public void SumOpBatch()
{
var cns = new ConvNetSharp <T>();
var x = cns.Const(NewVolume(new[]
{
1.0, 2.0, 3.0,
4.0, 5.0, 6.0
}, new Shape(3, 1, 1, 2)), "x");
var op = cns.Sum(x, new Shape(1, 1, 1, 2));
using (var session = new Session <T>())
{
var result = op.Evaluate(session);
AssertNumber.AreEqual(6.0, result.Get(0));
AssertNumber.AreEqual(15.0, result.Get(1));
}
}
public void Shape()
{
var cns = new ConvNetSharp <T>();
var x = cns.PlaceHolder("x");
var op = cns.Shape(x);
using (var session = new Session <T>())
{
// Batch size = 1
var result = session.Run(op, new Dictionary <string, Volume <T> > {
{ "x", NewVolume(new[] { 1.0, 2.0, 3.0, 4.0 }, Volume.Shape.From(1, 1, 4, 1)) }
});
AssertNumber.AreEqual(1.0, result.Get(0));
AssertNumber.AreEqual(1.0, result.Get(1));
AssertNumber.AreEqual(4.0, result.Get(2));
AssertNumber.AreEqual(1.0, result.Get(3));
// Batch size = 2
result = session.Run(op, new Dictionary <string, Volume <T> >
{
{
"x", NewVolume(new[]
{
1.0, 2.0, 3.0, 4.0,
1.0, 2.0, 3.0, 4.0
}, Volume.Shape.From(1, 1, 4, 2))
}
});
AssertNumber.AreEqual(1.0, result.Get(0));
AssertNumber.AreEqual(1.0, result.Get(1));
AssertNumber.AreEqual(4.0, result.Get(2));
AssertNumber.AreEqual(2.0, result.Get(3));
}
}
public void CompareCoreVsFlow()
{
var inputWidth = 28;
var inputHeigth = 28;
var inputDepth = 3;
var batchSize = 20;
#region Flow network
var netFlow = new Net <T>();
netFlow.AddLayer(new InputLayer <T>());
var convLayerFlow1 = new ConvLayer <T>(5, 5, 8)
{
BiasPref = (T)Convert.ChangeType(0.1, typeof(T)), Stride = 1, Pad = 2
};
netFlow.AddLayer(convLayerFlow1);
netFlow.AddLayer(new ReluLayer <T>());
netFlow.AddLayer(new PoolLayer <T>(2, 2)
{
Stride = 2
});
var fullyConnLayerFlow = new FullyConnLayer <T>(10);
netFlow.AddLayer(fullyConnLayerFlow);
netFlow.AddLayer(new SoftmaxLayer <T>());
var trainerFlow = new SgdTrainer <T>(netFlow, (T)Convert.ChangeType(0.01f, typeof(T)))
{
BatchSize = batchSize
};
#endregion
#region Core network
var netCore = new Core.Net <T>();
netCore.AddLayer(new Core.Layers.InputLayer <T>(inputWidth, inputHeigth, inputDepth));
var convLayerCore1 = new Core.Layers.ConvLayer <T>(5, 5, 8)
{
BiasPref = (T)Convert.ChangeType(0.1, typeof(T)), Stride = 1, Pad = 2
};
netCore.AddLayer(convLayerCore1);
netCore.AddLayer(new Core.Layers.ReluLayer <T>());
netCore.AddLayer(new Core.Layers.PoolLayer <T>(2, 2)
{
Stride = 2
});
var fullyConnLayerCore = new Core.Layers.FullyConnLayer <T>(10);
netCore.AddLayer(fullyConnLayerCore);
netCore.AddLayer(new Core.Layers.SoftmaxLayer <T>(10));
var trainerCore = new Core.Training.SgdTrainer <T>(netCore)
{
LearningRate = (T)Convert.ChangeType(0.01f, typeof(T)),
BatchSize = batchSize
};
#endregion
// Same weights
var convfilterCore1 = netFlow.Session.GetVariableByName(netFlow.Op, (convLayerFlow1.Filter as IPersistable <T>).Name);
convfilterCore1.Result = BuilderInstance <T> .Volume.SameAs(convLayerCore1.Filters.ToArray(), convLayerCore1.Filters.Shape);
var fullyfilterCore = netFlow.Session.GetVariableByName(netFlow.Op, (fullyConnLayerFlow.Filter as IPersistable <T>).Name);
fullyfilterCore.Result = BuilderInstance <T> .Volume.SameAs(fullyConnLayerCore.Filters.ToArray(), fullyConnLayerCore.Filters.Shape);
// Create input
var xStorage = new double[inputWidth * inputHeigth * inputDepth * batchSize].Populate(1.0);
var x = NewVolume(xStorage, Volume.Shape.From(inputWidth, inputHeigth, inputDepth, batchSize));
// Create output
var yStorage = new double[10 * batchSize];
var y = NewVolume(yStorage, Volume.Shape.From(1, 1, 10, batchSize));
for (var i = 0; i < batchSize; i++)
{
y.Set(0, 0, i % 10, i, Ops <T> .One);
}
for (var k = 0; k < 10; k++)
{
xStorage = new double[inputWidth * inputHeigth * inputDepth * batchSize].Populate(1.0 + k);
x = NewVolume(xStorage, Volume.Shape.From(inputWidth, inputHeigth, inputDepth, batchSize));
var flowResult = netFlow.Forward(x);
var coreResult = netCore.Forward(x);
var sum1 = BuilderInstance <T> .Volume.SameAs(new Shape(1));
flowResult.DoSum(sum1);
var sum2 = BuilderInstance <T> .Volume.SameAs(new Shape(1));
coreResult.DoSum(sum2);
var diff = Ops <T> .Subtract(sum1.Get(0), sum2.Get(0));
Console.WriteLine(diff);
AssertNumber.AreSequenceEqual(flowResult.ToArray(), coreResult.ToArray(), 1e-6);
trainerCore.Train(x, y);
trainerFlow.Train(x, y);
}
}
