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 void SoftmaxForward()
{
OutputLayerBase
cpu = new SoftmaxLayer(TensorInfo.Linear(250), 127, WeightsInitializationMode.GlorotNormal, BiasInitializationMode.Gaussian),
gpu = new CuDnnSoftmaxLayer(cpu.InputInfo, cpu.OutputInfo.Size, cpu.Weights, cpu.Biases);
TestForward(cpu, gpu, 400);
}
public void SoftmaxForward()
{
float[,] x = WeightsProvider.NewFullyConnectedWeights(TensorInfo.Linear(400), 250, WeightsInitializationMode.GlorotNormal).AsSpan().AsMatrix(400, 250);
OutputLayerBase
cpu = new SoftmaxLayer(TensorInfo.Linear(250), 127, WeightsInitializationMode.GlorotNormal, BiasInitializationMode.Gaussian),
gpu = new CuDnnSoftmaxLayer(cpu.InputInfo, cpu.OutputInfo.Size, cpu.Weights, cpu.Biases);
TestForward(cpu, gpu, x);
}
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);
}
internal static INetworkLayer CuDnnLayerDeserialize([NotNull] Stream stream, LayerType type)
{
switch (type)
{
case LayerType.FullyConnected: return(CuDnnFullyConnectedLayer.Deserialize(stream));
case LayerType.Convolutional: return(CuDnnConvolutionalLayer.Deserialize(stream));
case LayerType.Pooling: return(CuDnnPoolingLayer.Deserialize(stream));
case LayerType.Softmax: return(CuDnnSoftmaxLayer.Deserialize(stream));
case LayerType.Inception: return(CuDnnInceptionLayer.Deserialize(stream));
default: return(null);
}
}
