tfa.losses.PinballLoss | TensorFlow Addons

Computes the pinball loss between y_true and y_pred. ... this loss yields an estimator of the tau conditional quantile. Install Learn Introduction NewtoTensorFlow? 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ViewsourceonGitHub Computesthepinballlossbetweeny_trueandy_pred. tfa.losses.PinballLoss( tau:tfa.types.FloatTensorLike=0.5, reduction:str=tf.keras.losses.Reduction.AUTO, name:str='pinball_loss' ) loss=maximum(tau*(y_true-y_pred),(tau-1)*(y_true-y_pred)) Inthecontextofregression,thislossyieldsanestimatorofthetau conditionalquantile. See:https://en.wikipedia.org/wiki/Quantile_regression Usage: pinball=tfa.losses.PinballLoss(tau=.1) loss=pinball([0.,0.,1.,1.],[1.,1.,1.,0.]) loss Usagewiththetf.kerasAPI: model=tf.keras.Model() model.compile('sgd',loss=tfa.losses.PinballLoss(tau=.1)) Args tau (Optional)Floatin[0,1]oratensortakingvaluesin[0,1]and shape=[d0,...,dn].Itdefinestheslopeofthepinballloss.In thecontextofquantileregression,thevalueoftaudeterminesthe conditionalquantilelevel.Whentau=0.5,thisamountstol1 regression,anestimatoroftheconditionalmedian(0.5quantile). reduction (Optional)Typeoftf.keras.losses.Reductiontoapplyto loss.DefaultvalueisAUTO.AUTOindicatesthatthereduction optionwillbedeterminedbytheusagecontext.Foralmostallcases thisdefaultstoSUM_OVER_BATCH_SIZE. Whenusedwithtf.distribute.Strategy,outsideofbuilt-intraining loopssuchastf.kerascompileandfit,usingAUTOor SUM_OVER_BATCH_SIZEwillraiseanerror.Pleasesee https://www.tensorflow.org/alpha/tutorials/distribute/training_loops formoredetailsonthis. name Optionalnamefortheop. References: https://en.wikipedia.org/wiki/Quantile_regression https://projecteuclid.org/download/pdfview_1/euclid.bj/1297173840 Methods from_config @classmethod from_config( config ) InstantiatesaLossfromitsconfig(outputofget_config()). Args config Outputofget_config(). Returns ALossinstance. get_config Viewsource get_config() ReturnstheconfigdictionaryforaLossinstance. __call__ __call__( y_true,y_pred,sample_weight=None ) InvokestheLossinstance. Args y_true Groundtruthvalues.shape=[batch_size,d0,..dN],except sparselossfunctionssuchassparsecategoricalcrossentropywhere shape=[batch_size,d0,..dN-1] y_pred Thepredictedvalues.shape=[batch_size,d0,..dN] sample_weight Optionalsample_weightactsasacoefficientforthe loss.Ifascalarisprovided,thenthelossissimplyscaledbythe givenvalue.Ifsample_weightisatensorofsize[batch_size],then thetotallossforeachsampleofthebatchisrescaledbythe correspondingelementinthesample_weightvector.Iftheshapeof sample_weightis[batch_size,d0,..dN-1](orcanbebroadcastedto thisshape),theneachlosselementofy_predisscaled bythecorrespondingvalueofsample_weight.(NoteondN-1:allloss functionsreduceby1dimension,usuallyaxis=-1.) Returns WeightedlossfloatTensor.IfreductionisNONE,thishas shape[batch_size,d0,..dN-1];otherwise,itisscalar.(NotedN-1 becausealllossfunctionsreduceby1dimension,usuallyaxis=-1.) Raises ValueError Iftheshapeofsample_weightisinvalid. 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