MyCaffe  1.12.2.41
Deep learning software for Windows C# programmers.
MyCaffe.param.InfogainLossParameter Class Reference

Specifies the parameters for the InfogainLossLayer. More...

Inheritance diagram for MyCaffe.param.InfogainLossParameter:
MyCaffe.param.LayerParameterBase MyCaffe.basecode.BaseParameter MyCaffe.basecode.IBinaryPersist

Public Member Functions

 InfogainLossParameter ()
 Constructor for the parameter. More...
 
override object Load (System.IO.BinaryReader br, bool bNewInstance=true)
 Load the parameter from a binary reader. More...
 
override void Copy (LayerParameterBase src)
 Copy on parameter to another. More...
 
override LayerParameterBase Clone ()
 Creates a new copy of this instance of the parameter. More...
 
override RawProto ToProto (string strName)
 Convert the parameter into a RawProto. More...
 
- Public Member Functions inherited from MyCaffe.param.LayerParameterBase
 LayerParameterBase ()
 Constructor for the parameter. More...
 
virtual string PrepareRunModelInputs ()
 This method gives derivative classes a chance specify model inputs required by the run model. More...
 
virtual void PrepareRunModel (LayerParameter p)
 This method gives derivative classes a chance to prepare the layer for a run-model. More...
 
void Save (BinaryWriter bw)
 Save this parameter to a binary writer. More...
 
abstract object Load (BinaryReader br, bool bNewInstance=true)
 Load the parameter from a binary reader. More...
 
- Public Member Functions inherited from MyCaffe.basecode.BaseParameter
 BaseParameter ()
 Constructor for the parameter. More...
 
virtual bool Compare (BaseParameter p)
 Compare this parameter to another parameter. More...
 

Static Public Member Functions

static InfogainLossParameter FromProto (RawProto rp)
 Parses the parameter from a RawProto. More...
 
- Static Public Member Functions inherited from MyCaffe.basecode.BaseParameter
static double ParseDouble (string strVal)
 Parse double values using the US culture if the decimal separator = '.', then using the native culture, and if then lastly trying the US culture to handle prototypes containing '.' as the separator, yet parsed in a culture that does not use '.' as a decimal. More...
 
static bool TryParse (string strVal, out double df)
 Parse double values using the US culture if the decimal separator = '.', then using the native culture, and if then lastly trying the US culture to handle prototypes containing '.' as the separator, yet parsed in a culture that does not use '.' as a decimal. More...
 
static float ParseFloat (string strVal)
 Parse float values using the US culture if the decimal separator = '.', then using the native culture, and if then lastly trying the US culture to handle prototypes containing '.' as the separator, yet parsed in a culture that does not use '.' as a decimal. More...
 
static bool TryParse (string strVal, out float f)
 Parse doufloatble values using the US culture if the decimal separator = '.', then using the native culture, and if then lastly trying the US culture to handle prototypes containing '.' as the separator, yet parsed in a culture that does not use '.' as a decimal. More...
 

Properties

string source [getset]
 Specifies the infogain matrix source. More...
 
int axis [getset]
 [optional, default = 1] Specifies the axis of the probability. More...
 

Additional Inherited Members

- Public Types inherited from MyCaffe.param.LayerParameterBase
enum  LABEL_TYPE { NONE , SINGLE , MULTIPLE , ONLY_ONE }
 Defines the label type. More...
 

Detailed Description

Specifies the parameters for the InfogainLossLayer.

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

Definition at line 18 of file InfogainLossParameter.cs.

Constructor & Destructor Documentation

◆ InfogainLossParameter()

MyCaffe.param.InfogainLossParameter.InfogainLossParameter ( )

Constructor for the parameter.

Definition at line 24 of file InfogainLossParameter.cs.

Member Function Documentation

◆ Clone()

override LayerParameterBase MyCaffe.param.InfogainLossParameter.Clone ( )
virtual

Creates a new copy of this instance of the parameter.

Returns
A new instance of this parameter is returned.

Implements MyCaffe.param.LayerParameterBase.

Definition at line 69 of file InfogainLossParameter.cs.

◆ Copy()

override void MyCaffe.param.InfogainLossParameter.Copy ( LayerParameterBase  src)
virtual

Copy on parameter to another.

Parameters
srcSpecifies the parameter to copy.

Implements MyCaffe.param.LayerParameterBase.

Definition at line 61 of file InfogainLossParameter.cs.

◆ FromProto()

static InfogainLossParameter MyCaffe.param.InfogainLossParameter.FromProto ( RawProto  rp)
static

Parses the parameter from a RawProto.

Parameters
rpSpecifies the RawProto to parse.
Returns
A new instance of the parameter is returned.

Definition at line 98 of file InfogainLossParameter.cs.

◆ Load()

override object MyCaffe.param.InfogainLossParameter.Load ( System.IO.BinaryReader  br,
bool  bNewInstance = true 
)

Load the parameter from a binary reader.

Parameters
brSpecifies the binary reader.
bNewInstanceWhen true a new instance is created (the default), otherwise the existing instance is loaded from the binary reader.
Returns
Returns an instance of the parameter.

Definition at line 49 of file InfogainLossParameter.cs.

◆ ToProto()

override RawProto MyCaffe.param.InfogainLossParameter.ToProto ( string  strName)
virtual

Convert the parameter into a RawProto.

Parameters
strNameSpecifies the name to associate with the RawProto.
Returns
The new RawProto is returned.

Implements MyCaffe.basecode.BaseParameter.

Definition at line 81 of file InfogainLossParameter.cs.

Property Documentation

◆ axis

int MyCaffe.param.InfogainLossParameter.axis
getset

[optional, default = 1] Specifies the axis of the probability.

Definition at line 42 of file InfogainLossParameter.cs.

◆ source

string MyCaffe.param.InfogainLossParameter.source
getset

Specifies the infogain matrix source.

Definition at line 32 of file InfogainLossParameter.cs.


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