AVh(*dZddlmZddlmZddlmZddlmZddlmZddlm Z ddl m Z iZ d d gZ d Zejd d de dg ddZejd d d ddZe dgGdd Zy)z5Registration and usage mechanisms for KL-divergences.)ops) array_ops)control_flow_assert)math_ops) deprecation) tf_inspect) tf_export RegisterKL kl_divergencectj|}tj|}d}d}t|D]D\}}t|D]1\}} ||z} tj || fd} |r | s(| |ks.| }| }3F|S)z3Get the KL function registered for classes a and b.N)rgetmro enumerate _DIVERGENCESget) type_atype_b hierarchy_a hierarchy_bdist_to_childrenkl_fnmro_to_aparent_amro_to_bparent_bcandidate_distcandidate_kl_fns d/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/distributions/kullback_leibler.py_registered_klr#s!!&)+!!&)+ %%k2 h' 4 ((*n$(((H)=tDo >*G*G I JJ  ~~'(9 d ;D 99   d .D ! !""  !4!4X__T5J!K L $$n&9&9;<@O # 9  < 89999999999s+-EBEE4 EE EE&ctj|d5|jt|||zcdddS#1swYyxYw)aComputes the (Shannon) cross entropy. Denote two distributions by `P` (`ref`) and `Q` (`other`). Assuming `P, Q` are absolutely continuous with respect to one another and permit densities `p(x) dr(x)` and `q(x) dr(x)`, (Shanon) cross entropy is defined as: ```none H[P, Q] = E_p[-log q(X)] = -int_F p(x) log q(x) dr(x) ``` where `F` denotes the support of the random variable `X ~ P`. Args: ref: `tfd.Distribution` instance. other: `tfd.Distribution` instance. allow_nan_stats: Python `bool`, default `True`. When `True`, statistics (e.g., mean, mode, variance) use the value "`NaN`" to indicate the result is undefined. When `False`, an exception is raised if one or more of the statistic's batch members are undefined. name: Python `str` prepended to names of ops created by this function. Returns: cross_entropy: `ref.dtype` `Tensor` with shape `[B1, ..., Bn]` representing `n` different calculations of (Shanon) cross entropy. cross_entropy)r6N)rr-entropyr )refotherr6r's rr9r9ysDF ~~dO,5 ;;== UO5 5555s AA zdistributions.RegisterKLcLeZdZdZej ddddZdZy) r zDecorator to register a KL divergence implementation function. Usage: @distributions.RegisterKL(distributions.Normal, distributions.Normal) def _kl_normal_mvn(norm_a, norm_b): # Return KL(norm_a || norm_b) r r!Tr"c||f|_y)zInitialize the KL registrar. Args: dist_cls_a: the class of the first argument of the KL divergence. dist_cls_b: the class of the second argument of the KL divergence. N)_key)self dist_cls_a dist_cls_bs r__init__zRegisterKL.__init__sZ(DIrc (t|std|z|jtvrSt d|jdj d|jdj dt|j|t|j<|S)a*Perform the KL registration. Args: kl_fn: The function to use for the KL divergence. Returns: kl_fn Raises: TypeError: if kl_fn is not a callable. ValueError: if a KL divergence function has already been registered for the given argument classes. z$kl_fn must be callable, received: %szKL(rz || z") has already been registered to: )callable TypeErrorr?r ValueErrorr,)r@rs r__call__zRegisterKL.__call__s| E? rTs<++5*.-6    %  ,-.-1:9/:9z% .255@ )*+00,0r