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) |
def | create_lars_inputs (self, param_init_net, weight_decay, trust, lr_max) |
Static Public Member Functions | |
def | dedup (net, sparse_dedup_aggregator, grad) |
Definition at line 30 of file optimizer.py.
def caffe2.python.optimizer.Optimizer.add_lr_multiplier | ( | self, | |
lr_multiplier | |||
) |
Set the global learning rate multiplier. If a multiplier already existed, this will overwrite the existing multiplier. The multiplier is used for all future calls to _run(), unless it is overwritten.
Definition at line 147 of file optimizer.py.
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 181 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 75 of file optimizer.py.