Parameterized Rectified Linear Unit non-linearity $y_i = \max(0, x_i) + a_i \min(0, x_i)$ . The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels. More...

#include <prelu_layer.hpp>

Inheritance diagram for caffe::PReLULayer< Dtype >:

Public Member Functions
	PReLULayer (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 const char *	type () const
	Returns the layer type.

Public Member Functions inherited from caffe::NeuronLayer< Dtype >
	NeuronLayer (const LayerParameter &param)

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...

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 PReLU 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)

Protected Attributes
bool	channel_shared_

Blob< Dtype >	multiplier_

Blob< Dtype >	backward_buff_

Blob< Dtype >	bottom_memory_

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::PReLULayer< Dtype >

Parameterized Rectified Linear Unit non-linearity $y_i = \max(0, x_i) + a_i \min(0, x_i)$ . The differences from ReLULayer are 1) negative slopes are learnable though backprop and 2) negative slopes can vary across channels. The number of axes of input blob should be greater than or equal to 2. The 1st axis (0-based) is seen as channels.

Constructor & Destructor Documentation

◆ PReLULayer()

template<typename Dtype >

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

inlineexplicit

Parameters

param

provides PReLUParameter prelu_param, with PReLULayer options:

filler (optional, FillerParameter, default {'type': constant 'value':0.25}).
channel_shared (optional, default false). negative slopes are shared across channels.

Member Function Documentation

◆ Backward_cpu()

template<typename Dtype >

void caffe::PReLULayer< 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 PReLU inputs.

Parameters

top	output Blob vector (length 1), providing the error gradient with respect to the outputs $(N \times C \times ...)$ containing error gradients $\frac{\partial E}{\partial y}$ with respect to computed outputs
propagate_down	see Layer::Backward.
bottom	input Blob vector (length 1) $(N \times C \times ...)$ the inputs ; For each channel , backward fills their diff with gradients $\frac{\partial E}{\partial x_i} = \left\{ \begin{array}{lr} a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0 \end{array} \right.$ . If param_propagate_down_[0] is true, it fills the diff with gradients $\frac{\partial E}{\partial a_i} = \left\{ \begin{array}{lr} \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ 0 & \mathrm{if} \; x_i > 0 \end{array} \right.$ .

Implements caffe::Layer< Dtype >.

◆ Forward_cpu()

template<typename Dtype >

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

protectedvirtual

Parameters

bottom	input Blob vector (length 1) $(N \times C \times ...)$ the inputs
top	output Blob vector (length 1) $(N \times C \times ...)$ the computed outputs for each channel $y_i = \max(0, x_i) + a_i \min(0, x_i)$ .

Implements caffe::Layer< Dtype >.

◆ LayerSetUp()

template<typename Dtype >

void caffe::PReLULayer< 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::Layer< Dtype >.

◆ Reshape()

template<typename Dtype >

void caffe::PReLULayer< 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::NeuronLayer< Dtype >.

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

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

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

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

Constructor & Destructor Documentation

◆ PReLULayer()

Member Function Documentation

◆ Backward_cpu()

◆ Forward_cpu()

◆ LayerSetUp()

◆ Reshape()

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