public void TestWeightedNetworkMergerMerge()
{
INetworkMerger merger = new WeightedNetworkMerger(3, 8);
INetwork netA = NetworkMergerTestUtils.GenerateNetwork(1);
INetwork netB = NetworkMergerTestUtils.GenerateNetwork(9);
merger.AddMergeEntry("layers.*.weights");
merger.Merge(netA, netB);
IRegistryResolver resolverA = new RegistryResolver(netA.Registry);
IRegistryResolver resolverB = new RegistryResolver(netB.Registry);
INDArray weightsA = resolverA.ResolveGet <INDArray>("layers.*.weights")[0];
INDArray weightsB = resolverB.ResolveGet <INDArray>("layers.*.weights")[0];
float firstValueA = weightsA.GetValue <float>(0, 0);
float firstValueB = weightsB.GetValue <float>(0, 0);
// the first value will change
Assert.AreEqual(6.82, System.Math.Round(firstValueA * 100) / 100);
// the second net may not be changed
Assert.AreEqual(9, firstValueB);
merger.RemoveMergeEntry("layers.*.weights");
merger.Merge(netA, netB);
weightsA = resolverA.ResolveGet <INDArray>("layers.*.weights")[0];
firstValueA = weightsA.GetValue <float>(0, 0);
// may not change
Assert.AreEqual(6.82, System.Math.Round(firstValueA * 100) / 100);
}
/// <summary>
/// Process a certain ndarray with a certain computation handler.
/// </summary>
/// <param name="array">The ndarray to process.</param>
/// <param name="handler">The computation handler to do the processing with.</param>
/// <returns>An ndarray with the processed contents of the given array (can be the same or a new one).</returns>
internal override INDArray ProcessDirect(INDArray array, IComputationHandler handler)
{
int recordLength = (int)(array.Length / array.Shape[0]);
long[] firstBufferIndices = new long[array.Shape.Length];
long[] secondBufferIndices = new long[array.Shape.Length];
_random = new Random(31415926); // fixed rng for reproducability
for (int i = 0; i < array.Shape[0]; i++)
{
int swapIndex = _random.Next((int)array.Shape[0]);
for (int y = 0; y < recordLength; y++)
{
NDArrayUtils.GetIndices(recordLength * i + y, array.Shape, array.Strides, firstBufferIndices);
NDArrayUtils.GetIndices(recordLength * swapIndex + y, array.Shape, array.Strides, secondBufferIndices);
double firstValue = array.GetValue <double>(firstBufferIndices);
double secondValue = array.GetValue <double>(secondBufferIndices);
array.SetValue(secondValue, firstBufferIndices);
array.SetValue(firstValue, secondBufferIndices);
}
}
return(array);
}
public void TestAverageNetworkMergerMerge()
{
INetworkMerger merger = new AverageNetworkMerger();
INetwork netA = NetworkMergerTestUtils.GenerateNetwork(1);
INetwork netB = NetworkMergerTestUtils.GenerateNetwork(5);
merger.AddMergeEntry("layers.*.weights");
merger.Merge(netA, netB);
IRegistryResolver resolverA = new RegistryResolver(netA.Registry);
IRegistryResolver resolverB = new RegistryResolver(netB.Registry);
INDArray weightsA = resolverA.ResolveGet <INDArray>("layers.*.weights")[0];
INDArray weightsB = resolverB.ResolveGet <INDArray>("layers.*.weights")[0];
float firstValueA = weightsA.GetValue <float>(0, 0);
float firstValueB = weightsB.GetValue <float>(0, 0);
// the first value will change
Assert.AreEqual(3, firstValueA);
// the second net may not be changed
Assert.AreEqual(5, firstValueB);
merger.RemoveMergeEntry("layers.*.weights");
merger.Merge(netA, netB);
weightsA = resolverA.ResolveGet <INDArray>("layers.*.weights")[0];
firstValueA = weightsA.GetValue <float>(0, 0);
Assert.AreEqual(3, firstValueA);
}
/// <summary>
/// Score an intermediate result (part of the entire validation dataset was used as specified by the validation iterator).
/// </summary>
/// <param name="predictions">The predictions.</param>
/// <param name="targets">The targets.</param>
/// <param name="handler">The computation handler.</param>
protected override void ScoreIntermediate(INDArray predictions, INDArray targets, IComputationHandler handler)
{
predictions = handler.FlattenTimeAndFeatures(predictions);
targets = handler.FlattenTimeAndFeatures(targets);
if (predictions.Shape[1] != 1)
{
throw new InvalidOperationException($"Cannot score uni-class classification accuracy on targets with != 1 feature shape (feature shape length was {predictions.Shape[1]}).");
}
int totalClassifications = ParameterRegistry.Get <int>("total_classifications");
int correctClassifications = ParameterRegistry.Get <int>("correct_classifications");
double lowerThreshold = ParameterRegistry.Get <double>("lower_threshold");
double upperThreshold = ParameterRegistry.Get <double>("upper_threshold");
for (int i = 0; i < predictions.Shape[0]; i++)
{
double value = predictions.GetValue <double>(i, 0);
int target = targets.GetValue <int>(i, 0);
if (value < lowerThreshold && target == 0 || value > upperThreshold && target == 1)
{
correctClassifications++;
}
}
totalClassifications += (int)predictions.Shape[0];
ParameterRegistry["total_classifications"] = totalClassifications;
ParameterRegistry["correct_classifications"] = correctClassifications;
}
/// <inheritdoc />
internal override INDArray ProcessDirect(INDArray array, IComputationHandler handler)
{
//BTF with single feature dimension, TODO couldn't feature dimension just be flattened and ignored?
if (array.Rank != 3)
{
throw new ArgumentException($"Cannot one-hot encode ndarrays which are not of rank 3 (BTF with single feature dimension), but given ndarray was of rank {array.Rank}.");
}
INDArray encodedArray = handler.NDArray(array.Shape[0], array.Shape[1], array.Shape[2] * _valueToIndexMapping.Count);
long[] bufferIndices = new long[3];
for (long i = 0; i < array.Shape[0] * array.Shape[1]; i++)
{
bufferIndices = NDArrayUtils.GetIndices(i, array.Shape, array.Strides, bufferIndices);
object value = array.GetValue <int>(bufferIndices);
if (!_valueToIndexMapping.ContainsKey(value))
{
throw new ArgumentException($"Cannot one-hot encode unknown value {value}, value was not registered as a possible value.");
}
bufferIndices[2] = _valueToIndexMapping[value];
encodedArray.SetValue(1, bufferIndices);
}
return(encodedArray);
}
internal override INDArray ProcessDirect(INDArray array, IComputationHandler handler)
{
double outputRange = MaxOutputValue - MinOutputValue;
for (int i = 0; i < array.Shape[0]; i++)
{
for (int j = 0; j < array.Shape[1]; j++)
{
foreach (int index in PerIndexMinMaxMappings.Keys)
{
double[] minMaxRange = PerIndexMinMaxMappings[index];
double value = array.GetValue <double>(i, j, index);
value = (value - minMaxRange[0]) / minMaxRange[2];
value = (value + MinOutputValue) * outputRange;
array.SetValue(value, i, j, index);
}
}
}
return(array);
}
