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The table below lists all the
cntk built-in
functions (in this namespace:
```
).
```

Function name | Description |
---|---|

cntk.abs | Create an instance of the CNTK built-in elementwise abs operation on the input operands. |

cntk.acos | Create an instance of the CNTK built-in elementwise acos operation on the input operands. |

cntk.adaDeltaLearner | Constructs an AdaDelta learner. |

cntk.adaGradLearner | Constructs an AdaGrad learner. |

cntk.adamLearner | Constructs an Adam learner. |

cntk.alias | Create a new Function instance which just aliases the specified ‘x’ Function/Variable such that the ‘Output’ of the new ‘Function’ is same as the ‘Output’ of the specified ‘x’ Function/Variable, and has the newly specified name. |

cntk.allAxes | Axis object representing all the axes (static and dynamic) of an operand. |

cntk.allStaticAxes | Axis object representing all the static axes of an operand. |

cntk.argmax | Create an instance of the CNTK built-in argmax operation on specified tensor input operand along the specified axis |

cntk.argmin | Computes the argmin of the input tensor’s elements across the specified axis. |

cntk.asBlock | Create a new block Function instance which just encapsulates the specified composite Function to create a new Function that appears to be a primitive. |

cntk.asComposite | Creates a composite Function that has the specified rootFunction as its root. |

cntk.asin | Create an instance of the CNTK built-in elementwise asin operation on the input operands. |

cntk.asinh | Create an instance of the CNTK built-in elementwise asinh operation on the input operands. |

cntk.assign | Assign the value in operand to ref and return the new value, ref need to be the same layout as operand. |

cntk.atan | Create an instance of the CNTK built-in elementwise atan operation on the input operands. |

cntk.atanh | Create an instance of the CNTK built-in elementwise atanh operation on the input operands. |

cntk.averagePoolingLayer | Layer factory function to create a average-pooling layer. |

cntk.axis | Construct an Axis object denoting a static axis with the specified index. |

cntk.axisIsBatch | Returns true if the axis is of type batch and false otherwise. |

cntk.axisIsDynamic | Returns true if the axis is of type dynamic and false otherwise. |

cntk.axisIsOrdered | Returns True if the axis is ordered; i.e. |

cntk.axisIsSequence | Returns true if the axis is of type sequence and false otherwise. |

cntk.axisIsStatic | Returns true if the axis is of type static and false otherwise. |

cntk.axisName | Returns the name of this axis. |

cntk.axisStaticIndex | Returns True if the axis is of type static and False otherwise |

cntk.batch | Constructs a batch of cntk:value as a cntk:value. |

cntk.batchNormalization | Normalizes layer outputs for every minibatch for each output (feature) independently and applies affine transformation to preserve representation of the layer. |

cntk.batchNormalizationLayer | Layer factory function to create a batch-normalization layer. |

cntk.batchOfSequences | Constructs a batch of CNTK sequences as a cntk:value. |

cntk.bernoulliRandom | Create an instance of a Bernoulli random operation. |

cntk.bernoulliRandomLike | Create an instance of a Bernoulli random operation. |

cntk.bilinearInitializer | Returns a bilinear parameter initializer. |

cntk.binaryCrossEntropy | Computes the binary cross entropy (aka logistic loss) between the output and target. |

cntk.broadcastAs | Creates a sequence out of a non-sequence by endowing the operand with dynamic axes of the same type as the broadcast_as_operand and broadcasting the value of the operand along those dynamic axes. |

cntk.ceil | Create an instance of the CNTK built-in elementwise ceil operation on the input operands. |

cntk.classificationError | This operation computes the classification error. |

cntk.clip | Computes a tensor with all of its values clipped to fall between min_value and max_value, i.e. |

cntk.combine | Create a new Function instance which just combines the outputs of the specified list of ‘operands’ Functions such that the ‘Outputs’ of the new ‘Function’ are union of the ‘Outputs’ of each of the specified ‘operands’ Functions. |

cntk.concatenateShape | Creates a new shape, concatenating the two supplied shapes. |

cntk.constant | Constructs a constant from a scalar value. |

cntk.constantCloneAs | Clone the constant as a different data type. |

cntk.constantFromValue | Constructs a constant from a cntk:value, that is not a sequence or batch. |

cntk.constantInitializer | Returns a constant parameter initializer. |

cntk.constantRecordValueUpdate | After setting the value of constant, update the record. |

cntk.constantSetValue | Sets the value of the constant using a cntk:value that is not a sequence or batch. |

cntk.constantValue | Returns the value of the constant. |

cntk.convolution | Computes the convolution of convolution_map (typically a tensor of learnable parameters) with operand (commonly an image or output of a previous convolution/pooling operation). |

cntk.convolution1dLayer | Layer factory function to create a convolution-1D layer. |

cntk.convolution2dLayer | Layer factory function to create a convolution-2D layer. |

cntk.convolution3dLayer | Layer factory function to create a convolution-3D layer. |

cntk.convolutionLayer | Layer factory function to create a convolution layer. |

cntk.convolutionTranspose | Layer factory function to create a convolution transpose layer. |

cntk.convolutionTranspose1dLayer | Layer factory function to create a convolution-transpose-1D layer. |

cntk.convolutionTranspose2dLayer | Layer factory function to create a convolution-transpose-2D layer. |

cntk.convolutionTranspose3dLayer | Layer factory function to create a convolution-transpose-3D layer. |

cntk.convolutionTransposeLayer | Layer factory function to create a convolution-transpose layer. |

cntk.convolutionTransposeWithExplicitPadding | Convolution transpose with explicit lower and upper pad values |

cntk.cos | Create an instance of the CNTK built-in elementwise cos operation on the input operands. |

cntk.cosh | Create an instance of the CNTK built-in elementwise cosh operation on the input operands. |

cntk.cosineDistance | Create an instance of the CNTK built-in operation to compute the cosine distance for the specified input operands. |

cntk.cosineDistanceWithNegativeSamples | Given minibatches for x and y, this function computes for each element in x the cosine distance between it and the corresponding y and additionally the cosine distance between x and some other elements of y (referred to a negative samples). |

cntk.cpu | Returns the CPU device. |

cntk.crossEntropyWithSoftmax | This operation computes the cross entropy between the target_vector and the softmax of the output_vector. |

cntk.customProxyOp | A proxy node that helps saving a model with different number of operands. |

cntk.defaultBatchAxis | Returns an Axis object representing the batch axis |

cntk.defaultDevice | Returns the default device. |

cntk.defaultInputVariableDynamicAxes | Returns the default input variable dynamic axes. |

cntk.denseLayer | The creates a dense layer and apply it to operand. |

cntk.depthToSpace | Rearranges elements in the input tensor from the depth dimension into spatial blocks. |

cntk.dropout | Each element of the input is independently set to 0 with probability dropout_rate or to 1 / (1 - dropout_rate) times its original value (with probability 1-dropout_rate). |

cntk.dropoutLayer | Layer factory function to create a dropout layer. |

cntk.dynamicAxis | Creates a dynamic axis. |

cntk.editDistanceError | Edit distance error evaluation function with the option of specifying penalty of substitution, deletion and insertion, as well as squashing the input sequences and ignoring certain samples. |

cntk.elementAnd | Create an instance of the CNTK built-in elementwise AND logical operation on the input operands. |

cntk.elementDivide | Create an instance of the CNTK built-in elementwise division operation on specified tensor input operands. |

cntk.elementMax | Compute the element wise maximum operation between the given operands. |

cntk.elementMin | Compute the element wise minimum operation between the given operands. |

cntk.elementNot | Create an instance of the CNTK built-in elementwise NOT logical operation on the input operands. |

cntk.elementOr | Create an instance of the CNTK built-in elementwise OR logical operation on the input operands. |

cntk.elementSelect | Return either value_if_true or value_if_false based on the value of flag. |

cntk.elementTimes | Create an instance of the CNTK built-in elementwise multiplication operation on specified tensor input operands. |

cntk.elementXor | Create an instance of the CNTK built-in elementwise XOR logical operation on the input operands. |

cntk.elu | Create an instance of the CNTK built-in elementwise exponential linear unit operation with the specified input operand. |

cntk.eluWithAlpha | Exponential linear unit operation. |

cntk.embeddingLayer | Construct an embedding layer based and apply on operand. |

cntk.endStaticAxis | Returns an axis representing the default end static axis. |

cntk.equal | Create an instance of the CNTK built-in elementwise equality comparison operation on specified tensor input operands. |

cntk.evaluate | Evaluate a CNTK model/function using specified input values. |

cntk.exp | Create an instance of the CNTK built-in elementwise exp operation on the input operands. |

cntk.expandDims | Adds a singleton (size 1) axis at position axis. |

cntk.eyeLike | Create an instance of a eye-like operation. |

cntk.flatten | Create an instance of a flatten operation that reshape the specified tensor into 2D tensor. |

cntk.flattenOnAxis | Create an instance of a flatten operation that reshape the specified tensor into 2D tensor. |

cntk.floor | Create an instance of the CNTK built-in elementwise floor operation on the input operands. |

cntk.forwardBackward | Criterion node for training methods that rely on forward-backward Viterbi-like passes, e.g. |

cntk.fsAdaGradLearner | Constructs an FSAdaGrad learner. |

cntk.function | Constructs a CNTK model/function from the binary node. |

cntk.functionArguments | Returns all arguments of a CNTK model, as a sequence of cntk:variable. |

cntk.functionConstants | Returns the constants of a CNTK model, as a sequence of cntk:constant |

cntk.functionFromVariable | Creates a pass-through function of the specified variable. |

cntk.functionIsBlock | Returns a boolean indicating if this Function is a block Function |

cntk.functionIsComposite | Returns a boolean indicating if this Function is a composite Function |

cntk.functionIsPrimitive | Returns a boolean indicating if this Function is a primitive Function |

cntk.functionName | Returns the name of this function. |

cntk.functionOpName | Returns the name of the operation that this function denotes. |

cntk.functionOutput | Returns the output variable of a CNTK model. |

cntk.functionOutputs | Returns the output variables of a CNTK model, as a sequence of cntk:variable |

cntk.functionParameters | Returns the parameters of a CNTK model, as a sequence of cntk:parameter |

cntk.functionSave | Serializes a cntk:function as a node. |

cntk.futureValue | This function returns the future value w.r.t. |

cntk.gatherOp | Create an instance of the CNTK build-in operation to get a tensor that is gathered from reference tensor by indices. |

cntk.gatherOpOnAxis | Create an instance of the CNTK build-in operation to get a tensor that is gathered from reference tensor by indices. |

cntk.globalAveragePoolingLayer | Layer factory function to create a global-average-pooling layer. |

cntk.globalMaxPoolingLayer | Layer factory function to create a global-max-pooling layer. |

cntk.glorotNormalInitializer | Returns a glorot normal parameter initializer. |

cntk.glorotUniformInitializer | Returns a glorot uniform parameter initializer. |

cntk.gpu | Returns the GPU device based on provided CUDA device ID. |

cntk.greater | Create an instance of the CNTK built-in elementwise greater than comparison operation on specified tensor input operands. |

cntk.greaterEqual | Create an instance of the CNTK built-in elementwise greater than or equal to comparison operation on specified tensor input operands. |

cntk.gruBlock | Layer factory function to create a gru block. |

cntk.gruLayer | Layer factory function to create a GRU layer. |

cntk.gumbelRandom | Create an instance of a Gumbel random operation. |

cntk.gumbelRandomLike | Create an instance of a Gumbel random operation. |

cntk.hardmax | Create an instance of the CNTK built-in elementwise hardmax operation on the input operands. |

cntk.hardSigmoid | Create an instance of hard sigmoid operation: f(x) = max(0,min(alpha*x+beta,1)) |

cntk.heNormalInitializer | Returns a He normal parameter initializer. |

cntk.heUniformInitializer | Returns a He uniform parameter initializer. |

cntk.imageScaler | Alteration of image by scaling its individual values. |

cntk.inputVariable | Constructs an input variable. |

cntk.labelLayer | Layer factory function to create a label layer. |

cntk.labelsToGraph | Conversion node from labels to graph. |

cntk.lambdaRank | Groups samples according to group, sorts them within each group based on output and computes the Normalized Discounted Cumulative Gain (NDCG) at infinity for each group. |

cntk.latticeSequenceWithSoftmax | lattice-sequence-with-softmax. |

cntk.layerNormalizationLayer | Layer factory function to create a layer-normalization layer. |

cntk.leakyReLu | Leaky Rectified linear operation. |

cntk.learningRateScheduleFromConstant | Returns a learning rate schedule, where the learning rate is fixed throughout the whole training process. |

cntk.learningRateScheduleFromPairs | Returns a learning rate schedule, based on a sequence of pairs of "number of epochs" and "their learning rate". |

cntk.learningRateScheduleFromSequence | Returns a learning rate schedule, based on a sequence of learning rates. |

cntk.less | Create an instance of the CNTK built-in elementwise less than comparison operation on specified tensor input operands. |

cntk.lessEqual | Create an instance of the CNTK built-in elementwise less than or equal to comparison operation on specified tensor input operands. |

cntk.localResponseNormalization | Local Response Normalization layer. |

cntk.log | Create an instance of the CNTK built-in elementwise log operation on the input operands. |

cntk.logAddExp | Create an instance of the CNTK built-in elementwise tensor operation that computes the log of the sum of the exponentials of the specified input operands. |

cntk.logSoftmax | Create an instance of the CNTK built-in log softmax operation on a specified tensor input operand |

cntk.logSoftmaxOnAxis | Create an instance of the CNTK built-in log softmax operation on specified axis on a specified tensor input operand |

cntk.lstmBlock | Layer factory function to create a lstm block. |

cntk.lstmLayer | Layer factory function to create a LSTM layer. |

cntk.maxPoolingLayer | Layer factory function to create a max-pooling layer. |

cntk.maxUnpoolingLayer | Layer factory function to create a global-average-pooling layer. |

cntk.mean | Create a new Function instance that computes element-wise mean of input tensors. |

cntk.meanVarianceNormalization | Computes mean-variance normalization of the specified input operand. |

cntk.minibatchSizeScheduleFromConstant | Returns a minibatch size schedule, where the minibatch size remains the same. |

cntk.minibatchSizeScheduleFromPairs | Returns a minibatch size schedule. |

cntk.minibatchSizeScheduleFromSequence | Returns a minibatch size schedule, based on a sequence of minibatch sizes. |

cntk.minus | Create an instance of the CNTK built-in elementwise tensor subtraction operation with the specified input operands. |

cntk.momentumScheduleFromConstant | Returns a momentum schedule where the momentum decay rate is fixed. |

cntk.momentumScheduleFromPairs | Returns a momentum schedule. |

cntk.momentumScheduleFromSequence | Returns a momentum schedule, based on a sequence of momentum decay rates. |

cntk.momentumSgdLearner | Constructs an momentum SGD learner. |

cntk.ndcgat1 | Create an instance of the CNTK built-in operation for evaluating the NDCG at 1 metric |

cntk.negate | Create an instance of the CNTK built-in elementwise negate operation on the input operands. |

cntk.nesterovLearner | Constructs an Nesterov learner. |

cntk.newUniqueDynamicAxis | Creates an Axis object representing a new unique dynamic axis. |

cntk.normalInitializer | Returns a normal parameter initializer. |

cntk.normalRandom | Create an instance of a uniform random operation. |

cntk.normalRandomLike | Create an instance of a normal random operation. |

cntk.notEqual | Create an instance of the CNTK built-in elementwise not-equal comparison operation on specified tensor input operands. |

cntk.oneHotOp | Create one hot tensor based on the input tensor. |

cntk.onesLike | Create an instance of a ones-like operation. |

cntk.operandSequenceAxis | Returns an axis representing the sequence axis (ordered dynamic axis) of an operand whose dynamic axes have not yet been inferred/resolved (i.e. |

cntk.optimizedRnnstack | An RNN implementation that uses the primitives in cuDNN. |

cntk.outputVariable | Constructs an output variable. |

cntk.pad | Pads a tensor according to the specified patterns. |

cntk.parameter | Constructs a parameter from a parameter initializer. |

cntk.parameterFromScalar | Constructs a parameter from a scalar value. |

cntk.parameterFromValue | Constructs a parameter from a cntk:value that is not a sequence or batch. |

cntk.parameterRecordValueUpdate | After setting the value of parameter, update the record. |

cntk.parameterSetValue | Sets the value of the parameter, using a cntk:value that is not a sequence or batch. |

cntk.parameterValue | Returns the value of the parameter. |

cntk.pastValue | This function returns the past value w.r.t. |

cntk.perDimMeanVarianceNormalize | Computes per dimension mean-variance normalization of the specified input operand. |

cntk.placeholderVariable | Constructs a placeholder variable. |

cntk.plus | Create an instance of the CNTK built-in elementwise tensor addition operation with the specified input operands. |

cntk.pooling | The pooling operations compute a new tensor by selecting the maximum or average value in the pooling input. |

cntk.poolingWithExplicitPadding | The pooling operations compute a new tensor by selecting the maximum or average value in the pooling input. |

cntk.pow | Create an instance of the CNTK built-in elementwise tensor operation that computes the leftOperand raised to the power of the right operand. |

cntk.preLu | Create an instance of the CNTK built-in elementwise parametric rectified linear Unit operation with the specified input operand and learning parameter alpha. |

cntk.previousMinibatchEvaluationAverage | Average evaluation function value of the previous minibatch. |

cntk.previousMinibatchLossAverage | Average loss function value of the previous minibatch. |

cntk.previousMinibatchSampleCount | Number of sample encountered in the previous minibatch. |

cntk.randomInitializerWithRank | Overrides $output-rank and $filter-rank specification in a random initializer constructed without an explicit rank specification. |

cntk.randomSample | Estimates inclusion frequencies for random sampling with or without replacement. |

cntk.randomSampleInclusionFrequency | For weighted sampling with the specified sample size (num_samples) this operation computes the expected number of occurrences of each class in the sampled set. |

cntk.reciprocal | Create an instance of the CNTK built-in elementwise reciprocal operation on the input operands. |

cntk.reconcileDynamicAxes | Create a new Function instance which reconciles the dynamic axes of the specified tensor operands. |

cntk.reduceL1 | Computes the L1 norm of the input tensor’s element along the provided axes. |

cntk.reduceL2 | Computes the L2 norm of the input tensor’s element along the provided axes. |

cntk.reduceLogSum | Create an instance of the CNTK built-in LogSum reduction operation on specified tensor input operand along the specified axis |

cntk.reduceLogSumOnAxes | Create an instance of the CNTK built-in LogSum reduction operation on specified tensor input operand along the specified axis |

cntk.reduceMax | Create an instance of the CNTK built-in Max reduction operation on specified tensor input operand along the specified axis |

cntk.reduceMaxOnAxes | Create an instance of the CNTK built-in Max reduction operation on specified tensor input operand along the specified axis |

cntk.reduceMean | Computes the mean of the input tensor’s elements across a specified axis or a list of specified axes. |

cntk.reduceMeanOnAxes | Create an instance of the CNTK built-in Mean reduction operation on specified tensor input operand along the specified axis |

cntk.reduceMin | Create an instance of the CNTK built-in Min reduction operation on specified tensor input operand along the specified axis |

cntk.reduceMinOnAxes | Create an instance of the CNTK built-in Mean reduction operation on specified tensor input operand along the specified axis |

cntk.reduceProd | Computes the min of the input tensor’s elements across the specified axis. |

cntk.reduceProdOnAxes | Create an instance of the CNTK built-in Prod reduction operation on specified tensor input operand along the specified axis |

cntk.reduceSum | Computes the sum of the input tensor’s elements across one axis or a list of axes. |

cntk.reduceSumOnAxes | Computes the sum of the input tensor’s elements across one axis or a list of axes. |

cntk.reduceSumSquare | Computes the sum square of the input tensor’s element along the provided axes. |

cntk.relu | Create an instance of the CNTK built-in elementwise relu operation on the input operands. |

cntk.reshape | Reinterpret input samples as having different tensor dimensions One dimension may be specified as 0 and will be inferred. |

cntk.rmsPropLearner | Constructs an RMSProp learner. |

cntk.rnnStepBlock | Layer factory function to create a rnn-step block. |

cntk.rnnStepLayer | Layer factory function to create a RNN Step layer. |

cntk.ROIPooling | The ROI-pooling operation computes a new matrix by selecting the maximum (max pooling) value in the pooling input for each region of interest (ROI). |

cntk.round | Create an instance of the CNTK built-in elementwise round operation on the input operands. |

cntk.scalar | Constructs a scalar, which is a zero dimensional constant. |

cntk.selu | Scaled exponential linear unit operation. |

cntk.sequence | Constructs a CNTK sequence as a cntk:value type. |

cntk.sequenceDelay | This function combines past_value() and future_value() into a single function. |

cntk.sequenceFirst | Returns the first element of its symbolic input sequence |

cntk.sequenceGather | Takes two sequences of the same length and returns a new sequence whose elements are those elements of sequence whose corresponding element in condition is True, preserving the ordering of sequence. |

cntk.sequenceGatherWithFactor | Takes two sequences of the same length and returns a new sequence whose elements are those elements of sequence whose corresponding element in condition is True, preserving the ordering of sequence. |

cntk.sequenceIsFirst | Returns a symbolic sequence of booleans with the same length as ``seq``. |

cntk.sequenceIsLast | Returns a symbolic sequence of booleans with the same length as ``seq``. |

cntk.sequenceLast | Returns the last element of its symbolic input sequence |

cntk.sequenceReduceMax | Create an instance of the CNTK built-in max reduction operation on specified tensor input operand along the operands lone dynamic sequence axis |

cntk.sequenceReduceSum | Computes the sum of the input sequence’s elements across the sequence axis. |

cntk.sequenceScatter | Performs the inverse of gather. |

cntk.sequenceScatterWithFactor | Performs the inverse of gather. |

cntk.sequenceSlice | Slice the input sequence. |

cntk.sequenceSoftmax | Create an instance of the CNTK built-in softmax operation on specified tensor input operand along the operands lone dynamic sequence axis |

cntk.sequenceUnpack | Create an instance of the CNTK built-in operator for unpacking the specified sequence operand along the most significant static axis [-1] and padding any gaps with the specified padding value. |

cntk.sequenceWhere | Given a symbolic sequence condition of boolean-like (1/0) values, it will return a new sequence containing the indices for which the values were true. |

cntk.sgdLearner | Constructs an SGD learner. |

cntk.shape | Returns a shape object recognized by CNTK. |

cntk.shapeDimensions | Returns the dimensions represented by a cntk:shape object, as a sequence of unsigned long interger. |

cntk.shapeFreeDimension | Returns a placeholder value to use for an axis whose dimension is unbound and is only determined when actual data is bound to the variable. |

cntk.shapeHasFreeDimension | Returns a boolean value indicating if the dimension size for any of the axes of this shape is free. |

cntk.shapeHasInferredDimension | Returns a boolean value indicating if the dimension size for any of the axes of this shape is unknown/inferred. |

cntk.shapeHasUnboundDimension | Returns a boolean value indicating if the dimension size for any of the axes of this shape is free or inferred. |

cntk.shapeInferredDimension | Returns a placeholder value to use for an axis whose dimension is unknown and is to be inferred by the system. |

cntk.shapeIsScalar | Returns true if the shape represents a scalar. |

cntk.shapeIsUnknown | Returns true if the shape is an unknown shape. |

cntk.shapeRank | Returns the rank of the shape. |

cntk.shapeTotalSize | Returns the total size of the rectangular shape that this shape denotes. |

cntk.sigmoid | Create an instance of the CNTK built-in elementwise sigmoid operation on the input operands. |

cntk.sin | Create an instance of the CNTK built-in elementwise sin operation on the input operands. |

cntk.sinh | Create an instance of the CNTK built-in elementwise sinh operation on the input operands. |

cntk.slice | Create an instance of the slice operation on specified tensor input operand |

cntk.sliceWithStrides | Create an instance of the slice operation on specified tensor input operand |

cntk.softmax | Create an instance of the CNTK built-in softmax operation on specified tensor input operand |

cntk.softmaxOnAxis | Create an instance of the CNTK built-in softmax operation on specified axis on a specified tensor input operand |

cntk.softplus | Softplus operation. |

cntk.softsign | Computes the element-wise softsign of x. |

cntk.spaceToDepth | Rearranges elements in the input tensor from the spatial dimensions to the depth dimension. |

cntk.splice | Concatenate the input tensors along an axis. |

cntk.sqrt | Create an instance of the CNTK built-in elementwise sqrt operation on the input operands. |

cntk.square | Create an instance of the CNTK built-in elementwise square operation on the input operands. |

cntk.squaredError | This operation computes the sum of the squared difference between elements in the two input matrices. |

cntk.squeeze | Removes axes whose size is 1. |

cntk.squeezeAxes | Create an instance of the squeeze operation on specified tensor input operand, for the specified axis |

cntk.stopGradient | Outputs its input as it is and prevents any gradient contribution from its output to its input. |

cntk.straightThrough | Element-wise binarization node using the straight through estimator |

cntk.subshape | Constructs and returns a new cntk:shape object that is a subshape of $shape. |

cntk.sum | Create a new Function instance that computes element-wise sum of input tensors. |

cntk.tan | Create an instance of the CNTK built-in elementwise tan operation on the input operands. |

cntk.tanh | Create an instance of the CNTK built-in elementwise tanh operation on the input operands. |

cntk.times | The output of this operation is the matrix product of the two input matrices. |

cntk.toBatch | Create an instance of attach dynamic axis operation that convert the input's first static axis to dynamic axis. |

cntk.topK | Create an instance of the CNTK built-in top k operation over the first static axis on a specified tensor input operand |

cntk.topKOnAxis | Create an instance of the CNTK built-in top k operation over the first static axis on a specified tensor input operand |

cntk.toSequence | Create an instance of the CNTK built-in operator for converting the specified tensor operand into a sequence |

cntk.toSequenceLike | Create an instance of the CNTK built-in operator for converting the specified tensor operand into a sequence. |

cntk.toSequenceWithLengths | Create an instance of the CNTK built-in operator for converting the specified tensor operand into a sequence This overload allows specifying an additional operand containing the lengths of individual sequences |

cntk.totalNumberOfSampleSeen | The total number of samples seen by this trainer. |

cntk.trainer | Constructs a trainer, used for training a model. |

cntk.trainerGetModel | Returns the model being trained by this trainer. |

cntk.trainMinibatch | Optimize model parameter using a minibatch of training data. |

cntk.transpose | Create an instance of the CNTK built-in log softmax operation on specified axis on a specified tensor input operand |

cntk.transposeAxes | Create an instance of the CNTK built-in transpose dimensions operation on specified tensor input operand. |

cntk.transposeOnAxes | Permutes the axes of the tensor. |

cntk.transposeTimes | The output of this operation is the product of the first (left) argument with the second (right) argument transposed. |

cntk.truncatedNormalInitializer | Truncated normal initializer. |

cntk.uniformInitializer | Returns a uniform parameter initializer. |

cntk.uniformRandom | Create an instance of a uniform random operation. |

cntk.uniformRandomLike | Create an instance of a uniform random operation. |

cntk.unknownDynamicAxes | Returns unknown dynamic axes. |

cntk.unknownShape | Returns a placeholder representing an unknown shape. |

cntk.unpackBatch | Create an instance of detach dynamic axis operation that convert the input's last dynamic axis to static axis. |

cntk.unpooling | Unpools the operand using information from pooling_input. |

cntk.value | Constructs a cntk:value. |

cntk.valueAlias | Creates a new value which is an alias of this value. |

cntk.valueCopyFrom | Copies the contents of the source value to this Value. |

cntk.valueDataType | Returns the data type of this value's contents. |

cntk.valueDeepClone | Creates a new Value with newly allocated storage on the same device as this value and copies this value's contents into the newly allocated Value. |

cntk.valueDevice | Returns the descriptor of the device that this value resides on |

cntk.valueErase | Erase the data of the value. |

cntk.valueIsReadonly | Returns true if the value is readonly. |

cntk.valueIsValid | Returns whether this object has been invalidated (by another forward and/or backward pass) |

cntk.valueShape | Returns the shape of this value's contents. |

cntk.valueToArray | Returns a json:array object representing the actual numeric values inside the cntk:value. |

cntk.variableAsString | Returns the string representation of the variable. |

cntk.variableCurrentValueTimeStamp | Returns the timestamp of current value held by the variable. |

cntk.variableDataType | Returns the data type of the variable. |

cntk.variableDynamicAxes | Returns dynamic axes of the variable, as a sequence. |

cntk.variableFromFunction | Constructs an output variable aliasing the output of the specified function. |

cntk.variableHasBatchAxis | Returns if the variable has batch axis. |

cntk.variableHasSequenceAxis | Returns if the variable has sequence axis. |

cntk.variableIsConstant | Returns if the variable is a constant variable. |

cntk.variableIsInitialized | Returns if the variable is initialized. |

cntk.variableIsInput | Returns if the variable is an input variable. |

cntk.variableIsOutput | Returns if the variable is an output variable. |

cntk.variableIsParameter | Returns if the variable is a parameter variable. |

cntk.variableIsPlaceholder | Returns if the variable is a placeholder variable. |

cntk.variableIsSparse | Returns if the variable is sparse. |

cntk.variableKind | Returns the variable-kind of the variable (input, output, etc). |

cntk.variableName | Returns the name of the variable. |

cntk.variableNeedsGradient | Returns if the variable needs gradient. |

cntk.variableOwner | Returns the function of which the specified variable is an output. |

cntk.variableShape | Returns the shape of the variable. |

cntk.variableUid | Returns the uid of the variable, as a string. |

cntk.variableValue | Returns the value of the variable. |

cntk.weightedBinaryCrossEntropy | This operation computes the weighted binary cross entropy (aka logistic loss) between the output and target. |

cntk.xavierInitializer | Returns a xavier parameter initializer. |

cntk.zerosLike | Create an instance of a zeros-like operation. |