2014年6月7日 · At the end after you exhaust the k-2, replace the largest with -infinity and the largest of the tournament will be the kth largest. The elements you have thrown away are the top k-1 elements. This takes at most n - k + (k-1) [log (n-k+2)] comparisons to find the top k. It uses O(n) memory though.

2018年8月21日 · I would like to get the top-k accuracy for my model in keras. I have found a post here:How to calculate top5 accuracy in keras? suggesting the following: from keras import backend as K import tensorflow as tf top_values, top_indices = K.get_session().run(tf.nn.top_k(_pred_test, k=5)) The output just gives me two arrays: top_values:

Thanks, Actually I tried with following formula which gave me exact same result as Recall_k, slim.metrics.streaming_mean(tf.nn.in_top_k(predictions=logits, targets=labels, k)) I am guessing Recall_k will give me similar result as top-k accuracy.

2018年6月1日 · Using tf.nn.top_k(): top_k_values, top_k_indices = tf.nn.top_k(predictions, k=k) If predictions is a vector of probabilities per class (i.e. predictions[i] = prediction probability for class i), then top_k_values will contain the k highest probabilities in predictions, and top_k_indices will contain the indices of these probabilities, i.e. the ...

2015年1月10日 · For example the list from which I want to find k largest number be list1 > list1 = [0.5, 0.7, 0.3, 0.3, 0.3, 0.4, 0.5] Here n = 7 and if k = 3, that is if I want to find 3 largest numbers from a list of 7 numbers then output should be 0.5, 0.7, 0.5. How can this be done?

2019年9月24日 · You're building a new list of 'm' items, from the top 3 items. The list comprehension keeps your items in order. The order of your example is such that you could just sort it and take the 3 top items, but I think you mean that you might have the top 3 items NOT in order, like: my_list = [3.5, 1.2, 0.3, 7.8, 3.3] which results in [3.5,7.8,3.3]

argpartition(a, k) function in numpy rearranges indices of input array a around the kth smallest element, so that all indices of smaller elements end up to the left, and all indices of bigger elements end up to the right.

2011年8月2日 · Strict ascend/descend top k indices code will be: Note that torch.topk accepts a torch tensor, and returns both top k values and top k indices in type torch.Tensor. Similar with np, torch.topk also accepts an axis argument so that you can …

2008年10月9日 · Actually, we just want top K words. Other words' frequency is not concern for us. So, we can use "partial Heap sorting". For step 2) and 3), we don't just do sorting. Instead, we change it to be. 2') build a heap of (word, word-frequency) pair with "word-frequency" as key. It takes O(n) time to build a heap; 3') extract top K words from the heap.

2012年9月4日 · so if you want top K frequent Elements to be printed from the Dictionary; you have to use heapq.nlargest funtcion. Here is the example for the same: return heapq.nlargest(k,count.keys(), key = count.get) Here, k is the number that helps us find out elements which are repeated in a dictionary k times or more than k times.