public virtual void PrepareNetwork()
{
if (Source != null)
{
Source.PrepareNetwork();
}
if (Verbose)
{
OperationsCount.Reset();
DateTime start = DateTime.Now;
Prepare();
layerPrepared = true;
var end = DateTime.Now;
Console.WriteLine("Prepare {0} computed in {1} seconds ({2} -- {3})", this.GetType().Name, (end - start).TotalSeconds, start.ToString("hh:mm:ss.fff"), end.ToString("hh:mm:ss.fff"));
OperationsCount.Print();
}
else
{
Prepare();
layerPrepared = true;
}
}
public void CryptoNetsPoolLayer()
{
OperationsCount.Reset();
int batchSize = 8192;
var Factory = new EncryptedSealBfvFactory(new ulong[] { 549764251649 /*, 549764284417*/ }, (ulong)batchSize);
int weightscale = 32;
var DenseLayer3 = new PoolLayer()
{
Source = new DummyLayer(),
InputShape = new int[] { 5 * 13 * 13 },
KernelShape = new int[] { 5 * 13 * 13 },
Stride = new int[] { 1000 },
MapCount = new int[] { 100 },
Weights = CryptoNets.CryptoNets.Transpose(Weights.Weights_1, 5 * 13 * 13, 100),
Bias = Weights.Biases_2,
WeightsScale = weightscale * weightscale,
Factory = Factory
};
DenseLayer3.Prepare();
var input = Matrix <double> .Build.Dense(8192, 5 * 13 * 13);
var m = Factory.GetEncryptedMatrix(input, EMatrixFormat.ColumnMajor, 1);
var start = DateTime.Now;
var z = DenseLayer3.Apply(m);
var stop = DateTime.Now;
var time = (stop - start).TotalMilliseconds;
Console.WriteLine("time {0}", time);
OperationsCount.Print();
z.Dispose();
m.Dispose();
DenseLayer3.Dispose();
}
public virtual IMatrix GetNext()
{
if (!layerPrepared)
{
Prepare();
}
var m = Source.GetNext();
#if DEBUG
Trace = Environment.StackTrace;
#endif
if (Verbose)
{
OperationsCount.Reset();
DateTime start = DateTime.Now;
var res = Apply(m);
var end = DateTime.Now;
Console.WriteLine("Layer {0} computed in {1} seconds ({2} -- {3}) layer width ({4},{5})", this.GetType().Name, (end - start).TotalSeconds, start.ToString("hh:mm:ss.fff"), end.ToString("hh:mm:ss.fff"), m.RowCount, m.ColumnCount);
CryptoTracker.TestBudget(res.GetColumn(0), Factory);
OperationsCount.Print();
if (res != m)
{
m.Dispose();
}
return(res);
}
else
{
var res = Apply(m);
if (res != m)
{
m.Dispose();
}
return(res);
}
}
static void Main(string[] args)
{
string fileName = "MNIST-28x28-test.txt";
int batchSize = 8192;
int numberOfRecords = 10000;
var Factory = new EncryptedSealBfvFactory(new ulong[] { 2148728833, 2148794369, 2149810177 }, 16384, DecompositionBitCount: 60, GaloisDecompositionBitCount: 60, SmallModulusCount: 7);
int weightscale = 32;
var ReaderLayer = new BatchReader
{
FileName = fileName,
SparseFormat = true,
MaxSlots = batchSize,
NormalizationFactor = 1.0 / 256.0,
Scale = 16.0
};
var EncryptedLayer = new EncryptLayer()
{
Source = ReaderLayer, Factory = Factory
};
var StartTimingLayer = new TimingLayer()
{
Source = EncryptedLayer, StartCounters = new string[] { "Batch-Time" }
};
var ConvLayer1 = new PoolLayer()
{
Source = StartTimingLayer,
InputShape = new int[] { 28, 28 },
KernelShape = new int[] { 5, 5 },
Upperpadding = new int[] { 1, 1 },
Stride = new int[] { 2, 2 },
MapCount = new int[] { 5, 1 },
WeightsScale = weightscale,
Weights = Weights.Weights_0
};
//var ActivationLayer2 = new SquareActivation() { Source = ConvLayer1 };
var ActivationLayer2 = new AppxReLUActivation()
{
Source = ConvLayer1
};
var DenseLayer3 = new PoolLayer()
{
Source = ActivationLayer2,
InputShape = new int[] { 5 * 13 * 13 },
KernelShape = new int[] { 5 * 13 * 13 },
Stride = new int[] { 1000 },
MapCount = new int[] { 100 },
Weights = Transpose(Weights.Weights_1, 5 * 13 * 13, 100),
Bias = Weights.Biases_2,
WeightsScale = weightscale * weightscale
};
//var ActivationLayer4 = new SquareActivation() { Source = DenseLayer3 };
var ActivationLayer4 = new AppxReLUActivation()
{
Source = DenseLayer3
};
var DenseLayer5 = new PoolLayer()
{
Source = ActivationLayer4,
InputShape = new int[] { 100 },
KernelShape = new int[] { 100 },
Stride = new int[] { 1000 },
MapCount = new int[] { 10 },
Weights = Weights.Weights_3,
Bias = Weights.Biases_3,
WeightsScale = weightscale
};
var StopTimingLayer = new TimingLayer()
{
Source = DenseLayer5, StopCounters = new string[] { "Batch-Time" }
};
var network = StopTimingLayer;
OperationsCount.Reset();
Console.WriteLine("Preparing");
network.PrepareNetwork();
OperationsCount.Print();
OperationsCount.Reset();
for (var p = (INetwork)network; p != null; p = p.Source)
{
if (p is BaseLayer b)
{
b.Verbose = true;
}
}
int count = 0;
int errs = 0;
while (count < numberOfRecords)
{
using (var m = network.GetNext())
Utils.ProcessInEnv(env =>
{
var decrypted = m.Decrypt(env);
for (int i = 0; i < decrypted.RowCount; i++)
{
int pred = 0;
for (int j = 1; j < decrypted.ColumnCount; j++)
{
if (decrypted[i, j] > decrypted[i, pred])
{
pred = j;
}
}
if (pred != ReaderLayer.Labels[i])
{
errs++;
}
count++;
if (count % 100 == 0)
{
Console.WriteLine("errs {0}/{1} accuracy {2:0.000}% prediction {3} label {4}", errs, count, 100 - (100.0 * errs / (count)), pred, ReaderLayer.Labels[i]);
}
}
Console.WriteLine("Batch size {0} {1}", batchSize, TimingLayer.GetStats());
}, Factory);
}
Console.WriteLine("errs {0}/{1} accuracy {2:0.000}%", errs, count, 100 - (100.0 * errs / (count)));
network.DisposeNetwork();
}
