org.bytedeco.javacpp

Class opencv_dnn.RNNLayer

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.opencv_core.Algorithm

org.bytedeco.javacpp.opencv_dnn.Layer

org.bytedeco.javacpp.opencv_dnn.RNNLayer

All Implemented Interfaces:

AutoCloseable

Enclosing class:

opencv_dnn

@Namespace(value="cv::dnn")
public static class opencv_dnn.RNNLayer
extends opencv_dnn.Layer

\brief Classical recurrent layer

Accepts two inputs \f$x_t\f$ and \f$h_{t-1}\f$ and compute two outputs \f$o_t\f$ and \f$h_t\f$.

- input: should contain packed input \f$x_t\f$. - output: should contain output \f$o_t\f$ (and \f$h_t\f$ if setProduceHiddenOutput() is set to true).

input[0] should have shape [T, N, data_dims] where T and N is number of timestamps and number of independent samples of \f$x_t\f$ respectively.

output[0] will have shape [T, N, \f$N_o\f$], where \f$N_o\f$ is number of rows in \f$ W_{xo} \f$ matrix.

If setProduceHiddenOutput() is set to true then \p output[1] will contain a Mat with shape [T, N, \f$N_h\f$], where \f$N_h\f$ is number of rows in \f$ W_{hh} \f$ matrix.

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.Pointer.CustomDeallocator, org.bytedeco.javacpp.Pointer.Deallocator, org.bytedeco.javacpp.Pointer.NativeDeallocator

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
RNNLayer(org.bytedeco.javacpp.Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
static opencv_dnn.RNNLayer	create(opencv_dnn.LayerParams params) Creates instance of RNNLayer
void	setProduceHiddenOutput()
void	setProduceHiddenOutput(boolean produce) \brief If this flag is set to true then layer will produce \f$ h_t \f$ as second output.
void	setWeights(opencv_core.Mat Wxh, opencv_core.Mat bh, opencv_core.Mat Whh, opencv_core.Mat Who, opencv_core.Mat bo) Setups learned weights.

Methods inherited from class org.bytedeco.javacpp.opencv_dnn.Layer

applyHalideScheduler, blobs, blobs, finalize, finalize, finalize, forward_fallback, forward_fallback, forward_fallback, forward, forward, forward, forward, getFLOPS, getMemoryShapes, getScaleShift, inputNameToIndex, inputNameToIndex, name, name, outputNameToIndex, outputNameToIndex, preferableTarget, preferableTarget, run, setActivation, setParamsFrom, supportBackend, tryAttach, tryFuse, type, type, unsetAttached

Methods inherited from class org.bytedeco.javacpp.opencv_core.Algorithm

clear, empty, getDefaultName, position, read, save, save, write, write, write

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

RNNLayer

public RNNLayer(org.bytedeco.javacpp.Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

Method Detail

create

@opencv_core.Ptr
public static opencv_dnn.RNNLayer create(@Const @ByRef
                                                          opencv_dnn.LayerParams params)

Creates instance of RNNLayer

setWeights

public void setWeights(@Const @ByRef
                       opencv_core.Mat Wxh,
                       @Const @ByRef
                       opencv_core.Mat bh,
                       @Const @ByRef
                       opencv_core.Mat Whh,
                       @Const @ByRef
                       opencv_core.Mat Who,
                       @Const @ByRef
                       opencv_core.Mat bo)

Setups learned weights.

Recurrent-layer behavior on each step is defined by current input \f$ x_t \f$, previous state \f$ h_t \f$ and learned weights as follows: \f{eqnarray*}{ h_t &= tanh&(W_{hh} h_{t-1} + W_{xh} x_t + b_h), \\ o_t &= tanh&(W_{ho} h_t + b_o), \f}

Parameters:

Wxh - is \f$ W_{xh} \f$ matrix

bh - is \f$ b_{h} \f$ vector

Whh - is \f$ W_{hh} \f$ matrix

Who - is \f$ W_{xo} \f$ matrix

bo - is \f$ b_{o} \f$ vector

setProduceHiddenOutput

public void setProduceHiddenOutput(@Cast(value="bool")
                                   boolean produce)

\brief If this flag is set to true then layer will produce \f$ h_t \f$ as second output. \details Shape of the second output is the same as first output.

setProduceHiddenOutput

public void setProduceHiddenOutput()