingLayer()
        {
            Matrix weightMatrix1 = new Matrix(2, 1);

            weightMatrix1.set(0, 0, -0.27);
            weightMatrix1.set(1, 0, -0.41);

            Vector biasVector1 = new Vector(2);

            biasVector1.setValue(0, -0.48);
            biasVector1.setValue(1, -0.13);

            Layer layer1 = new Layer(weightMatrix1, biasVector1,
                                     new LogSigActivationFunction());
            LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);

            Vector inputVector1 = new Vector(1);

            inputVector1.setValue(0, 1);

            layer1.feedForward(inputVector1);

            Matrix weightMatrix2 = new Matrix(1, 2);

            weightMatrix2.set(0, 0, 0.09);
            weightMatrix2.set(0, 1, -0.17);

            Vector biasVector2 = new Vector(1);

            biasVector2.setValue(0, 0.48);

            Layer layer2 = new Layer(weightMatrix2, biasVector2,
                                     new PureLinearActivationFunction());
            Vector inputVector2 = layer1.getLastActivationValues();

            layer2.feedForward(inputVector2);

            Vector errorVector = new Vector(1);

            errorVector.setValue(0, 1.261);
            LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);

            layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);

            layer1Sensitivity
            .sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);
            Matrix sensitivityMatrix = layer1Sensitivity.getSensitivityMatrix();

            Assert.AreEqual(2, sensitivityMatrix.getRowDimension());
            Assert.AreEqual(1, sensitivityMatrix.getColumnDimension());
            Assert.AreEqual(-0.0495, sensitivityMatrix.get(0, 0), 0.001);
            Assert.AreEqual(0.0997, sensitivityMatrix.get(1, 0), 0.001);
        }
Beispiel #2
0
        public void testSensitivityMatrixCalculationFromErrorVector()
        {
            Matrix weightMatrix1 = new Matrix(2, 1);

            weightMatrix1.Set(0, 0, -0.27);
            weightMatrix1.Set(1, 0, -0.41);

            Vector biasVector1 = new Vector(2);

            biasVector1.SetValue(0, -0.48);
            biasVector1.SetValue(1, -0.13);

            Layer layer1 = new Layer(weightMatrix1, biasVector1,
                                     new LogSigActivationFunction());

            Vector inputVector1 = new Vector(1);

            inputVector1.SetValue(0, 1);

            layer1.FeedForward(inputVector1);

            Matrix weightMatrix2 = new Matrix(1, 2);

            weightMatrix2.Set(0, 0, 0.09);
            weightMatrix2.Set(0, 1, -0.17);

            Vector biasVector2 = new Vector(1);

            biasVector2.SetValue(0, 0.48);

            Layer layer2 = new Layer(weightMatrix2, biasVector2,
                                     new PureLinearActivationFunction());
            Vector inputVector2 = layer1.GetLastActivationValues();

            layer2.FeedForward(inputVector2);

            Vector errorVector = new Vector(1);

            errorVector.SetValue(0, 1.261);
            LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);

            layer2Sensitivity.SensitivityMatrixFromErrorMatrix(errorVector);

            Matrix sensitivityMatrix = layer2Sensitivity.GetSensitivityMatrix();

            Assert.AreEqual(-2.522, sensitivityMatrix.Get(0, 0), 0.0001);
        }
Beispiel #3
0
        public void testWeightsAndBiasesUpdatedCorrectly()
        {
            Matrix weightMatrix1 = new Matrix(2, 1);

            weightMatrix1.set(0, 0, -0.27);
            weightMatrix1.set(1, 0, -0.41);

            Vector biasVector1 = new Vector(2);

            biasVector1.setValue(0, -0.48);
            biasVector1.setValue(1, -0.13);

            Layer layer1 = new Layer(weightMatrix1, biasVector1,
                                     new LogSigActivationFunction());
            LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);

            Vector inputVector1 = new Vector(1);

            inputVector1.setValue(0, 1);

            layer1.feedForward(inputVector1);

            Matrix weightMatrix2 = new Matrix(1, 2);

            weightMatrix2.set(0, 0, 0.09);
            weightMatrix2.set(0, 1, -0.17);

            Vector biasVector2 = new Vector(1);

            biasVector2.setValue(0, 0.48);

            Layer layer2 = new Layer(weightMatrix2, biasVector2,
                                     new PureLinearActivationFunction());
            Vector inputVector2 = layer1.getLastActivationValues();

            layer2.feedForward(inputVector2);

            Vector errorVector = new Vector(1);

            errorVector.setValue(0, 1.261);
            LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);

            layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);

            layer1Sensitivity
            .sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);

            BackPropLearning.calculateWeightUpdates(layer2Sensitivity, layer1
                                                    .getLastActivationValues(), 0.1);

            BackPropLearning.calculateBiasUpdates(layer2Sensitivity, 0.1);

            BackPropLearning.calculateWeightUpdates(layer1Sensitivity,
                                                    inputVector1, 0.1);

            BackPropLearning.calculateBiasUpdates(layer1Sensitivity, 0.1);

            layer2.updateWeights();
            Matrix newWeightMatrix2 = layer2.getWeightMatrix();

            Assert.AreEqual(0.171, newWeightMatrix2.get(0, 0), 0.001);
            Assert.AreEqual(-0.0772, newWeightMatrix2.get(0, 1), 0.001);

            layer2.updateBiases();
            Vector newBiasVector2 = layer2.getBiasVector();

            Assert.AreEqual(0.7322, newBiasVector2.getValue(0), 0.00001);

            layer1.updateWeights();
            Matrix newWeightMatrix1 = layer1.getWeightMatrix();

            Assert.AreEqual(-0.265, newWeightMatrix1.get(0, 0), 0.001);
            Assert.AreEqual(-0.419, newWeightMatrix1.get(1, 0), 0.001);

            layer1.updateBiases();
            Vector newBiasVector1 = layer1.getBiasVector();

            Assert.AreEqual(-0.475, newBiasVector1.getValue(0), 0.001);
            Assert.AreEqual(-0.139, newBiasVector1.getValue(1), 0.001);
        }
Beispiel #4
0
        public void testBiasUpdateMatrixesFormedCorrectly()
        {
            Matrix weightMatrix1 = new Matrix(2, 1);

            weightMatrix1.set(0, 0, -0.27);
            weightMatrix1.set(1, 0, -0.41);

            Vector biasVector1 = new Vector(2);

            biasVector1.setValue(0, -0.48);
            biasVector1.setValue(1, -0.13);

            Layer layer1 = new Layer(weightMatrix1, biasVector1,
                                     new LogSigActivationFunction());
            LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);

            Vector inputVector1 = new Vector(1);

            inputVector1.setValue(0, 1);

            layer1.feedForward(inputVector1);

            Matrix weightMatrix2 = new Matrix(1, 2);

            weightMatrix2.set(0, 0, 0.09);
            weightMatrix2.set(0, 1, -0.17);

            Vector biasVector2 = new Vector(1);

            biasVector2.setValue(0, 0.48);

            Layer layer2 = new Layer(weightMatrix2, biasVector2,
                                     new PureLinearActivationFunction());
            LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);
            Vector           inputVector2      = layer1.getLastActivationValues();

            layer2.feedForward(inputVector2);

            Vector errorVector = new Vector(1);

            errorVector.setValue(0, 1.261);
            layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);

            layer1Sensitivity
            .sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);

            Vector biasUpdateVector2 = BackPropLearning.calculateBiasUpdates(
                layer2Sensitivity, 0.1);

            Assert.AreEqual(0.2522, biasUpdateVector2.getValue(0), 0.001);

            Vector lastBiasUpdateVector2 = layer2.getLastBiasUpdateVector();

            Assert.AreEqual(0.2522, lastBiasUpdateVector2.getValue(0), 0.001);

            Vector penultimateBiasUpdateVector2 = layer2
                                                  .getPenultimateBiasUpdateVector();

            Assert.AreEqual(0.0, penultimateBiasUpdateVector2.getValue(0),
                            0.001);

            Vector biasUpdateVector1 = BackPropLearning.calculateBiasUpdates(
                layer1Sensitivity, 0.1);

            Assert.AreEqual(0.00495, biasUpdateVector1.getValue(0), 0.001);
            Assert.AreEqual(-0.00997, biasUpdateVector1.getValue(1), 0.001);

            Vector lastBiasUpdateVector1 = layer1.getLastBiasUpdateVector();

            Assert.AreEqual(0.00495, lastBiasUpdateVector1.getValue(0), 0.001);
            Assert.AreEqual(-0.00997, lastBiasUpdateVector1.getValue(1), 0.001);

            Vector penultimateBiasUpdateVector1 = layer1
                                                  .getPenultimateBiasUpdateVector();

            Assert.AreEqual(0.0, penultimateBiasUpdateVector1.getValue(0),
                            0.001);
            Assert.AreEqual(0.0, penultimateBiasUpdateVector1.getValue(1),
                            0.001);
        }
Beispiel #5
0
        public void testWeightUpdateMatrixesFormedCorrectly()
        {
            Matrix weightMatrix1 = new Matrix(2, 1);

            weightMatrix1.Set(0, 0, -0.27);
            weightMatrix1.Set(1, 0, -0.41);

            Vector biasVector1 = new Vector(2);

            biasVector1.SetValue(0, -0.48);
            biasVector1.SetValue(1, -0.13);

            Layer layer1 = new Layer(weightMatrix1, biasVector1,
                                     new LogSigActivationFunction());
            LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);

            Vector inputVector1 = new Vector(1);

            inputVector1.SetValue(0, 1);

            layer1.FeedForward(inputVector1);

            Matrix weightMatrix2 = new Matrix(1, 2);

            weightMatrix2.Set(0, 0, 0.09);
            weightMatrix2.Set(0, 1, -0.17);

            Vector biasVector2 = new Vector(1);

            biasVector2.SetValue(0, 0.48);

            Layer layer2 = new Layer(weightMatrix2, biasVector2,
                                     new PureLinearActivationFunction());
            Vector inputVector2 = layer1.GetLastActivationValues();

            layer2.FeedForward(inputVector2);

            Vector errorVector = new Vector(1);

            errorVector.SetValue(0, 1.261);
            LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);

            layer2Sensitivity.SensitivityMatrixFromErrorMatrix(errorVector);

            layer1Sensitivity
            .SensitivityMatrixFromSucceedingLayer(layer2Sensitivity);

            Matrix weightUpdateMatrix2 = BackPropagationLearning.CalculateWeightUpdates(
                layer2Sensitivity, layer1.GetLastActivationValues(), 0.1);

            Assert.AreEqual(0.0809, weightUpdateMatrix2.Get(0, 0), 0.001);
            Assert.AreEqual(0.0928, weightUpdateMatrix2.Get(0, 1), 0.001);

            Matrix lastWeightUpdateMatrix2 = layer2.GetLastWeightUpdateMatrix();

            Assert.AreEqual(0.0809, lastWeightUpdateMatrix2.Get(0, 0), 0.001);
            Assert.AreEqual(0.0928, lastWeightUpdateMatrix2.Get(0, 1), 0.001);

            Matrix penultimateWeightUpdatematrix2 = layer2
                                                    .GetPenultimateWeightUpdateMatrix();

            Assert.AreEqual(0.0, penultimateWeightUpdatematrix2.Get(0, 0),
                            0.001);
            Assert.AreEqual(0.0, penultimateWeightUpdatematrix2.Get(0, 1),
                            0.001);

            Matrix weightUpdateMatrix1 = BackPropagationLearning.CalculateWeightUpdates(
                layer1Sensitivity, inputVector1, 0.1);

            Assert.AreEqual(0.0049, weightUpdateMatrix1.Get(0, 0), 0.001);
            Assert.AreEqual(-0.00997, weightUpdateMatrix1.Get(1, 0), 0.001);

            Matrix lastWeightUpdateMatrix1 = layer1.GetLastWeightUpdateMatrix();

            Assert.AreEqual(0.0049, lastWeightUpdateMatrix1.Get(0, 0), 0.001);
            Assert.AreEqual(-0.00997, lastWeightUpdateMatrix1.Get(1, 0), 0.001);
            Matrix penultimateWeightUpdatematrix1 = layer1
                                                    .GetPenultimateWeightUpdateMatrix();

            Assert.AreEqual(0.0, penultimateWeightUpdatematrix1.Get(0, 0),
                            0.001);
            Assert.AreEqual(0.0, penultimateWeightUpdatematrix1.Get(1, 0),
                            0.001);
        }