public unsafe void SoftmaxBackwardOutput()
{
float[,]
x = WeightsProvider.NewFullyConnectedWeights(TensorInfo.Linear(400), 250, WeightsInitializationMode.GlorotNormal).AsSpan().AsMatrix(400, 250),
y = new float[400, 127];
for (int i = 0; i < 400; i++)
{
y[i, ThreadSafeRandom.NextInt(max: 127)] = 1;
}
OutputLayerBase
cpu = new SoftmaxLayer(TensorInfo.Linear(250), 127, WeightsInitializationMode.GlorotNormal, BiasInitializationMode.Gaussian),
gpu = new CuDnnSoftmaxLayer(cpu.InputInfo, cpu.OutputInfo.Size, cpu.Weights, cpu.Biases);
fixed(float *px = x, py = y)
{
Tensor.Reshape(px, x.GetLength(0), x.GetLength(1), out Tensor xt);
cpu.Forward(xt, out Tensor z, out Tensor a);
a.Duplicate(out Tensor a2);
Tensor.Reshape(py, y.GetLength(0), y.GetLength(1), out Tensor yt);
cpu.Backpropagate(a, yt, z);
gpu.Backpropagate(a2, yt, z);
Assert.IsTrue(a.ContentEquals(a2));
a.Free();
a2.Free();
z.Free();
}
}
public Chromosome(int length)
{
Fitness = 0;
_length = length;
Genome = new int[_length];
for (var i = 0; i < _length; i++)
{
Genome[i] = ThreadSafeRandom.NextInt(int.MinValue, int.MaxValue);
}
}
public void SoftmaxBackwardOutput()
{
float[,] y = new float[400, 127];
for (int i = 0; i < 400; i++)
{
y[i, ThreadSafeRandom.NextInt(max: 127)] = 1;
}
OutputLayerBase
cpu = new SoftmaxLayer(TensorInfo.Linear(250), 127, WeightsInitializationMode.GlorotNormal, BiasInitializationMode.Gaussian),
gpu = new CuDnnSoftmaxLayer(cpu.InputInfo, cpu.OutputInfo.Size, cpu.Weights, cpu.Biases);
TestBackward(cpu, gpu, y);
}
protected override void CrossChromosomes(Chromosome parent1, Chromosome parent2, int index)
{
var start = ThreadSafeRandom.NextInt(parent1.GenomeLength());
var end = ThreadSafeRandom.NextInt(parent1.GenomeLength());
var child1 = parent1.Clone();
var child2 = parent2.Clone();
NewPopulation[index] = child1;
if (index + 1 < NewPopulation.Count)
{
NewPopulation[index + 1] = child2;
}
if (ThreadSafeRandom.NextDouble() > CrossoverProbability)
{
return;
}
if (start > end)
{
var tmp = start;
start = end;
end = tmp;
}
for (var i = start; i <= end; i++)
{
var gen = i / 32;
var bit = i - gen * 32;
if (bit == 0 && end / 32 != gen)
{
var genome = child1.Genome[gen];
child1.Genome[gen] = child2.Genome[gen];
child2.Genome[gen] = genome;
i += 31;
}
else if (((child1.Genome[gen] >> bit & 1) ^ (child2.Genome[gen] >> bit & 1)) == 1)
{
child1.Genome[gen] ^= (int)(1 << bit);
child2.Genome[gen] ^= (int)(1 << bit);
}
}
}
public override void SelectPopulation()
{
Parallel.For(0, _populationSize, i =>
{
var tournament = new Chromosome[_tournamentSize];
for (var j = 0; j < _tournamentSize; j++)
{
tournament[j] = Population[ThreadSafeRandom.NextInt(Population.Count)];
}
NewPopulation[i] = tournament.MinBy(ch => ch.Fitness).First();
});
Parallel.For(0, _populationSize, i => Population[i] = NewPopulation[i].Clone());
}
// Tries to process a random batch through the network (this method should just not crash)
private static void ValidateGraph([NotNull] INeuralNetwork network)
{
float[,]
x = new float[200, network.InputInfo.Size],
y = new float[200, network.OutputInfo.Size];
for (int i = 0; i < 200; i++)
{
for (int j = 0; j < x.GetLength(1); j++)
{
x[i, j] = ThreadSafeRandom.NextFloat();
}
y[i, ThreadSafeRandom.NextInt(max: y.GetLength(1))] = 1;
}
_ = network.Forward(x);
SamplesBatch batch = new SamplesBatch(x, y);
ComputationGraphNetwork graph = network.To <INeuralNetwork, ComputationGraphNetwork>();
graph.Backpropagate(batch, 0.5f, WeightsUpdaters.AdaDelta(TrainingAlgorithms.AdaDelta(), graph));
_ = network.ExtractDeepFeatures(x);
}
public void CrossPopulation()
{
for (var i = 0; i < _populationSize; i += 2)
{
var parent1 = Population[ThreadSafeRandom.NextInt(_populationSize)];
Chromosome parent2;
do
{
parent2 = Population[ThreadSafeRandom.NextInt(_populationSize)];
} while (parent1.Equals(parent2));
CrossChromosomes(parent1, parent2, i);
}
for (var i = 0; i < _populationSize; i++)
{
Population[i] = NewPopulation[i];
}
}
protected override void Mutate(Chromosome chromosome)
{
var index = ThreadSafeRandom.NextInt(chromosome.GenomeLength());
chromosome.Genome[index / 32] ^= (int)(1 << (index % 32));
}
