Data Format¶
Here is data format for exchange network structures and trained parameters.
Network Structure¶
Network structure and parameter will store with Google Protocol Buffer format internally.
Overview¶
Overview of network structure defined as following.
![skinparam monochrome true
hide circle
hide methods
class NNablaProtoBuf {
string version
GlobalConfig global_config
TrainingConfig training_config
Network[] network
Parameter[] parameter
Dataset[] dataset
Optimizer[] optimizer
Monitor[] monitor
Executor[] executor
}
package common <<Rectangle>> {
class GlobalConfig {
Context default_context
}
class Network {
string name
int batch_size
RepeatInfo[] repeat_info
Variable[] variable
Function[] function
}
class Parameter {
string variable_name
Shape shape
float[] data
bool need_grad
}
}
package training <<Rectangle>> {
class TrainingConfig {
int max_epoch
int iter_per_epoch
bool save_best
}
class Dataset {
string name
string type
string uri
int batch_size
string cache_dir
bool overwrite_cache
bool create_cache_explicitly
bool shuffle
bool no_image_normalization
string[] variable
}
class Optimizer {
string name
int order
string network_name
string dataset_name
Solver solver
int update_interval
DataVariable[] data_variable
GeneratorVariable[] generator_variable
LossVariable[] loss_variable
ParameterVariable[] parameter_variable
}
class Monitor {
string name
string network_name
string dataset_name
DataVariable[] data_variable
GeneratorVariable[] generator_variable
MonitorVariable[] monitor_variable
}
}
package inference <<Rectangle>> {
class Executor {
string name
string network_name
int num_evaluations
string repeat_evaluation_type
bool need_back_propagation
DataVariable[] data_variable
GeneratorVariable[] generator_variable
LossVariable[] loss_variable
OutputVariable[] output_variable
ParameterVariable[] parameter_variable
}
}
common <.. training
common <.. inference
NNablaProtoBuf "1" o-- "0,1" GlobalConfig
NNablaProtoBuf "1" o-- "0,1" Parameter
NNablaProtoBuf "1" o-- "0,1" TrainingConfig
NNablaProtoBuf "1" o-- "0..*" Network
NNablaProtoBuf "1" o-- "0..*" Dataset
NNablaProtoBuf "1" o-- "0..*" Optimizer
NNablaProtoBuf "1" o-- "0..*" Monitor
NNablaProtoBuf "1" o-- "0..*" Executor](_images/plantuml-d8936270b0ce0d37ff7a30c28f56cbc1b11cb7ae.png)
- NNablaProtoBuf
- Root message of NNabla network structure. This message could be store GlobalConfig, TrainingConfig, Network(s), Parameter(s), Dataset(s), Optimizer(s), Monitor(s) and Executor(s).
- Variable
- Internal data structure to store tensor for Neural network I/O and parameters.
- GlobalConfig
- Configuration of environment that suggest to do train or inference.
- TrainingConfig
- Configuration of training.
- Network
- Network structure.
- Parameter
- Special variable to store train result. (e.g Weight or Bias of affine layer)
- Dataset
- Specify dataset for training.
- Optimizer
- Define network, dataset, and input/output variables for train.
- Monitor
- Define network, dataset, and input/output variables for monitor training status..
- Executor
- Define network and input/output variables for train.
Structure for Training¶
TBD
Structure for Inference¶
TBD
Overall structure¶
![skinparam monochrome true
hide circle
hide methods
class Shape {
int[] dim
}
class Context {
string backend
string array_class
string device_id
string compute_backend
}
class GlobalConfig {
Context default_context
}
class NNablaProtoBuf {
string version
GlobalConfig global_config
TrainingConfig training_config
Network[] network
Parameter[] parameter
Dataset[] dataset
Optimizer[] optimizer
Monitor[] monitor
Executor[] executor
}
class TrainingConfig {
int max_epoch
int iter_per_epoch
bool save_best
}
class Network {
string name
int batch_size
RepeatInfo[] repeat_info
Variable[] variable
Function[] function
}
class RepeatInfo {
string id
int times
}
class RepeatParameter {
string repeat_id
int times
}
class RecurrentParameter {
string repeat_id
int length
int axis
}
class Variable {
string name
string type
string[] repeat_id
Shape shape
Initializer initializer
}
class Initializer {
string type
float multiplier
}
class Parameter {
string variable_name
Shape shape
float[] data
bool need_grad
}
class Dataset {
string name
string type
string uri
int batch_size
string cache_dir
bool overwrite_cache
bool create_cache_explicitly
bool shuffle
bool no_image_normalization
string[] variable
}
class Optimizer {
string name
int order
string network_name
string dataset_name
Solver solver
int update_interval
DataVariable[] data_variable
GeneratorVariable[] generator_variable
LossVariable[] loss_variable
ParameterVariable[] parameter_variable
}
class Solver {
string type
Context context
float weight_decay
float lr_decay
int lr_decay_interval
SolverParameter parameter
}
class DataVariable {
string variable_name
string data_name
}
class GeneratorVariable {
string variable_name
string type
float multiplier
}
class LossVariable {
string variable_name
}
class ParameterVariable {
string variable_name
float learning_rate_multiplier
}
class Monitor {
string name
string network_name
string dataset_name
DataVariable[] data_variable
GeneratorVariable[] generator_variable
MonitorVariable[] monitor_variable
}
class MonitorVariable {
string variable_name
string type
string data_name
float multiplier
}
class Executor {
string name
string network_name
int num_evaluations
string repeat_evaluation_type
bool need_back_propagation
DataVariable[] data_variable
GeneratorVariable[] generator_variable
LossVariable[] loss_variable
OutputVariable[] output_variable
ParameterVariable[] parameter_variable
}
class OutputVariable {
string variable_name
string type
string data_name
}
class Function {
string name
string type
string[] repeat_id
Context context
string[] input
string[] output
FunctionParameter parameter
// Loop Functions
RepeatParameter repeat_param
RecurrentParameter recurrent_param
}
abstract class SolverParameter
hide SolverParameter members
abstract class FunctionParameter
hide FunctionParameter members
NNablaProtoBuf "1" o-- "0,1" GlobalConfig
NNablaProtoBuf "1" o-- "0,1" TrainingConfig
NNablaProtoBuf "1" o-- "0..*" Network
NNablaProtoBuf "1" o-- "0..*" Parameter
NNablaProtoBuf "1" o-- "0..*" Dataset
NNablaProtoBuf "1" o-- "0..*" Optimizer
NNablaProtoBuf "1" o-- "0..*" Monitor
NNablaProtoBuf "1" o-- "0..*" Executor
GlobalConfig "1" o-- "1" Context
Network "1" o-- "0..*" RepeatInfo
Network "1" o-- "0..*" Variable
Network "1" o-- "0..*" Function
Parameter "1" ..> "1" Variable
Parameter "1" o-- "1" Shape
Variable "1" o-- "1" Shape
Variable "1" o-- "0,1" Initializer
Optimizer "1" ..> "1" Network
Optimizer "1" ..> "1" Dataset
Optimizer "1" o-- "1" Solver
Optimizer "1" o-- "0..*" DataVariable
Optimizer "1" o-- "0..*" GeneratorVariable
Optimizer "1" o-- "0..*" LossVariable
Optimizer "1" o-- "0..*" ParameterVariable
Monitor "1" ..> "1" Network
Monitor "1" ..> "1" Dataset
Monitor "1" o-- "1" Solver
Monitor "1" o-- "0..*" DataVariable
Monitor "1" o-- "0..*" GeneratorVariable
Monitor "1" o-- "0..*" MonitorVariable
Executor "1" ..> "1" Network
Executor "1" o-- "1" Solver
Executor "1" o-- "0..*" DataVariable
Executor "1" o-- "0..*" GeneratorVariable
Executor "1" o-- "0..*" LossVariable
Executor "1" o-- "0..*" OutputVariable
Executor "1" o-- "0..*" ParameterVariable
DataVariable "1" ..> "1" Variable
GeneratorVariable "1" ..> "1" Variable
LossVariable "1" ..> "1" Variable
ParameterVariable "1" ..> "1" Variable
MonitorVariable "1" ..> "1" Variable
OutputVariable "1" ..> "1" Variable
Function "1" o-- "0,1" FunctionParameter
Function "1" o-- "0,1" RepeatParameter
Function "1" o-- "0,1" RecurrentParameter
Solver "1" o-- "1" Context
Solver "1" o-- "0,1" SolverParameter](_images/plantuml-1ea2aabf2d3aab57c64808d36f73dec97e7dde7c.png)
Parameter¶
From the performance point of view, parameters can be saved in HDF 5 format.
File Format and extensions¶
- Protocol buffer text format file
- .nntxt or .prototxt
- Protocol buffer serialized binary file
- .protobuf
- HDF5
- .h5
- NNP (ZIP archived file with above formats.)
- .nnp