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_ad_hoc_plot_blob (self, blob, dtype=None)
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	prediction (self)
def	add_prediction (self, prediction, weight=1.0)
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, modify_output_record=False)
def	apply_final_net_modifiers (self, trainer_net, trainer_init_net, grad_map, blob_to_device=None, modify_output_record=False)
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	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)
def	GetCompleteNet (self)
def	ConstructInitTrainNetfromNet (self, net)

Public Attributes
	param_to_optim
	param_to_reg
	param_init_net
	ad_hoc_diagnose_blobs_and_operations
	ad_hoc_plot_blobs
	global_constants
	global_constant_initializers
Public Attributes inherited from caffe2.python.model_helper.ModelHelper
	name
	net
	param_init_net
	param_to_grad
	params
	gradient_ops_added
	init_params
	allow_not_known_ops
	skip_sparse_optim
	weights
	biases
	grad_map

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 layer_model_helper.py.

Constructor & Destructor Documentation

def caffe2.python.layer_model_helper.LayerModelHelper.__init__	(		self,
			name,
			input_feature_schema,
			trainer_extra_schema,
			keep_blobs = False
	)

TODO(amalevich): more documnetation on input args

Definition at line 43 of file layer_model_helper.py.

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 376 of file layer_model_helper.py.

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

• caffe2/python/layer_model_helper.py