public void NetworkSerialization()
{
INeuralNetwork network = NetworkManager.NewSequential(TensorInfo.Image <Rgb24>(120, 120),
CuDnnNetworkLayers.Convolutional(ConvolutionInfo.New(ConvolutionMode.CrossCorrelation), (10, 10), 20, ActivationType.AbsoluteReLU),
CuDnnNetworkLayers.Convolutional(ConvolutionInfo.New(ConvolutionMode.Convolution, 2, 2), (5, 5), 20, ActivationType.ELU),
CuDnnNetworkLayers.Convolutional(ConvolutionInfo.Default, (10, 10), 20, ActivationType.Identity),
CuDnnNetworkLayers.Pooling(PoolingInfo.New(PoolingMode.AverageIncludingPadding, 2, 2, 1, 1), ActivationType.ReLU),
CuDnnNetworkLayers.Convolutional(ConvolutionInfo.Default, (10, 10), 20, ActivationType.Identity),
CuDnnNetworkLayers.Pooling(PoolingInfo.Default, ActivationType.ReLU),
CuDnnNetworkLayers.FullyConnected(125, ActivationType.Tanh),
CuDnnNetworkLayers.FullyConnected(27, ActivationType.Tanh),
CuDnnNetworkLayers.Softmax(133));
using (MemoryStream stream = new MemoryStream())
{
network.Save(stream);
stream.Seek(0, SeekOrigin.Begin);
INeuralNetwork copy = NetworkLoader.TryLoad(stream, ExecutionModePreference.Cuda);
Assert.IsTrue(network.Equals(copy));
}
}
public bool CreateLayer(int nCount, ELayerType type, ActivationSettings activationSettings)
{
Layer.Utility.Layer layer;
switch (type)
{
case ELayerType.Invalid:
throw new ArgumentException("Invalid \"type\" argument.");
case ELayerType.AveragePooling:
layer = new AveragePooling(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
case ELayerType.AverageUnpooling:
layer = new AverageUnpooling(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
case ELayerType.Convolutional:
layer = new Convolutional(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
case ELayerType.Deconvolutional:
layer = new Deconvolutional(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
case ELayerType.Dropout:
layer = new Dropout(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
case ELayerType.FullyConnected:
layer = new FullyConnected(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
case ELayerType.GatedRecurrent:
layer = new GatedRecurrent(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
case ELayerType.LSTM:
layer = new LSTM(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
case ELayerType.MaxPooling:
layer = new MaxPooling(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
case ELayerType.MaxUnpooling:
layer = new MaxUnpooling(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
case ELayerType.Recurrent:
layer = new Recurrent(nCount, Layers.Count, activationSettings);
Layers.Add(layer);
return(true);
default:
throw new ArgumentException("Invalid \"type\" argument.");
}
}