BrainScript Full Function Reference

Article
03/09/2017

This section provides information on BrainScript built-in functions.

The declarations of all built-in functions can be found in the CNTK.core.bs located next to the CNTK binary.

The primitive operations and layers are declared in the global namespace. Additional operations are declared in namespaces, and will be given with the respective prefix (e.g. BS.RNN.LSTMP).

Layers

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{}

Layer building

Sequential()

Activation functions

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

Elementwise operations, 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 operations, binary

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 operations, ternary

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

Matrix product and convolution operations

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)

Learnable parameters and constants

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)

Inputs

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

Loss functions and metrics

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

Reductions

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)

Training operations

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)

Reshaping operations

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)

Recurrence

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)

Sequence-to-sequence support

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

Special-purpose operations

ClassBasedCrossEntropyWithSoftmax (labelClassDescriptorVectorSequence, mainInputInfo, mainWeight, classLogProbsBeforeSoftmax)

Model editing

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

Other

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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