public void Generate(Shape inpShape, Random random, double бесполезный_аргумент)
{
InputShape = inpShape;
double std = 1.0 / Math.Sqrt(OutputShape.H);
_w = NNValue.Random(OutputShape.H, inpShape.H, std, random);
_b = new NNValue(OutputShape.H);
}
public RnnLayer(int inputDimension, int outputDimension, INonlinearity hiddenUnit, double initParamsStdDev,
Random rng)
{
InputShape = new Shape(inputDimension);
OutputShape = new Shape(outputDimension);
_f = hiddenUnit;
_w = NNValue.Random(outputDimension, inputDimension + outputDimension, initParamsStdDev, rng);
_b = new NNValue(outputDimension);
ResetState();
}
public FeedForwardLayer(Shape inputShape, int outputDimension, INonlinearity f, Random rng)
{
double initParamsStdDev = 1.0 / Math.Sqrt(outputDimension);
InputShape = inputShape;
OutputShape = new Shape(outputDimension);
_w = NNValue.Random(OutputShape.H, InputShape.H, initParamsStdDev, rng);
_b = new NNValue(outputDimension);
this._f = f;
}
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 GruLayer(int inputDimension, int outputDimension, Random rng)
{
InputShape = new Shape(inputDimension);
OutputShape = new Shape(outputDimension);
double initParamsStdDev = 1.0 / Math.Sqrt(outputDimension);
_hmix = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rng);
_hHmix = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rng);
_bmix = new NNValue(outputDimension);
_hnew = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rng);
_hHnew = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rng);
_bnew = new NNValue(outputDimension);
_hreset = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rng);
_hHreset = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rng);
_breset = new NNValue(outputDimension);
}
void Init(Shape inputShape, int outputDimension, Random rng)
{
double initParamsStdDev = 1.0 / Math.Sqrt(outputDimension);
int inputDimension = inputShape.H;
InputShape = new Shape(inputDimension);
OutputShape = new Shape(outputDimension);
_hmix = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rng);
_hHmix = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rng);
_bmix = new NNValue(outputDimension);
_hnew = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rng);
_hHnew = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rng);
_bnew = new NNValue(outputDimension);
_hreset = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rng);
_hHreset = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rng);
_breset = new NNValue(outputDimension);
}
public LstmLayer(int inputDimension, int outputDimension, double initParamsStdDev, Random rnd)
{
InputShape = new Shape(inputDimension);
OutputShape = new Shape(outputDimension);
_wix = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rnd);
_wih = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rnd);
_inputBias = new NNValue(outputDimension);
_wfx = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rnd);
_wfh = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rnd);
//set forget bias to 1.0, as described here: http://jmlr.org/proceedings/papers/v37/jozefowicz15.pdf
_forgetBias = NNValue.Ones(outputDimension, 1);
_wox = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rnd);
_woh = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rnd);
_outputBias = new NNValue(outputDimension);
_wcx = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rnd);
_wch = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rnd);
_cellWriteBias = new NNValue(outputDimension);
ResetState(); // Запуск НС
}
/// <summary>
/// Инициализация сети
/// </summary>
void Init(Shape inputShape, int outputDimension, double initParamsStdDev, Random rnd)
{
int inputDimension = inputShape.H;
InputShape = new Shape(inputDimension);
OutputShape = new Shape(outputDimension);
_wix = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rnd);
_wih = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rnd);
_inputBias = new NNValue(outputDimension);
_wfx = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rnd);
_wfh = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rnd);
_forgetBias = NNValue.Ones(outputDimension, 1);
_wox = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rnd);
_woh = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rnd);
_outputBias = new NNValue(outputDimension);
_wcx = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rnd);
_wch = NNValue.Random(outputDimension, outputDimension, initParamsStdDev, rnd);
_cellWriteBias = new NNValue(outputDimension);
ResetState();
}
void Init(Shape inputShape, FilterStruct filterStruct, INonlinearity func, Random rnd)
{
InputShape = inputShape;
fs = filterStruct;
int outputH = inputShape.H - filterStruct.FilterH + 1 + _padY,
outputW = inputShape.W - filterStruct.FilterW + 1 + _padX;
OutputShape = new Shape(outputH, outputW, filterStruct.FilterCount);
double initParamsStdDev = 1.0 / Math.Sqrt(OutputShape.Len);
int d = InputShape.D;
Function = func;
Filters = new NNValue[filterStruct.FilterCount];
for (int i = 0; i < filterStruct.FilterCount; i++)
{
Filters[i] = NNValue.Random(filterStruct.FilterH, filterStruct.FilterW, d, initParamsStdDev, rnd);
}
}
void Init(Shape inputShape, int outputDimension, double initParamsStdDev, Random rng)
{
InputShape = inputShape;
OutputShape = new Shape(outputDimension);
_w = NNValue.Random(outputDimension, inputShape.H, initParamsStdDev, rng);
}
//no biases
public LinearLayer(int inputDimension, int outputDimension, double initParamsStdDev, Random rng)
{
InputShape = new Shape(inputDimension);
OutputShape = new Shape(outputDimension);
_w = NNValue.Random(outputDimension, inputDimension, initParamsStdDev, rng);
}
