Пример #1
0
        public void SquaredErrorRegressionLayer_Backward()
        {
            var batchSize       = 1;
            var width           = 28;
            var height          = 28;
            var depth           = 3;
            var numberOfClasses = 10;
            var random          = new Random(232);

            var sut = new SquaredErrorRegressionLayer(numberOfClasses);

            sut.Initialize(width, height, depth, batchSize, Initialization.GlorotUniform, random);

            var input = Matrix <float> .Build.Random(batchSize, numberOfClasses, random.Next());

            sut.Forward(input);

            var delta = Matrix <float> .Build.Random(batchSize, numberOfClasses, random.Next());

            var actual = sut.Backward(delta);

            Trace.WriteLine(string.Join(", ", actual.ToColumnMajorArray()));

            var expected = Matrix <float> .Build.Dense(batchSize, numberOfClasses, new float[] { -0.3353941f, 0.0191406f, -0.9314069f, 1.202553f, 1.69809f, -1.126425f, 1.06249f, 0.06901796f, -1.057676f, -0.5987452f });

            MatrixAsserts.AreEqual(expected, actual);
        }
Пример #2
0
        public void SquaredErrorRegressionLayer_CopyLayerForPredictionModel()
        {
            var batchSize       = 1;
            var width           = 28;
            var height          = 28;
            var depth           = 3;
            var numberOfTargets = 10;
            var random          = new Random(232);

            var sut = new SquaredErrorRegressionLayer(numberOfTargets);

            sut.Initialize(width, height, depth, batchSize, Initialization.GlorotUniform, random);

            var layers = new List <ILayer>();

            sut.CopyLayerForPredictionModel(layers);

            var actual = (SquaredErrorRegressionLayer)layers.Single();

            Assert.AreEqual(sut.Width, actual.Width);
            Assert.AreEqual(sut.NumberOfTargets, actual.NumberOfTargets);
        }
Пример #3
0
        public void SquaredErrorRegressionLayer_Forward()
        {
            var batchSize       = 1;
            var width           = 28;
            var height          = 28;
            var depth           = 3;
            var numberOfClasses = 10;
            var random          = new Random(232);

            var sut = new SquaredErrorRegressionLayer(numberOfClasses);

            sut.Initialize(width, height, depth, batchSize, Initialization.GlorotUniform, random);

            var input = Matrix <float> .Build.Random(batchSize, numberOfClasses, random.Next());

            var actual = sut.Forward(input);

            Trace.WriteLine(string.Join(", ", actual.ToColumnMajorArray()));

            var expected = Matrix <float> .Build.Dense(batchSize, numberOfClasses, new float[] { 0.1234713f, 0.7669879f, -0.9698473f, 1.814438f, 0.2316814f, -0.05312517f, 0.5537131f, -0.2031853f, 0.01274186f, -0.4482329f });

            MatrixAsserts.AreEqual(expected, actual);
        }