Caffe2 - Python API
A deep learning, cross platform ML framework
Public Member Functions | Static Public Member Functions | List of all members
caffe2.python.optimizer.Optimizer Class Reference
Inheritance diagram for caffe2.python.optimizer.Optimizer:
caffe2.python.optimizer.AdagradOptimizer caffe2.python.optimizer.AdamOptimizer caffe2.python.optimizer.FtrlOptimizer caffe2.python.optimizer.RmsPropOptimizer caffe2.python.optimizer.SgdOptimizer caffe2.python.optimizer.WeightDecayBuilder caffe2.python.optimizer.YellowFinOptimizer caffe2.python.optimizer.FP16SgdOptimizer caffe2.python.optimizer.MultiPrecisionSgdOptimizer

Public Member Functions

def __init__ (self)
 
def __call__ (self, net, param_init_net, param, grad=None)
 
def get_cpu_blob_name (self, base_str, node_name='')
 
def get_gpu_blob_name (self, base_str, gpu_id, node_name)
 
def make_unique_blob_name (self, base_str)
 
def build_lr (self, net, param_init_net, base_learning_rate, learning_rate_blob=None, policy="fixed", iter_val=0, kwargs)
 
def add_lr_multiplier (self, lr_multiplier)
 
def get_auxiliary_parameters (self)
 
def scale_learning_rate (self, args, kwargs)
 

Static Public Member Functions

def dedup (net, sparse_dedup_aggregator, grad)
 

Detailed Description

Definition at line 40 of file optimizer.py.

Member Function Documentation

def caffe2.python.optimizer.Optimizer.get_auxiliary_parameters (   self)
Returns a list of auxiliary parameters.

Returns:
    aux_params: A namedtuple, AuxParams.

    aux_params.local stores a list of blobs. Each blob is a local
    auxiliary parameter. A local auxiliary parameter is a parameter in
    parallel to a learning rate parameter. Take adagrad as an example,
    the local auxiliary parameter is the squared sum parameter, because
    every learning rate has a squared sum associated with it.

    aux_params.shared also stores a list of blobs. Each blob is a shared
    auxiliary parameter. A shared auxiliary parameter is a parameter
    that is shared across all the learning rate parameters. Take adam as
    an example, the iteration parameter is a shared parameter, because
    all the learning rates share the same iteration parameter.

Definition at line 155 of file optimizer.py.

def caffe2.python.optimizer.Optimizer.make_unique_blob_name (   self,
  base_str 
)
Returns a blob name that will be unique to the current device
and optimizer instance.

Definition at line 80 of file optimizer.py.


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