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append ( metric ( rand ( 10 ), randint ( 2 ,( 10 ,)))) >>> fig_ , ax_ = metric . Bônus de aposta esportiva vencedor ao intervalo e vencedor final Boas-vindas 100% até R$500 + 25 Giros grátis. MulticlassFBetaScore ( beta , num_classes , top_k = 1 , average = 'macro' , multidim_average = 'global' , ignore_index = None , validate_args = True , ** kwargs ) [source] ¶ \[F_ = (1 + \beta^2) * \frac * \text> ) + \text>\] As input to forward and update the metric accepts the following input: As output to forward and compute the metric returns the following output: If multidim_average is set to global : If average='micro'/'macro'/'weighted' , the output will be a scalar tensor If average=None/'none' , the shape will be (C,) If average='micro'/'macro'/'weighted' , the shape will be (N,) If average=None/'none' , the shape will be (N, C) global : Additional dimensions are flatted along the batch dimension samplewise : Statistic will be calculated independently for each sample on the N axis. The statistics in this case are calculated over the additional dimensions. Example (preds is float tensor): Example (multidim tensors): plot ( val = None , ax = None ) [source] ¶ val ¶ ( Union [ Tensor , Sequence [ Tensor ], None ]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result. ax ¶ ( Optional [ Axes ]) – An matplotlib axis object. Dinamarca superligaen.“Too many stars, too many dreams. The reality is that in front of these things, I’m just a speck of dust.” – BTS.
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