BrainScript Full Function Reference

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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')

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] (./Loss-Functions-and-Metrics#logistic-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)