Referencia de función completa de BrainScript

Artículo
03/09/2017

En esta sección se proporciona información sobre las funciones integradas de BrainScript.

Las declaraciones de todas las funciones integradas se pueden encontrar en el CNTK.core.bs situado junto al binario de CNTK.

Las operaciones y capas primitivas se declaran en el espacio de nombres global. Las operaciones adicionales se declaran en espacios de nombres y se proporcionarán con el prefijo correspondiente (por ejemplo, BS.RNN.LSTMP).

Capas

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{}

Creación de capas

Sequential()

Funciones de activación

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

Operaciones en forma de elemento, unaria

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)

Operaciones en forma de elemento, binarios

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)

Operaciones de elemento, ternario

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

Operaciones de convolución y productos de matriz

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)

Parámetros y constantes que se pueden aprender

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)

Entradas

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

Funciones y métricas de pérdida

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

Reducciones

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)

Operaciones de entrenamiento

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)

Volver a dar forma a las operaciones

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)

Repetición

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)

Compatibilidad con secuencia a secuencia

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

Operaciones de propósito especial

ClassBasedCrossEntropyWithSoftmax (labelClassDescriptorVectorSequence, mainInputInfo, mainWeight, classLogProbsBeforeSoftmax)

Edición de modelos

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

Otro

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

Obsolescente

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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