Net Optimizer Pro Apk

The accuracy would reset to around 15% - 20%, instead of starting around 86%,and my loss would be much higher. Even if I used a small learning rate, and recompiled, I would still startoff from a very low accuracy.From browsing the internet it seems some optimizers like ADAM or RMSPROP havea problem with resetting weights after recompiling (can't find the link at the moment)

However, these changes don't seem to be reflected in my training.Despite raising the lr significantly, I'm still floundering around 86% with the same loss. During each epoch, I'm seeing very little loss or accuracy movement. I would expect the loss to be a lot more volatile. This leads me to believe that my change in optimizer and lr isn't beingrealized by the model.

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Adaptive optimizers such as ADAM RMSPROP, ADAGRAD, ADADELTA, and any variation on these, rely on previous update steps to improve the direction and magnitude of any current adjustment to the weights of the model.

Unfortunately I'm not sure how you can avoid this... Perhaps pretrain with one optimizer --> save weights --> replace optimizer --> restore weights --> train for a few epochs and hope the new adaptive optimizer learns a "useful history" --> than restore the weights agin from the saved weights of the pretrained model and without recompiling start training again, now with a better optimizer "history".

The optimizer is part of the r.js adapter for Node and Nashorn, and it is designed to be run as part of a build or packaging step after you are done with development and are ready to deploy the code for your users.

The optimizer will only combine modules that are specified in arrays of string literals that are passed to top-level require and define calls, or the require('name') string literal calls in a simplified CommonJS wrapping. So, it will not find modules that are loaded via a variable name:

This behavior allows dynamic loading of modules even after optimization. You can always explicitly add modules that are not found via the optimizer's static analysis by using the include option.

The optimizer can be run using Node, Java with Rhino or Nashorn, or in the browser. The requirements for each option: Node: (preferred) Node 0.4.0 or later. Java: Java 1.6 or later. Browser: as of 2.1.2, the optimizer can run in a web browser that has array extras. While the optimizer options are the same as shown below, it is called via JavaScript instead of command line options. It is also only good for generating optimized single files, not a directory optimization. See the browser example. This option is really only useful for providing web-based custom builds of your library.For command line use, Node is the preferred execution environment. The optimizer runs much faster with Node.

With this local install, you can run the optimizer by running the r.js or r.js.cmd file found in the project's node_modules/.bin directory.With the local install, you can also use the optimizer via a function call inside a node program.

The examples in this page will assume you downloaded and saved r.js in a directory that is a sibling to your project directory. The optimizer that is part of r.js can live anywhere you want, but you will likely need to adjust the paths accordingly in these examples.

There is a limitation on the command line argument syntax. Dots are viewed as object property separators, to allow something like paths.jquery=lib/jquery to be transformed to the following in the optimizer:

For properties that are module IDs, they should be module IDs, and not file paths. Examples arename, include, exclude, excludeShallow, deps.Config settings in your main JS module that is loaded in the browser at runtime are not read by default by the optimizer

In version 1.0.5+ of the optimizer, the mainConfigFile option can be used to specify the location of the runtime config. If specified with the path to your main JS file, the first requirejs({}), requirejs.config({}), require({}), or require.config({}) found in that file will be parsed out and used as part of the configuration options passed to the optimizer:

The optimizer cannot load network resources, so if you want it included in the build, be sure to create a paths config to map the file to a module name. Then, for running the optimizer, download the CDN script and pass a paths config to the optimizer that maps the module name to the local file path.

This build profile tells RequireJS to copy all of appdirectory to a sibling directory called appdirectory-build and apply all the optimizations in the appdirectory-build directory. It is strongly suggested you use a different output directory than the source directory -- otherwise bad things will likely happen as the optimizer overwrites your source.

The default for the optimizer is to do the safest, most robust set of actions that avoid surprises after a build. However, depending on your project setup, you may want to turn off some of these features to get faster builds:

As of version 2.1.2, there are some speed shortcuts the optimizer will take by default if optimize is set to "none". However, if you are using "none" for optimize and you are planning to minify the built files after the optimizer runs, then you should turn set normalizeDirDefines to "all" so that define() calls are normalized correctly to withstand minification. If you are doing minification via the optimize option, then you do not need to worry about setting this option.

The optimizer has supported sourceURL (by setting useSourceUrl to true), for debugging combined modules as individual files. However, that only works with non-minified code. Source maps translate a minified file to a non-minified version. It does not make sense to use useSourceUrl with generateSourceMaps since useSourceUrl needs the source values as strings, which prohibits the useful minification done in combination with generateSourceMaps.

The r.js optimizer is designed to offer some primitives that can be used for different deployment scenarios by adding other code on top of it. See the deployment techniques wiki page for ideas on how to use the optimizer in that fashion.

I am working on two projects, one is on F5419, the other one is on FG4618. I am experiencing a interesting problem (bug!?) of the CCE 3.1 optimizer. In F5419 project, my code works OK no matter what optimization level I am using; And on the FG4618 project, in which I am sharing a major portion of the code with F5419 project, the program fails in the USCIA0 TX ISR while using optimization level 2 (default of release build), and the same code works OK while using debug build and optimization level 1.

By inspecting the generated ASM, I noticed that the optimizer extacted a portion of commonly used code to a routine called "abproc0" in almost each ASM file, and normally the the routine is returned with an "RETA" (non ISR code). However, while optimizing the FG4618 USCIA0 TX ISR, the optimizer extracted my buffer management code to a "abproc0" and the routine returns with RETI, I think the RETI caused the code run back to the _c_init00 and restart the software.

In the FG4618 project, the optimizer is doing that because I handled both USCIA0 and USCIB0 TX ISR in the same file, (they are sharing the same IRQ vector), so it can see the duplicated code (inline C++ code in my source), while in the F5419 project, each USCI port is handled in seperate source files, so I don't see the same problem.

A function call inside the ISR will be returned with RETI or RETA? I know the ISR itself should be returned by a RETI. the optimizer generated the "abproc0", which is a sub routine called from ISR, has a RETI at the end of it, and I believe that is wrong.

A SQL optimizer analyzes a SQL query and chooses the most efficient way to execute it. While the simplest queries might have only one way to execute, more complex queries can have thousands, or even millions, of ways. The better the optimizer, the closer it gets to choosing the optimal execution plan, which is the most efficient way to execute a query.

Enter the cost-based optimizer. A cost-based optimizer will enumerate possible execution plans and assign a cost to each plan, which is an estimate of the time and resources required to execute that plan. Once the possibilities have been enumerated, the optimizer picks the lowest cost plan and hands it off for execution. While a cost model is typically designed to maximize throughput (i.e. queries per second), it can be designed to favor other desirable query behavior, such as minimizing latency (i.e. time to retrieve first row) or minimizing memory usage.

The goal of gathering database statistics is to provide information to the optimizer so that it can make more accurate row count estimates. Useful statistics include row counts for tables, distinct and null value counts for columns, and histograms for understanding the distribution of values. This information feeds into the cost model and helps decide questions of join type, join ordering, index selection, and more.

CockroachDB started with a simple heuristic optimizer that grew more complicated over time, as optimizers tend to do. By our 2.0 release, we had started running into limitations of the heuristic design that we could not easily circumvent. Carefully-tuned heuristic rules were beginning to conflict with one another, with no clear way to decide between them. A simple heuristic like:

Every new heuristic rule we added had to be examined with respect to every heuristic rule already in place, to make sure that they played nicely with one another. And while even cost-based optimizers sometimes behave like a delicately balanced Jenga tower, heuristic optimizers fall over at much lower heights. 2351a5e196