&Vf,ddlmZddlmZddlmZddlmZddlmZddlmZddl m Z dZ ed Gd d eZ ed Gd deZ edGdde ZdS))backend) initializers)losses)ops) keras_export)Metric)serialization_libc |ptj}t|dd}tj||}|tj||}|#t j|||d}t j ||\}}t|j }t|j }||kr(||tt||}||z}|dkr(||ttd|}t|j }|dkr,||ttd|}||fS||fS)N _keras_maskdtypesumr reduction)axis)rfloatxgetattrrcastconvert_to_tensorrloss apply_masksqueeze_or_expand_to_same_ranklenshapelistrange)values sample_weight reduce_fnr mask weight_ndim values_ndims `/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/metrics/reduction_metrics.pyreduce_to_samplewise_valuesr% s  %W^%%E 6=$ / /D XfE * * *F -m5III  "K22t5E3M!' J J M! !  --.. &,''  $ $YT% ["A"ABBF-' ??%IDq+)>)>$?$?Mfl##KQ6U1k-B-B(C(CDDD}$$ =  zkeras.metrics.Sumc8eZdZdZdfd Zd dZdZdZxZS) SumaCompute the (weighted) sum of the given values. For example, if `values` is `[1, 3, 5, 7]` then their sum is 16. If `sample_weight` was specified as `[1, 1, 0, 0]` then the sum would be 4. This metric creates one variable, `total`. This is ultimately returned as the sum value. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Example: >>> m = metrics.Sum() >>> m.update_state([1, 3, 5, 7]) >>> m.result() 16.0 >>> m = metrics.Sum() >>> m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0]) >>> m.result() 4.0 rNct|||dtj|jd|_dS)Nnamer totalr initializerr r+)super__init__ add_variablerZerosr r-selfr+r __class__s r$r1z Sum.__init__HsW d%000&&$*,,* '   r&ct||tj|j\}}|j|jtj|zdS)Nr r )r%rrr r-assign)r5rr_s r$ update_statezSum.update_stateQsT/ MSWDJ    $*swv677777r&c:|jddS)N)r-r9r5s r$ reset_statezSum.reset_stateWs #r&c@tj|j|jSN)rrr-r r>s r$resultz Sum.resultZsx DJ///r&)rNrA __name__ __module__ __qualname____doc__r1r;r?rB __classcell__r6s@r$r(r(-sy2      8888 0000000r&r(zkeras.metrics.Meanc8eZdZdZdfd Zd dZdZdZxZS) MeanaCompute the (weighted) mean of the given values. For example, if values is `[1, 3, 5, 7]` then the mean is 4. If `sample_weight` was specified as `[1, 1, 0, 0]` then the mean would be 2. This metric creates two variables, `total` and `count`. The mean value returned is simply `total` divided by `count`. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Example: >>> m = Mean() >>> m.update_state([1, 3, 5, 7]) >>> m.result() 4.0 >>> m.reset_state() >>> m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0]) >>> m.result() 2.0 ``` meanNct|||dtj|jd|_|dtj|jd|_dS)Nr*r,r-r.count)r0r1r2rr3r r-rNr4s r$r1z Mean.__init__zs d%000&&$*,,* '   &&$*,,* '   r&ct||tj|j\}}|j|jtj|zt|jdkrtj|d}nd}|tj|}|j |j tj ||jzdS)Nr8rrr ) r%rrLr r-r9rrrrNr)r5rr num_sampless r$r;zMean.update_states ; MSXTZ! ! !   $*swv6777 v|   ! !)F++A.KKK  $'-00K $*sx 4:'N'N'NNOOOOOr&cn|jd|jddS)Nr=r)r-r9rNr>s r$r?zMean.reset_states4 # !r&crtj|jtj|j|jS)Nr )r divide_no_nanr-rrNr r>s r$rBz Mean.results1 J4:>>>   r&)rLNrArCrIs@r$rKrK^s}4        P P P P       r&rKzkeras.metrics.MeanMetricWrappercPeZdZdZdfd Zdfd ZfdZedZxZ S) MeanMetricWrapperaWrap a stateless metric function with the `Mean` metric. You could use this class to quickly build a mean metric from a function. The function needs to have the signature `fn(y_true, y_pred)` and return a per-sample loss array. `MeanMetricWrapper.result()` will return the average metric value across all samples seen so far. For example: ```python def mse(y_true, y_pred): return (y_true - y_pred) ** 2 mse_metric = MeanMetricWrapper(fn=mse) ``` Args: fn: The metric function to wrap, with signature `fn(y_true, y_pred, **kwargs)`. name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. **kwargs: Keyword arguments to pass on to `fn`. Nc t||||_||_|jtjvs-t |jdr!|jjtjvr d|_dSdSdS)Nr*r6down) r0r1_fn _fn_kwargsr ALL_OBJECTShasattrr6 _direction)r5fnr+r kwargsr6s r$r1zMeanMetricWrapper.__init__s d%000  H* * *tx-- +"f&888$DOOO + *88r&ct|dd}|j||fi|j}|*|(tj|||jd}t||S)Nr rr)r) rrXrYrrrr r0r;)r5y_truey_predrr!rr6s r$r;zMeanMetricWrapper.update_statesv}d33&&<tOOPPP(+(((r&cBd|vrtj|}|di|S)Nr]r,)r deserialize_keras_object)clsrgs r$ from_configzMeanMetricWrapper.from_configs- 6>>&?GGFs}}V}}r&)NNrA) rDrErFrGr1r;rc classmethodrkrHrIs@r$rUrUs0 % % % % % %IIIIII))))) [r&rUN) keras.srcrrrrkeras.src.api_exportrkeras.src.metrics.metricrkeras.src.savingr r%r(rKrUr,r&r$rqse""""""------++++++...... ! ! !F!""-0-0-0-0-0&-0-0#"-0`"##> > > > > 6> > $#> B/00:::::::10:::r&