o *ѹgR7@s~ddlZddlmZddlmZmZmZmZmZmZm Z m Z ddl m Z m Z ddgZGdddeZd d e d e d ed e_   d"de ede ede ede edede edededededededefddZddZde ede ede ede ededededededededefddZde ede ede ede ededededededededefd d!ZdS)#N)Tensor) Optimizer_use_grad_for_differentiable _get_value _view_as_real_default_to_fused_or_foreach_differentiable_doc _foreach_doc _maximize_doc)ListOptionalAdagradadagradcsneZdZ      dddddeeded effd d Zfd d ZddZddZe dddZ Z S)r{Gz?r绽|=NF)maximizedifferentiableforeachrrc sd|ks td|d|kstd|d|ks!td|d|ks,td|d|ks7td|t|||||||| d} t|| |jD]/} | dD](} |j| } tjdtjd | d <t | rnt ||n|}tj | |tj d | d <qSqMdS) NgzInvalid learning rate: zInvalid lr_decay value: zInvalid weight_decay value: z)Invalid initial_accumulator_value value: zInvalid epsilon value: )lrlr_decayeps weight_decayinitial_accumulator_valuerrrparamsZdtypestep)Z memory_formatsum) ValueErrordictsuper__init__ param_groupsstatetorchtensorfloat32 is_complexcomplexZ full_likeZpreserve_format)selfrrrrrrrrrdefaultsgrouppr#Z init_value __class__W/Users/admin/.pyenv/versions/3.10.0/lib/python3.10/site-packages/torch/optim/adagrad.pyr! sH      zAdagrad.__init__cst||jD]}|dd|dd|ddq t|j}t|dko3t |dd}|sI|D]}tj t |dtj d|d<q8dSdS)NrrFrrrr) r __setstate__r" setdefaultlistr#valueslenr$Z is_tensorr%floatr&)r)r#r+Z state_valuesZstep_is_tensorsr-r/r0r1?s     zAdagrad.__setstate__cCs4|jD]}|dD] }|j|}|dq qdS)Nrr)r"r#Z share_memory_)r)r+r,r#r/r/r0 share_memoryNs   zAdagrad.share_memoryc Cs~d\}}|dD]2}|jdur:||jjO}|t|O}||||j|j|} || d|| dq||fS)N)FFrrr)grad is_sparser$r'appendr#) r)r+params_with_gradgrads state_sums state_stepshas_sparse_grad has_complexr,r#r/r/r0 _init_groupTs      zAdagrad._init_groupc Csd}|durt |}Wdn1swY|jD]4}g}g}g}g}||||||\}} t|||||d|d|d|d||d|d|d| d q |S) zPerform a single optimization step. 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Nrrrrrrr) rrrrr@rrrrA)r$Z enable_gradr"rBr) r)closureZlossr+r<r=r>r?r@rAr/r/r0rbs6  z Adagrad.step)rrrrrNN) __name__ __module__ __qualname__r boolr!r1r8rBrr __classcell__r/r/r-r0r s,    3a[Implements Adagrad algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \lambda \text{ (weight decay)}, \\ &\hspace{12mm} \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\ &\textbf{initialize} : state\_sum_0 \leftarrow 0 \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm} \tilde{\gamma} \leftarrow \gamma / (1 +(t-1) \eta) \\ &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}state\_sum_t \leftarrow state\_sum_{t-1} + g^2_t \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon} \\ &\rule{110mm}{0.4pt} \\[-1.ex] &\bf{return} \: \theta_t \\[-1.ex] &\rule{110mm}{0.4pt} \\[-1.ex] \end{aligned} For further details regarding the algorithm we refer to `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization`_. a Args: 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) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-10) z z .. _Adaptive Subgradient Methods for Online Learning and Stochastic Optimization: http://jmlr.org/papers/v12/duchi11a.html Frr=r>r?r@rrrArrrrrcCstdd|Ds td|durt||dd\} }|r%tjr%td|r/tjs/t}nt}||||||| | | || ||d dS) ztFunctional API that performs Adagrad algorithm computation. 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