torch.optim.adagrad.Adagrad Class Reference

Inheritance diagram for torch.optim.adagrad.Adagrad:

Public Member Functions
def	__init__ (self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0)
def	share_memory (self)
def	step (self, closure=None)

Detailed Description

Implements Adagrad algorithm.

It has been proposed in `Adaptive Subgradient Methods for Online Learning
and Stochastic Optimization`_.

Arguments:
    params (iterable): iterable of parameters to optimize or dicts defining
        parameter groups
    lr (float, optional): learning rate (default: 1e-2)
    lr_decay (float, optional): learning rate decay (default: 0)
    weight_decay (float, optional): weight decay (L2 penalty) (default: 0)

.. _Adaptive Subgradient Methods for Online Learning and Stochastic
    Optimization: http://jmlr.org/papers/v12/duchi11a.html

Definition at line 5 of file adagrad.py.

Member Function Documentation

def torch.optim.adagrad.Adagrad.step	(		self,
			closure = None
	)

Performs a single optimization step.

Arguments:
    closure (callable, optional): A closure that reevaluates the model
and returns the loss.

Definition at line 48 of file adagrad.py.

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

• torch/optim/adagrad.py