Caffe2 - Python API
A deep learning, cross platform ML framework
Public Member Functions | Public Attributes | List of all members
caffe2.python.layer_model_helper.LayerModelHelper Class Reference
Inheritance diagram for caffe2.python.layer_model_helper.LayerModelHelper:

Public Member Functions

def __init__ (self, name, input_feature_schema, trainer_extra_schema, keep_blobs=False)
def clear_output_schema (self)
def set_initialize_params (self, initialize_params)
def add_metric_field (self, name, value)
def add_global_constant (self, name, array=None, dtype=None, initializer=None)
def maybe_add_global_constant (self, name, args, kwargs)
def create_init_net (self, name)
def create_param (self, param_name, shape, initializer, optimizer=None, ps_param=None, regularizer=None)
def next_layer_name (self, prefix)
def add_layer (self, layer)
def get_parameter_blobs (self)
def add_post_grad_net_modifiers (self, modifier)
def add_final_net_modifiers (self, modifier)
def seed (self)
def sequence_seed (self)
def store_seed (self, seed, sequence_seed=True)
def apply_seed (self, net)
def default_optimizer (self)
def default_optimizer (self, optimizer)
def input_feature_schema (self)
def trainer_extra_schema (self)
def metrics_schema (self)
def output_schema (self)
def output_schema (self, schema)
def preproc_output_schema (self)
def preproc_output_schema (self, schema)
def loss (self)
def loss (self, loss)
def has_loss (self)
def add_loss (self, loss, name='unnamed')
def add_output_schema (self, name, value)
def add_trainer_extra_schema (self, trainer_extra_schema)
def __getattr__ (self, layer)
def layers (self)
def apply_regularizers_on_loss (self, train_net, train_init_net, blob_to_device=None)
def apply_regularizers_after_optimizer (self, train_net, train_init_net, grad_map, blob_to_device=None)
def apply_post_grad_net_modifiers (self, trainer_net, trainer_init_net, grad_map, blob_to_device=None)
def apply_final_net_modifiers (self, trainer_net, trainer_init_net, grad_map, blob_to_device=None)
def apply_optimizers (self, train_net, train_init_net, grad_map, blob_to_device=None)
def NoOptim (self, args, kwargs)
def breakdown_map (self)
def breakdown_map (self, breakdown_map)
- Public Member Functions inherited from caffe2.python.model_helper.ModelHelper
def __init__ (self, name=None, init_params=True, allow_not_known_ops=True, skip_sparse_optim=False, param_model=None, arg_scope=None)
def arg_scope (self)
def get_name (self)
def create_param (self, param_name, shape, initializer, tags=None)
def get_param_info (self, param)
def add_param_DEPRECATED (self, param, key=None, shape=None, length=None)
def param_info (self, grad_type=None, id=None)
def AddParameter (self, param, tags=None)
def GetParams (self, namescope=None, top_scope=False)
def Proto (self)
def InitProto (self)
def RunAllOnGPU (self, args, kwargs)
def CreateDB (self, blob_out, db, db_type, kwargs)
def AddGradientOperators (self, args, kwargs)
def get_param_to_grad (self, params)
def GetOptimizationParamInfo (self, params=None)
def Validate (self)
def GetComputedParams (self, namescope=None)
def GetAllParams (self, namescope=None)
def TensorProtosDBInput (self, unused_blob_in, blob_out, batch_size, db, db_type, kwargs)
def GetDevices (self)
def __getattr__ (self, op_type)
def __dir__ (self)

Public Attributes

- Public Attributes inherited from caffe2.python.model_helper.ModelHelper

Detailed Description

Model helper for building models on top of layers abstractions.

Each layer is the abstraction that is higher level than Operator. Layer
is responsible for ownership of it's own parameters and can easily be
instantiated in multiple nets possible with different sets of ops.
As an example: one can easily instantiate predict and train nets from
the same set of layers, where predict net will have subset of the
operators from train net.

Definition at line 30 of file

Constructor & Destructor Documentation

def caffe2.python.layer_model_helper.LayerModelHelper.__init__ (   self,
  keep_blobs = False 
TODO(amalevich): more documnetation on input args

Definition at line 43 of file

Member Function Documentation

def caffe2.python.layer_model_helper.LayerModelHelper.metrics_schema (   self)
Returns the schema that represents model output that should be used for
metric reporting.

During the training/evaluation this schema will be appended to the
schema that represents model output.

Definition at line 362 of file

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