/// <summary>
/// Create Value object from sparse input as sequence data with sequenceStartFlag, for N-dimensional tensor. Only CreateSequence() for now.
/// </summary>
/// <typeparam name="T">data type of the created Value object.Currently, float and double are supported.</typeparam>
/// <param name="sampleShape">the tensor shape. For sparse input, the tensor shape leading dimensionality must be the same as the total size of the tensor shape.</param>
/// <param name="sequenceLength">the sequence length.</param>
/// <param name="colStarts">column start indices</param>
/// <param name="rowIndices">row indices</param>
/// <param name="nonZeroValues">sparse values</param>
/// <param name="sequenceStartFlag">true indicates that it is a new sequence.
/// false means a continuation of a previous sequence.</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether it a readonly value</param>
/// <returns></returns>
public static Value CreateSequence <T>(NDShape sampleShape, int sequenceLength,
int[] colStarts, int[] rowIndices, T[] nonZeroValues,
bool sequenceStartFlag,
DeviceDescriptor device,
bool readOnly = false)
{
if (nonZeroValues.Length != rowIndices.Length)
{
throw new ArgumentException("The length of rowIndicies must be same as the length of nonZeroValues.");
}
if (colStarts.Length != sequenceLength + 1)
{
throw new ArgumentException("The length of colStarts must be equal to (sequenceLength + 1)");
}
uint numNonZeroValues = (uint)nonZeroValues.Length;
if (typeof(T).Equals(typeof(float)))
{
return(Value._CreateSequenceFloat(sampleShape, (uint)sequenceLength, colStarts, rowIndices, nonZeroValues as float[], numNonZeroValues, sequenceStartFlag, device, readOnly));
}
else if (typeof(T).Equals(typeof(double)))
{
return(Value._CreateSequenceDouble(sampleShape, (uint)sequenceLength, colStarts, rowIndices, nonZeroValues as double[], numNonZeroValues, sequenceStartFlag, device, readOnly));
}
else
{
throw new ArgumentException("The data type " + typeof(T).ToString() + " is not supported. Only float or double is supported by CNTK.");
}
}
/// <summary>
/// Create Value object from dense input as batch of sequences data.
/// </summary>
/// <typeparam name="T">data type</typeparam>
/// <param name="sampleShape">data shape</param>
/// <param name="batchOfSequences">the data to be stored in the Value.
/// The outer vector represents a collection of sequences with variable length,
/// and the inner vector represents each individual sequence.</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether it a readonly value</param>
/// <returns></returns>
public static Value CreateBatchOfSequences <T>(NDShape sampleShape,
IEnumerable <IEnumerable <T> > batchOfSequences,
DeviceDescriptor device,
bool readOnly = false)
{
return(Create(sampleShape, batchOfSequences, new List <bool>(0), device, readOnly));
}
/// <summary>
/// Create Value object from OneHotVector input, for N-dimenstional tensor. Only Create() method for now.
/// </summary>
/// <typeparam name="T">data type</typeparam>
/// <param name="sampleShape">data shape</param>
/// <param name="sequences">data sequence</param>
/// <param name="sequenceStartFlag">true indicates that it is a new sequence.
/// false means a continuation of a previous sequence.</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether it a readonly value</param>
/// <returns></returns>
public static Value Create <T>(NDShape sampleShape,
IEnumerable <IEnumerable <int> > sequences,
IEnumerable <bool> sequenceStartFlags,
DeviceDescriptor device,
bool readOnly = false)
{
var seqFlags = Helper.AsBoolVector(sequenceStartFlags);
var inputSeqVector = new SizeTVectorVector();
foreach (var seq in sequences)
{
var s = Helper.AsSizeTVector(seq);
inputSeqVector.Add(s);
}
if (typeof(T).Equals(typeof(float)))
{
return(Value._CreateOneHotFloat(sampleShape, inputSeqVector, seqFlags, device, readOnly));
}
else if (typeof(T).Equals(typeof(double)))
{
return(Value._CreateOneHotDouble(sampleShape, inputSeqVector, seqFlags, device, readOnly));
}
else
{
throw new ArgumentException("The data type " + typeof(T).ToString() + " is not supported. Only float or double is supported by CNTK.");
}
}
/// <summary>
/// Create Value object from NDArrayViews.
/// </summary>
/// <param name="sampleShape">data shape</param>
/// <param name="sequences">data sequence</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether it a readonly value</param>
/// <returns></returns>
public static Value Create(NDShape sampleShape,
IEnumerable <NDArrayView> sequences,
DeviceDescriptor device,
bool readOnly = false)
{
return(Create(sampleShape, sequences, new List <bool>(0), device, readOnly));
}
/// <summary>
/// Create Value object from sparse input as sequence data, for N-dimensional tensor. Only CreateSequence() for now.
/// </summary>
/// <typeparam name="T">data type</typeparam>
/// <param name="sampleShape">the tensor shape. For sparse input, the tensor shape leading dimensionality must be the same as the total size of the tensor shape.</param>
/// <param name="sequenceLength">the sequence length.</param>
/// <param name="colStarts">column start indices</param>
/// <param name="rowIndices">row indices</param>
/// <param name="nonZeroValues">sparse values</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether it a readonly value</param>
/// <returns></returns>
public static Value CreateSequence <T>(NDShape sampleShape, int sequenceLength,
int[] colStarts, int[] rowIndices, T[] nonZeroValues,
DeviceDescriptor device,
bool readOnly = false)
{
return(Value.CreateSequence <T>(sampleShape, sequenceLength, colStarts, rowIndices, nonZeroValues, true, device, readOnly));
}
/// <summary>
/// Create Value object from dense input as sequence data.
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="sampleShape">the shape fo the value</param>
/// <param name="sequence">daat sequence</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether it is a readonly value</param>
/// <returns></returns>
public static Value CreateSequence <T>(NDShape sampleShape,
IEnumerable <T> sequence,
DeviceDescriptor device,
bool readOnly = false)
{
return(CreateSequence <T>(sampleShape, sequence, true, device, readOnly));
}
/// <summary>
/// Create Value object from dense input as batch of sequences data with sequenceStartFlags.
/// </summary>
/// <typeparam name="T">data type</typeparam>
/// <param name="sampleShape">data shape</param>
/// <param name="sequences">data sequence</param>
/// <param name="sequenceStartFlag">true indicates that it is a new sequence.
/// false means a continuation of a previous sequence.</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether it a readonly value</param>
/// <returns></returns>
public static Value Create <T>(NDShape sampleShape,
IEnumerable <IEnumerable <T> > sequences,
IEnumerable <bool> sequenceStartFlags,
DeviceDescriptor device,
bool readOnly = false)
{
var seqFlags = Helper.AsBoolVector(sequenceStartFlags);
if (typeof(T).Equals(typeof(float)))
{
var inputAsSequencesVector = new FloatVectorVector();
foreach (var seq in sequences)
{
var seqVector = Helper.AsFloatVector(seq);
// The seqVector is copied when adding to inputAsSequencesVector.
inputAsSequencesVector.Add(seqVector);
}
return(Value._CreateDenseFloat(sampleShape, inputAsSequencesVector, seqFlags, device, readOnly));
}
else if (typeof(T).Equals(typeof(double)))
{
var inputAsSequencesVector = new DoubleVectorVector();
foreach (var seq in sequences)
{
var seqVector = Helper.AsDoubleVector(seq);
inputAsSequencesVector.Add(seqVector);
}
return(Value._CreateDenseDouble(sampleShape, inputAsSequencesVector, seqFlags, device, readOnly));
}
else
{
throw new ArgumentException("The data type " + typeof(T).ToString() + " is not supported. Only float or double is supported by CNTK.");
}
}
/// <summary>
/// Create Value object from NDArrayViews with sequenceStartFlags
/// </summary>
/// <param name="sampleShape">data shape</param>
/// <param name="sequences">data sequences</param>
/// <param name="sequenceStartFlag">true indicates that it is a new sequence.
/// false means a continuation of a previous sequence.</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether it a readonly value</param>
/// <returns></returns>
public static Value Create(NDShape sampleShape,
IEnumerable <NDArrayView> sequences,
IEnumerable <bool> sequenceStartFlags,
DeviceDescriptor device,
bool readOnly = false)
{
return(Create(sampleShape, sequences, sequenceStartFlags, device, readOnly, /*createNewCopy = */ false));
}
/// <summary>
/// Create an 'Input' Variable denoting sparse data and specify if gradients are to be computed for this input
/// </summary>
/// <param name="shape"></param>
/// <param name="isSparse"></param>
/// <param name="dataType"></param>
/// <param name="needsGradient"></param>
/// <param name="name"></param>
/// <param name="dynamicAxes"></param>
/// <returns></returns>
public static Variable InputVariable(NDShape shape, DataType dataType, string name = "", IList <Axis> dynamicAxes = null, bool isSparse = false, bool needsGradient = false)
{
if (dynamicAxes == null)
{
dynamicAxes = Axis.DefaultInputVariableDynamicAxes();
}
AxisVector dynamicAxesVector = Helper.AsAxisVector(dynamicAxes);
return(CNTKLib.InputVariable(shape, isSparse, dataType, needsGradient, name, dynamicAxesVector));
}
//
// Copy the data of the Value object into the buffer provided by 'sequences'.
// The 'sequences' is a list of sequences with variable length.
// The number of items contained in the outer list of 'sequences' is the number of sequences in the Value object.
// Each element of the outer list represents a sequence.
// Each sequence, represented by List<T>, contains a variable number of samples.
// Each sample consits of a fixed number of elements with type of 'T'. The number of elements is determined by the sample shape.
// The number of samples = the count of elements in List<T> / the count of elements of the sample
// The sampleShape should match the shape of the Value object.
//
public static void CopyTo <T>(this Value value, NDShape sampelShape, List <List <T> > sequences)
{
if ((value.GetDataType() == DataType.Float) && (!typeof(T).Equals(typeof(float))) ||
(value.GetDataType() == DataType.Double) && (!typeof(T).Equals(typeof(double))))
{
throw new ArgumentException("The value type does not match the list type.");
}
throw new Exception("Not implemented yet.");
}
/// <summary>
/// Value Equality.
/// </summary>
/// <param name="p"></param>
/// <returns></returns>
public bool Equals(NDShape p)
{
// If parameter is null return false:
if ((object)p == null)
{
return(false);
}
// Return true if the fields match:
return(CNTKLib.AreEqual(this, p));
}
/// <summary>
/// Constructor using double sparse input.
/// </summary>
/// <param name="viewShape">shape of the data</param>
/// <param name="colStarts">starting column</param>
/// <param name="rowIndices">list of row indices</param>
/// <param name="nonZeroValues">sparse data</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether the data is readonly</param>
public NDArrayView(NDShape viewShape, int[] colStarts, int[] rowIndices, double[] nonZeroValues, DeviceDescriptor device, bool readOnly = false) : this(viewShape, colStarts, rowIndices, nonZeroValues, (uint)nonZeroValues.Length, device, readOnly)
{
if (rowIndices.Length != nonZeroValues.Length)
{
throw new ArgumentException("The length of rowIndicies must be same as the length of nonZeroValues.");
}
if (viewShape[viewShape.Rank - 1] + 1 != colStarts.Length)
{
throw new ArgumentException("The length of colStarts does not match the number of rows, i.e. the dimension size of the last rank of viewShape.");
}
}
/// <summary>
/// Create batch data as a Value object from a dense source buffer.
/// Batch data of the specified dataSize are copied from the source buffer
/// starting at the specified starting position
/// </summary>
/// <typeparam name="T">float or double</typeparam>
/// <param name="sampleShape">shape of the Value</param>
/// <param name="dataBuffer">source data buffer</param>
/// <param name="dataStart">index to the source data buffer where batch data start</param>
/// <param name="dataSize">size of the data</param>
/// <param name="device">device where data are allocated</param>
/// <param name="readOnly">whether data are mutable</param>
/// <returns>the batch data value</returns>
public static Value CreateBatch <T>(NDShape sampleShape, T[] dataBuffer, int dataStart, int dataSize,
DeviceDescriptor device, bool readOnly = false)
{
if (typeof(T).Equals(typeof(float)))
{
return(Value._CreateBatchFloat(sampleShape, dataBuffer as float[], dataStart, dataSize, device, readOnly));
}
else if (typeof(T).Equals(typeof(double)))
{
return(Value._CreateBatchDouble(sampleShape, dataBuffer as double[], dataStart, dataSize, device, readOnly));
}
else
{
throw new ArgumentException("The data type " + typeof(T).ToString() + " is not supported. Only float or double is supported by CNTK.");
}
}
/// <summary>
/// Create Value object from NDArrayViews with sequenceStartFlags
/// </summary>
/// <param name="sampleShape">data shape</param>
/// <param name="sequences">data sequence</param>
/// <param name="sequenceStartFlag">true indicates that it is a new sequence.
/// false means a continuation of a previous sequence.</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether it a readonly value</param>
/// <param name="createNewCopy"></param>
/// <returns></returns>
public static Value Create(NDShape sampleShape,
IEnumerable <NDArrayView> sequences,
IEnumerable <bool> sequenceStartFlags,
DeviceDescriptor device,
bool readOnly,
bool createNewCopy)
{
var seqVector = new NDArrayViewPtrVector();
foreach (var element in sequences)
{
seqVector.Add(element);
}
var startFlags = Helper.AsBoolVector(sequenceStartFlags);
return(_Create(sampleShape, seqVector, startFlags, device, readOnly, createNewCopy));
}
/// <summary>
/// Create Value object from dense input as batch data.
/// </summary>
/// <typeparam name="T">float or double</typeparam>
/// <param name="sampleShape">shape of the Value</param>
/// <param name="batch">batch of data</param>
/// <param name="device">device</param>
/// <param name="readOnly">readonly value</param>
/// <returns>the value</returns>
public static Value CreateBatch <T>(NDShape sampleShape, IEnumerable <T> batch, DeviceDescriptor device, bool readOnly = false)
{
if (typeof(T).Equals(typeof(float)))
{
var inputVector = Helper.AsFloatVector(batch);
return(Value._CreateBatchFloat(sampleShape, inputVector, device, readOnly));
}
else if (typeof(T).Equals(typeof(double)))
{
var inputVector = Helper.AsDoubleVector(batch);
return(Value._CreateBatchDouble(sampleShape, inputVector, device, readOnly));
}
else
{
throw new ArgumentException("The data type " + typeof(T).ToString() + " is not supported. Only float or double is supported by CNTK.");
}
}
/// <summary>
/// Generate a uniform distribution random data NDArrayView
/// </summary>
/// <typeparam name="T">the data type</typeparam>
/// <param name="shape">shape of the data</param>
/// <param name="rangeBegin">low end value</param>
/// <param name="rangeEnd">high end value</param>
/// <param name="seed">seed</param>
/// <param name="device">device</param>
/// <returns></returns>
public static NDArrayView RandomUniform <T>(NDShape shape, double rangeBegin, double rangeEnd, uint seed, DeviceDescriptor device)
{
if (device == null)
{
device = DeviceDescriptor.UseDefaultDevice();
}
if (typeof(T).Equals(typeof(float)))
{
return(_RandomUniformFloat(shape, rangeBegin, rangeEnd, seed, device));
}
else if (typeof(T).Equals(typeof(double)))
{
return(_RandomUniformDouble(shape, rangeBegin, rangeEnd, seed, device));
}
else
{
throw new ArgumentException("The data type " + typeof(T).ToString() + " is not supported. Only float or double is supported by CNTK.");
}
}
/// <summary>
/// Value equality.
/// </summary>
/// <param name="obj"></param>
/// <returns></returns>
public override bool Equals(Object obj)
{
// If parameter is null return false.
if (obj == null)
{
return(false);
}
// If parameter cannot be cast to Point return false.
NDShape p = obj as NDShape;
if ((Object)p == null)
{
return(false);
}
// Return true if the fields match:
return(CNTKLib.AreEqual(this, p));
}
/// <summary>
/// Create Value object from dense input as sequence data with sequenceStartFlag.
/// </summary>
/// <typeparam name="T">data type</typeparam>
/// <param name="sampleShape">data shape</param>
/// <param name="sequence">data sequence</param>
/// <param name="sequenceStartFlag">true indicates that it is a new sequence. false means a continuation of a previous sequence.</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether it a readonly value</param>
/// <returns></returns>
public static Value CreateSequence <T>(NDShape sampleShape,
IEnumerable <T> sequence,
bool sequenceStartFlag,
DeviceDescriptor device,
bool readOnly = false)
{
if (typeof(T).Equals(typeof(float)))
{
float[] sequenceBuffer = sequence.ToArray() as float[];
return(Value._CreateSequenceFloat(sampleShape, sequenceBuffer, sequenceBuffer.Length, sequenceStartFlag, device, readOnly));
}
else if (typeof(T).Equals(typeof(double)))
{
double[] sequenceBuffer = sequence.ToArray() as double[];
return(Value._CreateSequenceDouble(sampleShape, sequenceBuffer, sequenceBuffer.Length, sequenceStartFlag, device, readOnly));
}
else
{
throw new ArgumentException("The data type " + typeof(T).ToString() + " is not supported. Only float or double is supported by CNTK.");
}
}
/// <summary>
/// Invidates a section of a NDShape.
/// </summary>
/// <param name="sectionOffset"></param>
/// <param name="sectionShape"></param>
public void InvalidateSection(IEnumerable <int> sectionOffset, NDShape sectionShape)
{
var offsetVector = Helper.AsSizeTVector(sectionOffset);
_InvalidateSection(offsetVector, sectionShape);
}
/// <summary>
/// Constructor using double dense input.
/// </summary>
/// <param name="viewShape">shape of the data</param>
/// <param name="dataBuffer">data buffer</param>
/// <param name="device">device</param>
/// <param name="readOnly">whether the data is readonly</param>
public NDArrayView(NDShape viewShape, double[] dataBuffer, DeviceDescriptor device, bool readOnly = false) : this(viewShape, dataBuffer, (uint)dataBuffer.Length, device, readOnly)
{
}
/// <summary>
/// create a constant
/// </summary>
/// <param name="shape">shape of the constant</param>
/// <param name="initValue">initial value</param>
/// <param name="device">device</param>
/// <param name="name">name</param>
public Constant(NDShape shape, float initValue, DeviceDescriptor device, string name = "") :
this(shape, DataType.Float, initValue, device, name)
{
}
/// <summary>
/// create a pooling function
/// </summary>
/// <param name="operand">input</param>
/// <param name="poolingType">pooling type</param>
/// <param name="poolingWindowShape">pooiling window dimensions</param>
/// <param name="strides">strides to apply the pooling</param>
/// <param name="autoPadding"></param>
/// <returns></returns>
public static Function Pooling(Variable operand, PoolingType poolingType, NDShape poolingWindowShape, NDShape strides, IEnumerable <bool> autoPadding)
{
BoolVector autoPaddingVector = Helper.AsBoolVector(autoPadding);
return(Pooling(operand, poolingType, poolingWindowShape, strides, autoPaddingVector));
}
/// <summary>
/// build a convolution function
/// </summary>
/// <param name="convolutionMap">convolution parameters (shape, type of the kernal)</param>
/// <param name="operand">input variable</param>
/// <param name="strides">strides to apply convolution</param>
/// <param name="sharing">whether to share parameters (default = true)</param>
/// <param name="autoPadding"></param>
/// <returns></returns>
public static Function Convolution(Variable convolutionMap, Variable operand, NDShape strides, IEnumerable <bool> sharing, IEnumerable <bool> autoPadding)
{
BoolVector sharingVec = Helper.AsBoolVector(sharing);
BoolVector autoPaddingVec = Helper.AsBoolVector(autoPadding);
return(CNTKLib.Convolution(convolutionMap, operand, strides, sharingVec, autoPaddingVec));
}
/// <summary>
/// Create a Placeholder variable to be used as a temporary/placeholder input to a Function.
/// All placeholder inputs of a Function must be replaced with non-placeholder Variables before Forward evaluation of the Function.
/// </summary>
/// <param name="shape"></param>
/// <param name="dynamicAxes"></param>
/// <returns></returns>
public static Variable PlaceholderVariable(NDShape shape, IList <Axis> dynamicAxes)
{
AxisVector dynamicAxesVector = Helper.AsAxisVector(dynamicAxes);
return(CNTKLib.PlaceholderVariable(shape, dynamicAxesVector));
}
/// <summary>
/// Create Value object from dense input as batch data.
/// </summary>
/// <typeparam name="T">float or double</typeparam>
/// <param name="sampleShape">shape of the Value</param>
/// <param name="batch">batch of data</param>
/// <param name="device">device</param>
/// <param name="readOnly">readonly value</param>
/// <returns>the value</returns>
public static Value CreateBatch <T>(NDShape sampleShape, IEnumerable <T> batch, DeviceDescriptor device, bool readOnly = false)
{
T[] batchAsArray = batch.ToArray();
return(CreateBatch <T>(sampleShape, batchAsArray, 0, batchAsArray.Count(), device, readOnly));
}
/// <summary>
/// Marks sequence begins in a NDShape.
/// </summary>
/// <param name="offset"></param>
/// <param name="sectionShape"></param>
public void MarkSequenceBegin(IEnumerable <int> offset, NDShape sectionShape)
{
var offsetVector = Helper.AsSizeTVector(offset);
_MarkSequenceBegin(offsetVector, sectionShape);
}