Modifier and Type | Method and Description |
---|---|
ByteBuffer |
BasicGraphExecutioner.convertToFlatBuffers(SameDiff diff,
ExecutorConfiguration configuration) |
ByteBuffer |
GraphExecutioner.convertToFlatBuffers(SameDiff diff,
ExecutorConfiguration configuration)
This method converts given SameDiff instance to FlatBuffers representation
|
INDArray[] |
BasicGraphExecutioner.executeGraph(SameDiff sd)
This method executes given graph and returns results
PLEASE NOTE: Default configuration is used
|
INDArray[] |
GraphExecutioner.executeGraph(SameDiff graph) |
INDArray[] |
BasicGraphExecutioner.executeGraph(SameDiff graph,
ExecutorConfiguration configuration)
This method executes given graph and returns results
|
INDArray[] |
GraphExecutioner.executeGraph(SameDiff graph,
ExecutorConfiguration configuration)
This method executes given graph and returns results
|
int |
BasicGraphExecutioner.registerGraph(SameDiff graph)
This method stores given graph for future execution
|
int |
GraphExecutioner.registerGraph(SameDiff graph)
This method stores given graph for future execution
|
INDArray[] |
BasicGraphExecutioner.reuseGraph(SameDiff graph,
Map<Integer,INDArray> inputs) |
INDArray[] |
GraphExecutioner.reuseGraph(SameDiff graph,
Map<Integer,INDArray> inputs) |
Modifier and Type | Field and Description |
---|---|
protected SameDiff |
DifferentialFunction.sameDiff |
Modifier and Type | Method and Description |
---|---|
abstract void |
DifferentialFunction.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph)
Iniitialize the function from the given
Onnx.NodeProto |
abstract void |
DifferentialFunction.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph)
Initialize the function from the given
NodeDef |
Constructor and Description |
---|
DifferentialFunction(SameDiff sameDiff,
boolean inPlace,
Object[] extraArgs) |
DifferentialFunction(SameDiff sameDiff,
boolean inPlace,
SDVariable[] args)
Add the various arguments for
this function
|
DifferentialFunction(SameDiff sameDiff,
NodeDef nodeDef,
Map<String,AttrValue> attributesForNode,
GraphDef graph)
Initialize the function from the given
NodeDef |
DifferentialFunction(SameDiff sameDiff,
Object[] extraArgs) |
DifferentialFunction(SameDiff sameDiff,
Onnx.NodeProto node,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph)
Initialize the function from the given
Onnx.NodeProto |
DifferentialFunction(SameDiff sameDiff,
SDVariable[] args) |
Modifier and Type | Method and Description |
---|---|
void |
BaseListener.activationAvailable(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
String varName,
INDArray activation) |
void |
Listener.activationAvailable(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
String varName,
INDArray activation)
Called when any activation becomes available.
|
void |
BaseEvaluationListener.activationAvailable(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
String varName,
INDArray activation) |
void |
BaseEvaluationListener.activationAvailableEvaluations(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
String varName,
INDArray activation)
|
ListenerResponse |
BaseListener.epochEnd(SameDiff sd,
At at,
LossCurve lossCurve,
long epochTimeMillis) |
ListenerResponse |
Listener.epochEnd(SameDiff sd,
At at,
LossCurve lossCurve,
long epochTimeMillis)
Called at the end of every epoch, when fitting from an iterator
|
ListenerResponse |
BaseEvaluationListener.epochEnd(SameDiff sd,
At at,
LossCurve lossCurve,
long epochTimeMillis) |
ListenerResponse |
BaseEvaluationListener.epochEndEvaluations(SameDiff sd,
At at,
LossCurve lossCurve,
long epochTimeMillis,
EvaluationRecord evaluations)
See
Listener.epochEnd(SameDiff, At, LossCurve, long) , also provided the requested evaluations |
void |
BaseListener.epochStart(SameDiff sd,
At at) |
void |
Listener.epochStart(SameDiff sd,
At at)
Called at the start of every epoch, when fitting from an iterator
|
void |
BaseEvaluationListener.epochStart(SameDiff sd,
At at) |
void |
BaseEvaluationListener.epochStartEvaluations(SameDiff sd,
At at)
|
void |
BaseListener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss) |
void |
Listener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss)
Called at the end of every iteration, after all operations (including updating parameters) has been completed
|
void |
BaseListener.iterationStart(SameDiff sd,
At at,
MultiDataSet data,
long etlMs) |
void |
Listener.iterationStart(SameDiff sd,
At at,
MultiDataSet data,
long etlTimeMs)
Called at the start of every iteration (minibatch), before any operations have been executed
|
void |
BaseListener.operationEnd(SameDiff sd,
Operation op) |
void |
Listener.operationEnd(SameDiff sd,
Operation op)
Called at the end of an operation, e.g.
|
void |
BaseListener.operationStart(SameDiff sd,
Operation op) |
void |
Listener.operationStart(SameDiff sd,
Operation op)
Called at the start of an operation, e.g.
|
void |
BaseListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
OpContext opContext,
INDArray[] outputs) |
void |
Listener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
OpContext opContext,
INDArray[] outputs)
Called at the end of each operation execution
|
ListenerVariables |
BaseEvaluationListener.otherRequiredVariables(SameDiff sd)
Return any requested variables that are not part of the evaluations
|
void |
BaseListener.preOpExecution(SameDiff sd,
At at,
SameDiffOp op,
OpContext opContext) |
void |
Listener.preOpExecution(SameDiff sd,
At at,
SameDiffOp op,
OpContext opContext)
Called just before each operation is executed (native code called, etc) - after all inputs etc have been set
|
void |
BaseListener.preUpdate(SameDiff sd,
At at,
Variable v,
INDArray update) |
void |
Listener.preUpdate(SameDiff sd,
At at,
Variable v,
INDArray update)
Called just before each parameter is to be updated - i.e., just before each parameter is modified.
|
ListenerVariables |
BaseListener.requiredVariables(SameDiff sd) |
ListenerVariables |
Listener.requiredVariables(SameDiff sd)
Required variables for this listener.
|
ListenerVariables |
BaseEvaluationListener.requiredVariables(SameDiff sd) |
ListenerResponse |
BaseListener.validationDone(SameDiff sd,
At at,
long validationTimeMillis) |
ListenerResponse |
Listener.validationDone(SameDiff sd,
At at,
long validationTimeMillis)
Called after the end of every epoch, once validation evaluation is done, when training
|
ListenerResponse |
BaseEvaluationListener.validationDone(SameDiff sd,
At at,
long validationTimeMillis) |
ListenerResponse |
BaseEvaluationListener.validationDoneEvaluations(SameDiff sd,
At at,
long validationTimeMillis,
EvaluationRecord evaluations)
See
Listener.validationDone(SameDiff, At, long) , also provided the requested evaluations |
Modifier and Type | Method and Description |
---|---|
static SameDiff |
CheckpointListener.loadCheckpoint(File rootDir,
int checkpointNum,
boolean loadUpdaterState)
Load a SameDiff instance for the given checkpoint that resides in the specified root directory
|
SameDiff |
CheckpointListener.loadCheckpoint(int checkpointNum,
boolean loadUpdaterState)
Load a given checkpoint number
|
static SameDiff |
CheckpointListener.loadLastCheckpoint(File rootDir,
boolean loadUpdaterState)
Load the last (most recent) checkpoint from the specified root directory
|
Modifier and Type | Method and Description |
---|---|
ListenerResponse |
CheckpointListener.epochEnd(SameDiff sameDiff,
At at,
LossCurve lossCurve,
long epochTimeMillis) |
void |
CheckpointListener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss) |
Modifier and Type | Method and Description |
---|---|
void |
OpBenchmarkListener.operationEnd(SameDiff sd,
Operation op) |
void |
OpBenchmarkListener.operationStart(SameDiff sd,
Operation op) |
void |
OpBenchmarkListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
OpContext opContext,
INDArray[] outputs) |
void |
ArraySavingListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
OpContext opContext,
INDArray[] outputs) |
void |
OpBenchmarkListener.preOpExecution(SameDiff sd,
At at,
SameDiffOp op,
OpContext opContext) |
void |
ExecDebuggingListener.preOpExecution(SameDiff sd,
At at,
SameDiffOp op,
OpContext opContext) |
Modifier and Type | Method and Description |
---|---|
protected void |
UIListener.checkStructureForRestore(SameDiff sd) |
ListenerResponse |
UIListener.epochEnd(SameDiff sd,
At at,
LossCurve lossCurve,
long epochTimeMillis) |
ListenerResponse |
ScoreListener.epochEnd(SameDiff sd,
At at,
LossCurve lossCurve,
long epochTimeMillis) |
ListenerResponse |
HistoryListener.epochEndEvaluations(SameDiff sd,
At at,
LossCurve lossCurve,
long epochTimeMillis,
EvaluationRecord evaluations) |
void |
UIListener.epochStart(SameDiff sd,
At at) |
void |
ScoreListener.epochStart(SameDiff sd,
At at) |
protected void |
UIListener.initalizeWriter(SameDiff sd) |
protected void |
UIListener.initializeHelper(SameDiff sd) |
void |
UIListener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss) |
void |
ScoreListener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss) |
void |
UIListener.iterationStart(SameDiff sd,
At at,
MultiDataSet data,
long etlMs) |
void |
ScoreListener.iterationStart(SameDiff sd,
At at,
MultiDataSet data,
long etlMs) |
void |
HistoryListener.operationEnd(SameDiff sd,
Operation op) |
void |
HistoryListener.operationStart(SameDiff sd,
Operation op) |
void |
UIListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
OpContext opContext,
INDArray[] outputs) |
void |
UIListener.preUpdate(SameDiff sd,
At at,
Variable v,
INDArray update) |
ListenerResponse |
HistoryListener.validationDoneEvaluations(SameDiff sd,
At at,
long validationTimeMillis,
EvaluationRecord evaluations) |
Modifier and Type | Method and Description |
---|---|
void |
ProfilingListener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss) |
void |
ProfilingListener.operationEnd(SameDiff sd,
Operation op) |
void |
ProfilingListener.operationStart(SameDiff sd,
Operation op) |
void |
ProfilingListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
OpContext opContext,
INDArray[] outputs) |
void |
ProfilingListener.preOpExecution(SameDiff sd,
At at,
SameDiffOp op,
OpContext opContext) |
Modifier and Type | Field and Description |
---|---|
protected SameDiff |
SDVariable.sameDiff |
Modifier and Type | Method and Description |
---|---|
static SameDiff |
SameDiff.create()
Create a new (empty) SameDiff instance without any functions or variables
|
SameDiff |
SameDiff.defineFunction(String function,
SameDiffFunctionDefinition functionDefinition,
SDVariable[] variables) |
SameDiff |
SameDiff.disableDebugging()
Clears debugging state and disables debug mode.
|
SameDiff |
SameDiff.dup()
Clone/duplicate the SameDiff instance, including arrays etc.
|
SameDiff |
SameDiff.enableDebugMode()
Enables tracing of graphs automatically.
|
static SameDiff |
SameDiff.fromFlatBuffers(ByteBuffer bbIn)
Create a
SameDiff
instance from a byte buffers
instance. |
static SameDiff |
SameDiff.fromFlatBuffers(ByteBuffer bbIn,
boolean loadUpdaterState)
Create a
SameDiff
instance from a byte buffers
instance. |
static SameDiff |
SameDiff.fromFlatFile(@NonNull File file)
Create a
SameDiff instance from a file, including the updater state
The method to save the file is save(File, boolean) |
static SameDiff |
SameDiff.fromFlatFile(@NonNull File file,
boolean loadUpdaterState)
Create a
SameDiff instance from a file, optionally also loading the updater state
The method to save the file is save(File, boolean) |
SameDiff |
SameDiff.getFunction(String functionName)
Get a SameDiff function instance given the name of the function
|
static SameDiff |
SameDiff.importFrozenTF(File graphFile)
Import a frozen Tensorflow graph to a new SameDiff graph.
|
static SameDiff |
SameDiff.importFrozenTF(GraphDef graphDef)
|
static SameDiff |
SameDiff.importFrozenTF(InputStream graph)
|
static SameDiff |
SameDiff.load(@NonNull File file,
boolean loadUpdaterState)
Load the SameDiff instance previously saved with
save(File, boolean) |
static SameDiff |
SameDiff.load(@NonNull InputStream is,
boolean loadUpdaterState)
As per
load(File, boolean) but the SameDiff instance |
Modifier and Type | Method and Description |
---|---|
protected int |
SameDiff.asFlatNode(String name,
@NonNull SameDiff scope,
@NonNull com.google.flatbuffers.FlatBufferBuilder bufferBuilder) |
SDVariable |
SDVariable.clone(SameDiff sd) |
SDVariable |
SameDiffNoArgSingleLambda.define(SameDiff sameDiff) |
SDVariable[] |
SameDiffFunctionDefinition.define(SameDiff sameDiff,
Map<String,INDArray> inputs,
SDVariable[] variableInputs) |
SDVariable |
SameDiffSingleLambda.define(SameDiff sameDiff,
SDVariable[] inputs) |
SDVariable[] |
SameDiffLambda.define(SameDiff sameDiff,
SDVariable[] inputs) |
SDVariable |
SameDiffConditional.eval(SameDiff context,
SameDiffFunctionDefinition body,
SDVariable[] inputVars) |
SDVariable |
SameDiff.invokeFunctionOn(String functionName,
SameDiff with) |
SDVariable |
SameDiff.invokeGraphOn(SameDiff sameDiff) |
void |
SameDiff.putSubFunction(String name,
SameDiff nameSpace)
Associate a
SameDiff namespace as a sub function. |
Constructor and Description |
---|
NameScope(SameDiff sameDiff,
String name) |
SDVariable(@NonNull String varName,
@NonNull VariableType varType,
@NonNull SameDiff sameDiff,
long[] shape,
DataType dataType) |
Constructor and Description |
---|
BatchOutputConfig(@NonNull SameDiff sd) |
EvaluationConfig(@NonNull SameDiff sd) |
FitConfig(@NonNull SameDiff sd) |
OutputConfig(@NonNull SameDiff sd) |
Modifier and Type | Method and Description |
---|---|
SDVariable |
DefaultSameDiffConditional.eval(SameDiff context,
SameDiffFunctionDefinition body,
SDVariable[] inputVars) |
Modifier and Type | Field and Description |
---|---|
protected SameDiff |
AbstractSession.sameDiff |
Constructor and Description |
---|
AbstractSession(@NonNull SameDiff sameDiff) |
InferenceSession(@NonNull SameDiff sameDiff) |
TrainingSession(SameDiff sameDiff) |
Constructor and Description |
---|
CloseValidationMemoryMgr(SameDiff sd,
SessionMemMgr underlying) |
Modifier and Type | Field and Description |
---|---|
protected SameDiff |
SDOps.sd |
protected SameDiff |
SDBaseOps.sd |
Constructor and Description |
---|
SDBaseOps(SameDiff sameDiff) |
SDBitwise(SameDiff sameDiff) |
SDCNN(SameDiff sameDiff) |
SDImage(SameDiff sameDiff) |
SDLinalg(SameDiff sameDiff) |
SDLoss(SameDiff sameDiff) |
SDMath(SameDiff sameDiff) |
SDNN(SameDiff sameDiff) |
SDOps(SameDiff sameDiff) |
SDRandom(SameDiff sameDiff) |
SDRNN(SameDiff sameDiff) |
Modifier and Type | Method and Description |
---|---|
static int |
FlatBuffersMapper.asFlatNode(@NonNull SameDiff sameDiff,
@NonNull DifferentialFunction node,
@NonNull com.google.flatbuffers.FlatBufferBuilder bufferBuilder,
List<SDVariable> variables,
Map<String,Integer> reverseMap,
Map<String,Integer> forwardMap,
Map<String,Integer> framesMap,
AtomicInteger idCounter,
Integer id) |
static DifferentialFunction |
FlatBuffersMapper.cloneViaSerialize(SameDiff sd,
DifferentialFunction df) |
static DifferentialFunction |
FlatBuffersMapper.cloneViaSerialize(SameDiff sd,
DifferentialFunction df,
Map<String,Integer> nameToIdxMap) |
Modifier and Type | Field and Description |
---|---|
protected SameDiff |
SubGraph.sameDiff |
Modifier and Type | Method and Description |
---|---|
static SameDiff |
GraphTransformUtil.replaceSubgraphsMatching(@NonNull SameDiff sd,
@NonNull SubGraphPredicate p,
@NonNull SubGraphProcessor processor)
Find all of the subgraphs that match the specified SubGraphPredicate and then replace them with a different subgraph.
Note that the original SameDiff instance is not modified; a copy is made, which is then modified and returned. |
Modifier and Type | Method and Description |
---|---|
SubGraph |
SubGraphPredicate.getSubGraph(SameDiff sd,
DifferentialFunction rootFn)
Get the SubGraph that matches the predicate
|
static List<SubGraph> |
GraphTransformUtil.getSubgraphsMatching(SameDiff sd,
SubGraphPredicate p)
Get a list of all the subgraphs that match the specified predicate
|
boolean |
SubGraphPredicate.matches(SameDiff sameDiff,
DifferentialFunction rootFn)
Determine if the subgraph, starting with the root function, matches the predicate
|
abstract boolean |
OpPredicate.matches(SameDiff sameDiff,
DifferentialFunction function) |
List<SDVariable> |
SubGraphProcessor.processSubgraph(SameDiff sd,
SubGraph subGraph)
Replace the subgraph, and return the new outputs that should replace the old outputs.
Note that the order of the outputs you return matters! If the original outputs are [A,B,C] and you return output variables [X,Y,Z], then anywhere "A" was used as input will now use "X"; similarly Y replaces B, and Z replaces C. |
static SameDiff |
GraphTransformUtil.replaceSubgraphsMatching(@NonNull SameDiff sd,
@NonNull SubGraphPredicate p,
@NonNull SubGraphProcessor processor)
Find all of the subgraphs that match the specified SubGraphPredicate and then replace them with a different subgraph.
Note that the original SameDiff instance is not modified; a copy is made, which is then modified and returned. |
Modifier and Type | Method and Description |
---|---|
static ExternalErrorsFunction |
SameDiffUtils.externalErrors(SameDiff sameDiff,
Map<String,INDArray> externalGradients,
SDVariable... inputs) |
static ExternalErrorsFunction |
SameDiffUtils.externalErrors(SameDiff sameDiff,
SDVariable[] inputs) |
static void |
SameDiffUtils.validateDifferentialFunctionSameDiff(SameDiff sameDiff,
SDVariable function,
DifferentialFunction op) |
Modifier and Type | Method and Description |
---|---|
static void |
OpValidation.checkDeserializedEquality(SameDiff original,
ByteBuffer bbSerialized,
TestCase tc) |
static boolean |
GradCheckUtil.checkGradients(SameDiff sd,
Map<String,INDArray> placeholderValues,
boolean print,
boolean exitOnFirstFailure) |
static boolean |
GradCheckUtil.checkGradients(SameDiff sd,
Map<String,INDArray> placeholderValues,
double eps,
double maxRelError,
double minAbsError,
boolean print,
boolean exitOnFirstFailure) |
static boolean |
GradCheckUtil.checkGradients(SameDiff sd,
Map<String,INDArray> placeholderValues,
double eps,
double maxRelError,
double minAbsError,
boolean print,
boolean exitOnFirstFailure,
boolean skipValidation,
boolean debugMode,
Set<String> skipVariables,
Map<String,INDArray> gradCheckMask) |
static boolean |
GradCheckUtil.checkGradients(SameDiff sd,
Map<String,INDArray> placeholderValues,
double eps,
double maxRelError,
double minAbsError,
boolean print,
boolean exitOnFirstFailure,
boolean skipValidation,
boolean debugMode,
Set<String> skipVariables,
Map<String,INDArray> gradCheckMask,
int maxPerParam,
GradCheckUtil.Subset subset) |
static boolean |
GradCheckUtil.checkGradients(SameDiff sd,
Map<String,INDArray> placeholderValues,
String... skipVariables) |
static void |
OpValidation.collectTensorflowImportCoverage(SameDiff graph) |
void |
ActivationGradientCheckListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
OpContext opContext,
INDArray[] outputs) |
static void |
GradCheckUtil.validateInternalState(SameDiff sd,
boolean generateAndCheckGradFn) |
Constructor and Description |
---|
TestCase(SameDiff sameDiff) |
Modifier and Type | Method and Description |
---|---|
void |
NonInplaceValidationListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
OpContext opContext,
INDArray[] outputs) |
void |
NonInplaceValidationListener.preOpExecution(SameDiff sd,
At at,
SameDiffOp op,
OpContext oc) |
Modifier and Type | Method and Description |
---|---|
protected int |
LogFileWriter.encodeGraphStructure(com.google.flatbuffers.FlatBufferBuilder fbb,
SameDiff sd) |
long |
LogFileWriter.writeGraphStructure(SameDiff sd)
Write the graph structure
|
Modifier and Type | Method and Description |
---|---|
List<SDVariable> |
OpImportOverride.initFromTensorFlow(List<SDVariable> inputs,
List<SDVariable> controlDepInputs,
NODE_TYPE nodeDef,
SameDiff initWith,
Map<String,ATTR_TYPE> attributesForNode,
GRAPH_TYPE graph)
Initialize the operation and return its output variables
|
boolean |
OpImportFilter.skipOp(NODE_TYPE nodeDef,
SameDiff initWith,
Map<String,ATTR_TYPE> attributesForNode,
GRAPH_TYPE graph)
If true: the op should be skipped for import, and its output variables should not be created.
|
Modifier and Type | Method and Description |
---|---|
static SameDiff |
TFGraphMapper.importGraph(@NonNull File f)
Import a frozen TensorFlow protobuf (.pb) file from the specified file
|
static SameDiff |
TFGraphMapper.importGraph(@NonNull File f,
Map<String,TFImportOverride> importOverride,
TFOpImportFilter opFilter)
Import a frozen TensorFlow protobuf (.pb) file from the specified file, with optional overrides
|
static SameDiff |
TFGraphMapper.importGraph(@NonNull GraphDef tfGraph)
Import a TensorFlow model from a GraphDef
|
static SameDiff |
TFGraphMapper.importGraph(@NonNull GraphDef tfGraph,
Map<String,TFImportOverride> importOverride,
TFOpImportFilter opFilter)
Import a TensorFlow model from a GraphDef, with optional import overrides
|
static SameDiff |
TFGraphMapper.importGraph(@NonNull InputStream is)
Import a frozen TensorFlow protobuf (.pb) file, via an input stream
|
static SameDiff |
TFGraphMapper.importGraph(@NonNull InputStream is,
Map<String,TFImportOverride> importOverride,
TFOpImportFilter opFilter)
Import a frozen TensorFlow protobuf (.pb) file via an input stream, with optional overrides
|
static SameDiff |
TFGraphMapper.importGraphTxt(@NonNull InputStream is,
Map<String,TFImportOverride> importOverride,
TFOpImportFilter opFilter)
Import a frozen TensorFlow protobuf file in text format (.pb.txt) file via an input stream, with optional overrides
|
Modifier and Type | Method and Description |
---|---|
SDVariable |
Activation.asSameDiff(SameDiff sd,
SDVariable input)
Get the Activation as a SameDiff variable
|
SDVariable |
Activation.asSameDiff(String variableName,
SameDiff sd,
SDVariable input)
Get the Activation as a SameDiff variable
|
Modifier and Type | Method and Description |
---|---|
void |
BaseOp.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
BaseBroadcastBoolOp.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
DynamicCustomOp.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
BaseReduceOp.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
BaseBroadcastOp.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
NoOp.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
BaseOp.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
BaseBroadcastBoolOp.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
DynamicCustomOp.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
BaseReduceOp.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
BaseBroadcastOp.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
NoOp.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
BaseBroadcastBoolOp(SameDiff sameDiff) |
BaseBroadcastBoolOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BaseBroadcastBoolOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BaseBroadcastBoolOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BaseBroadcastBoolOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BaseBroadcastBoolOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BaseBroadcastBoolOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BaseBroadcastOp(SameDiff sameDiff) |
BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BaseIndexAccumulation(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
BaseIndexAccumulation(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
boolean keepDims,
int[] dimensions) |
BaseOp(SameDiff sameDiff,
boolean inPlace,
Object[] extraArgs) |
BaseOp(SameDiff sameDiff,
Object[] extraArgs) |
BaseReduceBoolOp(SameDiff sameDiff,
SDVariable input,
int... dimensions) |
BaseReduceBoolOp(SameDiff sameDiff,
SDVariable input,
int[] dimensions,
boolean keepDims) |
BaseReduceBoolOp(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
BaseReduceFloatOp(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
BaseReduceFloatOp(SameDiff sameDiff,
SDVariable input,
int... dimensions) |
BaseReduceFloatOp(SameDiff sameDiff,
SDVariable input,
int[] dimensions,
boolean keepDims) |
BaseReduceFloatOp(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
BaseReduceLongOp(SameDiff sameDiff,
SDVariable input,
int... dimensions) |
BaseReduceLongOp(SameDiff sameDiff,
SDVariable input,
int[] dimensions,
boolean keepDims) |
BaseReduceLongOp(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
BaseReduceOp(SameDiff sameDiff) |
BaseReduceOp(SameDiff sameDiff,
SDVariable i_v) |
BaseReduceOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
BaseReduceOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions,
boolean keepDims) |
BaseReduceOp(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
BaseReduceOp(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions,
boolean keepDims) |
BaseReduceSameOp(SameDiff sameDiff,
SDVariable input,
int... dimensions) |
BaseReduceSameOp(SameDiff sameDiff,
SDVariable input,
int[] dimensions,
boolean keepDims) |
BaseReduceSameOp(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
BaseScalarBoolOp(SameDiff sameDiff,
SDVariable i_v,
Number scalar) |
BaseScalarBoolOp(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace) |
BaseScalarBoolOp(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace,
Object[] extraArgs) |
BaseScalarBoolOp(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
Object[] extraArgs) |
BaseScalarOp(SameDiff sameDiff,
SDVariable i_v,
Number scalar) |
BaseScalarOp(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace) |
BaseScalarOp(SameDiff sameDiff,
@NonNull SDVariable i_v,
Number scalar,
boolean inPlace,
Object[] extraArgs) |
BaseScalarOp(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
Object[] extraArgs) |
BaseTransformAnyOp(SameDiff sameDiff) |
BaseTransformAnyOp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
BaseTransformAnyOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
Object[] extraArgs) |
BaseTransformAnyOp(SameDiff sameDiff,
SDVariable i_v,
Object[] extraArgs) |
BaseTransformAnyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
BaseTransformAnyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
BaseTransformAnyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
Object[] extraArgs) |
BaseTransformBoolOp(SameDiff sameDiff) |
BaseTransformBoolOp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
BaseTransformBoolOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
Object[] extraArgs) |
BaseTransformBoolOp(SameDiff sameDiff,
SDVariable i_v,
Object[] extraArgs) |
BaseTransformBoolOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
BaseTransformBoolOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
BaseTransformBoolOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
Object[] extraArgs) |
BaseTransformFloatOp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
BaseTransformFloatOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
Object[] extraArgs) |
BaseTransformFloatOp(SameDiff sameDiff,
SDVariable i_v,
Object[] extraArgs) |
BaseTransformOp(SameDiff sameDiff) |
BaseTransformOp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
BaseTransformOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
Object[] extraArgs) |
BaseTransformOp(SameDiff sameDiff,
SDVariable i_v,
Object[] extraArgs) |
BaseTransformOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
BaseTransformOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
BaseTransformOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
Object[] extraArgs) |
BaseTransformSameOp(SameDiff sameDiff) |
BaseTransformSameOp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
BaseTransformSameOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
Object[] extraArgs) |
BaseTransformSameOp(SameDiff sameDiff,
SDVariable i_v,
Object[] extraArgs) |
BaseTransformSameOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
BaseTransformSameOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
BaseTransformSameOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
Object[] extraArgs) |
BaseTransformStrictOp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
BaseTransformStrictOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
Object[] extraArgs) |
BaseTransformStrictOp(SameDiff sameDiff,
SDVariable i_v,
Object[] extraArgs) |
BaseTransformStrictOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
BaseTransformStrictOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
DynamicCustomOp(SameDiff sameDiff,
SDVariable arg) |
DynamicCustomOp(SameDiff sameDiff,
SDVariable[] args) |
DynamicCustomOp(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
DynamicCustomOp(String opName,
SameDiff sameDiff,
SDVariable[] args) |
DynamicCustomOp(String opName,
SameDiff sameDiff,
SDVariable[] args,
boolean inPlace)
Initialize this for
SameDiff execution
Any extra int or float arguments for operations
must be added to the respective TArguments
or IArguments lists upon construction |
NoOp(SameDiff sd,
SDVariable in) |
Modifier and Type | Method and Description |
---|---|
void |
FakeQuantWithMinMaxVarsPerChannel.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
LinearSolve.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
TriangularSolve.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
FusedBatchNorm.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
BitCast.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Lu.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
AdjustContrast(@NonNull SameDiff sameDiff,
@NonNull SDVariable in,
double factor) |
AdjustContrast(@NonNull SameDiff sameDiff,
@NonNull SDVariable in,
@NonNull SDVariable factor) |
AdjustContrastV2(@NonNull SameDiff sameDiff,
@NonNull SDVariable in,
@NonNull SDVariable factor) |
AdjustHue(@NonNull SameDiff sameDiff,
@NonNull SDVariable in,
double factor) |
AdjustHue(@NonNull SameDiff sameDiff,
@NonNull SDVariable in,
@NonNull SDVariable factor) |
AdjustSaturation(@NonNull SameDiff sameDiff,
@NonNull SDVariable in,
double factor) |
AdjustSaturation(@NonNull SameDiff sameDiff,
@NonNull SDVariable in,
@NonNull SDVariable factor) |
BaseAdjustContrast(@NonNull SameDiff sameDiff,
@NonNull SDVariable[] vars) |
BetaInc(@NonNull SameDiff sameDiff,
@NonNull SDVariable a,
@NonNull SDVariable b,
@NonNull SDVariable x) |
BitCast(SameDiff sameDiff,
SDVariable in,
SDVariable dataType) |
CompareAndBitpack(SameDiff sameDiff,
SDVariable threshold) |
Digamma(@NonNull SameDiff sameDiff,
@NonNull SDVariable x) |
DivideNoNan(SameDiff sameDiff,
SDVariable in1,
SDVariable in2) |
DrawBoundingBoxes(SameDiff sameDiff,
SDVariable boxes,
SDVariable colors) |
FakeQuantWithMinMaxVarsPerChannel(SameDiff sameDiff,
SDVariable x,
SDVariable min,
SDVariable max,
int num_bits,
boolean narrow) |
Flatten(SameDiff sameDiff,
char order,
SDVariable... inputs) |
FusedBatchNorm(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable scale,
@NonNull SDVariable offset,
int dataFormat,
int isTraining) |
FusedBatchNorm(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable scale,
@NonNull SDVariable offset,
@NonNull SDVariable dataFormat,
@NonNull SDVariable isTraining) |
HsvToRgb(SameDiff sameDiff,
SDVariable input) |
Igamma(@NonNull SameDiff sameDiff,
@NonNull SDVariable n,
@NonNull SDVariable x) |
Igammac(@NonNull SameDiff sameDiff,
@NonNull SDVariable n,
@NonNull SDVariable x) |
Lgamma(@NonNull SameDiff sameDiff,
@NonNull SDVariable x) |
LinearSolve(SameDiff sameDiff,
SDVariable a,
SDVariable b,
boolean adjoint) |
LinearSolve(SameDiff sameDiff,
SDVariable a,
SDVariable b,
SDVariable adjoint) |
Logdet(SameDiff sameDiff,
SDVariable input) |
Lstsq(@NonNull SameDiff sameDiff,
@NonNull SDVariable matrix,
@NonNull SDVariable rhs,
double l2_regularizer,
boolean fast) |
Lu(SameDiff sameDiff,
SDVariable input) |
MatrixBandPart(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
int minLower,
int maxUpper) |
MatrixBandPart(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
SDVariable minLower,
SDVariable maxUpper) |
Polygamma(@NonNull SameDiff sameDiff,
@NonNull SDVariable n,
@NonNull SDVariable x) |
RandomCrop(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable shape) |
RgbToGrayscale(SameDiff sameDiff,
SDVariable image) |
RgbToHsv(SameDiff sameDiff,
SDVariable input) |
RgbToYiq(SameDiff sameDiff,
SDVariable input) |
RgbToYuv(SameDiff sameDiff,
SDVariable input) |
Roll(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
int shift) |
Roll(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable shift) |
Roll(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable axes,
@NonNull SDVariable shift) |
ToggleBits(@NonNull SameDiff sameDiff,
@NonNull SDVariable input) |
Tri(SameDiff sameDiff,
DataType dataType,
int row,
int column,
int diag) |
Tri(SameDiff sameDiff,
int row,
int column,
int diag) |
TriangularSolve(SameDiff sameDiff,
SDVariable matrix,
SDVariable rhs,
boolean lower,
boolean adjoint) |
TriangularSolve(SameDiff sameDiff,
SDVariable matrix,
SDVariable rhs,
SDVariable lower,
SDVariable adjoint) |
Triu(SameDiff sameDiff,
SDVariable in) |
Triu(SameDiff sameDiff,
SDVariable in,
int diag) |
TriuBp(SameDiff sameDiff,
SDVariable in,
SDVariable grad) |
TriuBp(SameDiff sameDiff,
SDVariable in,
SDVariable grad,
int diag) |
YiqToRgb(SameDiff sameDiff,
SDVariable input) |
YuvToRgb(SameDiff sameDiff,
SDVariable input) |
Modifier and Type | Method and Description |
---|---|
void |
BroadcastTo.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
BiasAdd.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
BiasAdd(SameDiff sameDiff,
SDVariable input,
SDVariable bias,
boolean nchw) |
BiasAddGrad(SameDiff sameDiff,
SDVariable input,
SDVariable bias,
SDVariable gradient,
boolean nchw) |
BroadcastAddOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastAddOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastAddOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastAMax(SameDiff sameDiff) |
BroadcastAMax(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastAMax(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastAMax(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastAMax(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastAMax(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastAMax(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastAMin(SameDiff sameDiff) |
BroadcastAMin(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastAMin(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastAMin(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastAMin(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastAMin(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastAMin(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastCopyOp(SameDiff sameDiff) |
BroadcastCopyOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastCopyOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastCopyOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastCopyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastCopyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastCopyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastDivOp(SameDiff sameDiff) |
BroadcastDivOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastDivOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastDivOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastDivOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastDivOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastDivOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastGradientArgs(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastGradientArgs(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastGradientArgs(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastMax(SameDiff sameDiff) |
BroadcastMax(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastMax(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastMax(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastMax(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastMax(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastMax(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastMin(SameDiff sameDiff) |
BroadcastMin(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastMin(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastMin(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastMin(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastMin(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastMin(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastMulOp(SameDiff sameDiff) |
BroadcastMulOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastMulOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastMulOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastMulOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastMulOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastMulOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastRDivOp(SameDiff sameDiff) |
BroadcastRDivOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastRDivOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastRDivOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastRDivOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastRDivOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastRDivOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastRSubOp(SameDiff sameDiff) |
BroadcastRSubOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastRSubOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastRSubOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastRSubOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastRSubOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastRSubOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastSubOp(SameDiff sameDiff) |
BroadcastSubOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastSubOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastSubOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastSubOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastSubOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastSubOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastTo(SameDiff sameDiff,
SDVariable input,
SDVariable shape) |
Constructor and Description |
---|
BroadcastEqualTo(SameDiff sameDiff) |
BroadcastEqualTo(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastEqualTo(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastEqualTo(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastGreaterThan(SameDiff sameDiff) |
BroadcastGreaterThan(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastGreaterThan(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastGreaterThan(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastGreaterThan(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastGreaterThan(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastGreaterThan(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastGreaterThanOrEqual(SameDiff sameDiff) |
BroadcastGreaterThanOrEqual(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastGreaterThanOrEqual(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastGreaterThanOrEqual(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastGreaterThanOrEqual(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastGreaterThanOrEqual(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastGreaterThanOrEqual(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastLessThan(SameDiff sameDiff) |
BroadcastLessThan(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastLessThan(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastLessThan(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastLessThan(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastLessThan(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastLessThan(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastLessThanOrEqual(SameDiff sameDiff) |
BroadcastLessThanOrEqual(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastLessThanOrEqual(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastLessThanOrEqual(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastLessThanOrEqual(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastLessThanOrEqual(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastLessThanOrEqual(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
BroadcastNotEqual(SameDiff sameDiff) |
BroadcastNotEqual(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
boolean inPlace) |
BroadcastNotEqual(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) |
BroadcastNotEqual(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) |
BroadcastNotEqual(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) |
BroadcastNotEqual(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) |
BroadcastNotEqual(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) |
Modifier and Type | Method and Description |
---|---|
void |
Select.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
Select(SameDiff sameDiff,
SDVariable[] args) |
Select(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
Where(SameDiff sameDiff,
SDVariable[] args) |
Where(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
WhereNumpy(SameDiff sameDiff,
SDVariable[] args) |
WhereNumpy(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
Modifier and Type | Method and Description |
---|---|
void |
Exit.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Switch.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
LoopCond.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
NextIteration.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
BaseCompatOp.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Enter.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Merge.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
BaseCompatOp(SameDiff sameDiff,
SDVariable[] inputs) |
Enter(SameDiff sameDiff,
SDVariable[] inputs) |
Enter(SameDiff sameDiff,
String frameName,
SDVariable input) |
Enter(SameDiff sameDiff,
String frameName,
SDVariable input,
boolean isConstant) |
Exit(SameDiff sameDiff,
SDVariable x) |
Merge(SameDiff sd,
SDVariable[] inputs) |
Merge(SameDiff sd,
SDVariable a,
SDVariable b) |
NextIteration(SameDiff sameDiff,
SDVariable x) |
StopGradient(SameDiff sd,
SDVariable in) |
Switch(SameDiff sameDiff,
SDVariable input,
SDVariable predicate) |
Modifier and Type | Method and Description |
---|---|
void |
CropAndResize.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ResizeNearestNeighbor.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ResizeBilinear.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ResizeBicubic.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ResizeArea.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ExtractImagePatches.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
CropAndResize(@NonNull SameDiff sameDiff,
@NonNull SDVariable image,
@NonNull SDVariable cropBoxes,
@NonNull SDVariable boxIndices,
@NonNull SDVariable cropOutSize,
@NonNull CropAndResize.Method method,
double extrapolationValue) |
CropAndResize(@NonNull SameDiff sameDiff,
SDVariable image,
SDVariable cropBoxes,
SDVariable boxIndices,
SDVariable cropOutSize,
double extrapolationValue) |
ExtractImagePatches(@NonNull SameDiff samediff,
@NonNull SDVariable input,
@NonNull int[] kSizes,
@NonNull int[] strides,
@NonNull int[] rates,
boolean sameMode) |
ExtractImagePatches(@NonNull SameDiff samediff,
@NonNull SDVariable input,
int kH,
int kW,
int sH,
int sW,
int rH,
int rW,
boolean sameMode) |
ImageResize(@NonNull SameDiff sameDiff,
@NonNull SDVariable in,
@NonNull SDVariable size,
boolean preserveAspectRatio,
boolean antialias,
ImageResizeMethod method) |
NonMaxSuppression(SameDiff sameDiff,
SDVariable boxes,
SDVariable scores,
int maxOutSize,
double iouThreshold,
double scoreThreshold) |
NonMaxSuppression(SameDiff sameDiff,
@NonNull SDVariable boxes,
@NonNull SDVariable scores,
@NonNull SDVariable maxOutSize,
@NonNull SDVariable iouThreshold,
@NonNull SDVariable scoreThreshold) |
NonMaxSuppressionV3(SameDiff sameDiff,
@NonNull SDVariable boxes,
@NonNull SDVariable scores,
@NonNull SDVariable maxOutSize,
@NonNull SDVariable iouThreshold,
@NonNull SDVariable scoreThreshold) |
ResizeArea(@NonNull SameDiff sd,
@NonNull SDVariable image,
int height,
int width,
boolean alignCorners) |
ResizeBicubic(@NonNull SameDiff sameDiff,
@NonNull SDVariable image,
SDVariable size,
boolean alignCorners,
boolean alignPixelCenters) |
ResizeBilinear(@NonNull SameDiff sd,
@NonNull SDVariable input,
int height,
int width,
boolean alignCorners,
boolean halfPixelCenters) |
Constructor and Description |
---|
FirstIndex(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
Condition condition,
int... dimensions) |
FirstIndex(SameDiff sameDiff,
SDVariable i_v,
Condition condition,
boolean keepDims,
int... dimensions) |
IAMax(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
IAMin(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
IMax(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
IMin(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
LastIndex(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
Condition condition,
int... dimensions) |
LastIndex(SameDiff sameDiff,
SDVariable i_v,
Condition condition,
boolean keepDims,
int... dimensions) |
LastIndex(SameDiff sameDiff,
SDVariable x,
@NonNull Condition condition,
int... dimensions) |
Modifier and Type | Method and Description |
---|---|
void |
ArgMin.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ArgMax.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Modifier and Type | Method and Description |
---|---|
void |
ExternalErrorsFunction.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
ExternalErrorsFunction.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
ExternalErrorsFunction(SameDiff sd,
List<SDVariable> inputs,
Map<String,INDArray> gradients) |
Modifier and Type | Method and Description |
---|---|
void |
Pooling2D.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
MaxPooling3D.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
DeConv2D.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
LocalResponseNormalization.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
BatchNorm.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
DepthwiseConv2D.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
AvgPooling2D.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
AvgPooling3D.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Conv2D.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
MaxPooling2D.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Pooling2D.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
DeConv2D.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
DeConv2DTF.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
LocalResponseNormalization.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Conv3D.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
SpaceToDepth.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Pooling3D.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
BatchNorm.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
DepthwiseConv2D.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
AvgPooling2D.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
DeConv3DTF.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
MaxPoolWithArgmax.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
DepthToSpace.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Conv2D.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
MaxPooling2D.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
AvgPooling2D(SameDiff sameDiff,
SDVariable input,
Pooling2DConfig config) |
AvgPooling3D(SameDiff sameDiff,
SDVariable input,
Pooling3DConfig config) |
BatchNorm(SameDiff sameDiff,
SDVariable[] inputFunctions,
INDArray[] inputArrays,
INDArray[] outputArrays,
boolean inPlace,
boolean applyGamma,
boolean applyBeta,
double epsilon,
int[] axis) |
BatchNorm(SameDiff sameDiff,
SDVariable input,
SDVariable mean,
SDVariable variance,
SDVariable gamma,
SDVariable beta,
double epsilon,
int[] axis) |
BatchNormDerivative(SameDiff sameDiff,
SDVariable[] inputFunctions,
INDArray[] inputArrays,
INDArray[] outputArrays,
boolean inPlace,
boolean applyGamma,
boolean applyBeta,
double epsilon,
int[] axis) |
Col2Im(SameDiff sameDiff,
SDVariable[] inputFunctions,
INDArray[] inputArrays,
INDArray[] outputs,
Conv2DConfig conv2DConfig) |
Col2Im(@NonNull SameDiff sd,
@NonNull SDVariable input,
@NonNull Conv2DConfig config) |
Conv1D(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv1DConfig config) |
Conv1D(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
@NonNull Conv1DConfig conv1DConfig) |
Conv1DDerivative(@NonNull SameDiff sameDiff,
@NonNull SDVariable[] inputs,
@NonNull Conv1DConfig config) |
Conv1DDerivative(@NonNull SameDiff sd,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
SDVariable gradOut,
@NonNull Conv1DConfig config) |
Conv2D(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv2DConfig config) |
Conv2D(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
@NonNull Conv2DConfig conv2DConfig) |
Conv2DDerivative(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv2DConfig config) |
Conv3D(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv3DConfig config) |
Conv3D(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
@NonNull Conv3DConfig config) |
Conv3DDerivative(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv3DConfig conv3DConfig) |
DeConv2D(SameDiff sameDiff,
SDVariable[] inputs,
DeConv2DConfig config) |
DeConv2D(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
DeConv2DConfig config) |
DeConv2DDerivative(SameDiff sameDiff,
SDVariable[] inputs,
DeConv2DConfig config) |
DeConv2DTF(SameDiff sameDiff,
SDVariable[] inputs,
DeConv2DConfig config) |
DeConv3D(SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
@NonNull DeConv3DConfig config) |
DeConv3D(SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
@NonNull DeConv3DConfig config) |
DeConv3DDerivative(SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
SDVariable grad,
DeConv3DConfig config) |
DeConv3DTF(@NonNull SameDiff sameDiff,
@NonNull SDVariable shape,
@NonNull SDVariable weights,
@NonNull SDVariable input,
@NonNull DeConv3DConfig config) |
DepthToSpace(SameDiff sameDiff,
SDVariable[] args,
int blockSize,
DataFormat dataFormat) |
DepthToSpace(SameDiff sameDiff,
SDVariable args,
int blockSize,
DataFormat dataFormat) |
DepthwiseConv2D(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv2DConfig config) |
DepthwiseConv2D(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
@NonNull Conv2DConfig conv2DConfig) |
DepthwiseConv2DBp(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
@NonNull SDVariable gradO,
@NonNull Conv2DConfig config) |
DepthwiseConv2DBp(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable weights,
SDVariable bias,
@NonNull SDVariable gradO,
@NonNull Conv2DConfig config) |
Im2col(SameDiff sameDiff,
SDVariable[] inputFunctions,
INDArray[] inputArrays,
INDArray[] outputs,
Conv2DConfig conv2DConfig) |
Im2col(SameDiff sd,
SDVariable input,
Conv2DConfig config) |
Im2colBp(SameDiff sd,
SDVariable input,
Conv2DConfig config) |
Im2colBp(SameDiff sameDiff,
SDVariable i2cInput,
SDVariable gradAtOutput,
Conv2DConfig conv2DConfig) |
LocalResponseNormalization(SameDiff sameDiff,
SDVariable[] inputFunctions,
boolean inPlace,
LocalResponseNormalizationConfig config) |
LocalResponseNormalization(SameDiff sameDiff,
SDVariable input,
LocalResponseNormalizationConfig config) |
LocalResponseNormalizationDerivative(SameDiff sameDiff,
SDVariable[] inputFunctions,
boolean inPlace,
LocalResponseNormalizationConfig config) |
MaxPooling2D(SameDiff sameDiff,
SDVariable input,
Pooling2DConfig config) |
MaxPooling3D(SameDiff sameDiff,
SDVariable input,
Pooling3DConfig config) |
MaxPoolWithArgmax(SameDiff sameDiff,
SDVariable input,
Pooling2DConfig config) |
Pooling2D(SameDiff sameDiff,
SDVariable[] inputs,
Pooling2DConfig config) |
Pooling2DDerivative(SameDiff sameDiff,
SDVariable[] inputs,
Pooling2DConfig config) |
Pooling3D(SameDiff sameDiff,
SDVariable[] inputs,
INDArray[] inputArrays,
INDArray[] outputs,
boolean inPlace,
Pooling3DConfig pooling3DConfig,
Pooling3D.Pooling3DType type) |
Pooling3DDerivative(SameDiff sameDiff,
SDVariable[] inputs,
INDArray[] inputArrays,
INDArray[] outputs,
boolean inPlace,
Pooling3DConfig pooling3DConfig,
Pooling3D.Pooling3DType type) |
SConv2D(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv2DConfig conv2DConfig) |
SConv2D(@NonNull SameDiff sameDiff,
@NonNull SDVariable layerInput,
@NonNull SDVariable depthWeights,
SDVariable pointWeights,
SDVariable bias,
@NonNull Conv2DConfig conv2DConfig) |
SConv2DDerivative(SameDiff sameDiff,
SDVariable[] inputFunctions,
Conv2DConfig conv2DConfig) |
SpaceToDepth(SameDiff sameDiff,
SDVariable[] args,
int blockSize,
DataFormat dataFormat) |
SpaceToDepth(SameDiff sameDiff,
SDVariable x,
int blockSize,
DataFormat dataFormat) |
Upsampling2d(SameDiff sameDiff,
SDVariable input,
boolean nchw,
int scaleH,
int scaleW) |
Upsampling2d(SameDiff sameDiff,
SDVariable input,
int scale) |
Upsampling2d(SameDiff sameDiff,
SDVariable input,
int scaleH,
int scaleW,
boolean nchw) |
Upsampling2dDerivative(SameDiff sameDiff,
SDVariable input,
SDVariable gradient,
boolean nchw,
int scaleH,
int scaleW) |
Upsampling3d(SameDiff sameDiff,
SDVariable input,
boolean ncdhw,
int scaleD,
int scaleH,
int scaleW) |
Upsampling3dBp(SameDiff sameDiff,
SDVariable input,
SDVariable grad0,
boolean ncdhw) |
Modifier and Type | Method and Description |
---|---|
void |
SRU.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
SRUCell.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
SRU.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
LSTMBlockCell.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
LSTMBlock.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
SRUCell.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
GRU(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable hI,
@NonNull SDVariable Wx,
@NonNull SDVariable Wh,
@NonNull SDVariable biases) |
GRUBp(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable hI,
@NonNull SDVariable Wx,
@NonNull SDVariable Wh,
@NonNull SDVariable biases,
@NonNull SDVariable dLdh) |
GRUCell(SameDiff sameDiff,
SDVariable x,
SDVariable hLast,
GRUWeights weights) |
LSTMBlock(@NonNull SameDiff sameDiff,
SDVariable maxTSLength,
SDVariable x,
SDVariable cLast,
SDVariable yLast,
LSTMWeights weights,
LSTMConfiguration configuration) |
LSTMBlockCell(SameDiff sameDiff,
SDVariable x,
SDVariable cLast,
SDVariable yLast,
LSTMWeights weights,
LSTMConfiguration configuration) |
LSTMCell(SameDiff sameDiff,
LSTMCellConfiguration configuration) |
LSTMLayer(@NonNull SameDiff sameDiff,
SDVariable x,
SDVariable cLast,
SDVariable yLast,
SDVariable maxTSLength,
LSTMLayerWeights weights,
LSTMLayerConfig configuration) |
LSTMLayerBp(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
SDVariable cLast,
SDVariable yLast,
SDVariable maxTSLength,
@NonNull LSTMLayerWeights weights,
@NonNull LSTMLayerConfig configuration,
SDVariable dLdh,
SDVariable dLdhL,
SDVariable dLdcL) |
SRU(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable initialC,
SDVariable mask,
@NonNull SRUWeights weights) |
SRUCell(SameDiff sameDiff,
SDVariable x,
SDVariable cLast,
SRUWeights weights) |
Modifier and Type | Method and Description |
---|---|
protected static SDVariable |
BaseLoss.getWeights(SameDiff sd,
SDVariable weights,
SDVariable predictions) |
void |
SoftmaxCrossEntropyLoss.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
SparseSoftmaxCrossEntropyLossWithLogits.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
AbsoluteDifferenceLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels) |
AbsoluteDifferenceLoss(SameDiff sameDiff,
SDVariable label,
SDVariable predictions,
SDVariable weights,
LossReduce lossReduce) |
BaseLoss(@NonNull SameDiff sameDiff,
@NonNull LossReduce lossReduce,
@NonNull SDVariable predictions,
SDVariable weights,
@NonNull SDVariable labels) |
CosineDistanceLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels,
int dimension) |
CosineDistanceLoss(SameDiff sameDiff,
SDVariable labels,
SDVariable predictions,
SDVariable weights,
LossReduce lossReduce,
int dimension) |
HingeLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels) |
HingeLoss(SameDiff sameDiff,
SDVariable labels,
SDVariable predictions,
SDVariable weights,
LossReduce lossReduce) |
HuberLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels,
double delta) |
HuberLoss(SameDiff sameDiff,
SDVariable labels,
SDVariable predictions,
SDVariable weights,
LossReduce lossReduce,
double delta) |
L2Loss(SameDiff sameDiff,
SDVariable var) |
LogLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels,
double epsilon) |
LogLoss(SameDiff sameDiff,
SDVariable labels,
SDVariable predictions,
SDVariable weights,
LossReduce lossReduce,
double epsilon) |
LogPoissonLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels) |
LogPoissonLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels,
boolean full) |
LogPoissonLoss(SameDiff sameDiff,
SDVariable labels,
SDVariable predictions,
SDVariable weights,
LossReduce lossReduce,
boolean full) |
MeanPairwiseSquaredErrorLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels) |
MeanPairwiseSquaredErrorLoss(SameDiff sameDiff,
SDVariable labels,
SDVariable predictions,
SDVariable weights,
LossReduce lossReduce) |
MeanSquaredErrorLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels) |
MeanSquaredErrorLoss(SameDiff sameDiff,
SDVariable labels,
SDVariable predictions,
SDVariable weights,
LossReduce lossReduce) |
SigmoidCrossEntropyLoss(SameDiff sameDiff,
LossReduce reductionMode,
SDVariable logits,
SDVariable weights,
SDVariable labels) |
SigmoidCrossEntropyLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable logits,
SDVariable weights,
SDVariable labels,
double labelSmoothing) |
SigmoidCrossEntropyLoss(SameDiff sameDiff,
SDVariable labels,
SDVariable logits,
SDVariable weights,
LossReduce lossReduce,
double labelSmoothing) |
SoftmaxCrossEntropyLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable logits,
SDVariable weights,
SDVariable labels) |
SoftmaxCrossEntropyLoss(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable logits,
SDVariable weights,
SDVariable labels,
double labelSmoothing) |
SoftmaxCrossEntropyLoss(SameDiff sameDiff,
SDVariable labels,
SDVariable logits,
SDVariable weights,
LossReduce lossReduce,
double labelSmoothing) |
SoftmaxCrossEntropyWithLogitsLoss(SameDiff sameDiff,
SDVariable logits,
SDVariable labels,
int classesDim) |
SparseSoftmaxCrossEntropyLossWithLogits(@NonNull SameDiff sameDiff,
@NonNull SDVariable logits,
@NonNull SDVariable labels) |
WeightedCrossEntropyLoss(SameDiff sameDiff,
SDVariable targets,
SDVariable inputs,
SDVariable weights) |
Constructor and Description |
---|
AbsoluteDifferenceLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels) |
BaseLossBp(@NonNull SameDiff sameDiff,
@NonNull LossReduce lossReduce,
@NonNull SDVariable predictions,
@NonNull SDVariable weights,
@NonNull SDVariable labels) |
CosineDistanceLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels,
int dimension) |
HingeLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels) |
HuberLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels,
double delta) |
LogLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels,
double epsilon) |
LogPoissonLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels) |
LogPoissonLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels,
boolean full) |
MeanPairwiseSquaredErrorLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels) |
MeanSquaredErrorLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable predictions,
SDVariable weights,
SDVariable labels) |
SigmoidCrossEntropyLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable logits,
SDVariable weights,
SDVariable labels,
double labelSmoothing) |
SoftmaxCrossEntropyLossBp(SameDiff sameDiff,
LossReduce lossReduce,
SDVariable logits,
SDVariable weights,
SDVariable labels,
double labelSmoothing) |
SoftmaxCrossEntropyWithLogitsLossBp(SameDiff sameDiff,
SDVariable logits,
SDVariable labels,
int classesDim) |
SparseSoftmaxCrossEntropyLossWithLogitsBp(SameDiff sameDiff,
SDVariable logits,
SDVariable labels) |
Modifier and Type | Method and Description |
---|---|
void |
TensorMmul.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Mmul.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
TensorMmul.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Mmul.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
NormalizeMoments.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Moments.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
Mmul(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
Mmul(SameDiff sameDiff,
SDVariable x,
SDVariable y,
boolean transposeX,
boolean transposeY,
boolean transposeZ) |
Mmul(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
MMulTranspose mt) |
MmulBp(SameDiff sameDiff,
SDVariable x,
SDVariable y,
SDVariable eps) |
MmulBp(SameDiff sameDiff,
SDVariable x,
SDVariable y,
SDVariable eps,
MMulTranspose mt) |
Moments(SameDiff sameDiff,
SDVariable input) |
Moments(SameDiff sameDiff,
SDVariable input,
int[] axes) |
NormalizeMoments(SameDiff sameDiff,
SDVariable counts,
SDVariable means,
SDVariable variances) |
NormalizeMoments(SameDiff sameDiff,
SDVariable counts,
SDVariable means,
SDVariable variances,
double shift) |
SufficientStatistics(SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable axis,
SDVariable shift) |
TensorMmul(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[][] dimensions) |
TensorMmul(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[][] dimensions,
MMulTranspose mMulTranspose) |
TensorMmul(SameDiff sameDiff,
SDVariable x,
SDVariable y,
int[] dimensionsX,
int[] dimensionsY,
boolean transposeX,
boolean transposeY,
boolean transposeZ) |
ZeroFraction(SameDiff sameDiff,
SDVariable input) |
Constructor and Description |
---|
All(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
Any(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
IsInf(SameDiff sameDiff,
SDVariable i_v,
int[] dims) |
IsInf(SameDiff sameDiff,
SDVariable i_v,
int[] dims,
boolean keepDims) |
IsNaN(SameDiff sameDiff,
SDVariable i_v,
int[] dims) |
IsNaN(SameDiff sameDiff,
SDVariable i_v,
int[] dims,
boolean keepDims) |
Constructor and Description |
---|
BaseReductionBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
BaseReductionBp(SameDiff sameDiff,
SDVariable origInput1,
SDVariable origInput2,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
CumProdBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean exclusive,
boolean reverse,
int... axis) |
CumSumBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean exclusive,
boolean reverse,
int... axis) |
DotBp(SameDiff sameDiff,
SDVariable origInput1,
SDVariable origInput2,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
MaxBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
MeanBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
MinBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
Norm1Bp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
Norm2Bp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
NormMaxBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
PowBp(SameDiff sameDiff,
SDVariable x,
SDVariable y,
SDVariable dLdz) |
ProdBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
SquaredNormBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
StandardDeviationBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean biasCorrected,
boolean keepDims,
int... dimensions) |
SumBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean keepDims,
int... dimensions) |
VarianceBp(SameDiff sameDiff,
SDVariable origInput,
SDVariable gradAtOutput,
boolean biasCorrected,
boolean keepDims,
int... dimensions) |
Constructor and Description |
---|
BatchMmul(SameDiff sameDiff,
SDVariable[] matrices,
boolean transposeA,
boolean transposeB) |
BatchMmul(SameDiff sameDiff,
SDVariable[] matricesA,
SDVariable[] matricesB,
boolean transposeA,
boolean transposeB) |
LogSumExp(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
LogSumExp(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
Modifier and Type | Method and Description |
---|---|
static List<SDVariable> |
Entropy.grad(SameDiff sd,
SDVariable arg,
SDVariable grad,
int[] dimensions) |
Constructor and Description |
---|
AMean(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
AMean(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
Entropy(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
LogEntropy(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
Mean(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
Norm1(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
Norm2(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
NormMax(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
NormMax(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
ShannonEntropy(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
ShannonEntropy(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
SquaredNorm(SameDiff sameDiff,
SDVariable input,
boolean keepDims,
int... dimensions) |
Constructor and Description |
---|
CountNonZero(SameDiff sameDiff,
SDVariable input,
int... dimensions) |
CountZero(SameDiff sameDiff,
SDVariable input,
int... dimensions) |
MatchCondition(SameDiff sameDiff,
SDVariable in,
Condition condition) |
MatchCondition(SameDiff sameDiff,
SDVariable in,
Condition condition,
boolean keepDims,
int... dimensions) |
Constructor and Description |
---|
AMax(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
AMax(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
AMin(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
AMin(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
ASum(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
ASum(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
Max(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
Max(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
Min(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
Prod(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
Prod(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
Sum(SameDiff sameDiff,
SDVariable i_v,
boolean keepDims,
int[] dimensions) |
Sum(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
Modifier and Type | Method and Description |
---|---|
static List<SDVariable> |
CosineSimilarity.doDiff(SameDiff sameDiff,
SDVariable x,
SDVariable y,
SDVariable gradOut,
boolean keepDims,
int... dimensions) |
Constructor and Description |
---|
BaseReduce3Op(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
BaseReduce3Op(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int... dimensions) |
CosineDistance(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int... dimensions) |
CosineSimilarity(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
CosineSimilarity(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
Dot(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int... dimensions) |
EqualsWithEps(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions,
double eps) |
EqualsWithEps(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions,
double eps) |
EuclideanDistance(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
EuclideanDistance(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) |
HammingDistance(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int... dimensions) |
JaccardDistance(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int... dimensions) |
ManhattanDistance(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) |
ManhattanDistance(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int... dimensions) |
Modifier and Type | Method and Description |
---|---|
void |
Relu6.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
LeakyReLU.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
LeakyReLU(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double alpha) |
LeakyReLU(SameDiff sameDiff,
SDVariable i_v,
double alpha) |
LeakyReLU(SameDiff sameDiff,
SDVariable i_v,
Object[] extraArgs,
double alpha) |
LogX(SameDiff sameDiff,
SDVariable i_v,
double base) |
Pow(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double pow) |
Pow(SameDiff sameDiff,
SDVariable i_v,
double pow) |
Pow(SameDiff sameDiff,
SDVariable i_v,
Object[] extraArgs,
double pow) |
PowDerivative(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double pow) |
PRelu(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable alpha,
int... sharedAxes) |
RectifiedLinear(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double cutoff) |
RectifiedLinear(SameDiff sameDiff,
SDVariable i_v,
double cutoff) |
RectifiedLinearDerivative(SameDiff sd,
SDVariable input,
SDVariable gradient) |
Relu6(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double cutoff) |
Relu6(SameDiff sameDiff,
SDVariable i_v,
double cutoff) |
ReplaceNans(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double set) |
ReplaceNans(SameDiff sameDiff,
SDVariable i_v,
Object[] extraArgs,
double set) |
ScalarAdd(@NonNull SameDiff sameDiff,
@NonNull SDVariable i_v,
Number scalar) |
ScalarAdd(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace) |
ScalarAdd(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace,
Object[] extraArgs) |
ScalarAdd(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
Object[] extraArgs) |
ScalarDivision(SameDiff sameDiff,
SDVariable i_v,
Number scalar) |
ScalarDivision(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace) |
ScalarFMod(SameDiff sd,
SDVariable in,
Number number) |
ScalarMax(SameDiff sd,
SDVariable in,
Number number) |
ScalarMin(SameDiff sd,
SDVariable in,
Number number) |
ScalarMultiplication(SameDiff sameDiff,
SDVariable i_v,
Number scalar) |
ScalarMultiplication(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace) |
ScalarRemainder(SameDiff sameDiff,
SDVariable i_v,
Number scalar) |
ScalarRemainder(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace) |
ScalarReverseDivision(SameDiff sameDiff,
SDVariable i_v,
Number scalar) |
ScalarReverseDivision(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace) |
ScalarReverseSubtraction(SameDiff sameDiff,
SDVariable i_v,
Number scalar) |
ScalarReverseSubtraction(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace) |
ScalarSet(SameDiff sameDiff,
SDVariable i_v,
Number scalar) |
ScalarSet(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace) |
ScalarSubtraction(SameDiff sameDiff,
SDVariable i_v,
Number scalar) |
ScalarSubtraction(SameDiff sameDiff,
SDVariable i_v,
Number scalar,
boolean inPlace) |
Step(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double cutoff) |
Step(SameDiff sameDiff,
SDVariable i_v,
double cutoff) |
Modifier and Type | Method and Description |
---|---|
void |
ScatterNd.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ScatterAdd.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ScatterNdAdd.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ScatterSub.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ScatterDiv.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ScatterMin.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ScatterUpdate.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ScatterMul.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ScatterMax.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ScatterNdSub.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ScatterNdUpdate.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Modifier and Type | Method and Description |
---|---|
void |
DiagPart.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
ParallelStack.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
MergeMax.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Gather.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Repeat.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Diag.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Unstack.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Shape.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Rank.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
MergeAvg.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Reshape.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Transpose.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
MergeSum.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Stack.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
DiagPart.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Concat.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ConfusionMatrix.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Create.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Split.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
OneHot.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
StridedSlice.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ZerosLike.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ParallelStack.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Squeeze.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
MergeMax.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
OnesLike.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Gather.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ApplyGradientDescent.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Repeat.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Diag.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Unstack.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
SplitV.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Shape.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Linspace.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
SequenceMask.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Size.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
MergeAvg.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Reshape.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Tile.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Transpose.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ShapeN.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ExpandDims.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
MergeSum.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Stack.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
BroadcastDynamicShape(SameDiff sameDiff,
SDVariable in,
SDVariable shape) |
Concat(SameDiff sameDiff,
int concatDimension,
SDVariable... inputs) |
Concat(SameDiff sameDiff,
SDVariable[] inputs,
int concatDimension) |
ConfusionMatrix(SameDiff sameDiff,
SDVariable labels,
SDVariable pred,
DataType dataType) |
ConfusionMatrix(SameDiff sameDiff,
SDVariable labels,
SDVariable pred,
Integer numClasses) |
ConfusionMatrix(SameDiff sameDiff,
SDVariable labels,
SDVariable pred,
Integer numClasses,
SDVariable weights) |
ConfusionMatrix(SameDiff sameDiff,
SDVariable labels,
SDVariable pred,
SDVariable weights) |
ConfusionMatrix(SameDiff sameDiff,
SDVariable labels,
SDVariable pred,
SDVariable weights,
DataType dataType) |
ConfusionMatrix(SameDiff sameDiff,
SDVariable labels,
SDVariable pred,
SDVariable weights,
Integer numClasses) |
Create(String name,
SameDiff sameDiff,
SDVariable input,
boolean initialize) |
Create(String name,
SameDiff sameDiff,
SDVariable input,
char order,
boolean initialize,
DataType dataType) |
Cross(SameDiff sameDiff,
SDVariable[] args) |
Cross(SameDiff sameDiff,
SDVariable a,
SDVariable b) |
Diag(SameDiff sameDiff,
SDVariable input) |
Diag(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
DiagPart(SameDiff sameDiff,
SDVariable in) |
DiagPart(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
ExpandDims(SameDiff sameDiff,
SDVariable[] args) |
ExpandDims(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
ExpandDims(SameDiff sameDiff,
SDVariable[] args,
int axis) |
ExpandDims(SameDiff sameDiff,
SDVariable args,
int axis) |
Eye(SameDiff sameDiff,
int numRows) |
Eye(SameDiff sameDiff,
int numRows,
int numCols) |
Eye(SameDiff sameDiff,
int numRows,
int numCols,
DataType dataType) |
Eye(SameDiff sameDiff,
int numRows,
int numCols,
DataType dataType,
int[] batchDimension) |
Eye(SameDiff sameDiff,
SDVariable numRows) |
Eye(SameDiff sameDiff,
SDVariable numRows,
SDVariable numCols) |
Eye(SameDiff sameDiff,
SDVariable numRows,
SDVariable numCols,
DataType dataType,
int[] batchDimension) |
Eye(SameDiff sameDiff,
SDVariable numRows,
SDVariable numCols,
SDVariable batch_shape) |
Gather(SameDiff sameDiff,
SDVariable df,
int[] indices,
int axis) |
Gather(SameDiff sameDiff,
SDVariable input,
int[] indices,
int axis,
boolean inPlace) |
Gather(SameDiff sameDiff,
SDVariable df,
SDVariable indices,
int axis) |
Gather(SameDiff sameDiff,
SDVariable input,
SDVariable indices,
int axis,
boolean inPlace) |
GatherNd(SameDiff sameDiff,
SDVariable input,
SDVariable indices) |
Linspace(SameDiff sameDiff,
DataType dataType,
double start,
double stop,
long number) |
Linspace(SameDiff sameDiff,
SDVariable from,
SDVariable to,
SDVariable length,
DataType dataType) |
MergeAvg(SameDiff sameDiff,
SDVariable... inputs) |
MergeMax(SameDiff sameDiff,
SDVariable... inputs) |
MergeMaxIndex(@NonNull SameDiff sameDiff,
SDVariable... inputs) |
MergeMaxIndex(@NonNull SameDiff sd,
@NonNull SDVariable[] x,
@NonNull DataType dataType) |
MergeSum(SameDiff sameDiff,
SDVariable... inputs) |
MeshGrid(SameDiff sd,
boolean cartesian,
SDVariable... inputs) |
MeshGrid(SameDiff sd,
SDVariable[] inputs,
boolean cartesian) |
OneHot(SameDiff sameDiff,
SDVariable indices,
int depth) |
OneHot(SameDiff sameDiff,
SDVariable indices,
int depth,
int axis,
double on,
double off,
DataType dataType) |
OnesLike(SameDiff sameDiff,
SDVariable input) |
OnesLike(SameDiff sameDiff,
SDVariable input,
DataType dataType) |
OnesLike(String name,
SameDiff sameDiff,
SDVariable input) |
OnesLike(String name,
SameDiff sameDiff,
SDVariable input,
DataType dataType) |
ParallelStack(SameDiff sameDiff,
SDVariable[] values) |
Permute(SameDiff sameDiff,
SDVariable i_v,
int... permuteDims) |
Permute(SameDiff sd,
SDVariable input,
SDVariable permuteDims) |
Rank(SameDiff sameDiff,
SDVariable input) |
Rank(SameDiff sameDiff,
SDVariable input,
boolean inPlace) |
ReductionShape(@NonNull SameDiff sameDiff,
@NonNull SDVariable shape,
@NonNull SDVariable axis,
boolean keepDims) |
Repeat(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace,
int axis) |
Repeat(SameDiff sameDiff,
SDVariable[] args,
int axis) |
Reshape(SameDiff sameDiff,
SDVariable i_v,
long[] shape) |
Reshape(SameDiff sameDiff,
SDVariable i_v,
SDVariable shape) |
SequenceMask(SameDiff sameDiff,
SDVariable input,
DataType dataType) |
SequenceMask(SameDiff sameDiff,
SDVariable input,
int maxLen,
DataType dataType) |
SequenceMask(SameDiff sameDiff,
SDVariable input,
SDVariable maxLen,
DataType dataType) |
Shape(SameDiff sameDiff,
SDVariable input) |
Shape(SameDiff sameDiff,
SDVariable input,
boolean inPlace) |
ShapeN(SameDiff sameDiff,
SDVariable[] inputs,
boolean inPlace) |
Size(SameDiff sameDiff,
SDVariable input) |
SizeAt(SameDiff sameDiff,
SDVariable input,
int dimension) |
Slice(SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull int[] begin,
@NonNull int[] size) |
Slice(SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable begin,
@NonNull SDVariable end) |
Squeeze(SameDiff sameDiff,
SDVariable arg,
int squeezeDims) |
Squeeze(SameDiff sameDiff,
SDVariable arg,
int[] squeezeDims) |
Stack(SameDiff sameDiff,
SDVariable[] values,
int axis) |
Stack(SameDiff sameDiff,
SDVariable values,
int axis) |
StridedSlice(SameDiff sameDiff,
SDVariable in,
int[] begin,
int[] end,
int[] strides) |
StridedSlice(SameDiff sameDiff,
SDVariable in,
@NonNull int[] begin,
@NonNull int[] end,
@NonNull int[] strides,
int beginMask,
int endMask,
int ellipsisMask,
int newAxisMask,
int shrinkAxisMask) |
StridedSlice(SameDiff sameDiff,
SDVariable in,
long[] begin,
long[] end,
long[] strides) |
StridedSlice(SameDiff sameDiff,
SDVariable in,
@NonNull long[] begin,
@NonNull long[] end,
@NonNull long[] strides,
int beginMask,
int endMask,
int ellipsisMask,
int newAxisMask,
int shrinkAxisMask) |
Tile(SameDiff sameDiff,
SDVariable i_v,
int[] axis) |
Tile(SameDiff sameDiff,
SDVariable i_v,
SDVariable axis) |
Transpose(SameDiff sameDiff,
SDVariable i_v) |
Transpose(SameDiff sameDiff,
SDVariable in,
int[] permuteDims) |
Transpose(SameDiff sameDiff,
SDVariable in,
SDVariable permuteDims) |
Unstack(SameDiff sameDiff,
SDVariable value,
int axis) |
Unstack(SameDiff sameDiff,
SDVariable value,
int axis,
int num) |
ZerosLike(SameDiff sameDiff,
SDVariable input) |
ZerosLike(String name,
SameDiff sameDiff,
SDVariable input) |
ZerosLike(String name,
SameDiff sameDiff,
SDVariable input,
boolean inPlace) |
ZerosLike(String name,
SameDiff sameDiff,
SDVariable input,
boolean inPlace,
DataType dataType) |
ZerosLike(String name,
SameDiff sameDiff,
SDVariable input,
DataType dataType) |
Constructor and Description |
---|
ConcatBp(@NonNull SameDiff sameDiff,
int concatDimension,
SDVariable... inputsAndGrad) |
ConcatBp(@NonNull SameDiff sameDiff,
SDVariable... inputsGradAxis) |
MergeAvgBp(SameDiff sameDiff,
@NonNull SDVariable[] inputs,
@NonNull SDVariable gradO) |
MergeMaxBp(SameDiff sameDiff,
@NonNull SDVariable[] inputs,
@NonNull SDVariable gradO) |
SliceBp(SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable gradient,
@NonNull int[] begin,
@NonNull int[] size) |
SliceBp(SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable gradient,
@NonNull SDVariable begin,
@NonNull SDVariable size) |
StridedSliceBp(SameDiff sameDiff,
@NonNull SDVariable in,
@NonNull SDVariable grad,
@NonNull long[] begin,
@NonNull long[] end,
@NonNull long[] strides,
int beginMask,
int endMask,
int ellipsisMask,
int newAxisMask,
int shrinkAxisMask) |
StridedSliceBp(SameDiff sameDiff,
@NonNull SDVariable in,
@NonNull SDVariable grad,
@NonNull SDVariable begin,
@NonNull SDVariable end,
@NonNull SDVariable strides,
int beginMask,
int endMask,
int ellipsisMask,
int newAxisMask,
int shrinkAxisMask) |
TileBp(SameDiff sameDiff,
SDVariable in,
SDVariable grad,
int[] repeat) |
TileBp(SameDiff sameDiff,
SDVariable in,
SDVariable repeat,
SDVariable grad) |
Modifier and Type | Method and Description |
---|---|
SameDiff |
TensorArray.getSameDiff() |
Modifier and Type | Method and Description |
---|---|
void |
TensorArraySize.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
TensorArrayRead.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
TensorArraySplit.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
TensorArrayConcat.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
TensorArrayScatter.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
TensorArrayGather.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
TensorArray.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
TensorArraySize.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
TensorArrayRead.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
BaseTensorOp.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
StandardDeviation(SameDiff sameDiff,
SDVariable i_v,
boolean biasCorrected,
boolean keepDims,
int[] dimensions) |
Variance(SameDiff sameDiff,
SDVariable i_v,
boolean biasCorrected,
boolean keepDims,
int[] dimensions) |
Modifier and Type | Method and Description |
---|---|
void |
Cholesky.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Pad.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
BinCount.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
HistogramFixedWidth.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
CheckNumerics.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
NthElement.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
Assign(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
IsMax(SameDiff sameDiff,
SDVariable i_v) |
Constructor and Description |
---|
BooleanNot(SameDiff sameDiff,
SDVariable i_v) |
IsFinite(SameDiff sameDiff,
SDVariable i_v) |
IsFinite(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
IsInf(SameDiff sameDiff,
SDVariable i_v) |
IsInf(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
IsNaN(SameDiff sameDiff,
SDVariable i_v) |
IsNaN(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
MatchConditionTransform(SameDiff sameDiff,
SDVariable in,
Condition condition) |
Modifier and Type | Method and Description |
---|---|
void |
ClipByNorm.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
ClipByValue.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
ClipByNorm.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ClipByValue.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
ClipByAvgNorm(SameDiff sameDiff,
SDVariable x,
double clipValue,
int... dimensions) |
ClipByNorm(SameDiff sameDiff,
SDVariable x,
double clipValue,
int... dimensions) |
ClipByNormBp(SameDiff sameDiff,
SDVariable x,
SDVariable eps,
double clipValue,
int... dimensions) |
ClipByValue(SameDiff sameDiff,
SDVariable x,
double clipValueMin,
double clipValueMax) |
ClipByValue(SameDiff sameDiff,
SDVariable x,
double clipValueMin,
double clipValueMax,
boolean inPlace) |
Constructor and Description |
---|
CompareAndReplace(SameDiff sameDiff,
SDVariable to,
SDVariable from,
Condition condition) |
CompareAndSet(SameDiff sameDiff,
SDVariable to,
Number set,
Condition condition) |
Eps(SameDiff sameDiff) |
Eps(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
Eps(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
Eps(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
Object[] extraArgs) |
Modifier and Type | Method and Description |
---|---|
void |
Fill.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
CumProd.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Assign.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
CumSum.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Dilation2D.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
MirrorPad.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
DynamicPartition.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
UniqueWithCounts.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Fill.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ReverseV2.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
FakeQuantWithMinMaxArgs.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ParallelConcat.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Unique.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
ReverseSequence.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
InTopK.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
CumProd.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
TopK.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Assign.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
FakeQuantWithMinMaxVars.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
Svd.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
CumSum.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
DynamicStitch.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
Assign(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
ATan2(SameDiff sameDiff,
SDVariable y,
SDVariable x) |
BatchToSpace(SameDiff sameDiff,
SDVariable[] args,
int[] blocks,
int[][] crops,
boolean inPlace) |
BatchToSpace(SameDiff sameDiff,
SDVariable x,
int[] blocks,
int[][] crops,
boolean inPlace) |
BatchToSpace(SameDiff sameDiff,
SDVariable x,
int[] blocks,
int[] croppingTop,
int... croppingBottom) |
BatchToSpaceND(SameDiff sameDiff,
SDVariable[] args,
int[] blocks,
int[][] crops,
boolean inPlace) |
BitsHammingDistance(@NonNull SameDiff sd,
@NonNull SDVariable x,
@NonNull SDVariable y) |
BitwiseAnd(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
BitwiseOr(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
BitwiseXor(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
Choose(SameDiff sameDiff,
SDVariable[] args,
Condition condition) |
Choose(String opName,
SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
CReLU(SameDiff sd,
SDVariable input) |
CReluBp(SameDiff sd,
SDVariable input,
SDVariable epsilonNext) |
CumProd(SameDiff sameDiff,
SDVariable x,
boolean exclusive,
boolean reverse,
int... axis) |
CumProd(SameDiff sameDiff,
SDVariable x,
int... axis) |
CumSum(SameDiff sameDiff,
SDVariable x,
boolean exclusive,
boolean reverse,
int... axis) |
CumSum(SameDiff sameDiff,
SDVariable x,
int... axis) |
CyclicRShiftBits(SameDiff sameDiff,
SDVariable x,
SDVariable shift) |
CyclicShiftBits(SameDiff sameDiff,
SDVariable x,
SDVariable shift) |
Dilation2D(SameDiff sameDiff,
SDVariable[] inputAndWeights,
int[] strides,
int[] rates,
boolean isSameMode,
boolean inPlace) |
Dilation2D(SameDiff sameDiff,
SDVariable df,
SDVariable weights,
int[] strides,
int[] rates,
boolean isSameMode) |
DotProductAttention(SameDiff sameDiff,
SDVariable queries,
SDVariable keys,
SDVariable values,
SDVariable mask,
boolean scaled,
boolean withWeights) |
DotProductAttentionBp(SameDiff sameDiff,
SDVariable queries,
SDVariable keys,
SDVariable values,
SDVariable eps,
SDVariable mask,
boolean scaled) |
DynamicPartition(SameDiff sameDiff,
SDVariable input,
SDVariable[] partitions,
int numPartitions) |
DynamicPartition(SameDiff sameDiff,
SDVariable input,
SDVariable partitions,
int numPartitions) |
DynamicStitch(SameDiff sameDiff,
SDVariable[] indices,
SDVariable[] inputs) |
EqualTo(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
EqualTo(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
FakeQuantWithMinMaxArgs(SameDiff sd,
SDVariable input,
float min,
float max,
boolean narrowRange,
int numBits) |
FakeQuantWithMinMaxVars(SameDiff sd,
SDVariable input,
SDVariable min,
SDVariable max,
boolean narrowRange,
int numBits) |
Fill(SameDiff sameDiff,
SDVariable shape,
DataType outputDataType,
double value) |
GreaterThan(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
GreaterThan(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
GreaterThanOrEqual(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
GreaterThanOrEqual(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
InTopK(SameDiff sd,
SDVariable predictions,
SDVariable targets,
int k) |
InvertPermutation(SameDiff sameDiff,
SDVariable input) |
InvertPermutation(SameDiff sameDiff,
SDVariable input,
boolean inPlace) |
IsNonDecreasing(SameDiff sameDiff,
SDVariable input) |
IsNonDecreasing(SameDiff sameDiff,
SDVariable[] args) |
IsNonDecreasing(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
IsNumericTensor(SameDiff sameDiff,
SDVariable args) |
IsNumericTensor(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
IsStrictlyIncreasing(SameDiff sameDiff,
SDVariable input) |
IsStrictlyIncreasing(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
LayerNorm(SameDiff sameDiff,
SDVariable input,
SDVariable gain,
boolean channelsFirst,
int... dimensions) |
LayerNorm(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable gain,
SDVariable bias,
boolean channelsFirst,
int... dimensions) |
LayerNormBp(SameDiff sameDiff,
SDVariable input,
SDVariable gain,
SDVariable gradient,
boolean channelsFirst,
int... dimensions) |
LayerNormBp(@NonNull SameDiff sameDiff,
@NonNull SDVariable input,
@NonNull SDVariable gain,
SDVariable bias,
@NonNull SDVariable gradient,
boolean channelsFirst,
int... dimensions) |
LessThan(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
LessThan(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
LessThanOrEqual(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
LessThanOrEqual(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
ListDiff(@NonNull SameDiff sd,
@NonNull SDVariable x,
@NonNull SDVariable y) |
LogicalAnd(SameDiff sd,
SDVariable in1,
SDVariable in2) |
LogicalNot(SameDiff sd,
SDVariable in1,
SDVariable in2) |
LogicalOr(SameDiff sd,
SDVariable in1,
SDVariable in2) |
LogicalXor(SameDiff sd,
SDVariable in1,
SDVariable in2) |
LogMatrixDeterminant(SameDiff sameDiff,
SDVariable in,
boolean inPlace) |
LogSoftMax(SameDiff sameDiff,
SDVariable i_v) |
LogSoftMax(SameDiff sameDiff,
SDVariable i_v,
int dimension) |
MatrixDeterminant(SameDiff sameDiff,
SDVariable in) |
MatrixDeterminant(SameDiff sameDiff,
SDVariable in,
boolean inPlace) |
MatrixDiag(SameDiff sameDiff,
SDVariable in,
boolean inPlace) |
MatrixDiagPart(SameDiff sameDiff,
SDVariable in,
boolean inPlace) |
MatrixInverse(SameDiff sameDiff,
SDVariable in) |
MatrixInverse(SameDiff sameDiff,
SDVariable in,
boolean inPlace) |
MatrixSetDiag(SameDiff sameDiff,
SDVariable in,
SDVariable diag) |
MatrixSetDiag(SameDiff sameDiff,
SDVariable in,
SDVariable diag,
boolean inPlace) |
Max(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
Max(SameDiff sameDiff,
@NonNull SDVariable first,
@NonNull SDVariable second) |
MaximumBp(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable y,
@NonNull SDVariable gradO) |
Min(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
Min(SameDiff sameDiff,
@NonNull SDVariable first,
@NonNull SDVariable second) |
MultiHeadDotProductAttention(SameDiff sameDiff,
SDVariable queries,
SDVariable keys,
SDVariable values,
SDVariable Wq,
SDVariable Wk,
SDVariable Wv,
SDVariable Wo,
SDVariable mask,
boolean scaled,
boolean withWeights) |
MultiHeadDotProductAttentionBp(SameDiff sameDiff,
SDVariable queries,
SDVariable keys,
SDVariable values,
SDVariable Wq,
SDVariable Wk,
SDVariable Wv,
SDVariable Wo,
SDVariable eps,
SDVariable mask,
boolean scaled) |
NotEqualTo(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
NotEqualTo(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
Pow(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
Qr(SameDiff sameDiff,
SDVariable input,
boolean fullMatrices) |
Reverse(@NonNull SameDiff sameDiff,
@NonNull SDVariable i_v,
int... dimensions) |
ReverseBp(@NonNull SameDiff sameDiff,
@NonNull SDVariable i_v,
@NonNull SDVariable grad,
int... dimensions) |
ReverseSequence(SameDiff sameDiff,
SDVariable i_v,
SDVariable seqLengths) |
ReverseSequence(SameDiff sameDiff,
SDVariable i_v,
SDVariable seqLengths,
int seqDim,
int batchDim) |
RShiftBits(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
ShiftBits(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
SoftMax(SameDiff sameDiff,
SDVariable[] args) |
SoftMax(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
SoftMax(SameDiff sameDiff,
SDVariable[] args,
int dimension) |
SoftMax(SameDiff sameDiff,
SDVariable[] args,
int dimension,
boolean inPlace) |
SoftMax(SameDiff sameDiff,
SDVariable x,
int dimension) |
SpaceToBatch(SameDiff sameDiff,
SDVariable[] args,
int[] blocks,
int[][] padding,
boolean inPlace) |
SpaceToBatch(SameDiff sameDiff,
SDVariable x,
int[] blocks,
int[] paddingTop,
int... paddingBottom) |
SpaceToBatchND(SameDiff sameDiff,
SDVariable[] args,
int[] blocks,
int[][] padding,
boolean inPlace) |
Standardize(SameDiff sameDiff,
SDVariable i_v,
int... dimensions) |
StandardizeBp(SameDiff sameDiff,
SDVariable i_v,
SDVariable grad,
int... dimensions) |
Svd(SameDiff sd,
SDVariable input,
boolean fullUV,
boolean computeUv) |
Svd(SameDiff sd,
SDVariable input,
boolean fullUV,
boolean computeUv,
int switchNum) |
ThresholdRelu(SameDiff sd,
SDVariable input,
boolean inPlace,
double cutoff) |
ThresholdRelu(SameDiff sd,
SDVariable input,
double cutoff) |
TopK(SameDiff sd,
SDVariable in,
int k,
boolean sorted) |
Trace(SameDiff sd,
SDVariable in) |
Unique(SameDiff sd,
SDVariable in) |
UniqueWithCounts(SameDiff sd,
SDVariable in) |
XwPlusB(SameDiff sameDiff,
SDVariable input,
SDVariable weights,
SDVariable bias) |
Zeta(SameDiff sameDiff,
SDVariable x,
SDVariable q) |
Constructor and Description |
---|
SegmentMax(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds) |
SegmentMean(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds) |
SegmentMin(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds) |
SegmentProd(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds) |
SegmentSum(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds) |
Modifier and Type | Method and Description |
---|---|
void |
Cast.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
Cast(SameDiff sameDiff,
SDVariable arg,
@NonNull DataType dst) |
Constructor and Description |
---|
RSqrt(SameDiff sameDiff,
SDVariable i_v) |
RSqrt(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Sqrt(SameDiff sameDiff,
SDVariable i_v) |
Sqrt(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Constructor and Description |
---|
BinaryMinimalRelativeError(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
BinaryMinimalRelativeError(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
BinaryMinimalRelativeError(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
BinaryRelativeError(SameDiff sameDiff) |
BinaryRelativeError(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
BinaryRelativeError(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
BinaryRelativeError(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
RelativeError(SameDiff sameDiff) |
RelativeError(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
RelativeError(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
RelativeError(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
Set(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Constructor and Description |
---|
AddOp(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable y) |
Axpy(SameDiff sameDiff,
double p) |
Axpy(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double p) |
Axpy(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
double p) |
Axpy(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
double p) |
CopyOp(SameDiff sameDiff) |
CopyOp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
CopyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
CopyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
DivOp(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable y) |
FloorDivOp(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
FloorDivOp(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
FloorModOp(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
FModOp(SameDiff sameDiff) |
FModOp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
FModOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
FModOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
MergeAddOp(SameDiff sameDiff,
SDVariable[] args) |
MergeAddOp(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
ModOp(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable y) |
MulOp(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable y) |
PowPairwise(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
PowPairwise(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
RDivOp(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable y) |
RealDivOp(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
RemainderOp(SameDiff sameDiff) |
RemainderOp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
RemainderOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
RemainderOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
RSubOp(SameDiff sameDiff,
SDVariable[] args,
boolean inPlace) |
RSubOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
RSubOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
SquaredDifferenceOp(SameDiff sameDiff,
SDVariable x,
SDVariable y) |
SquaredDifferenceOp(SameDiff sameDiff,
SDVariable x,
SDVariable y,
boolean inPlace) |
SubOp(@NonNull SameDiff sameDiff,
@NonNull SDVariable x,
@NonNull SDVariable y) |
TruncateDivOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
TruncateDivOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
Constructor and Description |
---|
And(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
And(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double comparable) |
And(SameDiff sameDiff,
SDVariable ix,
SDVariable iy) |
Not(SameDiff sameDiff,
SDVariable i_v) |
Or(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double comparable) |
Or(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
Or(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
Xor(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double comparable) |
Xor(SameDiff sameDiff,
SDVariable ix,
SDVariable iy) |
Constructor and Description |
---|
Abs(SameDiff sameDiff,
SDVariable i_v) |
Abs(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
AMax(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2) |
AMin(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2) |
Ceil(SameDiff sameDiff,
SDVariable i_v) |
Ceil(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Cube(SameDiff sameDiff,
SDVariable i_v) |
Cube(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Floor(SameDiff sameDiff,
SDVariable i_v) |
Floor(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Identity(SameDiff sd,
SDVariable input) |
Max(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2) |
Min(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2) |
Negative(SameDiff sameDiff,
SDVariable i_v) |
Negative(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
OneMinus(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Reciprocal(SameDiff sameDiff,
SDVariable in) |
Round(SameDiff sameDiff,
SDVariable i_v) |
Round(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Sign(SameDiff sameDiff,
SDVariable i_v) |
Sign(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Square(SameDiff sameDiff,
SDVariable i_v) |
Square(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
TimesOneMinus(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Constructor and Description |
---|
UnsortedSegmentMax(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds,
int numSegments) |
UnsortedSegmentMean(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds,
int numSegments) |
UnsortedSegmentMin(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds,
int numSegments) |
UnsortedSegmentProd(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds,
int numSegments) |
UnsortedSegmentSqrtN(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds,
int numSegments) |
UnsortedSegmentSum(SameDiff sameDiff,
SDVariable data,
SDVariable segmentIds,
int numSegments) |
Modifier and Type | Method and Description |
---|---|
void |
RectifiedTanh.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
RectifiedTanh.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
ACos(SameDiff sameDiff,
SDVariable i_v) |
ACos(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
ACosh(SameDiff sameDiff,
SDVariable i_v) |
ACosh(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
ASin(SameDiff sameDiff,
SDVariable i_v) |
ASin(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
ASinh(SameDiff sameDiff,
SDVariable i_v) |
ASinh(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
ATan(SameDiff sameDiff,
SDVariable i_v) |
ATan(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
ATanh(SameDiff sameDiff,
SDVariable i_v) |
ATanh(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Cos(SameDiff sameDiff,
SDVariable i_v) |
Cos(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Cosh(SameDiff sameDiff,
SDVariable i_v) |
Cosh(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
ELU(SameDiff sameDiff,
SDVariable i_v) |
Erf(SameDiff sameDiff,
SDVariable i_v) |
Erf(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Erfc(SameDiff sameDiff,
SDVariable i_v) |
Erfc(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Exp(SameDiff sameDiff,
SDVariable i_v) |
Exp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Expm1(SameDiff sameDiff,
SDVariable i_v) |
Expm1(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
GELU(SameDiff sameDiff,
SDVariable i_v) |
GELU(SameDiff sameDiff,
SDVariable i_v,
boolean precise) |
GELU(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
boolean precise) |
GELUDerivative(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
HardSigmoid(SameDiff sameDiff,
SDVariable in) |
HardSigmoid(SameDiff sameDiff,
SDVariable in,
boolean inPlace) |
HardTanh(SameDiff sameDiff,
SDVariable i_v) |
HardTanh(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Log(SameDiff sameDiff,
SDVariable i_v) |
Log(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Log1p(SameDiff sameDiff,
SDVariable i_v) |
Log1p(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
LogSigmoid(SameDiff sameDiff,
SDVariable i_v) |
LogSigmoid(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Mish(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
MishDerivative(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
MishDerivative(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
MishDerivative(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
PreciseGELU(SameDiff sameDiff,
SDVariable i_v) |
PreciseGELU(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
boolean precise) |
PreciseGELUDerivative(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
boolean precise) |
RationalTanh(SameDiff sameDiff,
SDVariable i_v) |
RationalTanh(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
RectifiedTanh(SameDiff sameDiff,
SDVariable i_v) |
RectifiedTanh(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Rint(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
SELU(SameDiff sameDiff,
SDVariable i_v) |
SELU(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
SetRange(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double min,
double max) |
Sigmoid(SameDiff sameDiff,
SDVariable i_v) |
Sigmoid(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
SigmoidDerivative(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace)
Deprecated.
|
SigmoidDerivative(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2)
Deprecated.
|
SigmoidDerivative(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace)
Deprecated.
|
Sin(SameDiff sameDiff,
SDVariable i_v) |
Sin(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Sinh(SameDiff sameDiff,
SDVariable i_v) |
Sinh(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
SoftPlus(SameDiff sameDiff,
SDVariable i_v) |
SoftPlus(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
SoftSign(SameDiff sameDiff,
SDVariable i_v) |
SoftSign(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Stabilize(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace,
double realMin,
double cutOff,
double k) |
Swish(SameDiff sameDiff,
SDVariable i_v) |
Swish(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
SwishDerivative(SameDiff sameDiff,
SDVariable i_v) |
SwishDerivative(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
SwishDerivative(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
Tan(SameDiff sameDiff,
SDVariable i_v) |
Tan(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
Tanh(SameDiff sameDiff,
SDVariable i_v) |
Tanh(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
TanhDerivative(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace)
Deprecated.
|
Modifier and Type | Method and Description |
---|---|
void |
SaveV2.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
RestoreV2.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
BaseRandomOp(SameDiff sd,
long[] shape) |
BaseRandomOp(SameDiff sameDiff,
SDVariable i_v) |
Constructor and Description |
---|
RandomStandardNormal(SameDiff sameDiff,
SDVariable[] args) |
Modifier and Type | Method and Description |
---|---|
void |
RandomPoisson.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
RandomGamma.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
DistributionUniform.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
DistributionUniform(SameDiff sd,
SDVariable shape,
double min,
double max) |
DistributionUniform(SameDiff sd,
SDVariable shape,
double min,
double max,
DataType dataType) |
RandomBernoulli(SameDiff sd,
SDVariable shape,
double p) |
RandomExponential(SameDiff sd,
double lambda,
DataType dataType,
long... shape) |
RandomExponential(SameDiff sd,
SDVariable shape,
double lambda) |
RandomGamma(@NonNull SameDiff sameDiff,
@NonNull SDVariable shape,
@NonNull SDVariable alpha,
SDVariable beta,
int... seeds) |
RandomNormal(SameDiff sameDiff,
SDVariable shape,
double mean,
double stdev) |
RandomPoisson(@NonNull SameDiff sameDiff,
@NonNull SDVariable shape,
@NonNull SDVariable rate,
int... seeds) |
RandomShuffle(@NonNull SameDiff sameDiff,
@NonNull SDVariable value,
int... seeds) |
Modifier and Type | Method and Description |
---|---|
void |
DropOutInverted.initFromOnnx(Onnx.NodeProto node,
SameDiff initWith,
Map<String,Onnx.AttributeProto> attributesForNode,
Onnx.GraphProto graph) |
void |
Range.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
void |
DropOutInverted.initFromTensorFlow(NodeDef nodeDef,
SameDiff initWith,
Map<String,AttrValue> attributesForNode,
GraphDef graph) |
Constructor and Description |
---|
BernoulliDistribution(SameDiff sd,
double prob,
DataType dataType,
long[] shape) |
BernoulliDistribution(SameDiff sd,
double prob,
long[] shape) |
BinomialDistribution(SameDiff sd,
int trials,
double probability,
DataType dataType,
long[] shape) |
BinomialDistribution(SameDiff sd,
int trials,
double probability,
long[] shape) |
DropOut(SameDiff sameDiff,
SDVariable input,
double p) |
DropOutInverted(SameDiff sameDiff,
SDVariable input,
double p) |
GaussianDistribution(SameDiff sd,
double mean,
double stddev,
DataType dataType,
long[] shape) |
GaussianDistribution(SameDiff sd,
double mean,
double stddev,
long[] shape) |
Linspace(SameDiff sd,
double from,
double to,
long length) |
LogNormalDistribution(SameDiff sd,
double mean,
double stdev,
DataType dataType,
long... shape) |
LogNormalDistribution(SameDiff sd,
double mean,
double stdev,
long... shape) |
Range(SameDiff sd,
double from,
double to,
double step,
DataType dataType) |
Range(SameDiff sd,
SDVariable from,
SDVariable to,
SDVariable step,
DataType dataType) |
TruncatedNormalDistribution(SameDiff sd,
double mean,
double stddev,
DataType dataType,
long[] shape) |
TruncatedNormalDistribution(SameDiff sd,
double mean,
double stddev,
long[] shape) |
UniformDistribution(SameDiff sd,
double from,
double to,
DataType dataType,
long[] shape) |
UniformDistribution(SameDiff sd,
double from,
double to,
long[] shape) |
Modifier and Type | Field and Description |
---|---|
protected SameDiff |
SameDiffLoss.sd |
Modifier and Type | Method and Description |
---|---|
abstract SDVariable |
SameDiffLoss.defineLoss(SameDiff sd,
SDVariable layerInput,
SDVariable labels)
Define the loss function.
NOTE: The score on a *per example* basis - should return a SDVariable with shape [minibatch], where out[i] is the score for the ith minibatch |
Copyright © 2020. All rights reserved.