public void FullyConnectedBackward()
{
FullyConnectedLayer
cpu = new FullyConnectedLayer(TensorInfo.Linear(250), 127, ActivationType.LeCunTanh, WeightsInitializationMode.GlorotNormal, BiasInitializationMode.Gaussian),
gpu = new CuDnnFullyConnectedLayer(cpu.InputInfo, cpu.OutputInfo.Size, cpu.Weights, cpu.Biases, cpu.ActivationType);
TestBackward(cpu, gpu, 400);
}
public void FullyConnectedForward()
{
float[,] x = WeightsProvider.NewFullyConnectedWeights(TensorInfo.Linear(400), 250, WeightsInitializationMode.GlorotNormal).AsSpan().AsMatrix(400, 250);
FullyConnectedLayer
cpu = new FullyConnectedLayer(TensorInfo.Linear(250), 127, ActivationFunctionType.LeCunTanh, WeightsInitializationMode.GlorotNormal, BiasInitializationMode.Gaussian),
gpu = new CuDnnFullyConnectedLayer(cpu.InputInfo, cpu.OutputInfo.Size, cpu.Weights, cpu.Biases, cpu.ActivationFunctionType);
TestForward(cpu, gpu, x);
}
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);
}
}
