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;
}
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();
}
