a Sic@sdZddlmmZddlmZddlmZddl m Z ddl m Z e e dgdGd d d ejZejd eje_dS) z!Adagrad optimizer implementation.N) initializers) optimizer) generic_utils) keras_exportz%keras.optimizers.experimental.Adagrad)v1c sBeZdZdZdfd d Zfd d Zd dZfddZZS)Adagrada6Optimizer that implements the Adagrad algorithm. Adagrad is an optimizer with parameter-specific learning rates, which are adapted relative to how frequently a parameter gets updated during training. The more updates a parameter receives, the smaller the updates. Args: learning_rate: Initial value for the learning rate: either a floating point value, or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance. Defaults to 0.001. Note that `Adagrad` tends to benefit from higher initial learning rate values compared to other optimizers. To match the exact form in the original paper, use 1.0. initial_accumulator_value: Floating point value. Starting value for the accumulators (per-parameter momentum values). Must be non-negative. epsilon: Small floating point value used to maintain numerical stability. {{base_optimizer_keyword_args}} Reference: - [Duchi et al., 2011]( http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf). MbP?皙?Hz>NFGz?Tc s@tjf|||||| | | d| |||_||_||_dS)N)clipnorm clipvalueglobal_clipnormuse_ema ema_momentumema_overwrite_frequency jit_compilename)super__init___build_learning_rate_learning_rateinitial_accumulator_valueepsilon) self learning_raterrr r rrrrrrkwargs __class__k/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/optimizers/optimizer_experimental/adagrad.pyr8s  zAdagrad.__init__c sjt|t|dr |jr dSd|_g|_t|j}|D](}|j|j |d||j |j ddqs     i