コード例 #1
0
ファイル: CTCTest.cs プロジェクト: arnavdas88/dnn
        public void TestMethod5()
        {
            const int T = 3;    // Length of utterance (time)
            const int L = 5;    // Alphabet size

            int[] axes = new[] { T, L };

            Tensor x = new Tensor(null, axes);

            x.Set(1.0f);
            Tensor y = new Tensor(null, axes);

            Vectors.SoftMax(x.Length, x.Weights, 0, L, y.Weights, 0);
            y.Weights[0] = 0;

            CTCLoss loss = new CTCLoss();

            int[] labels   = { 1, 2, 3 };
            float expected = -(float)Math.Log(y.Weights[(0 * L) + labels[0]] *
                                              y.Weights[(1 * L) + labels[1]] *
                                              y.Weights[(2 * L) + labels[2]]);

            float score = loss.Loss(y, labels, false);

            Assert.AreEqual(expected, score, 1e-6);

            score = loss.Loss(y, labels, true);
            Assert.AreEqual(expected, score, 1e-6);
        }
コード例 #2
0
ファイル: Program.cs プロジェクト: arnavdas88/dnn
                private ILoss <int[]> CreateLoss()
                {
                    ILoss <int[]> loss = null;

                    switch (this.TaskParameters.Loss.Name)
                    {
                    case "LogLikelihood":
                    default:
                        loss = new LogLikelihoodLoss();
                        break;

                    case "CTC":
                        loss = new CTCLoss();
                        break;
                    }

                    JsonSerializer jsonSerializer = new JsonSerializer();

                    using (JTokenReader jtokenReader = new JTokenReader(this.TaskParameters.Loss.Parameters))
                    {
                        jsonSerializer.Populate(jtokenReader, loss);
                    }

                    return(loss);
                }
コード例 #3
0
ファイル: CTCTest.cs プロジェクト: arnavdas88/dnn
        public void TestMethod2()
        {
            const int T = 5;
            const int L = 6;

            int[] axes = new[] { T, L };

            Tensor y = new Tensor(null, axes);

            y.Set(new float[]
            {
                0.633766f, 0.221185f, 0.0917319f, 0.0129757f, 0.0142857f, 0.0260553f,
                0.111121f, 0.588392f, 0.278779f, 0.0055756f, 0.00569609f, 0.010436f,
                0.0357786f, 0.633813f, 0.321418f, 0.00249248f, 0.00272882f, 0.0037688f,
                0.0663296f, 0.643849f, 0.280111f, 0.00283995f, 0.0035545f, 0.00331533f,
                0.458235f, 0.396634f, 0.123377f, 0.00648837f, 0.00903441f, 0.00623107f
            });

            CTCLoss loss = new CTCLoss()
            {
                BlankLabelIndex = 5
            };

            int[] labels   = { 0, 1, 2, 1, 0 /*, 0, 1, 1, 0*/ };
            float expected = -(float)Math.Log(y.Weights[(0 * L) + labels[0]] *
                                              y.Weights[(1 * L) + labels[1]] *
                                              y.Weights[(2 * L) + labels[2]] *
                                              y.Weights[(3 * L) + labels[3]] *
                                              y.Weights[(4 * L) + labels[4]]);

            float score = loss.Loss(y, labels, true);

            Helpers.AreArraysEqual(
                new float[]
            {
                1, 0, 0, 0, 0, 0,
                0, 1, 0, 0, 0, 0,
                0, 0, 1, 0, 0, 0,
                0, 1, 0, 0, 0, 0,
                1, 0, 0, 0, 0, 0
            },
                y.Gradient);
        }
コード例 #4
0
ファイル: CTCTest.cs プロジェクト: arnavdas88/dnn
        public void TestMethod3()
        {
            const int T = 5;
            const int L = 6;

            int[] axes = new[] { T, L };

            Tensor y = new Tensor(null, axes);

            y.Set(new float[]
            {
                0.30176f, 0.28562f, 0.0831517f, 0.0862751f, 0.0816851f, 0.161508f,
                0.24082f, 0.397533f, 0.0557226f, 0.0546814f, 0.0557528f, 0.19549f,
                0.230246f, 0.450868f, 0.0389607f, 0.038309f, 0.0391602f, 0.202456f,
                0.280884f, 0.429522f, 0.0326593f, 0.0339046f, 0.0326856f, 0.190345f,
                0.423286f, 0.315517f, 0.0338439f, 0.0393744f, 0.0339315f, 0.154046f
            });

            CTCLoss loss = new CTCLoss()
            {
                BlankLabelIndex = 5
            };

            int[] labels   = { 0, 1, 1, 0 };
            float expected = 5.42262f; // from tensorflow

            float score = loss.Loss(y, labels, true);

            Assert.AreEqual(expected, score, 1e-4);
            Helpers.AreArraysEqual(
                new float[]
            {
                1, 0, 0, 0, 0, 0,
                0, 1, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 1,
                0, 1, 0, 0, 0, 0,
                1, 0, 0, 0, 0, 0
            },
                y.Gradient);
        }