Computes the hinge loss for a one-of-many classification task. More...

#include <hinge_loss_layer.hpp>

Inheritance diagram for caffe::HingeLossLayer< Dtype >:

Public Member Functions
	HingeLossLayer (const LayerParameter &param)

virtual const char *	type () const
	Returns the layer type.

Public Member Functions inherited from caffe::LossLayer< Dtype >
	LossLayer (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 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 int	ExactNumTopBlobs () const
	Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...

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 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	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 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)
	Computes the hinge loss for a one-of-many classification task. More...

virtual void	Backward_cpu (const vector< Blob< Dtype > > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > > &bottom)
	Computes the hinge loss error gradient w.r.t. the predictions. More...

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)

Additional Inherited Members
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::HingeLossLayer< Dtype >

Computes the hinge loss for a one-of-many classification task.

Parameters

bottom

input Blob vector (length 2)

$(N \times C \times H \times W)$ the predictions , a Blob with values in $[-\infty, +\infty]$ indicating the predicted score for each of the classes. In an SVM, is the result of taking the inner product of the D-dimensional features $X \in \mathcal{R}^{D \times N}$ and the learned hyperplane parameters $W \in \mathcal{R}^{D \times K}$ , so a Net with just an InnerProductLayer (with num_output = D) providing predictions to a HingeLossLayer and no other learnable parameters or losses is equivalent to an SVM.
$(N \times 1 \times 1 \times 1)$ the labels , an integer-valued Blob with values $l_n \in [0, 1, 2, ..., K - 1]$ indicating the correct class label among the classes

top

output Blob vector (length 1)

$(1 \times 1 \times 1 \times 1)$ the computed hinge loss: $E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p$ , for the norm (defaults to , the L1 norm; L2 norm, as in L2-SVM, is also available), and $\delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right.$

In an SVM, $t \in \mathcal{R}^{N \times K}$ is the result of taking the inner product $ X^T W $ of the features $X \in \mathcal{R}^{D \times N}$ and the learned hyperplane parameters $W \in \mathcal{R}^{D \times K}$ . So, a Net with just an InnerProductLayer (with num_output = $k$ ) providing predictions to a HingeLossLayer is equivalent to an SVM (assuming it has no other learned outside the InnerProductLayer and no other losses outside the HingeLossLayer).

Member Function Documentation

◆ Backward_cpu()

template<typename Dtype >

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

protectedvirtual

Computes the hinge 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.

Parameters

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.)
propagate_down	see Layer::Backward. propagate_down[1] must be false as we can't compute gradients with respect to the labels.
bottom	input Blob vector (length 2) $(N \times C \times H \times W)$ the predictions ; Backward computes diff $\frac{\partial E}{\partial t}$ $(N \times 1 \times 1 \times 1)$ the labels – ignored as we can't compute their error gradients

Implements caffe::Layer< Dtype >.

◆ Forward_cpu()

template<typename Dtype >

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

protectedvirtual

Computes the hinge loss for a one-of-many classification task.

Parameters

bottom

input Blob vector (length 2)

$(N \times C \times H \times W)$ the predictions , a Blob with values in $[-\infty, +\infty]$ indicating the predicted score for each of the classes. In an SVM, is the result of taking the inner product of the D-dimensional features $X \in \mathcal{R}^{D \times N}$ and the learned hyperplane parameters $W \in \mathcal{R}^{D \times K}$ , so a Net with just an InnerProductLayer (with num_output = D) providing predictions to a HingeLossLayer and no other learnable parameters or losses is equivalent to an SVM.
$(N \times 1 \times 1 \times 1)$ the labels , an integer-valued Blob with values $l_n \in [0, 1, 2, ..., K - 1]$ indicating the correct class label among the classes

top

output Blob vector (length 1)

$(1 \times 1 \times 1 \times 1)$ the computed hinge loss: $E = \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p$ , for the norm (defaults to , the L1 norm; L2 norm, as in L2-SVM, is also available), and $\delta\{\mathrm{condition}\} = \left\{ \begin{array}{lr} 1 & \mbox{if condition} \\ -1 & \mbox{otherwise} \end{array} \right.$

In an SVM, $t \in \mathcal{R}^{N \times K}$ is the result of taking the inner product $ X^T W $ of the features $X \in \mathcal{R}^{D \times N}$ and the learned hyperplane parameters $W \in \mathcal{R}^{D \times K}$ . So, a Net with just an InnerProductLayer (with num_output = $k$ ) providing predictions to a HingeLossLayer is equivalent to an SVM (assuming it has no other learned outside the InnerProductLayer and no other losses outside the HingeLossLayer).

Implements caffe::Layer< Dtype >.

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

include/caffe/layers/hinge_loss_layer.hpp
src/caffe/layers/hinge_loss_layer.cpp

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

template<typename Dtype> class caffe::HingeLossLayer< Dtype >

Member Function Documentation

◆ Backward_cpu()

◆ Forward_cpu()

template<typename Dtype>
class caffe::HingeLossLayer< Dtype >