Net Optimizer Pro
Penny Dale
You can either instantiate an optimizer before passing it to model.compile() , as in the above example,or you can pass it by its string identifier. In the latter case, the default parameters for the optimizer will be used.
To construct an Optimizer you have to give it an iterable containing theparameters (all should be Variable s) to optimize. Then,you can specify optimizer-specific options such as the learning rate, weight decay, etc.
Optimizer s also support specifying per-parameter options. To do this, insteadof passing an iterable of Variable s, pass in an iterable ofdict s. Each of them will define a separate parameter group, and should containa params key, containing a list of parameters belonging to it. Other keysshould match the keyword arguments accepted by the optimizers, and will be usedas optimization options for this group.
We have 3 major categories of implementations: for-loop, foreach (multi-tensor), andfused. The most straightforward implementations are for-loops over the parameters withbig chunks of computation. For-looping is usually slower than our foreachimplementations, which combine parameters into a multi-tensor and run the big chunksof computation all at once, thereby saving many sequential kernel calls. A few of ouroptimizers have even faster fused implementations, which fuse the big chunks ofcomputation into one kernel. We can think of foreach implementations as fusinghorizontally and fused implementations as fusing vertically on top of that.
Here the model model can be an arbitrary torch.nn.Module object. averaged_modelwill keep track of the running averages of the parameters of the model. To update theseaverages, you should use the update_parameters() function after the optimizer.step():
Knowledge optimizer is a tool that enables you to optimize, or improve your knowledge base content. The knowledge optimizer provides insights about possible gaps in the knowledge base and areas for improvement.
The number in the Hits column is not the total number of hits for a particular query or article, but the number of hits since a task related to the query or article was last completed in the knowledge optimizer. This view enables you to see tasks that need your attention based on recent traffic, as opposed to total traffic. Best practice recommends that you clear all tasks from the knowledge optimizer, and let the traffic insert tasks from scratch.
Citrix optimizer optimizes user environments for better performance. It runs a quick scan of user environments and then applies template-based optimization recommendations. You can optimize user environments in two ways:
Changes to Citrix optimizer settings take some time to take effect, depending on the value that you specified for the SQL Settings Refresh Delay option on the Advanced Settings > Configuration > Service Options tab.
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