public static INeuralNetwork TryLoad([NotNull] Stream stream, ExecutionModePreference preference)
{
try
{
using (GZipStream gzip = new GZipStream(stream, CompressionMode.Decompress))
{
if (!gzip.TryRead(out NetworkType model))
{
return(null);
}
switch (model)
{
case NetworkType.Sequential: return(SequentialNetwork.Deserialize(gzip, preference));
case NetworkType.ComputationGraph: return(ComputationGraphNetwork.Deserialize(gzip, preference));
default: return(null);
}
}
}
catch
{
// Locked or invalid file
return(null);
}
}
// 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);
}
