Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter. More...

#include <net.hpp>

Classes
class	Callback

Public Member Functions
	Net (const NetParameter &param)

	Net (const string &param_file, Phase phase, const int level=0, const vector< string > *stages=NULL)

void	Init (const NetParameter &param)
	Initialize a network with a NetParameter.

const vector< Blob< Dtype > * > &	Forward (Dtype *loss=NULL)
	Run Forward and return the result.

const vector< Blob< Dtype > * > &	ForwardPrefilled (Dtype *loss=NULL)
	DEPRECATED; use Forward() instead.

Dtype	ForwardFromTo (int start, int end)

Dtype	ForwardFrom (int start)

Dtype	ForwardTo (int end)

const vector< Blob< Dtype > * > &	Forward (const vector< Blob< Dtype > * > &bottom, Dtype *loss=NULL)
	DEPRECATED; set input blobs then use Forward() instead.

void	ClearParamDiffs ()
	Zeroes out the diffs of all net parameters. Should be run before Backward.

void	Backward ()

void	BackwardFromTo (int start, int end)

void	BackwardFrom (int start)

void	BackwardTo (int end)

void	Reshape ()
	Reshape all layers from bottom to top. More...

Dtype	ForwardBackward ()

void	Update ()
	Updates the network weights based on the diff values computed.

void	ShareWeights ()
	Shares weight data of owner blobs with shared blobs. More...

void	ShareTrainedLayersWith (const Net *other)
	For an already initialized net, implicitly copies (i.e., using no additional memory) the pre-trained layers from another Net.

void	CopyTrainedLayersFrom (const NetParameter &param)
	For an already initialized net, copies the pre-trained layers from another Net.

void	CopyTrainedLayersFrom (const string trained_filename)

void	CopyTrainedLayersFromBinaryProto (const string trained_filename)

void	CopyTrainedLayersFromHDF5 (const string trained_filename)

void	ToProto (NetParameter *param, bool write_diff=false) const
	Writes the net to a proto.

void	ToHDF5 (const string &filename, bool write_diff=false) const
	Writes the net to an HDF5 file.

const string &	name () const
	returns the network name.

const vector< string > &	layer_names () const
	returns the layer names

const vector< string > &	blob_names () const
	returns the blob names

const vector< shared_ptr< Blob< Dtype > > > &	blobs () const
	returns the blobs

const vector< shared_ptr< Layer< Dtype > > > &	layers () const
	returns the layers

Phase	phase () const
	returns the phase: TRAIN or TEST

const vector< vector< Blob< Dtype > * > > &	bottom_vecs () const
	returns the bottom vecs for each layer – usually you won't need this unless you do per-layer checks such as gradients.

const vector< vector< Blob< Dtype > * > > &	top_vecs () const
	returns the top vecs for each layer – usually you won't need this unless you do per-layer checks such as gradients.

const vector< int > &	top_ids (int i) const
	returns the ids of the top blobs of layer i

const vector< int > &	bottom_ids (int i) const
	returns the ids of the bottom blobs of layer i

const vector< vector< bool > > &	bottom_need_backward () const

const vector< Dtype > &	blob_loss_weights () const

const vector< bool > &	layer_need_backward () const

const vector< shared_ptr< Blob< Dtype > > > &	params () const
	returns the parameters

const vector< Blob< Dtype > * > &	learnable_params () const

const vector< float > &	params_lr () const
	returns the learnable parameter learning rate multipliers

const vector< bool > &	has_params_lr () const

const vector< float > &	params_weight_decay () const
	returns the learnable parameter decay multipliers

const vector< bool > &	has_params_decay () const

const map< string, int > &	param_names_index () const

const vector< int > &	param_owners () const

const vector< string > &	param_display_names () const

int	num_inputs () const
	Input and output blob numbers.

int	num_outputs () const

const vector< Blob< Dtype > * > &	input_blobs () const

const vector< Blob< Dtype > * > &	output_blobs () const

const vector< int > &	input_blob_indices () const

const vector< int > &	output_blob_indices () const

bool	has_blob (const string &blob_name) const

const shared_ptr< Blob< Dtype > >	blob_by_name (const string &blob_name) const

bool	has_layer (const string &layer_name) const

const shared_ptr< Layer< Dtype > >	layer_by_name (const string &layer_name) const

void	set_debug_info (const bool value)

const vector< Callback * > &	before_forward () const

void	add_before_forward (Callback *value)

const vector< Callback * > &	after_forward () const

void	add_after_forward (Callback *value)

const vector< Callback * > &	before_backward () const

void	add_before_backward (Callback *value)

const vector< Callback * > &	after_backward () const

void	add_after_backward (Callback *value)

Static Public Member Functions
static void	FilterNet (const NetParameter &param, NetParameter *param_filtered)
	Remove layers that the user specified should be excluded given the current phase, level, and stage.

static bool	StateMeetsRule (const NetState &state, const NetStateRule &rule, const string &layer_name)
	return whether NetState state meets NetStateRule rule

Protected Member Functions
void	AppendTop (const NetParameter &param, const int layer_id, const int top_id, set< string > available_blobs, map< string, int > blob_name_to_idx)
	Append a new top blob to the net.

int	AppendBottom (const NetParameter &param, const int layer_id, const int bottom_id, set< string > available_blobs, map< string, int > blob_name_to_idx)
	Append a new bottom blob to the net.

void	AppendParam (const NetParameter &param, const int layer_id, const int param_id)
	Append a new parameter blob to the net.

void	ForwardDebugInfo (const int layer_id)
	Helper for displaying debug info in Forward.

void	BackwardDebugInfo (const int layer_id)
	Helper for displaying debug info in Backward.

void	UpdateDebugInfo (const int param_id)
	Helper for displaying debug info in Update.

	DISABLE_COPY_AND_ASSIGN (Net)

Protected Attributes
string	name_
	The network name.

Phase	phase_
	The phase: TRAIN or TEST.

vector< shared_ptr< Layer< Dtype > > >	layers_
	Individual layers in the net.

vector< string >	layer_names_

map< string, int >	layer_names_index_

vector< bool >	layer_need_backward_

vector< shared_ptr< Blob< Dtype > > >	blobs_
	the blobs storing intermediate results between the layer.

vector< string >	blob_names_

map< string, int >	blob_names_index_

vector< bool >	blob_need_backward_

vector< vector< Blob< Dtype > * > >	bottom_vecs_

vector< vector< int > >	bottom_id_vecs_

vector< vector< bool > >	bottom_need_backward_

vector< vector< Blob< Dtype > * > >	top_vecs_
	top_vecs stores the vectors containing the output for each layer

vector< vector< int > >	top_id_vecs_

vector< Dtype >	blob_loss_weights_

vector< vector< int > >	param_id_vecs_

vector< int >	param_owners_

vector< string >	param_display_names_

vector< pair< int, int > >	param_layer_indices_

map< string, int >	param_names_index_

vector< int >	net_input_blob_indices_
	blob indices for the input and the output of the net

vector< int >	net_output_blob_indices_

vector< Blob< Dtype > * >	net_input_blobs_

vector< Blob< Dtype > * >	net_output_blobs_

vector< shared_ptr< Blob< Dtype > > >	params_
	The parameters in the network.

vector< Blob< Dtype > * >	learnable_params_

vector< int >	learnable_param_ids_

vector< float >	params_lr_
	the learning rate multipliers for learnable_params_

vector< bool >	has_params_lr_

vector< float >	params_weight_decay_
	the weight decay multipliers for learnable_params_

vector< bool >	has_params_decay_

size_t	memory_used_
	The bytes of memory used by this net.

bool	debug_info_
	Whether to compute and display debug info for the net.

vector< Callback * >	before_forward_

vector< Callback * >	after_forward_

vector< Callback * >	before_backward_

vector< Callback * >	after_backward_

Detailed Description

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

Connects Layers together into a directed acyclic graph (DAG) specified by a NetParameter.

TODO(dox): more thorough description.

Member Function Documentation

◆ Backward()

template<typename Dtype >

void caffe::Net< Dtype >::Backward ( )

The network backward should take no input and output, since it solely computes the gradient w.r.t the parameters, and the data has already been provided during the forward pass.

◆ ForwardFromTo()

template<typename Dtype >

Dtype caffe::Net< Dtype >::ForwardFromTo	(	int	start,
		int	end
	)

The From and To variants of Forward and Backward operate on the (topological) ordering by which the net is specified. For general DAG networks, note that (1) computing from one layer to another might entail extra computation on unrelated branches, and (2) computation starting in the middle may be incorrect if all of the layers of a fan-in are not included.

◆ Reshape()

template<typename Dtype >

void caffe::Net< Dtype >::Reshape ( )

Reshape all layers from bottom to top.

This is useful to propagate changes to layer sizes without running a forward pass, e.g. to compute output feature size.

◆ ShareWeights()

template<typename Dtype >

void caffe::Net< Dtype >::ShareWeights ( )

Shares weight data of owner blobs with shared blobs.

Note: this is called by Net::Init, and thus should normally not be called manually.

Member Data Documentation

◆ blob_loss_weights_

template<typename Dtype >

vector<Dtype> caffe::Net< Dtype >::blob_loss_weights_

protected

Vector of weight in the loss (or objective) function of each net blob, indexed by blob_id.

◆ bottom_vecs_

template<typename Dtype >

vector<vector<Blob<Dtype>*> > caffe::Net< Dtype >::bottom_vecs_

protected

bottom_vecs stores the vectors containing the input for each layer. They don't actually host the blobs (blobs_ does), so we simply store pointers.

◆ learnable_param_ids_

template<typename Dtype >

vector<int> caffe::Net< Dtype >::learnable_param_ids_

protected

The mapping from params_ -> learnable_params_: we have learnable_param_ids_.size() == params_.size(), and learnable_params_[learnable_param_ids_[i]] == params_[i].get() if and only if params_[i] is an "owner"; otherwise, params_[i] is a sharer and learnable_params_[learnable_param_ids_[i]] gives its owner.

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

include/caffe/net.hpp
src/caffe/net.cpp

Classes

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

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

Member Function Documentation

◆ Backward()

◆ ForwardFromTo()

◆ Reshape()

◆ ShareWeights()

Member Data Documentation

◆ blob_loss_weights_

◆ bottom_vecs_

◆ learnable_param_ids_

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