The InforgainLossLayer is a generalization of SoftmaxWithLossLayer that takes an 'information gain' (infogain) matrix specifying the 'value of all label pairs. This layer is initialized with the MyCaffe.param.InfogainLossParameter. More...

Inheritance diagram for MyCaffe.layers.InfogainLossLayer< T >:

Public Member Functions
	InfogainLossLayer (CudaDnn< T > cuda, Log log, LayerParameter p)
	The InfogainLossLayer constructor. More...

override void	LayerSetUp (BlobCollection< T > colBottom, BlobCollection< T > colTop)
	Setup the layer. More...

override void	Reshape (BlobCollection< T > colBottom, BlobCollection< T > colTop)
	Reshape the bottom (input) and top (output) blobs. More...

Public Member Functions inherited from MyCaffe.layers.LossLayer< T >
	LossLayer (CudaDnn< T > cuda, Log log, LayerParameter p)
	The LossLayer constructor. More...

double	GetNormalizer (LossParameter.NormalizationMode normalization_mode, int nOuterNum, int nInnerNum, int nValidCount)
	Returns the normalizer used to normalize the loss. More...

override bool	AllowForceBackward (int nBottomIdx)
	We usually cannot backpropagate to the labels; ignore force_backward for these inputs. More...

override void	LayerSetUp (BlobCollection< T > colBottom, BlobCollection< T > colTop)
	Setup the layer. More...

override void	Reshape (BlobCollection< T > colBottom, BlobCollection< T > colTop)
	Reshape the bottom (input) and top (output) blobs. More...

Public Member Functions inherited from MyCaffe.layers.Layer< T >
	Layer (CudaDnn< T > cuda, Log log, LayerParameter p)
	The Layer constructor. More...

void	Dispose ()
	Releases all GPU and host resources used by the Layer. More...

virtual void	ConnectLoss (LossLayer< T > layer)
	Called to connect the loss OnLoss event to a specified layer (typically the data layer). More...

virtual BlobCollection< T >	PreProcessInput (PropertySet customInput, out int nSeqLen, BlobCollection< T > colBottom=null)
	The PreprocessInput allows derivative data layers to convert a property set of input data into the bottom blob collection used as intput. More...

virtual bool	PreProcessInput (string strEncInput, int? nDecInput, BlobCollection< T > colBottom)
	Preprocess the input data for the RUN phase. More...

virtual List< Tuple< string, int, double > >	PostProcessOutput (Blob< T > blobSofmtax, int nK=1)
	The PostProcessOutput allows derivative data layers to post-process the results, converting them back into text results (e.g., detokenizing). More...

virtual List< Tuple< string, int, double > >	PostProcessLogitsOutput (int nCurIdx, Blob< T > blobLogits, Layer< T > softmax, int nAxis, int nK=1)
	The PostProcessLogitsOutput allows derivative data layers to post-process the results, converting them back into text results (e.g., detokenizing). More...

virtual string	PostProcessFullOutput (Blob< T > blobSoftmax)
	The PostProcessFullOutput allows derivative data layers to post-process the results, usually be detokenizing the data in the blobSoftmax. More...

virtual string	PostProcessOutput (int nIdx)
	Convert the index to the word. More...

virtual void	SetOnDebug (EventHandler< GetWorkBlobArgs< T > > fn)
	Set the OnDebug event. More...

virtual void	ResetOnDebug (EventHandler< GetWorkBlobArgs< T > > fn)
	Reset the OnDebug event, disabling it. More...

virtual bool	ReInitializeParameters (WEIGHT_TARGET target)
	Re-initialize the parameters of the layer. More...

void	SetNetReshapeRequest ()
	Called by the Net when requesting a reshape. More...

void	SetPhase (Phase phase)
	Changes the layer's Phase to the one specified. More...

void	Setup (BlobCollection< T > colBottom, BlobCollection< T > colTop)
	Implements common Layer setup functionality. More...

virtual void	SetNetParameterUsed (NetParameter np)
	This function allows other layers to gather needed information from the NetParameters if any, and is called when initialzing the Net. More...

void	ConvertToBase (BlobCollection< T > col)
	ConvertToBase converts any blobs in a collection that are in half size to the base size. More...

double	Forward (BlobCollection< T > colBottom, BlobCollection< T > colTop)
	Given the bottom (input) Blobs, this function computes the top (output) Blobs and the loss. More...

void	Backward (BlobCollection< T > colTop, List< bool > rgbPropagateDown, BlobCollection< T > colBottom)
	Given the top Blob error gradients, compute the bottom Blob error gradients. More...

double	loss (int nTopIdx)
	Returns the scalar loss associated with the top Blob at a given index. More...

void	set_loss (int nTopIdx, double dfLoss)
	Sets the loss associated with a top Blob at a given index. More...

bool	param_propagate_down (int nParamIdx)
	Returns whether or not the Layer should compute gradients w.r.t. a parameter at a particular index given by a parameter index. More...

void	set_param_propagate_down (int nParamIdx, bool bPropagate)
	Sets whether or not the Layer should compute gradients w.r.t. a parameter at a particular index given by a parameter index. More...

void	SetEnablePassthrough (bool bEnable)
	Enables/disables the pass-through mode. More...

Protected Member Functions
override void	dispose ()
	Releases all GPU and host resources used by the Layer. More...

override void	setup_internal_blobs (BlobCollection< T > col)
	Derivative layers should add all internal blobws to the 'col' provided. More...

virtual void	sum_rows_of_H (Blob< T > blobH)
	Fill m_blobSumRowsOfH according to matrix H. More...

override void	forward (BlobCollection< T > colBottom, BlobCollection< T > colTop)
	The forward computation. More...

override void	backward (BlobCollection< T > colTop, List< bool > rgbPropagateDown, BlobCollection< T > colBottom)
	Computes the infogain loss error gradient w.r.t the predictions. More...

Protected Member Functions inherited from MyCaffe.layers.LossLayer< T >
void	callLossEvent (Blob< T > blob)
	This method is called by the loss layer to pass the blob data to the OnLoss event (if implemented) More...

virtual double	get_normalizer (LossParameter.NormalizationMode normalization_mode, int nValidCount)
	Returns the normalizer used to normalize the loss. More...

Protected Member Functions inherited from MyCaffe.layers.Layer< T >
void	dispose (ref Layer< T > l)
	Helper method used to dispose internal layers. More...

void	dispose (ref Blob< T > b)
	Helper method used to dispose internal blobs. More...

void	dispose (ref BlobCollection< T > rg, bool bSetToNull=true)
	Dispose the blob collection. More...

GetIterationArgs	getCurrentIteration ()
	Fires the OnGetIteration event to query the current iteration. More...

long	convert_to_full (int nCount, long hMem)
	Convert half memory to full memory. More...

void	convert (BlobCollection< T > col)
	Convert a collection of blobs from / to half size. More...

virtual bool	reshapeNeeded (BlobCollection< T > colBottom, BlobCollection< T > colTop, bool bReset=true)
	Tests the shapes of both the bottom and top blobs and if they are the same as the previous sizing, returns false indicating that no reshape is needed. More...

bool	compareShapes (BlobCollection< T > colBottom, BlobCollection< T > colTop)
	Compare the shapes of the top and bottom and if the same, return true, otherwise false. More...

void	setShapes (BlobCollection< T > colBottom, BlobCollection< T > colTop)
	Set the internal shape sizes - used when determining if a Reshape is necessary. More...

void	CheckBlobCounts (BlobCollection< T > colBottom, BlobCollection< T > colTop)
	Called by the Layer::Setup function to check the number of bottom (input) and top (output) Blobs provided match the expected number of blobs expected via the {EactNum,Min,Max}{Bottom,Top}Blobs functions. More...

void	SetLossWeights (BlobCollection< T > colTop)
	Called by Layer::Setup to initialize the weights associated with any top (output) Blobs in the loss function ans store non-zero loss weights in the diff Blob. More...

LayerParameter	convertLayerParam (LayerParameter pChild, LayerParameter pParent)
	Called to convert a parent LayerParameterEx, used in blob sharing, with a child layer parameter. More...

bool	shareParameter (Blob< T > b, List< int > rgMinShape, bool bAllowEndsWithComparison=false)
	Attempts to share a parameter Blob if another parameter Blob with the same name and accpetable size is found. More...

bool	shareLayerBlob (Blob< T > b, List< int > rgMinShape)
	Attempts to share a Layer Blob if another parameter Blob with the same name and acceptable size is found. More...

bool	shareLayerBlobs (Layer< T > layer)
	Attempts to share the Layer blobs and internal_blobs with matching names and sizes with those in another matching layer. More...

virtual WorkspaceArgs	getWorkspace ()
	Returns the WorkspaceArgs used to share a workspace between Layers. More...

virtual bool	setWorkspace (ulong lSizeInBytes)
	Sets the workspace size (in items) and returns true if set, false otherwise. More...

void	check_nan (Blob< T > b)
	Checks a Blob for NaNs and throws an exception if found. More...

T	convert (double df)
	Converts a double to a generic. More...

T	convert (float f)
	Converts a float to a generic. More...

double	convertD (T df)
	Converts a generic to a double value. More...

float	convertF (T df)
	Converts a generic to a float value. More...

double[]	convertD (T[] rg)
	Converts an array of generic values into an array of double values. More...

T[]	convert (double[] rg)
	Converts an array of double values into an array of generic values. More...

float[]	convertF (T[] rg)
	Converts an array of float values into an array of generic values. More...

T[]	convert (float[] rg)
	Converts an array of float values into an array of generic values. More...

int	val_at (T[] rg, int nIdx)
	Returns the integer value at a given index in a generic array. More...

Size	size_at (Blob< T > b)
	Returns the Size of a given two element Blob, such as one that stores Blob size information. More...

Properties
override int	ExactNumBottomBlobs `[get]`
	InfogainLossLayer takes 2-3 bottom blobs; if there are 3 the third should be the infogain matrix. (Otherwise the infogain matrix is loaded from a file specified by the LayerParameter.) More...

override int	MinBottomBlobs `[get]`
	Returns the minimum number of required bottom (intput) Blobs: pred, label More...

override int	MaxBottomBlobs `[get]`
	Returns the maximum number of required bottom (intput) Blobs: pred, label, matrix More...

override int	ExactNumTopBlobs `[get]`
	InfogainLossLayer computes softmax probability internally. optional second 'top' outputs the sofmax probability. More...

override int	MinTopBlobs `[get]`
	Returns the minimum number of required top (output) Blobs: infogain More...

override int	MaxTopBlobs `[get]`
	Returns the maximum number of requried top (output) Blobs: infogain, softmax More...

Properties inherited from MyCaffe.layers.LossLayer< T >
override int	ExactNumBottomBlobs `[get]`
	Returns the exact number of required bottom (intput) Blobs: prediction, label More...

override int	ExactNumTopBlobs `[get]`
	Returns the exact number of required top (output) Blobs: loss More...

override bool	AutoTopBlobs `[get]`
	For convenience and backwards compatibility, insturct the Net to automatically allocate a single top Blob for LossLayers, into which they output their singleton loss, (even if the user didn't specify one in the prototxt, etc.). More...

Properties inherited from MyCaffe.layers.Layer< T >
LayerParameter.? LayerType	parent_layer_type `[get]`
	Optionally, specifies the parent layer type (e.g. LOSS, etc.) More...

virtual bool	SupportsPreProcessing `[get]`
	Should return true when PreProcessing methods are overriden. More...

virtual bool	SupportsPostProcessing `[get]`
	Should return true when pre PostProcessing methods are overriden. More...

virtual bool	SupportsPostProcessingLogits `[get]`
	Should return true when pre PostProcessingLogits methods are overriden. More...

virtual bool	SupportsPostProcessingFullOutput `[get]`
	Should return true when PostProcessingFullOutput is supported. More...

BlobCollection< T >	blobs `[get]`
	Returns the collection of learnable parameter Blobs for the Layer. More...

BlobCollection< T >	internal_blobs `[get]`
	Returns the collection of internal Blobs used by the Layer. More...

LayerParameter	layer_param `[get]`
	Returns the LayerParameter for this Layer. More...

LayerParameter.LayerType	type `[get]`
	Returns the LayerType of this Layer. More...

virtual int	ExactNumBottomBlobs `[get]`
	Returns the exact number of bottom (input) Blobs required by the Layer, or -1 if no exact number is required. More...

virtual int	MinBottomBlobs `[get]`
	Returns the minimum number of bottom (input) Blobs required by the Layer, or -1 if no minimum number is required. More...

virtual int	MaxBottomBlobs `[get]`
	Returns the maximum number of bottom (input) Blobs required by the Layer, or -1 if no maximum number is required. More...

virtual int	ExactNumTopBlobs `[get]`
	Returns the exact number of top (output) Blobs required by the Layer, or -1 if no exact number is required. More...

virtual int	MinTopBlobs `[get]`
	Returns the minimum number of top (output) Blobs required by the Layer, or -1 if no minimum number is required. More...

virtual int	MaxTopBlobs `[get]`
	Returns the maximum number of top (output) Blobs required by the Layer, or -1 if no maximum number is required. More...

virtual bool	EqualNumBottomTopBlobs `[get]`
	Returns true if the Layer requires and equal number of bottom (input) and top (output) Blobs. More...

virtual bool	AutoTopBlobs `[get]`
	Return whether "anonymous" top (output) Blobs are created automatically by the Layer. More...

double	forward_timing `[get]`
	Returns the timing of the last forward pass in milliseconds. More...

double	forward_timing_average `[get]`
	Returns the average timing of the forward passes in milliseconds. More...

double	backward_timing `[get]`
	Returns the timing of the last backward pass in milliseconds. More...

double	backward_timing_average `[get]`
	Returns the average timing of the backward passes in milliseconds. More...

Additional Inherited Members
Static Public Member Functions inherited from MyCaffe.layers.Layer< T >
static Layer< T >	Create (CudaDnn< T > cuda, Log log, LayerParameter p, CancelEvent evtCancel, IXDatabaseBase db=null, TransferInput trxinput=null)
	Create a new Layer based on the LayerParameter. More...

Static Public Attributes inherited from MyCaffe.layers.LossLayer< T >
const double	kLOG_THRESHOLD = 1e-20
	Specifies the minimum threshold for loss values. More...

Protected Attributes inherited from MyCaffe.layers.LossLayer< T >
bool	m_bIgnoreLabels = false
	Set to true when labels are to be ignored. More...

LossParameter.NormalizationMode	m_normalization = LossParameter.NormalizationMode.NONE
	Specifies the normalization mode used to normalize the loss. More...

int	m_nOuterNum = 0
	Specifies the outer num, such as the batch count (e.g. count(0, axis)). Each derivative class must set this value appropriately. More...

int	m_nInnerNum = 0
	Specifies the inner num, such as the channel + height + width (e.g. count(axis + 1)). Each derivative class must set this value appropriately. More...

Protected Attributes inherited from MyCaffe.layers.Layer< T >
LayerParameter.LayerType	m_type = LayerParameter.LayerType._MAX
	Specifies the Layer type. More...

CudaDnn< T >	m_cuda
	Specifies the CudaDnn connection to Cuda. More...

Log	m_log
	Specifies the Log for output. More...

LayerParameter	m_param
	Specifies the LayerParameter describing the Layer. More...

Phase	m_phase
	Specifies the Phase under which the Layer is run. More...

BlobCollection< T >	m_colBlobs
	Specifies the learnable parameter Blobs of the Layer. More...

BlobCollection< T >	m_colInternalBlobs = new BlobCollection<T>()
	Specifies internal blobs used by the layer. More...

DictionaryMap< bool >	m_rgbParamPropagateDown
	Specifies whether or not to compute the learnable diff of each parameter Blob. More...

DictionaryMap< double >	m_rgLoss
	Specifies the loss values that indeicate whether each top (output) Blob has a non-zero weight in the objective function.. More...

T	m_tOne
	Specifies a generic type equal to 1.0. More...

T	m_tZero
	Specifies a generic type equal to 0.0. More...

bool	m_bEnablePassthrough = false
	Enables/disables the pass-through mode for the layer. Default = false. More...

bool	m_bUseHalfSize = false
	Specifies that the half size of the top (if any) should be converted to the base size. More...

bool	m_bConvertTopOnFwd = false
	Specifies whether or not the layer should convert the top on the forward pass when using half sized memory (typically only done with input data). More...

bool	m_bConvertTopOnBwd = true
	Specifies whether or not to convert the top on the backward pass when using half sized memory (typically not done on loss layers). More...

bool	m_bConvertBottom = true
	Specifies whether or not the layer should convert the bottom when using half sized memory. More...

bool	m_bReshapeOnForwardNeeded = true
	Specifies whether or not the reshape on forward is needed or not. More...

bool	m_bNetReshapeRequest = false
	Specifies whether the reshape is requested from a Net.Reshape call or not. More...

LayerParameter.? LayerType	m_parentLayerType = null
	Specifies the layer type of the parent. More...

Events inherited from MyCaffe.layers.LossLayer< T >
EventHandler< LossArgs >	OnLoss
	Specifies the loss event called on each learning cycle. More...

Events inherited from MyCaffe.layers.Layer< T >
EventHandler< WorkspaceArgs >	OnGetWorkspace
	Specifies the OnGetWorkspace event that fires when the getWorkspace() function is called by a layer to get a shareable workspace to conserve GPU memory. More...

EventHandler< WorkspaceArgs >	OnSetWorkspace
	Specifies the OnSetWorkspace event that fires when the setWorkspace() function is called by a layer to get a shareable workspace to conserve GPU memory. More...

EventHandler< GetIterationArgs >	OnGetIteration
	Specifies the OnGetIteration event that fires when a layer needs to get the current iteration from the solver. More...

EventHandler< GetWorkBlobArgs< T > >	OnDebug
	Specifies the OnGetWorkBlob event that is only supported when debugging to get a work blob from the primary Net holding this layer. More...

Detailed Description

The InforgainLossLayer is a generalization of SoftmaxWithLossLayer that takes an 'information gain' (infogain) matrix specifying the 'value of all label pairs. This layer is initialized with the MyCaffe.param.InfogainLossParameter.

Equivalent to the SoftmaxWithLossLayer if the inforgain matrix is the identity.

See also: DeepGaze II: Reading fixations from deep features trained on object recognition by Matthias Kümmerer, Thomas S. A. Wallis, and Matthias Bethge, 2016.

Template Parameters

T	Specifies the base type float or double. Using float is recommended to conserve GPU memory.

Definition at line 24 of file InfogainLossLayer.cs.

Constructor & Destructor Documentation

◆ InfogainLossLayer()

MyCaffe.layers.InfogainLossLayer< T >.InfogainLossLayer	(	CudaDnn< T >	cuda,
		Log	log,
		LayerParameter	p
	)

The InfogainLossLayer constructor.

Parameters

cuda	Specifies the CudaDnn connection to Cuda.
log	Specifies the Log for output.
p	provides LossParameter loss_param, with options: ignore_label (optional) Specify a label value that whould be ignored when computing the loss. normalize (optional, default true) If true, the loss is normalized by the number of (nonignored) labels present; otherwise the loss is imply summed over spatial locations.

Definition at line 56 of file InfogainLossLayer.cs.

Member Function Documentation

◆ backward()

override void MyCaffe.layers.InfogainLossLayer< T >.backward	(	BlobCollection< T >	colTop,
		List< bool >	rgbPropagateDown,
		BlobCollection< T >	colBottom
	)

protectedvirtual

Computes the infogain loss error gradient w.r.t the predictions.

Gradients cannot be computed with respect to the label inputs (bottom[1]), so this method ignores bottom[1] and requires !propagate_down[1], crashing if propagate_down[1] == true. (The same applies to the infogain matrix, if provided as bottom[2] rather than in the layer_param.)

Parameters

colTop	top output blob vector (length 1), providing the error gradient with respect to the outputs. $(1 \times 1 \times 1 \times 1)$ This blob's diff will simply contain the loss_weight * $\lambda$ as $\lambda$ is the coefficient of this layer's output $\ell_i$ in the overall Net loss. $E = \lambda_i \ell_i + \mbox{other loss terms}$ ; hence $\frac{partial E}{\partial \ell_i} = \lambda_i$ (*Assuming that this top blob is not used as a bottom (input) by any other layer of the Net.)
rgbPropagateDown	see Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels (similarly for progagate_down[2] and the infogain matrix, if provided as bottom[2]).
colBottom	bottom input blob vector (length 2-3) $(N \times C \times H \times W)$ the predictions ; Backward computes diff $\frac{\partial E}{\partial x}$ $(N \times 1 \times 1 \times 1)$ the labels – ignored as we can't compute their error gradients. $(1 \times 1 \times K \times K)$ (optional) the information gain matrix – ignored as its error gradient computation is not implemented.

Implements MyCaffe.layers.Layer< T >.

Definition at line 393 of file InfogainLossLayer.cs.

◆ dispose()

override void MyCaffe.layers.InfogainLossLayer< T >.dispose ( )

protectedvirtual

Releases all GPU and host resources used by the Layer.

Reimplemented from MyCaffe.layers.Layer< T >.

Definition at line 70 of file InfogainLossLayer.cs.

◆ forward()

override void MyCaffe.layers.InfogainLossLayer< T >.forward	(	BlobCollection< T >	colBottom,
		BlobCollection< T >	colTop
	)

protectedvirtual

The forward computation.

Parameters

colBottom

bottom input blob vector (length 2-3)

$(N \times C \times H \times W)$ the predictions , a Blob with values in $[-\infty, +\infty]$ indicating the predicted score for each of the classes. This layer maps these scores to a probability distribution over classes using the softmax function $\hat{p}_{nk} = \exp(x_{nk}) / \left[\sum_{k'} \exp(x_{nk'})\right]$ (see SoftmaxLayer).
$(N \times 1 \times 1 \times 1)$ the labels , an integer-valued Blob with values $l_n \in [0, 1, 2, ..., K-1]$ indicating the correct class label among the classes
$(1 \times 1 \times K \times K)$ (optional) the infogain matrix . This must be provided as the third bottom blob input if not provided as the inforgain_mat in the InfogainLossParameter. If , this layer is equivalent to the SoftmaxWithLossLayer.

colTop

top output blob vector (length 1)

$(1 \times 1 \times 1 \times 1)$ the computed infogain multinomial logistic loss: $E = \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} \log(\hat{p}_{n,k})$ where $H_{l_n}$ denotes row of .

Implements MyCaffe.layers.Layer< T >.

Definition at line 280 of file InfogainLossLayer.cs.

◆ LayerSetUp()

override void MyCaffe.layers.InfogainLossLayer< T >.LayerSetUp	(	BlobCollection< T >	colBottom,
		BlobCollection< T >	colTop
	)

virtual

Setup the layer.

Parameters

colBottom	Specifies the collection of bottom (input) Blobs.
colTop	Specifies the collection of top (output) Blobs.

Implements MyCaffe.layers.Layer< T >.

Definition at line 183 of file InfogainLossLayer.cs.

◆ Reshape()

override void MyCaffe.layers.InfogainLossLayer< T >.Reshape	(	BlobCollection< T >	colBottom,
		BlobCollection< T >	colTop
	)

virtual

Reshape the bottom (input) and top (output) blobs.

Parameters

colBottom	Specifies the collection of bottom (input) Blobs.
colTop	Specifies the collection of top (output) Blobs.

Implements MyCaffe.layers.Layer< T >.

Definition at line 217 of file InfogainLossLayer.cs.

◆ setup_internal_blobs()

override void MyCaffe.layers.InfogainLossLayer< T >.setup_internal_blobs ( BlobCollection< T > col )

protectedvirtual

Derivative layers should add all internal blobws to the 'col' provided.

Parameters

col	Specifies the blob collection where internal blobs are added.

Reimplemented from MyCaffe.layers.Layer< T >.

Definition at line 94 of file InfogainLossLayer.cs.

◆ sum_rows_of_H()

virtual void MyCaffe.layers.InfogainLossLayer< T >.sum_rows_of_H ( Blob< T > blobH )

protectedvirtual

Fill m_blobSumRowsOfH according to matrix H.

Parameters

blobH Specifies the Sum Rows of H blob.

Definition at line 159 of file InfogainLossLayer.cs.

Property Documentation

◆ ExactNumBottomBlobs

override int MyCaffe.layers.InfogainLossLayer< T >.ExactNumBottomBlobs

get

InfogainLossLayer takes 2-3 bottom blobs; if there are 3 the third should be the infogain matrix. (Otherwise the infogain matrix is loaded from a file specified by the LayerParameter.)

Definition at line 109 of file InfogainLossLayer.cs.

◆ ExactNumTopBlobs

override int MyCaffe.layers.InfogainLossLayer< T >.ExactNumTopBlobs

get

InfogainLossLayer computes softmax probability internally. optional second 'top' outputs the sofmax probability.

Definition at line 134 of file InfogainLossLayer.cs.

◆ MaxBottomBlobs

override int MyCaffe.layers.InfogainLossLayer< T >.MaxBottomBlobs

get

Returns the maximum number of required bottom (intput) Blobs: pred, label, matrix

Definition at line 125 of file InfogainLossLayer.cs.

◆ MaxTopBlobs

override int MyCaffe.layers.InfogainLossLayer< T >.MaxTopBlobs

get

Returns the maximum number of requried top (output) Blobs: infogain, softmax

Definition at line 150 of file InfogainLossLayer.cs.

◆ MinBottomBlobs

override int MyCaffe.layers.InfogainLossLayer< T >.MinBottomBlobs

get

Returns the minimum number of required bottom (intput) Blobs: pred, label

Definition at line 117 of file InfogainLossLayer.cs.

◆ MinTopBlobs

override int MyCaffe.layers.InfogainLossLayer< T >.MinTopBlobs

get

Returns the minimum number of required top (output) Blobs: infogain

Definition at line 142 of file InfogainLossLayer.cs.

The documentation for this class was generated from the following file:

C:/Data/Data/SS_Projects/Intelligence/GitHub/MyCaffe/MyCaffe/layers/InfogainLossLayer.cs

Public Member Functions

Protected Member Functions

Properties

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ InfogainLossLayer()

Member Function Documentation

◆ backward()

◆ dispose()

◆ forward()

◆ LayerSetUp()

◆ Reshape()

◆ setup_internal_blobs()

◆ sum_rows_of_H()

Property Documentation

◆ ExactNumBottomBlobs

◆ ExactNumTopBlobs

◆ MaxBottomBlobs

◆ MaxTopBlobs

◆ MinBottomBlobs

◆ MinTopBlobs