/// <summary>
/// Converts a dataset into sets of training examples for use in CNTK. Used to train recursive models with multiple outputs.
/// </summary>
/// <typeparam name="T">Supported by<seealso cref="float"/>, <seealso cref="double"/></typeparam>
/// <param name="features">A set of attributes for each example (sample), with an example - a sequence</param>
/// <param name="labels">A set of labels for each output of the model, the dimension for each output may be different.</param>
/// <param name="minibatchSize">Minipack size</param>
/// <returns></returns>
public IEnumerable <MinibatchMultiOutput> ConvertDatasetToMinibatchMultiOutput <T>(IEnumerable <IList <T[]> > features, IEnumerable <T[][]> labels, int minibatchSize)
{
int inputDim = features.FirstOrDefault()?[0].Length ?? 0;
int outputCount = labels.FirstOrDefault()?.Length ?? 0;
var combined = features.Zip(labels, (f, l) => (f, l));
foreach (var segment in GetSegments(combined, minibatchSize))
{
var featuresData = segment.Select(p => p.f.SelectMany(q => q));
var labelsData = new T[outputCount][];
for (int i = 0; i < outputCount; i++)
{
labelsData[i] = segment.SelectMany(p => p.l[i]).ToArray();
}
MinibatchMultiOutput minibatch = new MinibatchMultiOutput();
minibatch.Size = segment.Count;
minibatch.Features = Value.CreateBatch(new int[] { inputDim }, featuresData, Device);
minibatch.Labels = labelsData
.Select(label => Value.CreateBatch(new int[] { label.Length / segment.Count }, label, Device))
.ToArray();
yield return(minibatch);
}
}
/// <summary>
/// Converts a 2D dataset into training case sets for use in CNTK. Used to train models with multiple outputs.
/// </summary>
/// <typeparam name="T">Supported by<seealso cref="float"/>, <seealso cref="double"/></typeparam>
/// <param name="features">A set of attributes for each example (sample) in 2D</param>
/// <param name="labels">A set of labels for each output of the model, the dimension for each output may be different.</param>
/// <param name="minibatchSize">Minipack size</param>
/// <returns></returns>
public IEnumerable <MinibatchMultiOutput> ConvertDatasetToMinibatchMultiOutput <T>(IEnumerable <T[, ]> features, IEnumerable <T[][]> labels, int minibatchSize) where T : IConvertible
{
int outputCount = labels.FirstOrDefault()?.Length ?? 0;
var combined = features.Zip(labels, (f, l) => (f, l));
foreach (var segment in GetSegments(combined, minibatchSize))
{
var featuresData = segment.SelectMany(p => MatrixToVector(p.f));
var labelsData = new T[outputCount][];
for (int i = 0; i < outputCount; i++)
{
labelsData[i] = segment.SelectMany(p => p.l[i]).ToArray();
}
MinibatchMultiOutput minibatch = new MinibatchMultiOutput();
minibatch.Size = segment.Count;
minibatch.Features = Value.CreateBatch(new int[] { GetRowsCount(segment[0].f), GetColumnsCount(segment[0].f), 1 }, featuresData, Device);
minibatch.Labels = labelsData
.Select(label => Value.CreateBatch(new int[] { label.Length / segment.Count }, label, Device))
.ToArray();
yield return(minibatch);
}
}