/usr/local/lib64/python3.6/site-packages/torch/optim/__pycache__
NameSizeModeActions
adadelta.cpython-36.pyc48290644editdlrm
adagrad.cpython-36.pyc47670644editdlrm
adam.cpython-36.pyc62500644editdlrm
adamax.cpython-36.pyc48690644editdlrm
adamw.cpython-36.pyc62580644editdlrm
asgd.cpython-36.pyc28480644editdlrm
lbfgs.cpython-36.pyc88020644editdlrm
lr_scheduler.cpython-36.pyc637920644editdlrm
nadam.cpython-36.pyc57270644editdlrm
optimizer.cpython-36.pyc118270644editdlrm
radam.cpython-36.pyc57120644editdlrm
rmsprop.cpython-36.pyc65430644editdlrm
rprop.cpython-36.pyc51820644editdlrm
sgd.cpython-36.pyc62670644editdlrm
sparse_adam.cpython-36.pyc33290644editdlrm
swa_utils.cpython-36.pyc113700644editdlrm
_functional.cpython-36.pyc104070644editdlrm
__init__.cpython-36.pyc10490644editdlrm
Edit: /usr/local/lib64/python3.6/site-packages/torch/optim/__pycache__/asgd.cpython-36.pyc (2848B)
3 ûEg1 ã@s<ddlZddlZddlmZddlmZGdd„deƒZdS)éNé)Ú _functional)Ú Optimizercs4eZdZdZd ‡fdd„ Zejƒd d d „ƒZ‡ZS)ÚASGDaÖImplements Averaged Stochastic Gradient Descent. It has been proposed in `Acceleration of stochastic approximation by averaging`_. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-2) lambd (float, optional): decay term (default: 1e-4) alpha (float, optional): power for eta update (default: 0.75) t0 (float, optional): point at which to start averaging (default: 1e6) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) .. _Acceleration of stochastic approximation by averaging: https://dl.acm.org/citation.cfm?id=131098 ç{®Gáz„?ç-Cëâ6?çè?瀄.ArcsTd|kstdj|ƒƒ‚d|ks,tdj|ƒƒ‚t|||||d}tt|ƒj||ƒdS)NgzInvalid learning rate: {}zInvalid weight_decay value: {})ÚlrÚlambdÚalphaÚt0Ú weight_decay)Ú ValueErrorÚformatÚdictÚsuperrÚ__init__)ÚselfÚparamsr r r r rÚdefaults)Ú __class__©ús