Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input. More...

#include <multinomial_logistic_loss_layer.hpp>

Inheritance diagram for caffe::MultinomialLogisticLossLayer< Dtype >:

Public Member Functions
	MultinomialLogisticLossLayer (const LayerParameter &param)

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 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 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 multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input. More...

virtual void	Backward_cpu (const vector< Blob< Dtype > > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > > &bottom)
	Computes the multinomial logistic 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::MultinomialLogisticLossLayer< Dtype >

Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input.

When predictions are not already a probability distribution, you should instead use the SoftmaxWithLossLayer, which maps predictions to a distribution using the SoftmaxLayer, before computing the multinomial logistic loss. The SoftmaxWithLossLayer should be preferred over separate SoftmaxLayer + MultinomialLogisticLossLayer as its gradient computation is more numerically stable.

Parameters

bottom

input Blob vector (length 2)

$(N \times C \times H \times W)$ the predictions $\hat{p}$ , a Blob with values in indicating the predicted probability of each of the classes. Each prediction vector $\hat{p}_n$ should sum to 1 as in a probability distribution: $\forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1$ .
$(N \times 1 \times 1 \times 1)$ 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 multinomial logistic loss: $E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})$

Member Function Documentation

◆ Backward_cpu()

template<typename Dtype >

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

protectedvirtual

Computes the multinomial logistic 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 $\hat{p}$ ; Backward computes diff $\frac{\partial E}{\partial \hat{p}}$ $(N \times 1 \times 1 \times 1)$ the labels – ignored as we can't compute their error gradients

Implements caffe::Layer< Dtype >.

◆ Forward_cpu()

template<typename Dtype >

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

protectedvirtual

Computes the multinomial logistic loss for a one-of-many classification task, directly taking a predicted probability distribution as input.

When predictions are not already a probability distribution, you should instead use the SoftmaxWithLossLayer, which maps predictions to a distribution using the SoftmaxLayer, before computing the multinomial logistic loss. The SoftmaxWithLossLayer should be preferred over separate SoftmaxLayer + MultinomialLogisticLossLayer as its gradient computation is more numerically stable.

Parameters

bottom

input Blob vector (length 2)

$(N \times C \times H \times W)$ the predictions $\hat{p}$ , a Blob with values in indicating the predicted probability of each of the classes. Each prediction vector $\hat{p}_n$ should sum to 1 as in a probability distribution: $\forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1$ .
$(N \times 1 \times 1 \times 1)$ 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 multinomial logistic loss: $E = \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})$

Implements caffe::Layer< Dtype >.

◆ Reshape()

template<typename Dtype >

void caffe::MultinomialLogisticLossLayer< 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/multinomial_logistic_loss_layer.hpp
src/caffe/layers/multinomial_logistic_loss_layer.cpp

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

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

Member Function Documentation

◆ Backward_cpu()

◆ Forward_cpu()

◆ Reshape()

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