Torch topk

Returns-----topk_hyps : torch.Tensor (batch, topk, max length of token_id sequences) This tensor stores the topk predicted hypothesis. topk_scores : torch.Tensor (batch, topk) ...torch.topk documentation says that the function returns a tuple (values, indices), where indices is "the indices of the elements in the original input tensor." The straightforward interpretation of that sentence is that indices is a collection containing n-tuples where n is the number of dimensions in the original tensor input.

Source code for torchnet.meter.classerrormeter. import numpy as np import torch import numbers from. import meterParameters-----top_ks : list, default [2, 5]) list of cutoffs labels_onehot : bool Enable transform the labels to one-hot representation """ def __init__ (self, top_ks = None, labels_onehot = False): super (RankingMetric, self). __init__ self. top_ks = top_ks or [2, 5] self. labels_onehot = labels_onehot # Store the mean of the batch metrics ...Urology oral boards.



Graph U-Nets Table 1. Summary of datasets used in our node classification experiments (Yang et al.,2016;Zitnik & Leskovec,2017). The Cora, Citeseer,pytorch.topk ()用于返回Tensor中的前k个元素以及元素对应的索引值。. 例:. import torch item =torch.IntTensor ( [1,2,4,7,3,2 ]) value,indices =torch.topk (item,3 ) print ( "value:",value) print ( "indices:" ,indices) 输出结果为:. 其中:value中存储的是对应的top3的元素,并按照从大到小的取值 ...Computes the solution X to the system torch_tensordot (A, X) = B. linalg_vector_norm () Computes a vector norm. load_state_dict () Load a state dict file. lr_lambda () Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr.

module ( torch.nn.Module) - The module to process. params ( Iterator[torch.nn.parameter.Parameter]) -. The parameters to requires_grad. Defaults to []. trojanzoo.utils.model.accuracy(_output, _label, num_classes, topk=(1, 5)) [source] Computes the accuracy over the k top predictions for the specified values of k.第一次作业:深度学习基础 本文代码使用Google的colab作为运行环境。 使用代码安装实验所需的d2l库 与深度学习相关的数据操作 1.张量基本操作 torch.arange(n)函数作用是生成一个长度为n的由0~n-1组成的张量 x.numel()可以输出张量的元素个数 torch.zeros(),torch.ones()生成一个指定大小的全0或全1的张量。 torch.topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor) Returns the k largest elements of the given input tensor along a given dimension. If dim is not given ...Definition at line 115 of file any_value.h.. Referenced by quantization.DQuantType::__str__(), torch.utils.data.datapipes.dataframe.dataframes.CaptureSetItem::__str__ ...