| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
DifferentialFunction.calculateOutputDataTypes(List<DataType> dataTypes)
Calculate the data types for the output arrays.
|
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
DifferentialFunction.calculateOutputDataTypes(List<DataType> dataTypes)
Calculate the data types for the output arrays.
|
| Modifier and Type | Field and Description |
|---|---|
protected DataType |
SDVariable.dataType |
| Modifier and Type | Method and Description |
|---|---|
DataType |
SDVariable.dataType() |
| Modifier and Type | Method and Description |
|---|---|
SDVariable |
SDVariable.castTo(@NonNull DataType dataType) |
SDVariable |
SDVariable.castTo(String name,
@NonNull DataType dataType) |
SDVariable |
SameDiff.constant(String name,
DataType dataType,
Number value)
Create a new scalar constant (rank 0) with the specified value and datatype
|
SDVariable |
SameDiff.one(String name,
DataType dataType,
int... shape)
Create a new variable with the specified shape, with all values initialized to 1.0.
|
SDVariable |
SameDiff.one(String name,
DataType dataType,
long... shape)
Create a new variable with the specified shape, with all values initialized to 1.0.
|
SDVariable |
SameDiff.placeHolder(@NonNull String name,
DataType dataType,
long... shape)
Create a a placeholder variable.
|
SDVariable |
SameDiff.scalar(String name,
DataType dataType,
Number value)
Create a new scalar (rank 0) SDVariable with the specified value and datatype
|
TensorArray |
SameDiff.tensorArray(DataType dataType)
Create a new TensorArray.
|
SDVariable |
SameDiff.var(DataType dataType,
int... shape)
Creates a
SDVariable with the specified shape and a generated nameAny array will be generated with all zeros for the values This method creates a VARIABLE type SDVariable - i.e., must be floating point, and is a trainable parameter. |
SDVariable |
SameDiff.var(DataType dataType,
long... shape)
Creates a
SDVariable with the specified shape and a generated nameAny array will be generated with all zeros for the values This method creates a VARIABLE type SDVariable - i.e., must be floating point, and is a trainable parameter. |
SDVariable |
SameDiff.var(String name,
DataType dataType,
int... shape)
Creates a
SDVariable with the given shape and nameAny array will be generated with all zeros for the values |
SDVariable |
SameDiff.var(String name,
DataType dataType,
long... shape)
Creates a
SDVariable with the given shape and nameAny array will be generated with all zeros for the values This is a VARIABLE type SDVariable - i.e., must be floating point, and is a trainable parameter. |
SDVariable |
SameDiff.var(@NonNull String name,
@NonNull VariableType variableType,
WeightInitScheme weightInitScheme,
DataType dataType,
long... shape)
Variable initialization with a specified
WeightInitScheme
This method creates VARIABLE type SDVariable - i.e., must be floating point, and is a trainable parameter. |
SDVariable |
SameDiff.var(@NonNull String name,
@NonNull WeightInitScheme weightInitScheme,
DataType dataType,
long... shape)
Variable initialization with a specified
WeightInitScheme
This method creates VARIABLE type SDVariable - i.e., must be floating point, and is a trainable parameter. |
SDVariable |
SameDiff.var(WeightInitScheme weightInitScheme,
DataType dataType,
long... shape)
Creates a
SDVariable with the specified shape and a generated name. |
SDVariable |
SameDiff.zero(String name,
DataType dataType,
int... shape)
Create a new variable with the specified shape, with all values initialized to 0.
|
SDVariable |
SameDiff.zero(String name,
DataType dataType,
long... shape)
Create a new variable with the specified shape, with all values initialized to 0.
|
| Constructor and Description |
|---|
SDVariable(@NonNull String varName,
@NonNull VariableType varType,
@NonNull SameDiff sameDiff,
long[] shape,
DataType dataType) |
| Modifier and Type | Method and Description |
|---|---|
INDArray |
SessionMemMgr.allocate(boolean detached,
DataType dataType,
long... shape)
Allocate an array with the specified datatype and shape.
NOTE: This array should be assumed to be uninitialized - i.e., contains random values. |
| Modifier and Type | Method and Description |
|---|---|
INDArray |
ArrayCloseMemoryMgr.allocate(boolean detached,
DataType dataType,
long... shape) |
INDArray |
NoOpMemoryMgr.allocate(boolean detached,
DataType dataType,
long... shape) |
INDArray |
ArrayCacheMemoryMgr.allocate(boolean detached,
DataType dataType,
long... shape) |
INDArray |
CloseValidationMemoryMgr.allocate(boolean detached,
DataType dataType,
long... shape) |
| Modifier and Type | Method and Description |
|---|---|
SDVariable |
SDRandom.bernoulli(double p,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Bernoulli distribution,
with the specified probability. |
SDVariable |
SDRandom.bernoulli(String name,
double p,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Bernoulli distribution,
with the specified probability. |
SDVariable |
SDRandom.binomial(int nTrials,
double p,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Binomial distribution,
with the specified number of trials and probability. |
SDVariable |
SDRandom.binomial(String name,
int nTrials,
double p,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Binomial distribution,
with the specified number of trials and probability. |
SDVariable |
SDBaseOps.castTo(SDVariable arg,
DataType datatype)
Cast the array to a new datatype - for example, Integer -> Float
|
SDVariable |
SDBaseOps.castTo(String name,
SDVariable arg,
DataType datatype)
Cast the array to a new datatype - for example, Integer -> Float
|
SDVariable |
SDMath.confusionMatrix(SDVariable labels,
SDVariable pred,
DataType dataType)
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values. |
SDVariable |
SDMath.confusionMatrix(String name,
SDVariable labels,
SDVariable pred,
DataType dataType)
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values. |
SDVariable |
SDRandom.exponential(double lambda,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a exponential distribution:
P(x) = lambda * exp(-lambda * x) Inputs must satisfy the following constraints: Must be positive: lambda > 0 |
SDVariable |
SDRandom.exponential(String name,
double lambda,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a exponential distribution:
P(x) = lambda * exp(-lambda * x) Inputs must satisfy the following constraints: Must be positive: lambda > 0 |
SDVariable |
SDMath.eye(int rows,
int cols,
DataType dataType,
int... dimensions)
Generate an identity matrix with the specified number of rows and columns
Example: |
SDVariable |
SDMath.eye(String name,
int rows,
int cols,
DataType dataType,
int... dimensions)
Generate an identity matrix with the specified number of rows and columns
Example: |
SDVariable |
SDBaseOps.fill(SDVariable shape,
DataType dataType,
double value)
Generate an output variable with the specified (dynamic) shape with all elements set to the specified value
|
SDVariable |
SDBaseOps.fill(String name,
SDVariable shape,
DataType dataType,
double value)
Generate an output variable with the specified (dynamic) shape with all elements set to the specified value
|
SDVariable |
SDBaseOps.linspace(DataType dataType,
double start,
double stop,
long number)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0] |
SDVariable |
SDBaseOps.linspace(SDVariable start,
SDVariable stop,
SDVariable number,
DataType dataType)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0] |
SDVariable |
SDBaseOps.linspace(String name,
DataType dataType,
double start,
double stop,
long number)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0] |
SDVariable |
SDBaseOps.linspace(String name,
SDVariable start,
SDVariable stop,
SDVariable number,
DataType dataType)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0] |
SDVariable |
SDRandom.logNormal(double mean,
double stddev,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Log Normal distribution,
i.e., log(x) ~ N(mean, stdev) |
SDVariable |
SDRandom.logNormal(String name,
double mean,
double stddev,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Log Normal distribution,
i.e., log(x) ~ N(mean, stdev) |
SDVariable |
SDMath.mergeMaxIndex(SDVariable[] x,
DataType dataType)
Return array of max elements indices with along tensor dimensions
|
SDVariable |
SDMath.mergeMaxIndex(String name,
SDVariable[] x,
DataType dataType)
Return array of max elements indices with along tensor dimensions
|
SDVariable |
SDRandom.normal(double mean,
double stddev,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
N(mean, stdev) |
SDVariable |
SDRandom.normal(String name,
double mean,
double stddev,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
N(mean, stdev) |
SDVariable |
SDRandom.normalTruncated(double mean,
double stddev,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
N(mean, stdev). |
SDVariable |
SDRandom.normalTruncated(String name,
double mean,
double stddev,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
N(mean, stdev). |
SDVariable |
SDBaseOps.oneHot(SDVariable indices,
int depth,
int axis,
double on,
double off,
DataType dataType)
Convert the array to a one-hot array with walues and for each entry
If input has shape [ a, ..., n] then output has shape [ a, ..., n, depth], with {out[i, ..., j, in[i,...,j]] with other values being set to |
SDVariable |
SDBaseOps.oneHot(String name,
SDVariable indices,
int depth,
int axis,
double on,
double off,
DataType dataType)
Convert the array to a one-hot array with walues and for each entry
If input has shape [ a, ..., n] then output has shape [ a, ..., n, depth], with {out[i, ..., j, in[i,...,j]] with other values being set to |
SDVariable |
SDBaseOps.onesLike(SDVariable input,
DataType dataType)
As per onesLike(String, SDVariable) but the output datatype may be specified
|
SDVariable |
SDBaseOps.onesLike(String name,
SDVariable input,
DataType dataType)
As per onesLike(String, SDVariable) but the output datatype may be specified
|
SDVariable |
SDBaseOps.range(double from,
double to,
double step,
DataType dataType)
Create a new variable with a 1d array, where the values start at from and increment by step
up to (but not including) limit. For example, range(1.0, 3.0, 0.5) will return [1.0, 1.5, 2.0, 2.5] |
SDVariable |
SDBaseOps.range(SDVariable from,
SDVariable to,
SDVariable step,
DataType dataType)
Create a new variable with a 1d array, where the values start at from and increment by step
up to (but not including) limit. For example, range(1.0, 3.0, 0.5) will return [1.0, 1.5, 2.0, 2.5] |
SDVariable |
SDBaseOps.range(String name,
double from,
double to,
double step,
DataType dataType)
Create a new variable with a 1d array, where the values start at from and increment by step
up to (but not including) limit. For example, range(1.0, 3.0, 0.5) will return [1.0, 1.5, 2.0, 2.5] |
SDVariable |
SDBaseOps.range(String name,
SDVariable from,
SDVariable to,
SDVariable step,
DataType dataType)
Create a new variable with a 1d array, where the values start at from and increment by step
up to (but not including) limit. For example, range(1.0, 3.0, 0.5) will return [1.0, 1.5, 2.0, 2.5] |
SDVariable |
SDBaseOps.sequenceMask(SDVariable lengths,
DataType dataType)
see sequenceMask(String, SDVariable, SDVariable, DataType)
|
SDVariable |
SDBaseOps.sequenceMask(SDVariable lengths,
int maxLen,
DataType dataType)
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? 1.0 : 0.0) |
SDVariable |
SDBaseOps.sequenceMask(SDVariable lengths,
SDVariable maxLen,
DataType dataType)
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? 1.0 : 0.0) |
SDVariable |
SDBaseOps.sequenceMask(String name,
SDVariable lengths,
DataType dataType)
see sequenceMask(String, SDVariable, SDVariable, DataType)
|
SDVariable |
SDBaseOps.sequenceMask(String name,
SDVariable lengths,
int maxLen,
DataType dataType)
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? 1.0 : 0.0) |
SDVariable |
SDBaseOps.sequenceMask(String name,
SDVariable lengths,
SDVariable maxLen,
DataType dataType)
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? 1.0 : 0.0) |
SDVariable |
SDLinalg.tri(DataType dataType,
int row,
int column,
int diagonal)
An array with ones at and below the given diagonal and zeros elsewhere.
|
SDVariable |
SDLinalg.tri(String name,
DataType dataType,
int row,
int column,
int diagonal)
An array with ones at and below the given diagonal and zeros elsewhere.
|
SDVariable |
SDRandom.uniform(double min,
double max,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a uniform distribution,
U(min,max) |
SDVariable |
SDRandom.uniform(String name,
double min,
double max,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a uniform distribution,
U(min,max) |
| Modifier and Type | Method and Description |
|---|---|
static DataType |
FlatBuffersMapper.getDataTypeFromByte(byte val)
This method converts enums for DataType
|
| Modifier and Type | Method and Description |
|---|---|
static byte |
FlatBuffersMapper.getDataTypeAsByte(DataType type)
This method converts enums for DataType
|
| Modifier and Type | Method and Description |
|---|---|
static DataType |
DataTypeAdapter.dtypeConv(DataType dataType) |
static DataType |
DataTypeAdapter.dtypeConv(int dataType) |
| Modifier and Type | Method and Description |
|---|---|
static DataType |
TFGraphMapper.convertType(DataType tfType)
Convert from TF proto datatype to ND4J datatype
|
| Modifier and Type | Method and Description |
|---|---|
DataType |
TFTensorMappers.BaseTensorMapper.dataType() |
DataType |
TFTensorMapper.dataType() |
| Modifier and Type | Field and Description |
|---|---|
static DataType |
DataType.FLOAT16 |
static DataType |
DataType.INT16 |
static DataType |
DataType.INT32 |
static DataType |
DataType.INT64 |
static DataType |
DataType.INT8 |
protected DataType |
BaseDataBuffer.type |
static DataType |
DataType.UINT8 |
| Modifier and Type | Method and Description |
|---|---|
DataType |
BaseDataBuffer.dataType()
The data opType of the buffer
|
DataType |
DataBuffer.dataType()
The data opType of the buffer
|
static DataType |
DataType.fromInt(int type) |
static DataType |
DataType.fromNumpy(String numpyDtypeName) |
static DataType |
DataType.valueOf(String name)
Returns the enum constant of this type with the specified name.
|
static DataType[] |
DataType.values()
Returns an array containing the constants of this enum type, in
the order they are declared.
|
| Modifier and Type | Method and Description |
|---|---|
static Triple<DataBuffer.AllocationMode,Long,DataType> |
BaseDataBuffer.readHeader(@NonNull InputStream is) |
| Modifier and Type | Method and Description |
|---|---|
abstract void |
BaseDataBuffer.pointerIndexerByCurrentType(DataType currentType) |
void |
BaseDataBuffer.putByDestinationType(long i,
Number element,
DataType globalType) |
void |
BaseDataBuffer.read(DataInputStream s,
@NonNull DataBuffer.AllocationMode allocMode,
long len,
@NonNull DataType dtype) |
void |
DataBuffer.read(DataInputStream s,
DataBuffer.AllocationMode allocationMode,
long length,
DataType dataType) |
void |
BaseDataBuffer.read(InputStream is,
DataBuffer.AllocationMode allocationMode,
long length,
DataType dataType) |
void |
DataBuffer.read(InputStream is,
DataBuffer.AllocationMode allocationMode,
long length,
DataType dataType)
Write this buffer to the input stream.
|
protected void |
BaseDataBuffer.readContent(DataInputStream s,
DataType sourceType,
DataType thisType) |
| Modifier and Type | Method and Description |
|---|---|
DataBuffer |
DataBufferFactory.create(ByteBuffer underlyingBuffer,
DataType type,
long length,
long offset)
Creates a DataBuffer from java.nio.ByteBuffer
|
DataBuffer |
DataBufferFactory.create(DataType dataType,
long length,
boolean initialize) |
DataBuffer |
DataBufferFactory.create(DataType dataType,
long length,
boolean initialize,
MemoryWorkspace workspace) |
DataBuffer |
DataBufferFactory.create(org.bytedeco.javacpp.Pointer pointer,
DataType type,
long length,
org.bytedeco.javacpp.indexer.Indexer indexer)
Create a data buffer based on the
given pointer, data buffer opType,
and length of the buffer
|
DataBuffer |
DataBufferFactory.create(org.bytedeco.javacpp.Pointer pointer,
org.bytedeco.javacpp.Pointer specialPointer,
DataType type,
long length,
org.bytedeco.javacpp.indexer.Indexer indexer) |
| Modifier and Type | Method and Description |
|---|---|
static DataType |
DataTypeUtil.getDtypeFromContext()
get the allocation mode from the context
|
static DataType |
DataTypeUtil.getDtypeFromContext(String dType)
Get the allocation mode from the context
|
| Modifier and Type | Method and Description |
|---|---|
static String |
DataTypeUtil.getDTypeForName(DataType allocationMode)
Gets the name of the alocation mode
|
static int |
DataTypeUtil.lengthForDtype(DataType type)
Returns the length for the given data opType
|
static void |
DataTypeUtil.setDTypeForContext(DataType allocationModeForContext)
Set the allocation mode for the nd4j context
The value must be one of: heap, java cpp, or direct
or an @link{IllegalArgumentException} is thrown
|
| Modifier and Type | Method and Description |
|---|---|
void |
DistributedINDArray.allocate(int entry,
DataType dataType,
long... shape)
This method allocates INDArray for specified entry
|
| Modifier and Type | Method and Description |
|---|---|
PagedPointer |
MemoryWorkspace.alloc(long requiredMemory,
DataType dataType,
boolean initialize)
This method does allocation from a given Workspace
|
PagedPointer |
MemoryWorkspace.alloc(long requiredMemory,
MemoryKind kind,
DataType dataType,
boolean initialize)
This method does allocation from a given Workspace
|
| Modifier and Type | Method and Description |
|---|---|
PagedPointer |
Nd4jWorkspace.alloc(long requiredMemory,
DataType type,
boolean initialize) |
PagedPointer |
DummyWorkspace.alloc(long requiredMemory,
DataType dataType,
boolean initialize)
This method does allocation from a given Workspace
|
PagedPointer |
Nd4jWorkspace.alloc(long requiredMemory,
MemoryKind kind,
DataType type,
boolean initialize) |
PagedPointer |
DummyWorkspace.alloc(long requiredMemory,
MemoryKind kind,
DataType dataType,
boolean initialize)
This method does allocation from a given Workspace
|
| Modifier and Type | Method and Description |
|---|---|
DataType |
INDArray.dataType()
This method returns dtype for this INDArray
|
DataType |
BaseNDArray.dataType() |
| Modifier and Type | Method and Description |
|---|---|
INDArray |
INDArray.castTo(DataType dataType)
This method cast elements of this INDArray to new data type
|
INDArray |
BaseNDArray.castTo(DataType dataType) |
protected static DataTypeEx |
BaseNDArray.convertType(DataType type) |
Pair<DataBuffer,long[]> |
BaseShapeInfoProvider.createShapeInformation(long[] shape,
char order,
DataType dataType)
This method creates shapeInformation buffer, based on shape & order being passed in
|
Pair<DataBuffer,long[]> |
ShapeInfoProvider.createShapeInformation(long[] shape,
char order,
DataType dataType)
This method creates long shapeInformation buffer, based on shape & order being passed in
|
Pair<DataBuffer,long[]> |
BaseShapeInfoProvider.createShapeInformation(long[] shape,
DataType dataType)
This method creates shapeInformation buffer, based on shape being passed in
|
Pair<DataBuffer,long[]> |
ShapeInfoProvider.createShapeInformation(long[] shape,
DataType dataType)
This method creates long shapeInformation buffer, based on shape being passed in
|
Pair<DataBuffer,long[]> |
BaseShapeInfoProvider.createShapeInformation(long[] shape,
long[] stride,
long elementWiseStride,
char order,
DataType dataType,
boolean empty) |
Pair<DataBuffer,long[]> |
ShapeInfoProvider.createShapeInformation(long[] shape,
long[] stride,
long elementWiseStride,
char order,
DataType dataType,
boolean empty)
This method creates long shapeInformation buffer, based on detailed shape info being passed in
|
| Constructor and Description |
|---|
BaseNDArray(DataBuffer buffer,
DataType dataType,
long[] shape,
long[] stride,
long offset,
char ordering) |
BaseNDArray(DataBuffer buffer,
long[] shape,
long[] stride,
char ordering,
DataType type) |
BaseNDArray(DataBuffer buffer,
long[] shape,
long[] stride,
char ordering,
DataType type,
MemoryWorkspace workspace) |
BaseNDArray(DataBuffer buffer,
long[] shape,
long[] stride,
long offset,
char ordering,
DataType dataType) |
BaseNDArray(DataBuffer buffer,
long[] shape,
long[] stride,
long offset,
long ews,
char ordering,
DataType dataType) |
BaseNDArray(DataType type,
long[] shape,
long[] stride,
long offset,
char ordering,
boolean initialize) |
BaseNDArray(DataType type,
long[] shape,
long[] stride,
long offset,
char ordering,
boolean initialize,
MemoryWorkspace workspace) |
| Modifier and Type | Field and Description |
|---|---|
protected List<DataType> |
DynamicCustomOp.dArguments |
protected List<DataType> |
BaseOpContext.fastpath_d |
| Modifier and Type | Method and Description |
|---|---|
DataType[] |
CustomOp.dArgs() |
DataType[] |
DynamicCustomOp.dArgs() |
DataType |
BaseTransformStrictOp.resultType() |
DataType |
BaseTransformFloatOp.resultType() |
DataType |
BaseReduceLongOp.resultType() |
DataType |
BaseTransformBoolOp.resultType() |
DataType |
TransformOp.resultType()
This method returns datatype for result array wrt given inputs
|
DataType |
ReduceOp.resultType()
This method returns datatype for result array wrt given inputs
|
DataType |
BaseTransformSameOp.resultType() |
DataType |
BaseReduceSameOp.resultType() |
DataType |
BaseTransformAnyOp.resultType() |
DataType |
BaseReduceBoolOp.resultType() |
DataType |
BaseReduceFloatOp.resultType() |
DataType |
BaseTransformStrictOp.resultType(OpContext opContext) |
DataType |
BaseTransformFloatOp.resultType(OpContext oc) |
DataType |
BaseReduceLongOp.resultType(OpContext oc) |
DataType |
BaseTransformBoolOp.resultType(OpContext oc) |
DataType |
TransformOp.resultType(OpContext opContext) |
DataType |
ReduceOp.resultType(OpContext oc) |
DataType |
BaseTransformSameOp.resultType(OpContext oc) |
DataType |
BaseReduceSameOp.resultType(OpContext oc) |
DataType |
BaseTransformAnyOp.resultType(OpContext oc) |
DataType |
BaseReduceBoolOp.resultType(OpContext oc) |
DataType |
BaseReduceFloatOp.resultType(OpContext oc) |
| Modifier and Type | Method and Description |
|---|---|
void |
CustomOp.addDArgument(DataType... arg) |
void |
DynamicCustomOp.addDArgument(DataType... arg) |
DataBuffer |
Op.extraArgsDataBuff(DataType bufferType)
Returns the extra args as a data buffer
|
DataBuffer |
BaseOp.extraArgsDataBuff(DataType dtype) |
void |
OpContext.setDArguments(DataType... arguments)
This method sets data type arguments required for operation
|
void |
BaseOpContext.setDArguments(DataType... arguments) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
EncodeBitmap.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
DecodeThreshold.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
DecodeBitmap.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
EncodeThreshold.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
EncodeBitmap.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
DecodeThreshold.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
DecodeBitmap.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
EncodeThreshold.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
DataType[] |
ScatterUpdate.dArgs() |
| Modifier and Type | Method and Description |
|---|---|
void |
ScatterUpdate.addDArgument(DataType... arg) |
| Constructor and Description |
|---|
BitCast(INDArray in,
DataType dataType) |
BitCast(INDArray in,
DataType dataType,
INDArray out) |
Tri(DataType dataType,
int row,
int column,
int diag) |
Tri(SameDiff sameDiff,
DataType dataType,
int row,
int column,
int diag) |
| Modifier and Type | Method and Description |
|---|---|
DataBuffer |
DefaultOpExecutioner.createConstantBuffer(double[] values,
DataType desiredType) |
DataBuffer |
OpExecutioner.createConstantBuffer(double[] values,
DataType desiredType) |
DataBuffer |
DefaultOpExecutioner.createConstantBuffer(float[] values,
DataType desiredType) |
DataBuffer |
OpExecutioner.createConstantBuffer(float[] values,
DataType desiredType) |
DataBuffer |
DefaultOpExecutioner.createConstantBuffer(int[] values,
DataType desiredType) |
DataBuffer |
OpExecutioner.createConstantBuffer(int[] values,
DataType desiredType) |
DataBuffer |
DefaultOpExecutioner.createConstantBuffer(long[] values,
DataType desiredType) |
DataBuffer |
OpExecutioner.createConstantBuffer(long[] values,
DataType desiredType)
This method returns constant buffer for the given jvm array
|
DataBuffer |
DefaultOpExecutioner.createShapeInfo(long[] shape,
long[] stride,
long elementWiseStride,
char order,
DataType dtype,
boolean empty) |
DataBuffer |
OpExecutioner.createShapeInfo(long[] shape,
long[] stride,
long elementWiseStride,
char order,
DataType dtype,
boolean empty)
This method returns shapeInfo DataBuffer
|
static void |
DefaultOpExecutioner.validateDataType(DataType expectedType,
Object op,
INDArray... operands) |
static void |
DefaultOpExecutioner.validateDataType(DataType expectedType,
Op op)
Validate the data types
for the given operation
|
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
BroadcastTo.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
BiasAdd.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BiasAddGrad.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
BroadcastTo.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
BiasAdd.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BiasAddGrad.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Where.calculateOutputDataTypes(List<DataType> inputTypes) |
List<DataType> |
Select.calculateOutputDataTypes(List<DataType> inputDataType) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Where.calculateOutputDataTypes(List<DataType> inputTypes) |
List<DataType> |
Select.calculateOutputDataTypes(List<DataType> inputDataType) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Exit.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
Switch.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
LoopCond.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
NextIteration.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
StopGradient.calculateOutputDataTypes(List<DataType> input) |
List<DataType> |
Enter.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
Merge.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Exit.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
Switch.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
LoopCond.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
NextIteration.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
StopGradient.calculateOutputDataTypes(List<DataType> input) |
List<DataType> |
Enter.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
Merge.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
CropAndResize.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ResizeNearestNeighbor.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ResizeBilinear.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
NonMaxSuppressionV3.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ResizeBicubic.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ImageResize.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
NonMaxSuppression.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ResizeArea.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ExtractImagePatches.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
CropAndResize.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ResizeNearestNeighbor.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ResizeBilinear.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
NonMaxSuppressionV3.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ResizeBicubic.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ImageResize.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
NonMaxSuppression.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ResizeArea.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ExtractImagePatches.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Field and Description |
|---|---|
protected DataType |
ArgMin.outputType |
protected DataType |
ArgMax.outputType |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
ArgMin.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ArgMax.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
ArgMin.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ArgMax.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Field and Description |
|---|---|
protected DataType |
MaxPoolWithArgmax.outputType |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
LSTMBlockCell.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
GRUBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
LSTMLayer.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
GRU.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
LSTMLayerBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
LSTMBlock.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
GRUCell.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
LSTMBlockCell.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
GRUBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
LSTMLayer.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
GRU.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
LSTMLayerBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
LSTMBlock.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
GRUCell.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
WeightedCrossEntropyLoss.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BaseLoss.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SoftmaxCrossEntropyWithLogitsLoss.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SparseSoftmaxCrossEntropyLossWithLogits.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
L2Loss.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
WeightedCrossEntropyLoss.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BaseLoss.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SoftmaxCrossEntropyWithLogitsLoss.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SparseSoftmaxCrossEntropyLossWithLogits.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
L2Loss.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
SparseSoftmaxCrossEntropyLossWithLogitsBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BaseLossBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SoftmaxCrossEntropyLossBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SoftmaxCrossEntropyWithLogitsLossBp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
SparseSoftmaxCrossEntropyLossWithLogitsBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BaseLossBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SoftmaxCrossEntropyLossBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SoftmaxCrossEntropyWithLogitsLossBp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
TensorMmul.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
MmulBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
Mmul.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
SufficientStatistics.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
NormalizeMoments.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ZeroFraction.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
Moments.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
TensorMmul.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
MmulBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
Mmul.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
SufficientStatistics.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
NormalizeMoments.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ZeroFraction.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
Moments.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
BaseReductionBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
DotBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
PowBp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
BaseReductionBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
DotBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
PowBp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
LogSumExp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
BatchMmul.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
LogSumExp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
BatchMmul.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
DataType |
JaccardDistance.resultType() |
DataType |
BaseReduce3Op.resultType() |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
BaseReduce3Op.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
BaseReduce3Op.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
RectifiedLinearDerivative.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
PRelu.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
RectifiedLinearDerivative.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
PRelu.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Field and Description |
|---|---|
protected DataType |
Shape.dataType |
protected DataType |
Size.dataType |
protected DataType |
ShapeN.dataType |
static DataType |
Eye.DEFAULT_DTYPE |
static DataType |
ConfusionMatrix.DEFAULT_DTYPE |
static DataType |
OneHot.DEFAULT_DTYPE |
static DataType |
SequenceMask.DEFAULT_DTYPE |
protected DataType |
Create.outputType |
protected DataType |
ZerosLike.outputType |
protected DataType |
OnesLike.outputType |
| Constructor and Description |
|---|
ConfusionMatrix(@NonNull INDArray labels,
@NonNull INDArray predicted,
@NonNull DataType dataType) |
ConfusionMatrix(@NonNull INDArray labels,
@NonNull INDArray predicted,
INDArray weights,
Integer numClasses,
@NonNull DataType dataType) |
ConfusionMatrix(@NonNull INDArray labels,
@NonNull INDArray predicted,
Integer numClasses,
@NonNull DataType dataType) |
ConfusionMatrix(SameDiff sameDiff,
SDVariable labels,
SDVariable pred,
DataType dataType) |
ConfusionMatrix(SameDiff sameDiff,
SDVariable labels,
SDVariable pred,
SDVariable weights,
DataType dataType) |
Create(INDArray shape,
boolean initialize,
DataType dataType) |
Create(@NonNull INDArray shape,
char order,
boolean initialize,
DataType dataType) |
Create(INDArray shape,
DataType dataType) |
Create(String name,
SameDiff sameDiff,
SDVariable input,
char order,
boolean initialize,
DataType dataType) |
Eye(int numRows,
int numCols,
DataType dataType) |
Eye(int numRows,
int numCols,
DataType dataType,
int[] batchDimension) |
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,
SDVariable numCols,
DataType dataType,
int[] batchDimension) |
Linspace(DataType dataType,
double start,
double stop,
long number) |
Linspace(DataType dataType,
INDArray start,
INDArray stop,
INDArray number) |
Linspace(double start,
double stop,
long number,
@NonNull DataType dataType) |
Linspace(@NonNull INDArray start,
@NonNull INDArray stop,
@NonNull INDArray number,
@NonNull DataType dataType) |
Linspace(SameDiff sameDiff,
DataType dataType,
double start,
double stop,
long number) |
Linspace(SameDiff sameDiff,
SDVariable from,
SDVariable to,
SDVariable length,
DataType dataType) |
MergeMaxIndex(@NonNull INDArray[] x,
@NonNull DataType dataType) |
MergeMaxIndex(@NonNull SameDiff sd,
@NonNull SDVariable[] x,
@NonNull DataType dataType) |
OneHot(INDArray indices,
int depth,
int axis,
double on,
double off,
DataType dataType) |
OneHot(SameDiff sameDiff,
SDVariable indices,
int depth,
int axis,
double on,
double off,
DataType dataType) |
OnesLike(@NonNull INDArray input,
DataType dataType) |
OnesLike(SameDiff sameDiff,
SDVariable input,
DataType dataType) |
OnesLike(String name,
SameDiff sameDiff,
SDVariable input,
DataType dataType) |
SequenceMask(@NonNull INDArray input,
@NonNull DataType dataType) |
SequenceMask(@NonNull INDArray input,
INDArray maxLength,
@NonNull DataType dataType) |
SequenceMask(@NonNull INDArray input,
int maxLen,
DataType dataType) |
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) |
ZerosLike(INDArray in,
INDArray out,
DataType dataType) |
ZerosLike(String name,
SameDiff sameDiff,
SDVariable input,
boolean inPlace,
DataType dataType) |
ZerosLike(String name,
SameDiff sameDiff,
SDVariable input,
DataType dataType) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
MergeAvgBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
StridedSliceBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
MergeMaxBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
TileBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
ConcatBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
SliceBp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
MergeAvgBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
StridedSliceBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
MergeMaxBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
TileBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
ConcatBp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
SliceBp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Field and Description |
|---|---|
protected DataType |
TensorArrayRead.importDataType |
protected DataType |
TensorArray.tensorArrayDataType |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
EmbeddingLookup.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
TensorArray.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArraySize.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArrayRead.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArraySplit.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArrayConcat.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArrayScatter.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArrayWrite.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArrayGather.calculateOutputDataTypes(List<DataType> inputDataType) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
EmbeddingLookup.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
TensorArray.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArraySize.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArrayRead.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArraySplit.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArrayConcat.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArrayScatter.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArrayWrite.calculateOutputDataTypes(List<DataType> inputDataType) |
List<DataType> |
TensorArrayGather.calculateOutputDataTypes(List<DataType> inputDataType) |
| Constructor and Description |
|---|
TensorArray(DataType dataType) |
TensorArray(SameDiff sameDiff,
DataType dataType) |
TensorArray(String name,
SameDiff sameDiff,
DataType dataType) |
| Modifier and Type | Method and Description |
|---|---|
DataType |
Variance.resultType() |
DataType |
Variance.resultType(OpContext oc) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Variance.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Variance.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
DataType |
MaxOut.resultType() |
DataType |
MaxOut.resultType(OpContext oc) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Cholesky.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
Pad.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BinCount.calculateOutputDataTypes(List<DataType> inputTypes) |
List<DataType> |
HistogramFixedWidth.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
CheckNumerics.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
IdentityN.calculateOutputDataTypes(List<DataType> list) |
List<DataType> |
Assert.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
NthElement.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BaseDynamicTransformOp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Cholesky.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
Pad.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BinCount.calculateOutputDataTypes(List<DataType> inputTypes) |
List<DataType> |
HistogramFixedWidth.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
CheckNumerics.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
IdentityN.calculateOutputDataTypes(List<DataType> list) |
List<DataType> |
Assert.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
NthElement.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BaseDynamicTransformOp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Constructor and Description |
|---|
BinCount(SameDiff sd,
SDVariable in,
SDVariable weights,
Integer minLength,
Integer maxLength,
DataType outputType) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
IsMax.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
IsMax.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
ClipByNorm.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ClipByNormBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ClipByValue.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ClipByAvgNorm.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
ClipByNorm.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ClipByNormBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ClipByValue.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
ClipByAvgNorm.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
CompareAndReplace.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
CompareAndReplace.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Field and Description |
|---|---|
static DataType |
UniqueWithCounts.DEFAULT_IDX_DTYPE |
static DataType |
Unique.DEFAULT_IDX_DTYPE |
| Constructor and Description |
|---|
Fill(INDArray shape,
DataType outputDataType,
double value) |
Fill(SameDiff sameDiff,
SDVariable shape,
DataType outputDataType,
double value) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
SegmentMax.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SegmentSum.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SegmentProd.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SegmentMean.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SegmentMin.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
SegmentMax.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SegmentSum.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SegmentProd.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SegmentMean.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SegmentMin.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Cast.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Cast.calculateOutputDataTypes(List<DataType> dataTypes) |
| Constructor and Description |
|---|
Cast(@NonNull INDArray arg,
@NonNull DataType dataType) |
Cast(SameDiff sameDiff,
SDVariable arg,
@NonNull DataType dst) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
FloorModOp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
MergeAddOp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
FloorModOp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
MergeAddOp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
MergeAddBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SquaredDifferenceBpOp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
BaseArithmeticBackpropOp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
MergeAddBp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
SquaredDifferenceBpOp.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
BaseArithmeticBackpropOp.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Identity.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
Abs.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
Identity.calculateOutputDataTypes(List<DataType> dataTypes) |
List<DataType> |
Abs.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
UnsortedSegmentSqrtN.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
UnsortedSegmentMean.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
UnsortedSegmentSum.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
UnsortedSegmentProd.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
UnsortedSegmentMin.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
UnsortedSegmentMax.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
UnsortedSegmentSqrtN.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
UnsortedSegmentMean.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
UnsortedSegmentSum.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
UnsortedSegmentProd.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
UnsortedSegmentMin.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
UnsortedSegmentMax.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
ELU.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
ELU.calculateOutputDataTypes(List<DataType> dataTypes) |
| Modifier and Type | Field and Description |
|---|---|
protected DataType |
BaseRandomOp.dataType |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
BaseRandomOp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
BaseRandomOp.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
RandomStandardNormal.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
RandomStandardNormal.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
RandomPoisson.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
RandomGamma.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
RandomBernoulli.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
RandomShuffle.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
RandomNormal.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
DistributionUniform.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
RandomExponential.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
RandomPoisson.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
RandomGamma.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
RandomBernoulli.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
RandomShuffle.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
RandomNormal.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
DistributionUniform.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
RandomExponential.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Constructor and Description |
|---|
DistributionUniform(INDArray shape,
INDArray out,
double min,
double max,
DataType dataType) |
DistributionUniform(SameDiff sd,
SDVariable shape,
double min,
double max,
DataType dataType) |
RandomExponential(double lambda,
DataType datatype,
long... shape) |
RandomExponential(SameDiff sd,
double lambda,
DataType dataType,
long... shape) |
| Modifier and Type | Field and Description |
|---|---|
static DataType |
Range.DEFAULT_DTYPE |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
UniformDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
Range.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BernoulliDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BinomialDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
LogNormalDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
GaussianDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
TruncatedNormalDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Modifier and Type | Method and Description |
|---|---|
List<DataType> |
UniformDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
Range.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BernoulliDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
BinomialDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
LogNormalDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
GaussianDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
List<DataType> |
TruncatedNormalDistribution.calculateOutputDataTypes(List<DataType> inputDataTypes) |
| Constructor and Description |
|---|
BernoulliDistribution(double p,
DataType datatype,
long... shape) |
BernoulliDistribution(SameDiff sd,
double prob,
DataType dataType,
long[] shape) |
BinomialDistribution(int trials,
double probability,
DataType dt,
long[] shape) |
BinomialDistribution(SameDiff sd,
int trials,
double probability,
DataType dataType,
long[] shape) |
GaussianDistribution(double mean,
double stddev,
DataType datatype,
long... shape) |
GaussianDistribution(SameDiff sd,
double mean,
double stddev,
DataType dataType,
long[] shape) |
Linspace(double from,
double to,
long length,
DataType dataType) |
Linspace(double from,
long length,
double step,
DataType dataType) |
LogNormalDistribution(double mean,
double stddev,
DataType datatype,
long... shape) |
LogNormalDistribution(SameDiff sd,
double mean,
double stdev,
DataType dataType,
long... shape) |
Range(double from,
double to,
double step,
DataType dataType) |
Range(INDArray from,
INDArray to,
INDArray step,
DataType dataType) |
Range(SameDiff sd,
double from,
double to,
double step,
DataType dataType) |
Range(SameDiff sd,
SDVariable from,
SDVariable to,
SDVariable step,
DataType dataType) |
TruncatedNormalDistribution(double mean,
double stddev,
DataType datatype,
long... shape) |
TruncatedNormalDistribution(SameDiff sd,
double mean,
double stddev,
DataType dataType,
long[] shape) |
UniformDistribution(double min,
double max,
DataType datatype,
long... shape) |
UniformDistribution(SameDiff sd,
double from,
double to,
DataType dataType,
long[] shape) |
| Modifier and Type | Method and Description |
|---|---|
DataType |
LongShapeDescriptor.dataType() |
static DataType |
Shape.pickPairwiseDataType(@NonNull DataType typeX,
@NonNull DataType typeY) |
static DataType |
Shape.pickPairwiseDataType(@NonNull DataType typeX,
@NonNull Number number) |
| Modifier and Type | Method and Description |
|---|---|
LongShapeDescriptor |
LongShapeDescriptor.asDataType(DataType dataType)
Return a new LongShapeDescriptor with the same shape, strides, order etc but with the specified datatype instead
|
static DataBuffer |
Shape.createShapeInformation(long[] shape,
long[] stride,
long elementWiseStride,
char order,
DataType dataType,
boolean empty) |
static LongShapeDescriptor |
LongShapeDescriptor.empty(@NonNull DataType dataType) |
static LongShapeDescriptor |
LongShapeDescriptor.fromShape(int[] shape,
@NonNull DataType dataType) |
static LongShapeDescriptor |
LongShapeDescriptor.fromShape(long[] shape,
@NonNull DataType dataType) |
static LongShapeDescriptor |
LongShapeDescriptor.fromShape(@NonNull long[] shape,
@NonNull long[] strides,
long ews,
char order,
@NonNull DataType dataType,
boolean empty) |
static boolean |
Shape.isB(@NonNull DataType x) |
static boolean |
Shape.isR(@NonNull DataType x) |
static boolean |
Shape.isS(@NonNull DataType x) |
static boolean |
Shape.isZ(@NonNull DataType x) |
static DataType |
Shape.pickPairwiseDataType(@NonNull DataType typeX,
@NonNull DataType typeY) |
static DataType |
Shape.pickPairwiseDataType(@NonNull DataType typeX,
@NonNull DataType typeY) |
static DataType |
Shape.pickPairwiseDataType(@NonNull DataType typeX,
@NonNull Number number) |
| Modifier and Type | Method and Description |
|---|---|
static DataType |
ArrayOptionsHelper.convertToDataType(DataType dataType) |
static DataType |
ArrayOptionsHelper.dataType(long opt) |
static DataType |
ArrayOptionsHelper.dataType(long[] shapeInfo) |
static DataType |
ArrayOptionsHelper.dataType(@NonNull String dataType) |
| Modifier and Type | Method and Description |
|---|---|
static long |
ArrayOptionsHelper.setOptionBit(long storage,
DataType type) |
| Modifier and Type | Method and Description |
|---|---|
DataBuffer |
ConstantHandler.getConstantBuffer(boolean[] array,
DataType dataType)
This method returns DataBuffer with
constant equal to input array.
|
DataBuffer |
ConstantHandler.getConstantBuffer(double[] array,
DataType dataType)
This method returns DataBuffer with contant equal to input array.
|
DataBuffer |
ConstantHandler.getConstantBuffer(float[] array,
DataType dataType)
This method returns DataBuffer with contant equal to input array.
|
DataBuffer |
ConstantHandler.getConstantBuffer(int[] array,
DataType dataType) |
DataBuffer |
ConstantHandler.getConstantBuffer(long[] array,
DataType dataType)
This method returns DataBuffer with
constant equal to input array.
|
| Constructor and Description |
|---|
ArrayDescriptor(boolean[] array,
DataType dtype) |
ArrayDescriptor(double[] array,
DataType dtype) |
ArrayDescriptor(float[] array,
DataType dtype) |
ArrayDescriptor(int[] array,
DataType dtype) |
ArrayDescriptor(long[] array,
DataType dtype) |
| Modifier and Type | Method and Description |
|---|---|
static INDArray |
CheckUtil.convertFromApacheMatrix(org.apache.commons.math3.linear.RealMatrix matrix,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dPermutedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dPermutedWithShape(long seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dReshapedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dReshapedWithShape(long seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dSubArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dSubArraysWithShape(long seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dTensorAlongDimensionWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dTensorAlongDimensionWithShape(long seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get4dPermutedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get4dReshapedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get4dSubArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get4dTensorAlongDimensionWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get5dPermutedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get5dReshapedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get5dSubArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get5dTensorAlongDimensionWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get6dPermutedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get6dReshapedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get6dSubArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll3dTestArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll3dTestArraysWithShape(long seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll4dTestArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll4dTestArraysWithShape(int seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll5dTestArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll6dTestArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAllTestMatricesWithShape(char ordering,
int rows,
int cols,
int seed,
DataType dataType)
Get an array of INDArrays (2d) all with the specified shape.
|
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAllTestMatricesWithShape(long rows,
long cols,
long seed,
DataType dataType)
Get an array of INDArrays (2d) all with the specified shape.
|
static Pair<INDArray,String> |
NDArrayCreationUtil.getPermutedWithShape(char ordering,
long rows,
long cols,
long seed,
DataType dataType) |
static Pair<INDArray,String> |
NDArrayCreationUtil.getPermutedWithShape(long rows,
long cols,
long seed,
DataType dataType) |
static Pair<INDArray,String> |
NDArrayCreationUtil.getReshapedWithShape(char ordering,
long rows,
long cols,
long seed,
DataType dataType) |
static Pair<INDArray,String> |
NDArrayCreationUtil.getReshapedWithShape(long rows,
long cols,
long seed,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getSubMatricesWithShape(char ordering,
long rows,
long cols,
long seed,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getSubMatricesWithShape(long rows,
long cols,
long seed,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getTensorAlongDimensionMatricesWithShape(char ordering,
long rows,
long cols,
long seed,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getTensorAlongDimensionMatricesWithShape(long rows,
long cols,
long seed,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getTestMatricesWithVaryingShapes(int rank,
char order,
DataType dataType)
Test utility to sweep shapes given a rank
Given a rank will generate random test matrices that will cover all cases of a shape with a '1' anywhere in the shape
as well a shape with random ints that are not 0 or 1
eg.
|
static Pair<INDArray,String> |
NDArrayCreationUtil.getTransposedMatrixWithShape(char ordering,
int rows,
int cols,
int seed,
DataType dataType) |
static Pair<INDArray,String> |
NDArrayCreationUtil.getTransposedMatrixWithShape(long rows,
long cols,
long seed,
DataType dataType) |
| Modifier and Type | Method and Description |
|---|---|
DataBuffer |
NDArrayCompressor.decompress(DataBuffer buffer,
DataType targetType)
Return a compressed databuffer
|
DataBuffer |
BasicNDArrayCompressor.decompress(DataBuffer buffer,
DataType targetType)
Decompress the given databuffer
|
void |
CompressedDataBuffer.pointerIndexerByCurrentType(DataType currentType) |
static DataBuffer |
CompressedDataBuffer.readUnknown(DataInputStream s,
DataBuffer.AllocationMode allocMode,
long length,
DataType type)
Drop-in replacement wrapper for BaseDataBuffer.read() method, aware of CompressedDataBuffer
|
| Modifier and Type | Field and Description |
|---|---|
protected static DataType |
Nd4j.dtype |
| Modifier and Type | Method and Description |
|---|---|
static DataType |
Nd4j.dataType()
Returns the data opType used for the runtime
|
static DataType |
Nd4j.defaultFloatingPointType() |
DataType |
BaseNDArrayFactory.dtype()
Returns the data opType for this ndarray
|
DataType |
NDArrayFactory.dtype()
Returns the data opType for this ndarray
|
| Modifier and Type | Method and Description |
|---|---|
static INDArray |
Nd4j.create(boolean[] data,
long[] shape,
DataType type)
|
static INDArray |
Nd4j.create(boolean[] data,
long[] shape,
long[] strides,
char order,
DataType type)
|
INDArray |
NDArrayFactory.create(boolean[] data,
long[] shape,
long[] stride,
char order,
DataType dataType,
MemoryWorkspace workspace) |
INDArray |
NDArrayFactory.create(boolean[] data,
long[] shape,
long[] stride,
DataType dataType,
MemoryWorkspace workspace) |
static INDArray |
Nd4j.create(byte[] data,
long[] shape,
DataType type)
|
static INDArray |
Nd4j.create(byte[] data,
long[] shape,
long[] strides,
char order,
DataType type)
|
INDArray |
NDArrayFactory.create(byte[] data,
long[] shape,
long[] stride,
char order,
DataType dataType,
MemoryWorkspace workspace) |
INDArray |
NDArrayFactory.create(byte[] data,
long[] shape,
long[] stride,
DataType dataType,
MemoryWorkspace workspace) |
static INDArray |
Nd4j.create(DataBuffer data,
long[] newShape,
long[] newStride,
long offset,
char ordering,
DataType dataType)
Create an array based on the data buffer with given shape, stride, offset and data type.
|
INDArray |
NDArrayFactory.create(DataBuffer data,
long[] newShape,
long[] newStride,
long offset,
char ordering,
DataType dataType) |
static INDArray |
Nd4j.create(DataType type,
long... shape)
Create an array with specified shape and datatype.
|
static INDArray |
Nd4j.create(@NonNull DataType dataType,
@NonNull long[] shape,
char ordering)
Create an array with given data type shape and ordering.
|
INDArray |
NDArrayFactory.create(DataType dataType,
long[] shape,
char ordering,
MemoryWorkspace workspace) |
static INDArray |
Nd4j.create(DataType dataType,
@NonNull long[] shape,
long[] strides,
char ordering)
Create an array with given shape, stride and ordering.
|
INDArray |
NDArrayFactory.create(DataType dataType,
long[] shape,
long[] strides,
char ordering,
MemoryWorkspace workspace) |
static INDArray |
Nd4j.create(double[] data,
long[] shape,
DataType type)
|
static INDArray |
Nd4j.create(double[] data,
long[] shape,
long[] strides,
char order,
DataType type)
|
INDArray |
NDArrayFactory.create(double[] data,
long[] shape,
long[] stride,
char order,
DataType dataType,
MemoryWorkspace workspace) |
INDArray |
NDArrayFactory.create(double[] data,
long[] shape,
long[] stride,
DataType dataType,
MemoryWorkspace workspace) |
static INDArray |
Nd4j.create(float[] data,
long[] shape,
DataType type)
|
static INDArray |
Nd4j.create(float[] data,
long[] shape,
long[] strides,
char order,
DataType type)
|
INDArray |
NDArrayFactory.create(float[] data,
long[] shape,
long[] stride,
char order,
DataType dataType) |
abstract INDArray |
BaseNDArrayFactory.create(float[] data,
long[] shape,
long[] stride,
char order,
DataType dataType,
MemoryWorkspace workspace) |
INDArray |
NDArrayFactory.create(float[] data,
long[] shape,
long[] stride,
char order,
DataType dataType,
MemoryWorkspace workspace) |
INDArray |
NDArrayFactory.create(float[] data,
long[] shape,
long[] stride,
DataType dataType,
MemoryWorkspace workspace) |
static INDArray |
Nd4j.create(int[] shape,
DataType dataType)
Create an array of given shape and data type.
|
INDArray |
BaseNDArrayFactory.create(int[] shape,
DataType dataType,
MemoryWorkspace workspace) |
INDArray |
NDArrayFactory.create(int[] shape,
DataType dataType,
MemoryWorkspace workspace) |
static INDArray |
Nd4j.create(int[] data,
long[] shape,
DataType type)
Create an array of the specified type and shape initialized with values from a java 1d array.
|
static INDArray |
Nd4j.create(int[] data,
long[] shape,
long[] strides,
char order,
DataType type)
Create an array of the specified type, shape and stride initialized with values from a java 1d array.
|
INDArray |
NDArrayFactory.create(int[] data,
long[] shape,
long[] stride,
char order,
DataType dataType,
MemoryWorkspace workspace) |
INDArray |
NDArrayFactory.create(int[] data,
long[] shape,
long[] stride,
DataType dataType,
MemoryWorkspace workspace) |
static INDArray |
Nd4j.create(long[] data,
long[] shape,
DataType type)
|
static INDArray |
Nd4j.create(long[] data,
long[] shape,
long[] strides,
char order,
DataType type)
|
INDArray |
NDArrayFactory.create(long[] data,
long[] shape,
long[] stride,
char order,
DataType dataType,
MemoryWorkspace workspace) |
INDArray |
NDArrayFactory.create(long[] data,
long[] shape,
long[] stride,
DataType dataType,
MemoryWorkspace workspace) |
static INDArray |
Nd4j.create(short[] data,
long[] shape,
DataType type)
|
static INDArray |
Nd4j.create(short[] data,
long[] shape,
long[] strides,
char order,
DataType type)
|
INDArray |
NDArrayFactory.create(short[] data,
long[] shape,
long[] stride,
char order,
DataType dataType,
MemoryWorkspace workspace) |
INDArray |
NDArrayFactory.create(short[] data,
long[] shape,
long[] stride,
DataType dataType,
MemoryWorkspace workspace) |
static DataBuffer |
Nd4j.createBuffer(ByteBuffer buffer,
DataType type,
int length)
Creates a buffer of the specified opType
and length with the given byte buffer.
|
static DataBuffer |
Nd4j.createBuffer(ByteBuffer buffer,
DataType type,
int length,
long offset)
Creates a buffer of the specified opType and length with the given byte buffer.
|
static DataBuffer |
Nd4j.createBuffer(DataType dataType,
long length,
boolean initialize)
Create a data buffer based on datatype.
|
static DataBuffer |
Nd4j.createBuffer(DataType dataType,
long length,
boolean initialize,
MemoryWorkspace workspace)
Create a data buffer based on datatype, workspace.
|
static DataBuffer |
Nd4j.createBuffer(@NonNull int[] shape,
@NonNull DataType type)
Create a buffer equal of length prod(shape)
|
static DataBuffer |
Nd4j.createBuffer(int[] shape,
DataType type,
long offset)
Create a buffer equal of length prod(shape)
|
static DataBuffer |
Nd4j.createBuffer(@NonNull long[] shape,
@NonNull DataType type)
|
static DataBuffer |
Nd4j.createBuffer(org.bytedeco.javacpp.Pointer pointer,
DataType type,
long length,
org.bytedeco.javacpp.indexer.Indexer indexer)
Create a data buffer
based on a pointer
with the given opType and length
|
static DataBuffer |
Nd4j.createBuffer(@NonNull org.bytedeco.javacpp.Pointer pointer,
long length,
@NonNull DataType dataType)
Creates a buffer of the specified type and length with the given pointer.
|
static DataBuffer |
Nd4j.createBuffer(@NonNull org.bytedeco.javacpp.Pointer pointer,
@NonNull org.bytedeco.javacpp.Pointer devicePointer,
long length,
@NonNull DataType dataType)
Creates a buffer of the specified type and length with the given pointer at the specified device.
|
static DataBuffer |
Nd4j.createBufferDetached(int[] shape,
DataType type)
Create a buffer equal of length prod(shape).
|
static DataBuffer |
Nd4j.createBufferDetached(long[] shape,
DataType type)
|
static DataBuffer |
Nd4j.createTypedBuffer(boolean[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBuffer(byte[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBuffer(double[] data,
DataType dataType)
Create a buffer based on the data of the underlying java array with the specified type..
|
static DataBuffer |
Nd4j.createTypedBuffer(double[] data,
DataType dataType,
MemoryWorkspace workspace)
Create a buffer based on the data of the underlying java array, specified type and workspace
|
static DataBuffer |
Nd4j.createTypedBuffer(float[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBuffer(float[] data,
DataType dataType,
MemoryWorkspace workspace)
|
static DataBuffer |
Nd4j.createTypedBuffer(int[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBuffer(long[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBuffer(short[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBufferDetached(boolean[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBufferDetached(byte[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBufferDetached(double[] data,
DataType dataType)
Create am uninitialized buffer based on the data of the underlying java array and specified type.
|
static DataBuffer |
Nd4j.createTypedBufferDetached(float[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBufferDetached(int[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBufferDetached(long[] data,
DataType dataType)
|
static DataBuffer |
Nd4j.createTypedBufferDetached(short[] data,
DataType dataType)
|
static INDArray |
Nd4j.createUninitialized(DataType type,
long... shape) |
static INDArray |
Nd4j.createUninitialized(DataType type,
long[] shape,
char ordering)
Creates an *uninitialized* array with the specified data type, shape and ordering.
|
INDArray |
NDArrayFactory.createUninitialized(DataType dataType,
long[] shape,
char ordering,
MemoryWorkspace workspace) |
static INDArray |
Nd4j.createUninitializedDetached(DataType dataType,
char ordering,
long... shape)
Create an uninitialized ndArray.
|
INDArray |
NDArrayFactory.createUninitializedDetached(DataType dataType,
char ordering,
long... shape)
Create an uninitialized ndArray.
|
static INDArray |
Nd4j.createUninitializedDetached(DataType dataType,
long... shape)
See
Nd4j.createUninitializedDetached(DataType, char, long...) with default ordering. |
static INDArray |
Nd4j.empty(DataType type)
This method creates "empty" INDArray of the specified datatype
|
INDArray |
NDArrayFactory.empty(DataType type) |
static INDArray |
Nd4j.linspace(@NonNull DataType dataType,
double lower,
double step,
long num)
Generate a linearly spaced 1d vector of the specified datatype
|
static INDArray |
Nd4j.linspace(@NonNull DataType dtype,
long lower,
long num,
long step)
Generate a linearly spaced vector
|
static INDArray |
Nd4j.linspace(double lower,
double upper,
long num,
@NonNull DataType dataType)
Generate a linearly spaced 1d vector of the specified datatype
|
static INDArray |
Nd4j.linspace(long lower,
long upper,
long num,
@NonNull DataType dtype)
Generate a linearly spaced vector
|
static INDArray |
Nd4j.ones(DataType dataType,
long... shape)
Creates an array with the specified datatype and shape, with values all set to 1
|
static INDArray |
Nd4j.rand(@NonNull DataType dataType,
char order,
int... shape)
Deprecated.
|
static INDArray |
Nd4j.rand(@NonNull DataType dataType,
char order,
long... shape)
Create a random ndarray with the given shape, data type, and array order
Values are sampled from a uniform distribution over (0, 1)
|
static INDArray |
Nd4j.rand(@NonNull DataType dataType,
int... shape)
Create a random ndarray with the given shape and data type
Values are sampled from a uniform distribution over (0, 1)
|
static INDArray |
Nd4j.rand(@NonNull DataType dataType,
int[] shape,
char order)
Deprecated.
use {@link Nd4j#rand(org.nd4j.linalg.api.buffer.DataType, char, long...))
|
static INDArray |
Nd4j.rand(@NonNull DataType dataType,
long... shape)
Create a random ndarray with values from a uniform distribution over (0, 1) with the given shape and data type
|
static INDArray |
Nd4j.randn(@NonNull DataType dataType,
char order,
long... shape)
Random normal N(0,1) with the specified shape and array order
|
static INDArray |
Nd4j.randn(@NonNull DataType dataType,
@NonNull int[] shape)
Create a ndarray of the given shape and data type with values from N(0,1)
|
static INDArray |
Nd4j.randn(@NonNull DataType dataType,
long... shape)
Create a ndarray of the given shape and data type with values from N(0,1)
|
static INDArray |
Nd4j.readNumpy(@NonNull DataType dataType,
@NonNull InputStream filePath,
@NonNull String split,
@NonNull Charset charset)
Read array from input stream.
|
static INDArray |
Nd4j.readNumpy(DataType dataType,
String filePath)
Read array.
See Nd4j.readNumpy(DataType, InputStream, String , Charset) with default split and UTF-8 encoding. |
static INDArray |
Nd4j.readNumpy(DataType dataType,
String filePath,
String split)
Read array via input stream.
|
static INDArray |
Nd4j.scalar(DataType dataType,
Number value)
Create a scalar ndarray with the specified value and datatype
|
static void |
Nd4j.setDataType(@NonNull DataType dtype)
Deprecated.
use
Nd4j.setDefaultDataTypes(DataType, DataType). Equivalent to setDefaultDataTypes(dtype, (dtype.isFPType() ? dtype : defaultFloatingPointType())) |
static void |
Nd4j.setDefaultDataTypes(@NonNull DataType defaultType,
@NonNull DataType defaultFloatingPointType)
Set the default data types.
The default data types are used for array creation methods where no data type is specified. When the user explicitly provides a datatype (such as in Nd4j.ones(DataType.FLOAT, 1, 10)) these default values will not be used. defaultType: used in methods such as Nd4j.ones(1,10) and Nd4j.zeros(10). defaultFloatingPointType: used internally where a floating point array needs to be created, but no datatype is specified. |
static void |
Nd4j.setDefaultDataTypes(@NonNull DataType defaultType,
@NonNull DataType defaultFloatingPointType)
Set the default data types.
The default data types are used for array creation methods where no data type is specified. When the user explicitly provides a datatype (such as in Nd4j.ones(DataType.FLOAT, 1, 10)) these default values will not be used. defaultType: used in methods such as Nd4j.ones(1,10) and Nd4j.zeros(10). defaultFloatingPointType: used internally where a floating point array needs to be created, but no datatype is specified. |
void |
BaseNDArrayFactory.setDType(DataType dtype)
Sets the data opType
|
void |
NDArrayFactory.setDType(DataType dtype)
Sets the data opType
|
static int |
Nd4j.sizeOfDataType(DataType dtype)
This method returns size of element for specified dataType, in bytes
|
static INDArray |
Nd4j.valueArrayOf(long[] shape,
double value,
DataType type)
Creates an ndarray with the specified value
as the only value in the ndarray.
|
static INDArray |
Nd4j.valueArrayOf(long[] shape,
long value,
DataType type)
|
static INDArray |
Nd4j.zeros(DataType dataType,
long... shape)
Creates an array with the specified data tyoe and shape initialized with zero.
|
static INDArray |
Nd4j.zeros(int[] shape,
DataType dataType) |
| Constructor and Description |
|---|
BaseNDArrayFactory(DataType dtype,
char order) |
BaseNDArrayFactory(DataType dtype,
Character order)
Initialize with the given data opType and ordering
The ndarray factory will use this for
|
| Modifier and Type | Method and Description |
|---|---|
INDArray |
NDRandom.bernoulli(double p,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Bernoulli distribution,
with the specified probability. |
INDArray |
NDRandom.binomial(int nTrials,
double p,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Binomial distribution,
with the specified number of trials and probability. |
INDArray |
NDBase.castTo(INDArray arg,
DataType datatype)
Cast the array to a new datatype - for example, Integer -> Float
|
INDArray |
NDMath.confusionMatrix(INDArray labels,
INDArray pred,
DataType dataType)
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values. |
INDArray |
NDRandom.exponential(double lambda,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a exponential distribution:
P(x) = lambda * exp(-lambda * x) Inputs must satisfy the following constraints: Must be positive: lambda > 0 |
INDArray |
NDMath.eye(int rows,
int cols,
DataType dataType,
int... dimensions)
Generate an identity matrix with the specified number of rows and columns
Example: |
INDArray |
NDBase.fill(INDArray shape,
DataType dataType,
double value)
Generate an output variable with the specified (dynamic) shape with all elements set to the specified value
|
INDArray |
NDBase.linspace(DataType dataType,
double start,
double stop,
long number)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0] |
INDArray |
NDBase.linspace(INDArray start,
INDArray stop,
INDArray number,
DataType dataType)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0] |
INDArray |
NDRandom.logNormal(double mean,
double stddev,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Log Normal distribution,
i.e., log(x) ~ N(mean, stdev) |
INDArray |
NDMath.mergeMaxIndex(INDArray[] x,
DataType dataType)
Return array of max elements indices with along tensor dimensions
|
INDArray |
NDRandom.normal(double mean,
double stddev,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
N(mean, stdev) |
INDArray |
NDRandom.normalTruncated(double mean,
double stddev,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a Gaussian (normal) distribution,
N(mean, stdev). |
INDArray |
NDBase.oneHot(INDArray indices,
int depth,
int axis,
double on,
double off,
DataType dataType)
Convert the array to a one-hot array with walues and for each entry
If input has shape [ a, ..., n] then output has shape [ a, ..., n, depth], with {out[i, ..., j, in[i,...,j]] with other values being set to |
INDArray |
NDBase.onesLike(INDArray input,
DataType dataType)
As per onesLike(String, SDVariable) but the output datatype may be specified
|
INDArray |
NDBase.range(double from,
double to,
double step,
DataType dataType)
Create a new variable with a 1d array, where the values start at from and increment by step
up to (but not including) limit. For example, range(1.0, 3.0, 0.5) will return [1.0, 1.5, 2.0, 2.5] |
INDArray |
NDBase.range(INDArray from,
INDArray to,
INDArray step,
DataType dataType)
Create a new variable with a 1d array, where the values start at from and increment by step
up to (but not including) limit. For example, range(1.0, 3.0, 0.5) will return [1.0, 1.5, 2.0, 2.5] |
INDArray |
NDBase.sequenceMask(INDArray lengths,
DataType dataType)
see sequenceMask(String, SDVariable, SDVariable, DataType)
|
INDArray |
NDBase.sequenceMask(INDArray lengths,
INDArray maxLen,
DataType dataType)
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? 1.0 : 0.0) |
INDArray |
NDBase.sequenceMask(INDArray lengths,
int maxLen,
DataType dataType)
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? 1.0 : 0.0) |
INDArray |
NDLinalg.tri(DataType dataType,
int row,
int column,
int diagonal)
An array with ones at and below the given diagonal and zeros elsewhere.
|
INDArray |
NDRandom.uniform(double min,
double max,
DataType datatype,
long... shape)
Generate a new random INDArray, where values are randomly sampled according to a uniform distribution,
U(min,max) |
| Modifier and Type | Method and Description |
|---|---|
protected void |
SameDiffLoss.createSameDiffInstance(DataType dataType) |
| Modifier and Type | Method and Description |
|---|---|
static INDArray |
Transforms.isMax(INDArray input,
DataType dataType) |
| Modifier and Type | Method and Description |
|---|---|
static ValidationResult |
Nd4jValidator.validateINDArrayFile(@NonNull File f,
DataType... allowableDataTypes)
Validate whether the file represents a valid INDArray (of one of the allowed/specified data types) saved previously
with
Nd4j.saveBinary(INDArray, File) to be read with Nd4j.readBinary(File) |
| Modifier and Type | Method and Description |
|---|---|
INDArray |
BaseWorkspaceMgr.castTo(T arrayType,
@NonNull DataType dataType,
@NonNull INDArray toCast,
boolean dupIfCorrectType) |
INDArray |
WorkspaceMgr.castTo(T arrayType,
@NonNull DataType dataType,
@NonNull INDArray toCast,
boolean dupIfCorrectType)
Cast the specified array to the specified datatype.
If the array is already the correct type, the bahaviour depends on the 'dupIfCorrectType' argument. dupIfCorrectType = false && toCast.dataType() == dataType: return input array as-is (unless workspace is wrong) dupIfCorrectType = true && toCast.dataType() == dataType: duplicate the array into the specified workspace |
INDArray |
BaseWorkspaceMgr.create(T arrayType,
@NonNull DataType dataType,
long... shape) |
INDArray |
WorkspaceMgr.create(T arrayType,
DataType dataType,
long... shape)
Create an array in the specified array type's workspace (or detached if none is specified).
|
INDArray |
BaseWorkspaceMgr.create(T arrayType,
@NonNull DataType dataType,
@NonNull long[] shape,
@NonNull char order) |
INDArray |
WorkspaceMgr.create(T arrayType,
DataType dataType,
long[] shape,
char ordering)
Create an array in the specified array type's workspace (or detached if none is specified).
|
INDArray |
BaseWorkspaceMgr.createUninitialized(T arrayType,
DataType dataType,
long... shape) |
INDArray |
WorkspaceMgr.createUninitialized(T arrayType,
DataType dataType,
long... shape)
Create an uninitialized array in the specified array type's workspace (or detached if none is specified).
|
INDArray |
BaseWorkspaceMgr.createUninitialized(T arrayType,
@NonNull DataType dataType,
@NonNull long[] shape,
char order) |
INDArray |
WorkspaceMgr.createUninitialized(T arrayType,
DataType dataType,
long[] shape,
char order)
Create an uninitialized array in the specified array type's workspace (or detached if none is specified).
|
| Constructor and Description |
|---|
NDArrayList(DataType dataType,
int size) |
| Modifier and Type | Method and Description |
|---|---|
INDArray |
BaseWeightInitScheme.create(DataType dataType,
long... shape) |
INDArray |
WeightInitScheme.create(DataType dataType,
long... shape)
Create the array
|
abstract INDArray |
BaseWeightInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
| Modifier and Type | Method and Description |
|---|---|
INDArray |
NDArraySupplierInitScheme.create(DataType dataType,
long[] shape) |
INDArray |
LecunUniformInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
SigmoidUniformInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
XavierInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
DistributionInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
VarScalingNormalUniformFanOutInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
ReluUniformInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
XavierFanInInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
ConstantInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
VarScalingNormalUniformFanInInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
OneInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
VarScalingNormalFanAvgInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
ZeroInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
VarScalingNormalFanInInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
XavierUniformInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
UniformInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
IdentityInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
ReluInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
VarScalingUniformFanAvgInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
INDArray |
VarScalingNormalFanOutInitScheme.doCreate(DataType dataType,
long[] shape,
INDArray paramsView) |
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