A Convolutional Net Model. More...

#include <convnet.h>

Inheritance diagram for ConvNet:

Public Member Functions
	ConvNet (const string &model_file)
	Instantiate a model using the config in model_file.

void	SetupDataset (const string &train_data_config_file)

virtual void	SetupDataset (const string &train_data_config_file, const string &val_data_config_file)

virtual void	Train ()
	Start training. More...

void	Validate (DataHandler *dataset, vector< float > &error)
	Validate the model on the specfied dataset and return the error. More...

void	Validate (vector< float > &error)
	Validate the model on the validation dataset and return the error. More...

void	Save ()
	Write the model to disk. More...

void	Save (const string &output_file)
	Write the model to disk in the file specified. More...

void	Load ()
	Load the model. More...

void	Load (const string &input_file)
	Load the model from the file specified. More...

void	Display ()
	Display the state of the model. More...

void	DumpOutputs (const string &output_file, DataHandler *dataset, const vector< string > &layer_names)
	Write the state of the layers to disk. More...

void	DumpOutputs (const string &output_file, const vector< string > &layer_names)
	Write the state of the layers to disk. More...

void	AllocateMemory (bool fprop_only)
	Allocate memory for the model. More...

Protected Member Functions
void	BuildNet ()
	Creates layers and edges. More...

void	DestroyNet ()
	Release all memory held by the model. More...

void	AllocateLayerMemory ()
	Allocate layer memory for using mini-batches of batch_size_. More...

void	AllocateEdgeMemory (bool fprop_only)
	Allocate memory for edges. More...

string	GetCheckpointFilename ()

void	TimestampModel ()

void	Sort ()
	Topologically sort layers. More...

void	Fprop (Layer &input, Layer &output, Edge &edge, bool overwrite)
	Forward propagate one layer. More...

virtual void	Bprop (Layer &output, Layer &input, Edge &edge, bool overwrite, bool update_weights)
	Back propagate through one layer. More...

virtual void	Fprop (bool train)
	Forward propagate through the network. More...

virtual void	Bprop (bool update_weights)
	Backpropagate through the network and update weights. More...

virtual void	ComputeDeriv ()
	Computes the derivative of the loss function. More...

virtual void	GetLoss (vector< float > &error)
	Computes the loss function (to be displayed). More...

void	DumpOutputs (const string &output_file, DataHandler dataset, vector< Layer > &layers)
	Write the state of the layers to disk. More...

virtual void	TrainOneBatch (vector< float > &error)
	Takes one optimization step. More...

void	DisplayLayers ()

void	DisplayEdges ()

void	InsertPolyak ()

void	LoadPolyakWeights ()

void	LoadCurrentWeights ()

void	WriteLog (int current_iter, float time, float training_error)

void	WriteLog (int current_iter, float time, const vector< float > &training_error)

void	WriteValLog (int current_iter, const vector< float > &error)

Layer *	GetLayerByName (const string &name)

bool	CheckReduceLearningRate (const vector< float > &val_error)
	Decides if learning rate should be reduced. More...

void	ReduceLearningRate (const float factor)
	Multiply learning rate by factor. More...

Static Protected Member Functions
static void	AddVectors (vector< float > &a, vector< float > &b)

Protected Attributes
config::Model	model_

vector< Layer * >	layers_
	The model protobuf config. More...

vector< Layer * >	data_layers_
	The layers in the network. More...

vector< Layer * >	input_layers_
	Layers which have data associated with them. More...

vector< Layer * >	output_layers_
	Input layers. More...

vector< Edge * >	edges_
	Output layers. More...

int	max_iter_
	The edges in the network. More...

int	batch_size_

int	current_iter_

int	lr_reduce_counter_

DataHandler *	train_dataset_

DataHandler *	val_dataset_

string	checkpoint_dir_

string	output_file_

string	model_name_

ImageDisplayer	displayer_

string	model_filename_

string	timestamp_

string	log_file_

string	val_log_file_

Detailed Description

A Convolutional Net Model.

This class provides the interface for training and using conv nets.

Member Function Documentation

void ConvNet::AllocateEdgeMemory ( bool fprop_only )

protected

Allocate memory for edges.

Parameters

fprop_only If true, does not allocate memory needed for optimization.

void ConvNet::AllocateLayerMemory ( )

protected

Allocate layer memory for using mini-batches of batch_size_.

void ConvNet::AllocateMemory ( bool fprop_only )

Allocate memory for the model.

Parameters

fprop_only If true, does not allocate memory needed for optimization.

void ConvNet::Bprop	(	Layer &	output,
		Layer &	input,
		Edge &	edge,
		bool	overwrite,
		bool	update_weights
	)

protectedvirtual

Back propagate through one layer.

Passes down the gradients from the output layer to the input layer. Also updates the weights on the edge (if update_weights is true).

Parameters

output	the output layer (gradients w.r.t this have been computed).
input	the input layer (gradients w.r.t this will be computed here).
edge	the edge connecting the input to the output.
overwrite	If true, overwrite the deriv present in input, else add to it.
update_weights	If true, the weights will be updated.

void ConvNet::Bprop ( bool update_weights )

protectedvirtual

Backpropagate through the network and update weights.

Reimplemented in MultiGPUConvNet.

void ConvNet::BuildNet ( )

protected

Creates layers and edges.

bool ConvNet::CheckReduceLearningRate ( const vector< float > & val_error )

protected

Decides if learning rate should be reduced.

void ConvNet::ComputeDeriv ( )

protectedvirtual

Computes the derivative of the loss function.

void ConvNet::DestroyNet ( )

protected

Release all memory held by the model.

void ConvNet::Display ( )

Display the state of the model.

Shows the layers and edges for which display is enabled.

void ConvNet::DumpOutputs	(	const string &	output_file,
		DataHandler *	dataset,
		const vector< string > &	layer_names
	)

Write the state of the layers to disk.

This method runs the model on the specified dataset and writes the layer states out to disk in a hdf5 file.

Parameters

output_file	The name of the output hdf5 file.
dataset	The dataset to be run.
layer_names	The names of the layers whose state needs to be written out.

void ConvNet::DumpOutputs	(	const string &	output_file,
		const vector< string > &	layer_names
	)

Write the state of the layers to disk.

This method runs the model on the train dataset and writes the layer states out to disk in a hdf5 file.

Parameters

output_file	The name of the output hdf5 file.
layer_names	The names of the layers whose state needs to be written out.

void ConvNet::DumpOutputs	(	const string &	output_file,
		DataHandler *	dataset,
		vector< Layer * > &	layers
	)

protected

Write the state of the layers to disk.

This method runs the model on the specified dataset and writes the layer states out to disk in a hdf5 file.

Parameters

output_file	The name of the output hdf5 file.
dataset	The dataset to be run.
layers	The layers whose state needs to be written out.

void ConvNet::Fprop	(	Layer &	input,
		Layer &	output,
		Edge &	edge,
		bool	overwrite
	)

protected

Forward propagate one layer.

Passes up input through the edge and updates the state of the output.

Parameters

input	the input layer.
output	the output layer.
edge	the edge connecting the input to the output.
overwrite	If true, overwrite the state present in output, else add to it.

void ConvNet::Fprop ( bool train )

protectedvirtual

Forward propagate through the network.

Parameters

train If true, this forward prop is being done during training, otherwise during test/validation. Used for determining whether to use drop units stochastcially or use all of them.

Reimplemented in MultiGPUConvNet.

void ConvNet::GetLoss ( vector< float > & error )

protectedvirtual

Computes the loss function (to be displayed).

Reimplemented in GradChecker.

void ConvNet::Load ( )

Load the model.

void ConvNet::Load ( const string & input_file )

Load the model from the file specified.

void ConvNet::ReduceLearningRate ( const float factor )

protected

Multiply learning rate by factor.

void ConvNet::Save ( )

Write the model to disk.

void ConvNet::Save ( const string & output_file )

Write the model to disk in the file specified.

void ConvNet::Sort ( )

protected

Topologically sort layers.

void ConvNet::Train ( )

virtual

Start training.

void ConvNet::TrainOneBatch ( vector< float > & error )

protectedvirtual

Takes one optimization step.

void ConvNet::Validate	(	DataHandler *	dataset,
		vector< float > &	error
	)

Validate the model on the specfied dataset and return the error.

Parameters

dataset	The dataset.
error	A vector of errors (one element for each output layer).

void ConvNet::Validate ( vector< float > & error )

Validate the model on the validation dataset and return the error.

Parameters

error A vector of errors (one element for each output layer).

Member Data Documentation

vector<Layer*> ConvNet::data_layers_

protected

The layers in the network.

vector<Edge*> ConvNet::edges_

protected

Output layers.

vector<Layer*> ConvNet::input_layers_

protected

Layers which have data associated with them.

vector<Layer*> ConvNet::layers_

protected

The model protobuf config.

int ConvNet::max_iter_

protected

The edges in the network.

vector<Layer*> ConvNet::output_layers_

protected

Input layers.

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

src/convnet.h
src/convnet.cc

Public Member Functions

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Detailed Description

Member Function Documentation

Member Data Documentation