AIMA.Test.Core.Unit.Learning.Neural.LayerTests.testWeightsAndBiasesUpdatedCorrectly C# (CSharp) Метод

        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);
        }
    }