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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.
The APU Dynamics Optimizer is a dynamics optimization tool designed to support modern loudness types (LUFS, True Peak). Underneath the hood, the optimizer uses the samecompression engine as the APU Loudness Compressor, but benefits from the full audio waveform instead of real-time measurements. This allows the gain envelope tobe meticulously optimized across the entire waveform.
The design of this tool allows you to very precisely target a specific average LUFS and dynamic range. Once the gain envelope has been optimized, you can playback the result inreal-time while adjusting the character of the sound with the same collection of parameters from the compressor plug-in.
Before continuing to the next step, you can make adjustments to the source percentiles. This allows you to exclude any unwanted audio from the optimization process. By default,the percentiles are set to match the LRA standard from LUFS. This generally does a good job capturing the main body of the audio.
True Peak loudness types are similar to traditional peak measurements, but they are more accurate as they take into consideration inter-sample peaks. These measurementsuse a time interval equal to the configured block size (1ms by default). This mode is extremely responsive, making it suitable for very fast attack and release times.
This software was developed by APU Software, LLC and is available as a standalone application (windows x64/x86, macOS universal). The software libraries below are utilized for portions of the software:
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.
Also consider the following example related to the distinct penalization of parameters.Remember that parameters() returns an iterable thatcontains all learnable parameters, including biases and otherparameters that may prefer distinct penalization. To address this, one can specifyindividual penalization weights for each parameter group:
Some optimization algorithms such as Conjugate Gradient and LBFGS need toreevaluate the function multiple times, so you have to pass in a closure thatallows them to recompute your model. The closure should clear the gradients,compute the loss, and return it.
Many of our algorithms have various implementations optimized for performance,readability and/or generality, so we attempt to default to the generally fastestimplementation for the current device if no particular implementation has beenspecified by the user.
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.
In general, the performance ordering of the 3 implementations is fused > foreach > for-loop.So when applicable, we default to foreach over for-loop. Applicable means the foreachimplementation is available, the user has not specified any implementation-specific kwargs(e.g., fused, foreach, differentiable), and all tensors are native and on CUDA. Note thatwhile fused should be even faster than foreach, the implementations are newer and we wouldlike to give them more bake-in time before flipping the switch everywhere. You are welcometo try them out though!
torch.optim.lr_scheduler provides several methods to adjust the learningrate based on the number of epochs. torch.optim.lr_scheduler.ReduceLROnPlateauallows dynamic learning rate reducing based on some validation measurements.
Most learning rate schedulers can be called back-to-back (also referred to aschaining schedulers). The result is that each scheduler is applied one after theother on the learning rate obtained by the one preceding it.
Receives the list of schedulers that is expected to be called sequentially during optimization process and milestone points that provides exact intervals to reflect which scheduler is supposed to be called at a given epoch.
torch.optim.swa_utils implements Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). In particular,the torch.optim.swa_utils.AveragedModel class implements SWA and EMA models,torch.optim.swa_utils.SWALR implements the SWA learning rate scheduler andtorch.optim.swa_utils.update_bn() is a utility function used to update SWA/EMA batchnormalization statistics at the end of training.
EMA is a widely known technique to reduce the training time by reducing the number of weight updates needed. It is a variation of Polyak averaging, but using exponential weights instead of equal weights across iterations.
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():
By default, torch.optim.swa_utils.AveragedModel computes a running equal average ofthe parameters that you provide, but you can also use custom averaging functions with theavg_fn or multi_avg_fn parameters:
multi_avg_fn allows defining more efficient operations acting on a tuple of parameter lists, (averaged parameter list, model parameter list), at the same time, for example using the torch._foreach* functions. This function must update the averaged parameters in-place.
Typically, in SWA the learning rate is set to a high constant value. SWALR is alearning rate scheduler that anneals the learning rate to a fixed value, and then keeps itconstant. For example, the following code creates a scheduler that linearly anneals thelearning rate from its initial value to 0.05 in 5 epochs within each parameter group:
update_bn() assumes that each batch in the dataloader loader is either a tensors or a list oftensors where the first element is the tensor that the network swa_model should be applied to.If your dataloader has a different structure, you can update the batch normalization statistics of theswa_model by doing a forward pass with the swa_model on each element of the dataset.
In the example below, swa_model is the SWA model that accumulates the averages of the weights.We train the model for a total of 300 epochs and we switch to the SWA learning rate scheduleand start to collect SWA averages of the parameters at epoch 160:
In the example below, ema_model is the EMA model that accumulates the exponentially-decayed averages of the weights with a decay rate of 0.999.We train the model for a total of 300 epochs and start to collect EMA averages immediately.
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To resolve the issue, in the SG optimizer plugin on the site head over to the front end optimization tab and toggle off Minify Javascript files. This minification is what is stopping the editor from loading.
Thank you. However, my question is broader. Are you communicating with SiteGround so we will not have ongoing issues with your theme and their plug-in? You both are big players whose products should work together without this type of breakage.
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.