public Seq2Seq()
        {
            trainer                   = new TrainerCPU(TrainType.MiniBatch, new Adamax());
            trainer.BatchSize         = 7;
            trainer.GradientClipValue = 5;

            network = new NeuralNetwork(random, 0.02);

            network.AddNewLayer(new Shape(11), new GruLayer(20));                       // Энкодер
            network.AddNewLayer(new FeedForwardLayer(7, new RectifiedLinearUnit(0.3))); // Компрессор
            network.AddNewLayer(new GruLayer(20));                                      //Декодер
            network.AddNewLayer(new FeedForwardLayer(10, new SoftmaxUnit()));           //Декодер


            Console.WriteLine("\n\tseq2seq\n\n" + network + "\n\n");


            network.ResetState();
        }
        static void Main(string[] args)
        {
            Random random = new Random(13);



            NeuralNetwork cNN = new NeuralNetwork(random, 0.2);

            var conv = new ConvolutionLayer(new RectifiedLinearUnit(0.01), 8, 3, 3);

            conv.IsSame = true;


            cNN.AddNewLayer(new Shape(28, 28), conv);
            cNN.AddNewLayer(new MaxPooling(2, 2));

            cNN.AddNewLayer(new ConvolutionLayer(new RectifiedLinearUnit(0.01), 16, 3, 3));
            cNN.AddNewLayer(new MaxPooling(2, 2));

            cNN.AddNewLayer(new ConvolutionLayer(new RectifiedLinearUnit(0.01), 32, 3, 3));
            cNN.AddNewLayer(new UnPooling(2, 2));

            cNN.AddNewLayer(new ConvolutionLayer(new RectifiedLinearUnit(0.01), 16, 3, 3));
            cNN.AddNewLayer(new MaxPooling(2, 2));

            cNN.AddNewLayer(new Flatten());

            cNN.AddNewLayer(new FeedForwardLayer(20, new RectifiedLinearUnit(0.01)));
            cNN.AddNewLayer(new FeedForwardLayer(2, new SoftmaxUnit()));

            Console.WriteLine(cNN);



            GraphCPU graph = new GraphCPU(false);

            NNValue nValue  = NNValue.Random(28, 28, 2, random);
            NNValue nValue1 = NNValue.Random(28, 28, 2, random);
            NNValue outp    = new NNValue(new double[] { 0, 1 });
            NNValue outp1   = new NNValue(new double[] { 1, 0 });



            DataSetNoReccurent data = new DataSetNoReccurent(new NNValue[] { nValue, nValue1 }, new NNValue[] { outp, outp1 }, new CrossEntropyWithSoftmax());



            TrainerCPU trainer = new TrainerCPU(TrainType.MiniBatch, new Adam());

            trainer.BatchSize = 2;
            trainer.Train(10000, 0.001, cNN, data, 2, 0.0001);
            double[] dbs  = cNN.Activate(nValue, graph).DataInTensor;
            double[] dbs1 = cNN.Activate(nValue1, graph).DataInTensor;
        }