caffe::Net< Dtype > Class Template Reference

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