Rectified Linear Unit non-linearity $y = \max(0, x)$ . The simple max is fast to compute, and the function does not saturate. More...

#include <relu_layer.hpp>

Inheritance diagram for caffe::ReLULayer< Dtype >:

Public Member Functions
	ReLULayer (const LayerParameter &param)

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

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

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...

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...

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...

virtual bool	AutoTopBlobs () const
	Return whether "anonymous" top blobs are created automatically by the layer. More...

virtual bool	AllowForceBackward (const int bottom_index) const
	Return whether to allow force_backward for a given bottom blob index. 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)

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_cpu (const vector< Blob< Dtype > > &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > > &bottom)
	Computes the error gradient w.r.t. the ReLU inputs. More...

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.

Protected Member Functions inherited from caffe::Layer< Dtype >
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::ReLULayer< Dtype >

Rectified Linear Unit non-linearity $y = \max(0, x)$ . The simple max is fast to compute, and the function does not saturate.

Constructor & Destructor Documentation

◆ ReLULayer()

template<typename Dtype >

caffe::ReLULayer< Dtype >::ReLULayer ( const LayerParameter & param )

inlineexplicit

Parameters

param

provides ReLUParameter relu_param, with ReLULayer options:

negative_slope (optional, default 0). the value $\nu$ by which negative values are multiplied.

Member Function Documentation

◆ Backward_cpu()

template<typename Dtype >

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

protectedvirtual

Computes the error gradient w.r.t. the ReLU inputs.

Parameters

top	output Blob vector (length 1), providing the error gradient with respect to the outputs $(N \times C \times H \times W)$ containing error gradients $\frac{\partial E}{\partial y}$ with respect to computed outputs
propagate_down	see Layer::Backward.
bottom	input Blob vector (length 1) $(N \times C \times H \times W)$ the inputs ; Backward fills their diff with gradients $\frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} 0 & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right.$ if propagate_down[0], by default. If a non-zero negative_slope $\nu$ is provided, the computed gradients are $\frac{\partial E}{\partial x} = \left\{ \begin{array}{lr} \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\ \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 \end{array} \right.$ .

Implements caffe::Layer< Dtype >.

◆ Forward_cpu()

template<typename Dtype >

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

protectedvirtual

Parameters

bottom	input Blob vector (length 1) $(N \times C \times H \times W)$ the inputs
top	output Blob vector (length 1) $(N \times C \times H \times W)$ the computed outputs $y = \max(0, x)$ by default. If a non-zero negative_slope $\nu$ is provided, the computed outputs are $y = \max(0, x) + \nu \min(0, x)$ .

Implements caffe::Layer< Dtype >.

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

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

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

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

Constructor & Destructor Documentation

◆ ReLULayer()

Member Function Documentation

◆ Backward_cpu()

◆ Forward_cpu()

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