熟悉RNN、LSTM的人都知道,在其典型的实现中,要想计算 ht必须等到前一时刻ht-1计算完成,这明显的限制了其实现并行化处理,然而论文提出的简单循环单元(SRU)解除了这种限制,ht的计算不在依赖于前一时刻的计算,这样就
SRU和LSTM一样,是一个词一个词的进行计算foriinrange(xt.size(0)):# x_t^ = W * x_tx_t = self.x_t(xt[i])# f_t = σ( W_f * x_t + b_f )ft = F.sigmoid(self.ft(xt[i]))# r_t = σ( W_r
We also analyzed the result of the intra-position scenario for the activity-specific case. For this analysis, we considered the evaluation metrics such as recall, precision and F1-Score to demonstrate how the heuristic features performed with the help of the 1D-CNN–LSTM model for each activity...
(usually four), which share L1 instruction and data caches. InFig. 1, we can see a block diagram of an NVIDIA Tesla V100’s SM[3]GPU: each sub-core has an L0 instruction cache, issue scheduler in charge of dispatching instructions into the different SIMD (single instruction multiple ...
1、SRU Networks Structure Diagram 熟悉LSTM的人很容易理解SRU的网络结构图,下图是SRU的网络结构图: xt代表 t 时刻的输入; W、b 代表权重和偏置; ft代表 t 时刻的遗忘门(forget gate); rt代表 t 时刻的重置门(reset gate); ct和 ht分别代表 t 时刻的状态和最终的输出; ...
Figure 13.Diagram of gaze-mapping system parallelization. Based on queued and multi-processing, resource use was maximized to improve speed, and CPU resources could be dynamically allocated according to the load of each task in order to improve overall inference speed. ...