Fullständig funktionsreferens för BrainScript

Artikel
03/09/2017

Det här avsnittet innehåller information om inbyggda Funktioner i BrainScript.

Deklarationerna för alla inbyggda funktioner finns i CNTK.core.bs som finns bredvid CNTK-binärfilen.

De primitiva åtgärderna och lagren deklareras i det globala namnområdet. Ytterligare åtgärder deklareras i namnområden och ges med respektive prefix (t.ex. BS.RNN.LSTMP).

Lager

DenseLayer {outDim, bias= true, activation=Identity, init='uniform', initValueScale=1}
ConvolutionalLayer {numOutputChannels, filterShape, activation = Identity,
init = "uniform", initValueScale = 1,
stride = 1, pad = false, lowerPad = 0, upperPad = 0, bias=true}
MaxPoolingLayer {filterShape, stride = 1, pad = false, lowerPad = 0, upperPad = 0}
AveragePoolingLayer {filterShape, stride = 1, pad = false, lowerPad = 0, upperPad = 0}
EmbeddingLayer {outDim, embeddingPath = '', transpose = false}
RecurrentLSTMLayer {outputDim, cellShape = None, goBackwards = false, enableSelfStabilization = false}
DelayLayer {T=1, defaultHiddenActivation=0}
Dropout
BatchNormalizationLayer {spatialRank = 0, initialScale = 1, normalizationTimeConstant = 0, blendTimeConstant = 0, epsilon = 0.00001, useCntkEngine = true}
LayerNormalizationLayer {initialScale = 1, initialBias = 0}
StabilizerLayer{}
FeatureMVNLayer{}

Lagerbyggnad

Sequential()

Aktiveringsfunktioner

Sigmoid(x)
Tanh(x)
ReLU(x)
Softmax(x, axis=0)
LogSoftmax(x)
Hardmax(x)

Elementwise-åtgärder, unary

Abs (x)
Ceil (x)
Cosine (x)
Clip (x, minValue, maxValue)
Exp (x)
Floor (x)
Log (x)
Negate (x)
-x
BS.Boolean.Not (b)
!x
Reciprocal (x)
Round (x)
Sin (x)
Sqrt (x)

Elementwise-åtgärder, binärt

ElementTimes (x, y)
x .* y
Minus (x, y)
x - y
Plus (x, y)
x + y`
LogPlus (x, y)
Less (x, y)
Equal (x, y)
Greater (x, y)
GreaterEqual (x, y)
NotEqual (x, y)
LessEqual (x, y)
BS.Boolean.And (a, b)
BS.Boolean.Or (a, b)
BS.Boolean.Xor (a, b)

Elementwise-åtgärder, ternary

BS.Boolean.If (condition, thenVal, elseVal)

Matrisprodukt- och convolution-åtgärder

Times(A, B, outputRank=1)
A * B
TransposeTimes(A, B, outputRank=1)
Convolution(weights, x, kernelShape, mapDims=(0), stride=(1), sharing=(true), autoPadding=(true), lowerPadding=(0), upperPadding=(0), imageLayout='CHW', maxTempMemSizeInSamples=0)
Pooling(x, poolKind/*'max'|'average'*/, kernelShape, stride=(1), autoPadding=(true), lowerPadding=(0), upperPadding=(0), imageLayout='CHW')
ROIPooling(x, rois, roiOutputShape, spatialScale=1.0/16.0)

Lärbara parametrar och konstanter

ParameterTensor {shape, learningRateMultiplier=1.0, init='uniform'/*|gaussian*/, initValueScale=1.0, initValue=0.0, randomSeed=-1, initFromFilePath=''}
Constant {scalarValue, rows = 1, cols = 1}
BS.Constants.Zero, BS.Constants.One
BS.Constants.True, BS.Constants.False, BS.Constants.None
BS.Constants.OnesTensor (shape)
BS.Constants.ZeroSequenceLike (x)

Ingångar

Input (shape, dynamicAxis='', sparse=false, tag='feature')
DynamicAxis{}
EnvironmentInput (propertyName)
Mean (x), InvStdDev (x)

Förlustfunktioner och mått

CrossEntropyWithSoftmax (targetDistribution, nonNormalizedLogClassPosteriors)
CrossEntropy (targetDistribution, classPosteriors)
Logistic (label, probability)
WeightedLogistic(label, probability, instanceWeight)
ClassificationError (labels, nonNormalizedLogClassPosteriors)
MatrixL1Reg(matrix)
MatrixL2Reg(matrix)
SquareError (x, y)

Minskningar

ReduceSum (z, axis=None)
ReduceLogSum (z, axis=None)
ReduceMean (z, axis=None)
ReduceMin (z, axis=None)
ReduceMax (z, axis=None)
CosDistance (x, y)
SumElements (z)

Träningsåtgärder

BatchNormalization (input, scale, bias, runMean, runInvStdDev, spatial, normalizationTimeConstant = 0, blendTimeConstant = 0, epsilon = 0.00001, useCntkEngine = true, imageLayout='CHW')
Dropout (x)
Stabilize (x, enabled=true)
StabilizeElements (x, inputDim=x.dim, enabled=true)
CosDistanceWithNegativeSamples (x, y, numShifts, numNegSamples)

Omforma åtgärder

CNTK2.Reshape (x, shape, beginAxis=0, endAxis=0)
ReshapeDimension (x, axis, shape) = CNTK2.Reshape (x, shape, beginAxis=axis, endAxis=axis + 1)
FlattenDimensions (x, axis, num) = CNTK2.Reshape (x, 0, beginAxis=axis, endAxis=axis + num)
SplitDimension (x, axis, N) = ReshapeDimension (x, axis, 0:N)
Slice (beginIndex, endIndex, input, axis=1)
BS.Sequences.First (x) = Slice (0, 1, x, axis=-1)
BS.Sequences.Last (x) = Slice (-1, 0, x, axis=-1)
Splice (inputs, axis=1)
TransposeDimensions (x, axis1, axis2)
Transpose (x) = TransposeDimensions (x, 1, 2)
BS.Sequences.BroadcastSequenceAs (type, data1)
BS.Sequences.Gather (where, x)
BS.Sequences.Scatter (where, y)
BS.Sequences.IsFirst (x)
BS.Sequences.IsLast (x)

Återkommande

OptimizedRNNStack(weights, input, hiddenDims, numLayers=1, bidirectional=false, recurrentOp='lstm')
BS.Loop.Previous (x, timeStep=1, defaultHiddenActivation=0)
PastValue (shape, x, defaultHiddenActivation=0.1, ...) = BS.Loop.Previous (0, shape, ...)
BS.Loop.Next (x, timeStep=1, defaultHiddenActivation=0)
FutureValue (shape, x, defaultHiddenActivation=0.1, ...) = BS.Loop.Next (0, shape, ...)
LSTMP (outputDim, cellDim=outputDim, x, inputDim=x.shape, aux=BS.Constants.None, auxDim=aux.shape, prevState, enableSelfStabilization=false)
BS.Boolean.Toggle (clk, initialValue=BS.Constants.False)
BS.RNNs.RecurrentLSTMP (outputDim, cellDim=outputDim, x, inputDim=x.shape, previousHook=BS.RNNs.PreviousHC, augmentInputHook=NoAuxInputHook, augmentInputDim=0, layerIndex=0, enableSelfStabilization=false)
BS.RNNs.RecurrentLSTMPStack (layerShapes, cellDims=layerShapes, input, inputShape=input.shape, previousHook=PreviousHC, augmentInputHook=NoAuxInputHook, augmentInputShape=0, enableSelfStabilization=false)
BS.RNNs.RecurrentBirectionalLSTMPStack (layerShapes, cellDims=layerShapes, input, inputShape=input.dim, previousHook=PreviousHC, nextHook=NextHC, enableSelfStabilization=false)

Stöd för sekvens-till-sekvens

BS.Seq2Seq.CreateAugmentWithFixedWindowAttentionHook (attentionDim, attentionSpan, decoderDynamicAxis, encoderOutput, enableSelfStabilization=false)
BS.Seq2Seq.GreedySequenceDecoderFrom (modelAsTrained)
BS.Seq2Seq.BeamSearchSequenceDecoderFrom (modelAsTrained, beamDepth)

Specialåtgärder

ClassBasedCrossEntropyWithSoftmax (labelClassDescriptorVectorSequence, mainInputInfo, mainWeight, classLogProbsBeforeSoftmax)

Modellredigering

BS.Network.Load (pathName)
BS.Network.Edit (inputModel, editFunctions, additionalRoots)
BS.Network.CloneFunction (inputNodes, outputNodes, parameters="learnable" /*|"constant"|"shared"*/)

Annan

Fail (what)
IsSameObject (a, b)
Trace (node, say='', logFrequency=traceFrequency, logFirst=10, logGradientToo=false, onlyUpToRow=100000000, onlyUpToT=100000000, format=[])

Deprecated

ErrorPrediction (labels, nonNormalizedLogClassPosteriors)
ColumnElementTimes (...) = ElementTimes (...)
DiagTimes (...) = ElementTimes (...)
LearnableParameter(...) = Parameter(...)
LookupTable (embeddingMatrix, inputTensor)
RowRepeat (input, numRepeats)
RowSlice (beginIndex, numRows, input) = Slice(beginIndex, beginIndex + numRows, input, axis = 1)
RowStack (inputs)
RowElementTimes (...) = ElementTimes (...)
Scale (...) = ElementTimes (...)
ConstantTensor (scalarVal, shape)
Parameter (outputDim, inputDim, ...) = ParameterTensor ((outputDim:input), ...)
WeightParam (outputDim, inputDim) = Parameter (outputDim, inputDim, init='uniform', initValueScale=1, initOnCPUOnly=true, randomSeed=1)
DiagWeightParam (outputDim) = ParameterTensor ((outputDim), init='uniform', initValueScale=1, initOnCPUOnly=true, randomSeed=1)
BiasParam (dim) = ParameterTensor ((dim), init='fixedValue', value=0.0)
ScalarParam() = BiasParam (1)
SparseInput (shape, dynamicAxis='', tag='feature')
ImageInput (imageWidth, imageHeight, imageChannels, imageLayout='CHW', dynamicAxis='', tag='feature')
SparseImageInput (imageWidth, imageHeight, imageChannels, imageLayout='CHW', dynamicAxis='', tag='feature')
MeanVarNorm(feat) = PerDimMeanVarNormalization(feat, Mean (feat), InvStdDev (feat))
PerDimMeanVarNormalization (x, mean, invStdDev),
PerDimMeanVarDeNormalization (x, mean, invStdDev)
ReconcileDynamicAxis (dataInput, layoutInput)

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