Modifier and Type | Method and Description |
---|---|
DifferentialFunction |
DifferentialFunction.dup()
Duplicate this function
|
Modifier and Type | Method and Description |
---|---|
int |
DifferentialFunctionFactory.getReductionLength(DifferentialFunction func) |
Modifier and Type | Class and Description |
---|---|
class |
SDVariable
A variable representing a component within a
graph.
|
Modifier and Type | Method and Description |
---|---|
DifferentialFunction[] |
SameDiff.functions() |
DifferentialFunction |
SameDiff.getFunctionById(String id)
Get the function by the
DifferentialFunction#getOwnName() |
Modifier and Type | Method and Description |
---|---|
org.nd4j.linalg.primitives.Pair<Map<SDVariable,DifferentialFunction>,List<DifferentialFunction>> |
SameDiff.exec()
Creates and executes a list of operations
|
org.nd4j.linalg.primitives.Pair<Map<SDVariable,DifferentialFunction>,List<DifferentialFunction>> |
SameDiff.exec()
Creates and executes a list of operations
|
List<DifferentialFunction> |
SameDiff.exec(List<DifferentialFunction> ops)
Executes the list of operations.
|
org.nd4j.linalg.primitives.Pair<Map<SDVariable,DifferentialFunction>,List<DifferentialFunction>> |
SameDiff.exec(String functionName)
Exec a given function
|
org.nd4j.linalg.primitives.Pair<Map<SDVariable,DifferentialFunction>,List<DifferentialFunction>> |
SameDiff.exec(String functionName)
Exec a given function
|
List<DifferentialFunction> |
SameDiff.exec(String functionName,
List<DifferentialFunction> cachedOps)
Exec the given function
given the ops
|
org.nd4j.linalg.primitives.Pair<Map<SDVariable,DifferentialFunction>,List<DifferentialFunction>> |
SameDiff.execBackwards()
Builds a backwards graph
and executes the operations
on that graph.
|
org.nd4j.linalg.primitives.Pair<Map<SDVariable,DifferentialFunction>,List<DifferentialFunction>> |
SameDiff.execBackwards()
Builds a backwards graph
and executes the operations
on that graph.
|
org.nd4j.linalg.primitives.Pair<Map<SDVariable,DifferentialFunction>,List<DifferentialFunction>> |
SameDiff.execWithPlaceHolder(Map<String,INDArray> inputs)
Creates and executes a list of operations
based on the given variables passed in.
|
org.nd4j.linalg.primitives.Pair<Map<SDVariable,DifferentialFunction>,List<DifferentialFunction>> |
SameDiff.execWithPlaceHolder(Map<String,INDArray> inputs)
Creates and executes a list of operations
based on the given variables passed in.
|
Modifier and Type | Method and Description |
---|---|
void |
SameDiff.addArgsFor(SDVariable[] variables,
DifferentialFunction function)
Adds incoming args to the graph
|
void |
SameDiff.addArgsFor(String[] variables,
DifferentialFunction function)
Adds incoming args to the graph
|
void |
SameDiff.addOutgoingFor(SDVariable[] variables,
DifferentialFunction function)
Adds outgoing args to the graph
|
void |
SameDiff.addOutgoingFor(String[] varNames,
DifferentialFunction function)
Adds outgoing arguments to the graph.
|
void |
SameDiff.addPropertyForFunction(DifferentialFunction functionFor,
String propertyName,
INDArray property)
Add a property for the given function
|
void |
SameDiff.addPropertyForFunction(DifferentialFunction functionFor,
String propertyName,
long property)
Add a property for the given function
|
void |
SameDiff.addPropertyToResolve(DifferentialFunction forFunction,
String arrayName)
Adds a property that needs to be resolve for later.
|
void |
SameDiff.addVariableMappingForField(DifferentialFunction function,
String fieldName,
String varName)
Adds a field name -> variable name
mapping for a given function.
|
protected int |
SameDiff.asFlatNode(DifferentialFunction node,
com.google.flatbuffers.FlatBufferBuilder bufferBuilder,
List<SDVariable> variables,
Map<String,Integer> reverseMap,
Map<String,Integer> forwardMap,
Map<String,Integer> framesMap,
AtomicInteger idCounter) |
SDVariable[] |
SameDiff.generateOutputVariableForOp(DifferentialFunction function)
Generate the variables based on the given input op
and return the output variable names.
|
SDVariable[] |
SameDiff.generateOutputVariableForOp(DifferentialFunction function,
String baseName)
Generate the variables based on the given input op
and return the output variable names.
|
String |
SameDiff.getBaseNameForFunction(DifferentialFunction function)
Returns the base name for the given function
if any (may return null)
|
String[] |
SameDiff.getInputsForFunction(DifferentialFunction function)
Returns the inputs for the given function
|
SDVariable[] |
SameDiff.getInputVariablesForFunction(DifferentialFunction function)
Get the input variables given a set of ids
from
SameDiff.getInputVariablesForFunction(DifferentialFunction) |
String[] |
SameDiff.getOutputsForFunction(DifferentialFunction function)
Returns the outputs for the given function
|
SDVariable[] |
SameDiff.getOutputVariablesForFunction(DifferentialFunction function)
Get the output variables given a set of ids
from
SameDiff.getOutputsForFunction(DifferentialFunction) |
<T> T |
SameDiff.getPropertyForFunction(DifferentialFunction functionInstance,
String propertyName)
Get the property for a given function
|
String |
SameDiff.getVarNameForFieldAndFunction(DifferentialFunction function,
String fieldName)
Get the variable name to use
for resolving a given field
for a given function during import time.
|
boolean |
SameDiff.hasArgs(DifferentialFunction function)
Returns true if this function already
has defined arguments
|
boolean |
SameDiff.hasPropertiesToResolve(DifferentialFunction function)
Returns true if the given function
has ndarray properties to resolve.
|
void |
SameDiff.printFunction(DifferentialFunction differentialFunction)
Print the given function for debugging (will not print functions)
|
List<String> |
SameDiff.propertiesToResolveForFunction(DifferentialFunction function)
Return the properties to resolve for the given function.
|
void |
SameDiff.putFunctionForId(String id,
DifferentialFunction function)
Put the function for id
|
void |
SameDiff.removeArgFromFunction(String varName,
DifferentialFunction function)
Remove an argument for a function.
|
void |
SameDiff.setBaseNameForFunctionInstanceId(String baseName,
DifferentialFunction function)
Sets a base name for the function id.
|
Modifier and Type | Method and Description |
---|---|
List<DifferentialFunction> |
SameDiff.exec(List<DifferentialFunction> ops)
Executes the list of operations.
|
List<DifferentialFunction> |
SameDiff.exec(String functionName,
List<DifferentialFunction> cachedOps)
Exec the given function
given the ops
|
INDArray |
SameDiff.execAndEndResult(List<DifferentialFunction> ops)
u
|
List<SDVariable> |
SameDiff.getVariablesAssociatedWithFunctions(List<DifferentialFunction> functions)
Get the
SDVariable
associated with each function
based on the outputVariables() ()} |
Modifier and Type | Method and Description |
---|---|
DifferentialFunction |
DifferentialFunctionClassHolder.getInstance(String name) |
DifferentialFunction |
DifferentialFunctionClassHolder.getOpWithOnnxName(String onnxName) |
DifferentialFunction |
DifferentialFunctionClassHolder.getOpWithTensorflowName(String tensorflowName)
Get the
|
Modifier and Type | Method and Description |
---|---|
Map<String,Field> |
DifferentialFunctionClassHolder.getFieldsForFunction(DifferentialFunction function)
Get the fields for a given
DifferentialFunction |
Modifier and Type | Method and Description |
---|---|
void |
AttributeAdapter.mapAttributeFor(Object inputAttributeValue,
Field fieldFor,
DifferentialFunction on)
Map the attribute using the specified field
on the specified function on
adapting the given input type to
the type of the field for the specified function.
|
Modifier and Type | Method and Description |
---|---|
void |
StringEqualsAdapter.mapAttributeFor(Object inputAttributeValue,
Field fieldFor,
DifferentialFunction on) |
void |
DataTypeAdapter.mapAttributeFor(Object inputAttributeValue,
Field fieldFor,
DifferentialFunction on) |
void |
ConditionalFieldValueNDArrayShapeAdapter.mapAttributeFor(Object inputAttributeValue,
Field fieldFor,
DifferentialFunction on) |
void |
ConditionalFieldValueIntIndexArrayAdapter.mapAttributeFor(Object inputAttributeValue,
Field fieldFor,
DifferentialFunction on) |
void |
IntArrayIntIndexAdpater.mapAttributeFor(Object inputAttributeValue,
Field fieldFor,
DifferentialFunction on) |
void |
BooleanAdapter.mapAttributeFor(Object inputAttributeValue,
Field fieldFor,
DifferentialFunction on) |
void |
SizeThresholdIntArrayIntIndexAdpater.mapAttributeFor(Object inputAttributeValue,
Field fieldFor,
DifferentialFunction on) |
void |
NDArrayShapeAdapter.mapAttributeFor(Object inputAttributeValue,
Field fieldFor,
DifferentialFunction on) |
Modifier and Type | Method and Description |
---|---|
DifferentialFunction |
GraphMapper.getMappedOp(String name)
Get the mapped op name
for a given op
relative to the type of node being mapped.
|
Modifier and Type | Method and Description |
---|---|
String |
GraphMapper.getTargetMappingForOp(DifferentialFunction function,
NODE_TYPE node)
Get the target mapping key (usually based on the node name)
for the given function
|
void |
BaseGraphMapper.mapProperties(DifferentialFunction on,
NODE_TYPE node,
GRAPH_TYPE graph,
SameDiff sameDiff,
Map<String,Map<String,PropertyMapping>> propertyMappings) |
void |
GraphMapper.mapProperties(DifferentialFunction on,
NODE_TYPE node,
GRAPH_TYPE graph,
SameDiff sameDiff,
Map<String,Map<String,PropertyMapping>> propertyMappings) |
void |
GraphMapper.mapProperty(String name,
DifferentialFunction on,
NODE_TYPE node,
GRAPH_TYPE graph,
SameDiff sameDiff,
Map<String,Map<String,PropertyMapping>> propertyMappingsForFunction) |
Modifier and Type | Method and Description |
---|---|
DifferentialFunction |
OnnxGraphMapper.getMappedOp(String name) |
Modifier and Type | Method and Description |
---|---|
String |
OnnxGraphMapper.getTargetMappingForOp(DifferentialFunction function,
OnnxProto3.NodeProto node) |
void |
OnnxGraphMapper.initFunctionFromProperties(String mappedTfName,
DifferentialFunction on,
Map<String,OnnxProto3.AttributeProto> attributesForNode,
OnnxProto3.NodeProto node,
OnnxProto3.GraphProto graph)
Init a function's attributes
|
void |
OnnxGraphMapper.mapProperty(String name,
DifferentialFunction on,
OnnxProto3.NodeProto node,
OnnxProto3.GraphProto graph,
SameDiff sameDiff,
Map<String,Map<String,PropertyMapping>> propertyMappingsForFunction) |
Modifier and Type | Method and Description |
---|---|
DifferentialFunction |
TFGraphMapper.getMappedOp(String name) |
Modifier and Type | Method and Description |
---|---|
String |
TFGraphMapper.getTargetMappingForOp(DifferentialFunction function,
NodeDef node) |
void |
TFGraphMapper.initFunctionFromProperties(DifferentialFunction on,
Map<String,AttrValue> attributesForNode,
NodeDef node,
GraphDef graph)
|
void |
TFGraphMapper.initFunctionFromProperties(String mappedTfName,
DifferentialFunction on,
Map<String,AttrValue> attributesForNode,
NodeDef node,
GraphDef graph)
Init a function's attributes
|
void |
TFGraphMapper.mapProperty(String name,
DifferentialFunction on,
NodeDef node,
GraphDef graph,
SameDiff sameDiff,
Map<String,Map<String,PropertyMapping>> propertyMappingsForFunction) |
Modifier and Type | Class and Description |
---|---|
class |
BaseAccumulation
Base class for accumulation, initiates the initial entry
with respect to the child class.
|
class |
BaseBroadcastOp |
class |
BaseGradientOp
A gradient op always makes the following assumptions:
there is always a y (beacuse of backpropagating
or using the chain rule)
and that it is special exec (for now)
This op opType sis meant to be used
to build derivative operations.
|
class |
BaseIndexAccumulation
Index based reduction algo
|
class |
BaseModule
Abstract base class for
Module
that handles Dynamic ops and handles nesting. |
class |
BaseOp
Base op.
|
class |
BaseScalarOp
Base scalar operation
|
class |
BaseTransformOp
A base op for basic getters and setters
|
class |
DefaultOpConverter |
class |
DynamicCustomOp
Basic implementation for CustomOp
|
class |
NoOp |
class |
ShapeOp
Shape manipulation ops
|
Modifier and Type | Method and Description |
---|---|
DynamicCustomOp.DynamicCustomOpsBuilder |
DynamicCustomOp.SameDiffBuilder.addInputs(DifferentialFunction... inputs) |
DynamicCustomOp.DynamicCustomOpsBuilder |
DynamicCustomOp.SameDiffBuilder.addOutputs(DifferentialFunction... outputs) |
Modifier and Type | Class and Description |
---|---|
class |
AMax
Calculate the absolute max over a vector
|
class |
AMean
Calculate the absolute mean of the given vector
|
class |
AMin
Calculate the absolute minimum over a vector
|
class |
ASum
Absolute sum the components
|
class |
Bias
Calculate a bias
|
class |
CountNonZero
Count the number of non-zero elements
|
class |
CountZero
Count the number of zero elements
|
class |
CumProd |
class |
CumSum
Cumulative sum operation, optionally along dimension.
|
class |
Dot
Dot product
|
class |
Entropy
Entropy Op - returns the entropy (information gain, or uncertainty of a random variable).
|
class |
EqualsWithEps
Operation for fast INDArrays equality checks
|
class |
LogEntropy
Log Entropy Op - returns the entropy (information gain, or uncertainty of a random variable).
|
class |
LogSumExp
LogSumExp - this op returns https://en.wikipedia.org/wiki/LogSumExp
|
class |
MatchCondition
Absolute sum the components
|
class |
Max
Calculate the max over a vector
|
class |
Mean
Calculate the mean of the vector
|
class |
Min
Calculate the min over a vector
|
class |
Mmul
Matrix multiplication/dot product
|
class |
Moments |
class |
Norm1
Sum of absolute values
|
class |
Norm2
Sum of squared values (real)
Sum of squared complex modulus (complex)
|
class |
NormalizeMoments |
class |
NormMax
The max absolute value
|
class |
Prod
Prod the components
|
class |
ShannonEntropy
Non-normalized Shannon Entropy Op - returns the entropy (information gain, or uncertainty of a random variable).
|
class |
SigmoidCrossEntropyLoss
Sigmoid cross entropy loss with logits
|
class |
SoftmaxCrossEntropyLoss
Softmax cross entropy loss with logits
|
class |
StandardDeviation
Standard deviation (sqrt of variance)
|
class |
Sum
Sum the components
|
class |
TensorMmul
TensorMmul
|
class |
Variance
Variance with bias correction.
|
class |
WeightedCrossEntropyLoss
Weighted cross entropy loss with logits
|
class |
ZeroFraction
Compute the fraction of zero elements
|
Modifier and Type | Class and Description |
---|---|
class |
CosineDistance
Cosine distance
Note that you need to initialize
a scaling constant equal to the norm2 of the
vector
|
class |
CosineSimilarity
Cosine similarity
Note that you need to initialize
a scaling constant equal to the norm2 of the
vector
|
class |
EuclideanDistance
Euclidean distance
|
class |
HammingDistance
Hamming distance (simple)
|
class |
JaccardDistance
Jaccard distance (dissimilarity)
|
class |
ManhattanDistance
Manhattan distance
|
Modifier and Type | Class and Description |
---|---|
class |
BiasAdd |
class |
BiasAddGrad |
class |
BroadcastAddOp |
class |
BroadcastAMax
Broadcast Abs Max comparison op
|
class |
BroadcastAMin
Broadcast Abs Min comparison op
|
class |
BroadcastCopyOp |
class |
BroadcastDivOp |
class |
BroadcastEqualTo |
class |
BroadcastGradientArgs |
class |
BroadcastGreaterThan |
class |
BroadcastGreaterThanOrEqual |
class |
BroadcastLessThan |
class |
BroadcastLessThanOrEqual |
class |
BroadcastMax
Broadcast Max comparison op
|
class |
BroadcastMin
Broadcast Min comparison op
|
class |
BroadcastMulOp |
class |
BroadcastNotEqual |
class |
BroadcastRDivOp
Broadcast reverse divide
|
class |
BroadcastRSubOp |
class |
BroadcastSubOp |
Modifier and Type | Class and Description |
---|---|
class |
If
Equivalent to tensorflow's conditional op.
|
class |
IfDerivative |
class |
Select |
class |
Where |
class |
WhereNumpy |
class |
While
Equivalent to tensorflow's while loop
Takes in:
loopVars
loop body
condition
runs loop till condition is false.
|
class |
WhileDerivative
While loop derivative
|
Modifier and Type | Class and Description |
---|---|
class |
BaseCompatOp |
class |
Enter |
class |
Exit |
class |
LoopCond |
class |
Merge |
class |
NextIteration |
class |
Switch |
Modifier and Type | Class and Description |
---|---|
class |
BaseGridOp |
class |
FreeGridOp
Simple GridOp that operates on arbitrary number of Ops, that have no relations between them.
|
Modifier and Type | Class and Description |
---|---|
class |
FirstIndex
Calculate the index
of max value over a vector
|
class |
IAMax
Calculate the index of the max absolute value over a vector
|
class |
IAMin
Calculate the index of the max absolute value over a vector
|
class |
IMax
Calculate the index
of max value over a vector
|
class |
IMin
Calculate the index of min value over a vector
|
class |
LastIndex
Calculate the index
of max value over a vector
|
Modifier and Type | Class and Description |
---|---|
class |
Linear
Linear:
a * bT
|
Modifier and Type | Class and Description |
---|---|
class |
AvgPooling2D
Average Pooling2D operation
|
class |
BatchNorm
BatchNorm operation
|
class |
BatchNormDerivative
BatchNormDerivative operation
|
class |
Col2Im
Col2Im operation.
|
class |
Conv1D
Conv2D operation
|
class |
Conv2D
Conv2D operation
|
class |
Conv2DDerivative
Conv2DDerivative operation
|
class |
Conv3D
Conv3D operation
|
class |
Conv3DDerivative
Conv3DDerivative operation
|
class |
DeConv2D
DeConv2D operation
|
class |
DeConv2DDerivative
DeConv2DDerivative operation
|
class |
DepthToSpace
Inverse operation to SpaceToDepth.
|
class |
DepthwiseConv2D
Depthwise Conv2D operation
|
class |
FullConv3D
FullConv3D operation
|
class |
FullConv3DDerivative
FullConv3DDerivative operation
|
class |
Im2col
Im2col operation
|
class |
LegacyPooling2D
Deprecated.
Note: This operation will be removed in a future release
|
class |
LocalResponseNormalization
LocalResponseNormalization operation
|
class |
LocalResponseNormalizationDerivative
LocalResponseNormalizationDerivative operation
|
class |
MaxPooling2D
Max Pooling2D operation
|
class |
Pooling2D
Pooling2D operation
|
class |
Pooling2DDerivative
Pooling2DDerivative operation
|
class |
Pooling3D
Pooling3D operation
|
class |
Pooling3DDerivative
Pooling3DDerivative operation
|
class |
SConv2D
Separable convolution 2D operation
|
class |
SConv2DDerivative
SConv2DDerivative operation
|
class |
SpaceToDepth
This operation takes 4D array in, in either NCHW or NHWC format, and moves data from spatial dimensions (HW)
to channels (C) for given blockSize
|
class |
Upsampling
Upsampling operation
|
class |
UpsamplingDerivative
UpsamplingDerivative operation
|
Modifier and Type | Class and Description |
---|---|
class |
GRUCell
GRU cell for RNNs
|
class |
LSTMCell
LSTM cell
|
class |
SRU
Simple recurrent unit
|
class |
SRUCell
A simple recurrent unit cell.
|
Modifier and Type | Class and Description |
---|---|
class |
BaseMetaOp |
class |
InvertedPredicateMetaOp
This MetaOp covers case, when Op A and Op B are both using linear memory access
You're NOT supposed to directly call this op.
|
class |
PostulateMetaOp
You're NOT supposed to directly call this op.
|
class |
PredicateMetaOp
This MetaOp covers case, when Op A and Op B are both using linear memory access
You're NOT supposed to directly call this op.
|
class |
ReduceMetaOp
This is special case PredicateOp, with opB being only either Accumulation, Variance or Reduce3 op
|
Modifier and Type | Class and Description |
---|---|
class |
ScalarAdd
Scalar addition
|
class |
ScalarDivision
Scalar division
|
class |
ScalarFMod
Scalar floating-point remainder (fmod)
|
class |
ScalarMax
Scalar max operation.
|
class |
ScalarMin
Scalar max operation.
|
class |
ScalarMultiplication
Scalar multiplication
|
class |
ScalarRemainder
Scalar floating-point remainder
|
class |
ScalarReverseDivision
Scalar reverse division
|
class |
ScalarReverseSubtraction
Scalar reverse subtraction
|
class |
ScalarSet
Scalar max operation.
|
class |
ScalarSubtraction
Scalar subtraction
|
Modifier and Type | Class and Description |
---|---|
class |
ScalarEquals
Return a binary (0 or 1) when greater than a number
|
class |
ScalarGreaterThan
Return a binary (0 or 1) when greater than a number
|
class |
ScalarGreaterThanOrEqual
Return a binary (0 or 1) when greater than or equal to a number
|
class |
ScalarLessThan
Return a binary (0 or 1) when less than a number
|
class |
ScalarLessThanOrEqual
Return a binary (0 or 1) when less than
or equal to a number
|
class |
ScalarNotEquals
Return a binary (0 or 1)
when greater than a number
|
class |
ScalarSetValue
Scalar value set operation.
|
Modifier and Type | Class and Description |
---|---|
class |
ScatterAdd
Created by farizrahman4u on 3/23/18.
|
class |
ScatterDiv
Created by farizrahman4u on 3/23/18.
|
class |
ScatterMul
Created by farizrahman4u on 3/23/18.
|
class |
ScatterSub
Created by farizrahman4u on 3/23/18.
|
Modifier and Type | Class and Description |
---|---|
class |
ApplyGradientDescent
Reshape function
|
class |
Broadcast
Broadcast function
|
class |
Concat |
class |
ConfusionMatrix |
class |
Cross
Pairwise cross-product of two tensors of the same shape.
|
class |
Diag
Computes a diagonal matrix of shape (n, n) from a vector of length n.
|
class |
DiagPart
Return the diagonal part of a tensor.
|
class |
ExpandDims
ExpandDims function
|
class |
Eye
Computes a batch of identity matrices of shape (numRows, numCols), returns a single tensor.
|
class |
Gather
Gather op
|
class |
GatherNd
GatherND op
|
class |
MergeAvg |
class |
MergeMax |
class |
MergeSum |
class |
OneHot
Created by susaneraly on 3/14/18.
|
class |
OnesLike
OnesLike function - gives an output array with all values/entries being 1, with the same shape as the input.
|
class |
ParallelStack
Stacks n input tensors of same shape to tensor of rank n + 1.
|
class |
Permute
Permute function
|
class |
Rank
Rank function
|
class |
Repeat
Repeat function
|
class |
Reshape
Reshape function
|
class |
RollAxis
Transpose function
|
class |
SequenceMask
Created by farizrahman4u on 3/28/18.
|
class |
Shape
Returns the shape of the input array.
|
class |
Slice
Slice function
|
class |
Split
SplitV op
|
class |
SplitV
SplitV op
|
class |
Squeeze |
class |
Stack
Stack operation.
|
class |
StridedSlice
Strided Slice function
|
class |
Tile
Tile function
|
class |
Transpose
Transpose function
|
class |
Unstack
Unstack op conversion
|
class |
ZerosLike
Reshape function
|
Modifier and Type | Class and Description |
---|---|
class |
BaseTensorOp |
class |
TensorArrayGatherV3 |
class |
TensorArrayReadV3 |
class |
TensorArrayScatterV3 |
class |
TensorArrayV3 |
class |
TensorArrayWriteV3 |
class |
TensorSizeV3 |
Modifier and Type | Class and Description |
---|---|
class |
Abs
Abs elementwise function
|
class |
ACos
Log elementwise function
|
class |
ACosh
ACosh elementwise function
|
class |
All
Boolean AND pairwise transform
|
class |
And
Boolean AND pairwise transform
|
class |
ASin
Arcsin elementwise function
|
class |
ASinh
Arcsin elementwise function
|
class |
Assign
Assign op: x = y, with broadcast as required
|
class |
ATan
Arc Tangent elementwise function
|
class |
ATan2
Arc Tangent elementwise function
|
class |
ATanh
tan elementwise function
|
class |
BaseDynamicTransformOp |
class |
BatchToSpace
N-dimensional batch to space operation.
|
class |
Ceil
Ceiling elementwise function
|
class |
Constant |
class |
Cos
Cosine elementwise function
|
class |
Cosh
Cosine Hyperbolic elementwise function
|
class |
Cube
Cube (x^3) elementwise function
|
class |
Dilation2D
Dilation2D op wrapper
|
class |
DynamicPartition
Transforms a given input tensor into numPartitions partitions, as indicated by the indices in "partitions".
|
class |
DynamicStitch
Transforms a given input tensor into numPartitions partitions, as indicated by the indices in "partitions".
|
class |
ELU
ELU: Exponential Linear Unit (alpha=1.0)
Introduced in paper: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Djork-Arn?? Clevert, Thomas Unterthiner, Sepp Hochreiter (2015) http://arxiv.org/abs/1511.07289 |
class |
Erf
Gaussian error function (erf) function, which is defined as
|
class |
Erfc
Complementary Gaussian error function (erfc), defined as
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class |
Exp
Element-wise exponential function
|
class |
Expm1
Element-wise exponential function minus 1, i.e.
|
class |
Fill
Fill an array of given "shape" with the provided "value", e.g.
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class |
Floor
Floor elementwise function
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class |
HardSigmoid
HardSigmoid function
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class |
HardTanh
Hard tanh elementwise function
|
class |
Histogram |
class |
Identity
Identity function
|
class |
InvertPermutation
Inverse of index permutation.
|
class |
IsFinite
IsFinite function
|
class |
IsInf
IsInf function
|
class |
IsMax
[1, 2, 3, 1] -> [0, 0, 1, 0]
|
class |
IsNaN
IsNaN function
|
class |
LeakyReLU
Leaky Rectified linear unit.
|
class |
LegacyDropOut
DropOut implementation as Op
PLEASE NOTE: This is legacy DropOut implementation, please consider using op with the same opName from randomOps
|
class |
LegacyDropOutInverted
Inverted DropOut implementation as Op
PLEASE NOTE: This is legacy DropOutInverted implementation, please consider using op with the same opName from randomOps
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class |
Log
Log elementwise function
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class |
Log1p
Log1p function
|
class |
LogSigmoid
LogSigmoid function
|
class |
LogSigmoidDerivative
LogSigmoid derivative
|
class |
LogSoftMax
Log(softmax(X))
|
class |
LogX
Log on arbitrary base op
|
class |
MatchConditionTransform
Absolute sum the components
|
class |
MatrixDiagPart |
class |
MatrixSetDiag |
class |
MaxOut
Max out activation:
http://arxiv.org/pdf/1302.4389.pdf
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class |
Negative
Negative function
|
class |
Not
Boolean AND pairwise transform
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class |
OldAtan2Op
atan2 operation
|
class |
OldIdentity
Identity function
|
class |
OldReverse
OldReverse op
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class |
OldSoftMax
Soft max function
row_maxes is a row vector (max for each row)
row_maxes = rowmaxes(input)
diff = exp(input - max) / diff.rowSums()
Outputs a probability distribution.
|
class |
OneMinus
1 - input
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class |
Or
Boolean OR pairwise transform
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class |
Pow
Pow function
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class |
PowDerivative
Pow derivative
z = n * x ^ (n-1)
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class |
RationalTanh
Rational Tanh Approximation elementwise function, as described at https://github.com/deeplearning4j/libnd4j/issues/351
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class |
Reciprocal
Created by susaneraly on 3/28/18.
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class |
RectifedLinear
Rectified linear units
|
class |
RectifiedTanh
RectifiedTanh
Essentially max(0, tanh(x))
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class |
Relu6
Rectified linear unit 6, i.e.
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class |
ReluLayer
Composed op: relu((X, W) + b)
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class |
ReplaceNans
Element-wise "Replace NaN" implementation as Op
|
class |
Reverse |
class |
ReverseSequence
Created by farizrahman4u on 3/16/18.
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class |
Rint
Rint function
|
class |
Round
Rounding function
|
class |
RSqrt
RSqrt function
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class |
SELU
SELU activation function
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class |
Set
Set
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class |
SetRange
Set range to a particular set of values
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class |
Sigmoid
Sigmoid function
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class |
SigmoidDerivative
Sigmoid derivative
|
class |
Sign
Signum function
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class |
Sin
Log elementwise function
|
class |
Sinh
Sinh function
|
class |
SoftMax
Soft max function
row_maxes is a row vector (max for each row)
row_maxes = rowmaxes(input)
diff = exp(input - max) / diff.rowSums()
Outputs a probability distribution.
|
class |
SoftPlus |
class |
SoftSign
Softsign element-wise activation function.
|
class |
SpaceToBatch
N-dimensional space to batch operation.
|
class |
Sqrt
Sqrt function
|
class |
Stabilize
Stabilization function, forces values to be within a range
|
class |
Step
Unit step function.
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class |
Swish
Swish function
|
class |
SwishDerivative
Swish derivative
|
class |
Tan
Tanh elementwise function
|
class |
TanDerivative
Tan Derivative elementwise function
|
class |
Tanh
Tanh elementwise function
|
class |
TanhDerivative
Tanh derivative
|
class |
TimesOneMinus
If x is input: output is x*(1-x)
|
class |
Xor
Boolean XOR pairwise transform
|
class |
XwPlusB
Composed op: mmul (X, W) + b
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Modifier and Type | Method and Description |
---|---|
DifferentialFunction |
Constant.dup() |
Modifier and Type | Class and Description |
---|---|
class |
AddOp
Addition operation
|
class |
Axpy
Level 1 blas op Axpy as libnd4j native op
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class |
CopyOp
Copy operation
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class |
DivOp
Division operation
|
class |
FloorDivOp
Truncated division operation
|
class |
FloorModOp
Floor mod
|
class |
FModOp
Floating-point mod
|
class |
MergeAddOp
Addition operation for n operands, called "mergeadd" in libnd4j
|
class |
MulOp
Multiplication operation
|
class |
OldAddOp
Add operation for two operands
|
class |
OldDivOp
Division operation
|
class |
OldFloorDivOp
Truncated division operation
|
class |
OldFModOp
Floating point remainder
|
class |
OldMulOp
Multiplication operation
|
class |
OldRDivOp
OldReverse Division operation
|
class |
OldSubOp
Division operation
|
class |
RDivOp
OldReverse Division operation
|
class |
RealDivOp
RealDivision operation
|
class |
RemainderOp
Floating-point remainder operation
|
class |
RSubOp
OldReverse 'Subtraction operation
|
class |
SquaredDifferenceOp
Squared difference operation, i.e.
|
class |
SubOp
Subtraction operation
|
class |
TruncateDivOp
Truncated division operation
|
Modifier and Type | Class and Description |
---|---|
class |
AddBpOp
Addition backprop operation.
|
class |
BaseArithmeticBackpropOp
Base arithmetic backprop operation
|
class |
DivBpOp
Division backprop operation.
|
class |
FloorDivBpOp
Floor div backprop operation.
|
class |
FloorModBpOp
Floor div backprop operation.
|
class |
MulBpOp
Division backprop operation.
|
class |
RDivBpOp
Division backprop operation.
|
class |
RSubBpOp
Division backprop operation.
|
class |
SubBpOp
Division backprop operation.
|
Modifier and Type | Class and Description |
---|---|
class |
ClipByNorm |
class |
ClipByValue |
Modifier and Type | Class and Description |
---|---|
class |
Choose
This op allows us to (based on the passed in condition)
to return the element fulfilling the condition.
|
class |
CompareAndReplace
Element-wise Compare-and-Replace implementation as Op
Basically this op does the same as Compare-and-Set, but op.X is checked against Condition instead
|
class |
CompareAndSet
Element-wise Compare-and-set implementation as Op
Please check javadoc to specific constructors, for detail information.
|
class |
Eps
Bit mask over the ndarrays as to whether
the components are equal or not
|
class |
EqualTo
Bit mask over the ndarrays as to whether
the components are equal or not
|
class |
GreaterThan
Bit mask over the ndarrays as to whether
the components are greater than or not
|
class |
GreaterThanOrEqual
Bit mask over the ndarrays as to whether
the components are greater than or equal or not
|
class |
IsNonDecreasing
This op takes 1 n-dimensional array as input,
and returns true if for every adjacent pair we have x[i] <= x[i+1].
|
class |
IsNumericTensor
This op takes 1 n-dimensional array as input, and returns true if input is a numeric array.
|
class |
IsStrictlyIncreasing
This op takes 1 n-dimensional array as input,
and returns true if for every adjacent pair we have x[i] < x[i+1].
|
class |
LessThan
Bit mask over the ndarrays as to whether
the components are less than or not
|
class |
LessThanOrEqual
Bit mask over the ndarrays as to whether
the components are less than or equal or not
|
class |
ListDiff |
class |
NotEqualTo
Not equal to function:
Bit mask over whether 2 elements are not equal or not
|
class |
OldEqualTo
Bit mask over the ndarrays as to whether
the components are equal or not
|
class |
OldGreaterThan
Bit mask over the ndarrays as to whether
the components are greater than or not
|
class |
OldGreaterThanOrEqual
Bit mask over the ndarrays as to whether
the components are greater than or equal or not
|
class |
OldLessThan
Bit mask over the ndarrays as to whether
the components are less than or not
|
class |
OldLessThanOrEqual
Bit mask over the ndarrays as to whether
the components are less than or equal or not
|
class |
OldMax
Max function
|
class |
OldMin
Min function
|
class |
OldNotEqualTo
Not equal to function:
Bit mask over whether 2 elements are not equal or not
|
Modifier and Type | Class and Description |
---|---|
class |
Cast
Cast op wrapper.
|
Modifier and Type | Class and Description |
---|---|
class |
CubeDerivative
Cube derivative, e.g.
|
class |
ELUDerivative
Derivative of ELU: Exponential Linear Unit (alpha=1.0)
Introduced in paper: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Djork-Arn?? Clevert, Thomas Unterthiner, Sepp Hochreiter (2015) http://arxiv.org/abs/1511.07289 |
class |
GradientBackwardsMarker |
class |
HardSigmoidDerivative
HardSigmoid derivative
|
class |
HardTanhDerivative
Hard tanh elementwise derivative function
|
class |
LeakyReLUDerivative
Leaky ReLU derivative.
|
class |
LogSoftMaxDerivative |
class |
RationalTanhDerivative
Rational Tanh Derivative, as described at as described at https://github.com/deeplearning4j/libnd4j/issues/351
|
class |
RectifiedTanhDerivative
Rectified Tanh Derivative
|
class |
SELUDerivative
SELU Derivative elementwise function
https://arxiv.org/pdf/1706.02515.pdf
|
class |
SoftMaxDerivative |
class |
SoftSignDerivative
SoftSign derivative.
|
Modifier and Type | Class and Description |
---|---|
class |
RestoreV2 |
class |
SaveV2 |
Modifier and Type | Class and Description |
---|---|
class |
BaseRandomOp |
Modifier and Type | Class and Description |
---|---|
class |
DistributionUniform
Uniform distribution wrapper
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Modifier and Type | Class and Description |
---|---|
class |
AlphaDropOut
AlphaDropOut implementation as Op
|
class |
BernoulliDistribution
BernoulliDistribution implementation
|
class |
BinomialDistribution
This Op generates binomial distribution
|
class |
BinomialDistributionEx
This Op generates binomial distribution
|
class |
Choice
This Op implements numpy.choice method
It fills Z from source, following probabilities for each source element
|
class |
DropOut
Inverted DropOut implementation as Op
|
class |
DropOutInverted
Inverted DropOut implementation as Op
|
class |
GaussianDistribution
This Op generates normal distribution over provided mean and stddev
|
class |
Linspace
Linspace/arange Op implementation, generates from..to distribution within Z
|
class |
LogNormalDistribution
This Op generates log-normal distribution over provided mean and stddev
|
class |
ProbablisticMerge |
class |
Range
Range Op implementation, generates from..to distribution within Z
|
class |
TruncatedNormalDistribution
This Op generates truncated normal distribution over provided mean and stddev
|
class |
UniformDistribution |
Modifier and Type | Class and Description |
---|---|
class |
LossBinaryXENT
Binary cross entropy loss function
https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression
Labels are assumed to take values 0 or 1
|
class |
LossCosineProximity
Created by susaneraly on 9/9/16.
|
class |
LossFMeasure
F???measure loss function is a loss function design for training on imbalanced datasets.
|
class |
LossHinge
Created by susaneraly on 8/15/16.
|
class |
LossKLD
Kullback Leibler Divergence loss function
|
class |
LossL1
L1 loss function: i.e., sum of absolute errors, L = sum_i abs(predicted_i - actual_i)
See also
LossMAE for a mathematically similar loss function (MAE has division by N, where N is output size) |
class |
LossL2
L2 loss function: i.e., sum of squared errors, L = sum_i (actual_i - predicted)^2
The L2 loss function is the square of the L2 norm of the difference between actual and predicted.
|
class |
LossMAE
Mean absolute error loss function: L = 1/N sum_i abs(predicted_i - actual_i)
See also
LossL1 for a mathematically similar loss function (LossL1 does not have division by N, where N is output size) |
class |
LossMAPE
Created by susaneraly on 8/15/16.
|
class |
LossMCXENT
Multi-Class Cross Entropy loss function:
L = sum_i actual_i * log( predicted_i ) |
class |
LossMixtureDensity
This is a cost function associated with a mixture-density network.
|
class |
LossMSE
Mean Squared Error loss function: L = 1/N sum_i (actual_i - predicted)^2
See also
LossL2 for a mathematically similar loss function (LossL2 does not have division by N, where N is output size) |
class |
LossMSLE
Mean Squared Logarithmic Error loss function: L = 1/N sum_i (log(1+predicted_i) - log(1+actual_i))^2
|
class |
LossMultiLabel
Multi-Label-Loss Function, maybe more commonly known as BPMLL
|
class |
LossNegativeLogLikelihood
Negative log likelihood loss function
|
class |
LossPoisson
Created by susaneraly on 9/9/16.
|
class |
LossSquaredHinge
Created by susaneraly on 9/9/16.
|
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