caffe::InfogainLossLayer< Dtype > Class Template Reference

A generalization of SoftmaxWithLossLayer that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs. More...

`#include <infogain_loss_layer.hpp>`

Inheritance diagram for caffe::InfogainLossLayer< Dtype >:

## Public Member Functions

| InfogainLossLayer (const LayerParameter &param)
virtual void | LayerSetUp (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
| Does layer-specific setup: your layer should implement this function as well as Reshape. More...
virtual void | Reshape (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
| Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
virtual int | ExactNumBottomBlobs () const
| Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
virtual int | MinBottomBlobs () const
| Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
virtual int | MaxBottomBlobs () const
| Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
virtual int | ExactNumTopBlobs () const
| Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
virtual int | MinTopBlobs () const
| Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
virtual int | MaxTopBlobs () const
| Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
virtual const char * | type () const
| Returns the layer type.
Public Member Functions inherited from caffe::LossLayer< Dtype >
| LossLayer (const LayerParameter &param)
virtual bool | AutoTopBlobs () const
| For convenience and backwards compatibility, instruct 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.).
virtual bool | AllowForceBackward (const int bottom_index) const
Public Member Functions inherited from caffe::Layer< Dtype >
| Layer (const LayerParameter &param)
void | SetUp (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
| Implements common layer setup functionality. More...
Dtype | Forward (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
| Given the bottom blobs, compute the top blobs and the loss. More...
void | Backward (const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
| Given the top blob error gradients, compute the bottom blob error gradients. More...
vector< shared_ptr< Blob< Dtype > > > & | blobs ()
| Returns the vector of learnable parameter blobs.
const LayerParameter & | layer_param () const
| Returns the layer parameter.
virtual void | ToProto (LayerParameter *param, bool write_diff=false)
| Writes the layer parameter to a protocol buffer.
Dtype | loss (const int top_index) const
| Returns the scalar loss associated with a top blob at a given index.
void | set_loss (const int top_index, const Dtype value)
| Sets the loss associated with a top blob at a given index.
virtual bool | EqualNumBottomTopBlobs () const
| Returns true if the layer requires an equal number of bottom and top blobs. More...
bool | param_propagate_down (const int param_id)
| Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
void | set_param_propagate_down (const int param_id, const bool value)
| Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.

## Protected Member Functions

virtual void | Forward_cpu (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
| A generalization of SoftmaxWithLossLayer that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs. More...
virtual void | Backward_cpu (const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
| Computes the infogain loss error gradient w.r.t. the predictions. More...
virtual Dtype | get_normalizer (LossParameter_NormalizationMode normalization_mode, int valid_count)
virtual void | sum_rows_of_H (const Blob< Dtype > *H)
| fill sum_rows_H_ according to matrix H
Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void | Forward_gpu (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
| Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
virtual void | Backward_gpu (const vector< Blob< Dtype > * > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > * > &bottom)
| Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
virtual void | CheckBlobCounts (const vector< Blob< Dtype > * > &bottom, const vector< Blob< Dtype > * > &top)
void | SetLossWeights (const vector< Blob< Dtype > * > &top)

## Protected Attributes

shared_ptr< Layer< Dtype > > | softmax_layer_
| The internal SoftmaxLayer used to map predictions to a distribution.
Blob< Dtype > | prob_
| prob stores the output probability predictions from the SoftmaxLayer.
vector< Blob< Dtype > * > | softmax_bottom_vec_
| bottom vector holder used in call to the underlying SoftmaxLayer::Forward
vector< Blob< Dtype > * > | softmax_top_vec_
| top vector holder used in call to the underlying SoftmaxLayer::Forward
Blob< Dtype > | infogain_
Blob< Dtype > | sum_rows_H_
bool | has_ignore_label_
| Whether to ignore instances with a certain label.
int | ignore_label_
| The label indicating that an instance should be ignored.
LossParameter_NormalizationMode | normalization_
| How to normalize the output loss.
int | infogain_axis_
int | outer_num_
int | inner_num_
int | num_labels_
Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter | layer_param_
Phase | phase_
vector< shared_ptr< Blob< Dtype > > > | blobs_
vector< bool > | param_propagate_down_
vector< Dtype > | loss_

## Detailed Description

### template<typename Dtype>
class caffe::InfogainLossLayer< Dtype >

A generalization of SoftmaxWithLossLayer that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs.

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

Parameters

  bottom

  input Blob vector (length 2-3)
  1. the predictions , a Blob with values in indicating the predicted score for each of the classes. This layer maps these scores to a probability distribution over classes using the softmax function (see SoftmaxLayer).
  2. the labels , an integer-valued Blob with values indicating the correct class label among the classes
  3. (optional) the infogain matrix . This must be provided as the third bottom blob input if not provided as the infogain_mat in the InfogainLossParameter. If , this layer is equivalent to the SoftmaxWithLossLayer.

  top

  output Blob vector (length 1)
  1. the computed infogain multinomial logistic loss: , where denotes row of .

## Member Function Documentation

## Backward_cpu()

template<typename Dtype >

void caffe::InfogainLossLayer< Dtype >::Backward_cpu | ( | const vector< Blob< Dtype > * > & | top,
|  | const vector< bool > & | propagate_down,
|  | const vector< Blob< Dtype > * > & | bottom
| ) |  |

protected virtual

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] is set. (The same applies to the infogain matrix, if provided as bottom[2] rather than in the layer_param.)

Parameters

  top

  output Blob vector (length 1), providing the error gradient with respect to the outputs
  1. This Blob's diff will simply contain the loss_weight* , as is the coefficient of this layer's output in the overall Net loss ; hence . (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)

  propagate_down | see Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels (similarly for propagate_down[2] and the infogain matrix, if provided as bottom[2])

  bottom

  input Blob vector (length 2-3)
  1. the predictions ; Backward computes diff
  2. the labels – ignored as we can't compute their error gradients
  3. (optional) the information gain matrix – ignored as its error gradient computation is not implemented.

Implements caffe::Layer< Dtype >.

## ExactNumBottomBlobs()

template<typename Dtype >

virtual int caffe::InfogainLossLayer< Dtype >::ExactNumBottomBlobs | ( |  | ) | const

inline virtual

Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required.

This method should be overridden to return a non-negative value if your layer expects some exact number of bottom blobs.

Reimplemented from caffe::LossLayer< Dtype >.

## ExactNumTopBlobs()

template<typename Dtype >

virtual int caffe::InfogainLossLayer< Dtype >::ExactNumTopBlobs | ( |  | ) | const

inline virtual

Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.

This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.

Reimplemented from caffe::LossLayer< Dtype >.

## Forward_cpu()

template<typename Dtype >

void caffe::InfogainLossLayer< Dtype >::Forward_cpu | ( | const vector< Blob< Dtype > * > & | bottom,
|  | const vector< Blob< Dtype > * > & | top
| ) |  |

protected virtual

A generalization of SoftmaxWithLossLayer that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs.

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

Parameters

  bottom

  input Blob vector (length 2-3)
  1. the predictions , a Blob with values in indicating the predicted score for each of the classes. This layer maps these scores to a probability distribution over classes using the softmax function (see SoftmaxLayer).
  2. the labels , an integer-valued Blob with values indicating the correct class label among the classes
  3. (optional) the infogain matrix . This must be provided as the third bottom blob input if not provided as the infogain_mat in the InfogainLossParameter. If , this layer is equivalent to the SoftmaxWithLossLayer.

  top

  output Blob vector (length 1)
  1. the computed infogain multinomial logistic loss: , where denotes row of .

Implements caffe::Layer< Dtype >.

## get_normalizer()

template<typename Dtype >

Dtype caffe::InfogainLossLayer< Dtype >::get_normalizer | ( | LossParameter_NormalizationMode | normalization_mode,
|  | int | valid_count
| ) |  |

protected virtual

Read the normalization mode parameter and compute the normalizer based on the blob size. If normalization_mode is VALID, the count of valid outputs will be read from valid_count, unless it is -1 in which case all outputs are assumed to be valid.

## LayerSetUp()

template<typename Dtype >

void caffe::InfogainLossLayer< Dtype >::LayerSetUp | ( | const vector< Blob< Dtype > * > & | bottom,
|  | const vector< Blob< Dtype > * > & | top
| ) |  |

virtual

Does layer-specific setup: your layer should implement this function as well as Reshape.

Parameters

  bottom | the preshaped input blobs, whose data fields store the input data for this layer
  top | the allocated but unshaped output blobs

This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the `layer_param_`. Setting up the shapes of top blobs and internal buffers should be done in `Reshape`, which will be called before the forward pass to adjust the top blob sizes.

Reimplemented from caffe::LossLayer< Dtype >.

## MaxBottomBlobs()

template<typename Dtype >

virtual int caffe::InfogainLossLayer< Dtype >::MaxBottomBlobs | ( |  | ) | const

inline virtual

Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required.

This method should be overridden to return a non-negative value if your layer expects some maximum number of bottom blobs.

Reimplemented from caffe::Layer< Dtype >.

## MaxTopBlobs()

template<typename Dtype >

virtual int caffe::InfogainLossLayer< Dtype >::MaxTopBlobs | ( |  | ) | const

inline virtual

Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required.

This method should be overridden to return a non-negative value if your layer expects some maximum number of top blobs.

Reimplemented from caffe::Layer< Dtype >.

## MinBottomBlobs()

template<typename Dtype >

virtual int caffe::InfogainLossLayer< Dtype >::MinBottomBlobs | ( |  | ) | const

inline virtual

Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.

This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.

Reimplemented from caffe::Layer< Dtype >.

## MinTopBlobs()

template<typename Dtype >

virtual int caffe::InfogainLossLayer< Dtype >::MinTopBlobs | ( |  | ) | const

inline virtual

Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required.

This method should be overridden to return a non-negative value if your layer expects some minimum number of top blobs.

Reimplemented from caffe::Layer< Dtype >.

## Reshape()

template<typename Dtype >

void caffe::InfogainLossLayer< Dtype >::Reshape | ( | const vector< Blob< Dtype > * > & | bottom,
|  | const vector< Blob< Dtype > * > & | top
| ) |  |

virtual

Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.

Parameters

  bottom | the input blobs, with the requested input shapes
  top | the top blobs, which should be reshaped as needed

This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.

Reimplemented from caffe::LossLayer< Dtype >.

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

- include/caffe/layers/infogain_loss_layer.hpp
- src/caffe/layers/infogain_loss_layer.cpp