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= square_error * valid_inds square_error, _ = tf.nn.top_k(square_error, k=keep_num) return tf.reduce_mean(square_error) ``` 我更
t进程内所有线程获取的id低12bit都是一致的（4K page对齐），所以vhost里所有线程都选中了相同的shard，多线程间产生大量的原子写竞争。 复现概率：必现 出现问题时的perf top截图：  return tf.reduce_mean(loss) ``` 我根据Tensorflow复现的：https://github
**hub run resnet50_vd_imagenet_ssld --input_path=test.jpg --top-k 5** /home/robert/anaconda3/envs/xzitv/lib/python3.7/site-pac
char c; char k; char *con; clrmous(MouseX, MouseY); setfillstyle(1,WHITE); setcolor(BLACK); settextjustify(LEFT_TEXT,TOP_TEXT);//前期准备，规定对齐方式
796910 2497127360 executor.cc:121] Memory used after operator top_k running: 666181632 I0111 15:00:04.797045 2497127360 executor
pooling layer top_model = base_model.output top_model = GlobalAveragePooling2D()(top_model) # or just flatten the layers # top_model =
https://github.com/PaddlePaddle/Paddle/pull/7527 CTC evaluator = top-k_op + ctc_align_op + edit_distance_op Test script: ``` import
**我们查看两种cell types 的RT，HM,RNA,Compartment的差值（top 5%）的相关系数，仔细观察我们发现H3K27me3与其他信号都是负相关，且H3K4me1，H3K4me2与RT的相关性最强**  05:00.0 3D controller: NVIDIA Corporation GK210GL [Tesla K80] (rev a1) 08:00.0 3D controller:
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