public void PreConvLayer()
{
var Factory = Defaults.RawFactory;
var preConvLayer = new LLPreConvLayer()
{
Factory = Factory,
InputShape = new int[] { 28, 28 },
KernelShape = new int[] { 5, 5 },
Upperpadding = new int[] { 1, 1 },
Stride = new int[] { 2, 2 },
};
preConvLayer.Prepare();
var lineLength = 28;
var inp = Enumerable.Range(0, lineLength * lineLength).Select(x => (double)(x + 1)).ToArray();
Utils.ProcessInEnv(env =>
{
var v = Factory.GetEncryptedVector(Vector <double> .Build.DenseOfArray(inp), EVectorFormat.dense, 1);
var res = preConvLayer.Apply(Factory.GetMatrix(new IVector[] { v }, EMatrixFormat.ColumnMajor));
var dec = res.Decrypt(env);
Assert.AreEqual(196, dec.RowCount);
Assert.AreEqual(25, dec.ColumnCount);
var values = new HashSet <int>();
for (int j = 0; j < dec.RowCount; j++)
{
var val = (int)(dec[j, 0]);
if (val != 0)
{
Assert.IsFalse(values.Contains(val));
values.Add(val);
var x = (val - 1) / 28;
var y = (val - 1) % 28;
Assert.AreEqual(0, x % 2);
Assert.AreEqual(0, y % 2);
Assert.IsTrue(x >= 0);
Assert.IsTrue(x < 26);
Assert.IsTrue(y >= 0);
Assert.IsTrue(y < 26);
}
}
Assert.AreEqual(13 * 13, values.Count);
for (int i = 1; i < 25; i++)
{
var dx = i / 5;
var dy = i % 5;
var delta = dy * 28 + dx;
for (int j = 0; j < dec.RowCount; j++)
{
var val = dec[j, i];
var val0 = dec[j, 0];
if (val0 == 0)
{
Assert.AreEqual(0, val);
}
else
{
var y = (int)(val0 - 1) / 28;
var x = (int)(val0 - 1) % 28;
if (x + dx >= 28)
{
Assert.AreEqual(0, val);
}
else if (y + dy >= 28)
{
Assert.AreEqual(0, val);
}
else
{
Assert.AreEqual(val0 + delta, val);
}
}
}
}
}, Factory);
}
public static Tuple<INetwork, IFactory> LoLaDense(string FileName, bool Encrypt)
{
Console.WriteLine("LoLa-Dense mode");
Console.Write("Generating keys in ");
var start = DateTime.Now;
var Factory = (Encrypt) ? (IFactory)new EncryptedSealBfvFactory(new ulong[] { 34359771137, 34360754177 }, 16384, DecompositionBitCount: 60, GaloisDecompositionBitCount: 60, SmallModulusCount: 7)
: (IFactory)new RawFactory(16384);
var end = DateTime.Now;
Console.WriteLine("{0} seconds", (end - start).TotalSeconds);
int weightscale = 32;
var readerLayer = new LLSingleLineReader()
{
FileName = FileName,
SparseFormat = true,
NormalizationFactor = 1.0 / 256.0,
Scale = 16.0,
};
var encryptLayer = new EncryptLayer() { Source = readerLayer, Factory = Factory };
var preConvLayer1 = new LLPreConvLayer()
{
Source = encryptLayer,
InputShape = new int[] { 28, 28 },
KernelShape = new int[] { 5, 5 },
Upperpadding = new int[] { 1, 1 },
Stride = new int[] { 2, 2 },
UseAxisForBlocks = new bool[] { true, true }
};
var ConvLayer2 = new LLPoolLayer()
{
Source = preConvLayer1,
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,
HotIndices = preConvLayer1.HotIndices
};
var VectorizeLayer3 = new LLVectorizeLayer() { Source = ConvLayer2 };
//var ActivationLayer4 = new SquareActivation() { Source = VectorizeLayer3 };
var ActivationLayer4 = new AppxReLUActivation() { Source = VectorizeLayer3 };
//var ActivationLayer4 = new LeakyReLUActivation() { Source = VectorizeLayer3 };
//var ActivationLayer4 = new ReLUActivation() { Source = VectorizeLayer3 };
var DuplicateLayer5 = new LLDuplicateLayer() { Source = ActivationLayer4, Count = 16};
var DenseLayer6 = new LLPackedDenseLayer()
{
Source = DuplicateLayer5,
Weights = preConvLayer1.RearrangeWeights(Transpose(Weights.Weights_1, 5 * 13 * 13, 100)),
Bias = Weights.Biases_2,
WeightsScale = weightscale * weightscale,
PackingCount = DuplicateLayer5.Count,
PackingShift = 1024,
};
//var ActivationLayer7 = new SquareActivation() { Source = DenseLayer6 };
var ActivationLayer7 = new AppxReLUActivation() { Source = DenseLayer6 };
//var ActivationLayer7 = new LeakyReLUActivation() { Source = DenseLayer6 };
//var ActivationLayer7 = new ReLUActivation() { Source = DenseLayer6 };
var InterleaveLayer8 = new LLInterleaveLayer()
{
Source = ActivationLayer7,
Shift = -1,
SelectedIndices = Enumerable.Range(0, (int)DuplicateLayer5.Count).Select(i => 1023 + i * 1024).ToList()
};
var DenseLayer8 = new LLInterleavedDenseLayer()
{
Source = InterleaveLayer8,
Weights = Weights.Weights_3,
Bias = Weights.Biases_3,
WeightsScale = weightscale,
Shift = -1,
SelectedIndices = Enumerable.Range(0, (int)DuplicateLayer5.Count).Select(i => 1023 + i * 1024).ToList()
};
var network = DenseLayer8;
return new Tuple<INetwork, IFactory>(network, Factory);
}
