&Vf tddlmZddlmZddlmZddlmZedGddeZdS) )backend)ops) keras_export)Layerzkeras.layers.AlphaDropoutcBeZdZdZd fd Zd dZdZdZfdZxZ S) AlphaDropoutaApplies Alpha Dropout to the input. Alpha Dropout is a `Dropout` that keeps mean and variance of inputs to their original values, in order to ensure the self-normalizing property even after this dropout. Alpha Dropout fits well to Scaled Exponential Linear Units (SELU) by randomly setting activations to the negative saturation value. Args: rate: Float between 0 and 1. The multiplicative noise will have standard deviation `sqrt(rate / (1 - rate))`. noise_shape: 1D integer tensor representing the shape of the binary alpha dropout mask that will be multiplied with the input. For instance, if your inputs have shape `(batch_size, timesteps, features)` and you want the alpha dropout mask to be the same for all timesteps, you can use `noise_shape=(batch_size, 1, features)`. seed: A Python integer to use as random seed. Call arguments: inputs: Input tensor (of any rank). training: Python boolean indicating whether the layer should behave in training mode (adding alpha dropout) or in inference mode (doing nothing). Nc tjdi|d|cxkrdksntd|||_||_||_|dkr$t j||_ d|_ dS)NrzcInvalid value received for argument `rate`. Expected a float value between 0 and 1. Received: rate=T) super__init__ ValueErrorrateseed noise_shaperrandom SeedGeneratorseed_generatorsupports_masking)selfrrrkwargs __class__s j/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/layers/regularization/alpha_dropout.pyr zAlphaDropout.__init__#s""6"""D~~~~A~~~~)"&))    & !88").">">t"D"DD  $Fc|r|jdkr|||j}d}d}| |z}tjtj||j|j}tj||j }d|jz d|j|dzzzzdz}| |z|jz} ||z|d|z zz} || z| zS|S)Nrg,x?g2֫?)rr g) r_get_concrete_noise_shaperr greater_equalruniformrcastdtype) rinputstrainingralphascalealpha_pkept_idxabxs rcallzAlphaDropout.call2s   A 88(K6E5EfunG( "";T5H"II Hx&,77Hdi-A GQJ(>$>?DHAW ty(A!Gq8|$<rDs------(((((()**Y)Y)Y)Y)Y)5Y)Y)+*Y)Y)Y)r