FreezeD | Simple Baseline for Fine-Tuning GANs | Machine Learning library

By default, to avoid using an unbounded amount of RAM, the Gensim Phrases class uses a default parameter max_vocab_size=40000000, per the source code & docs at:

https://radimrehurek.com/gensim/models/phrases.html#gensim.models.phrases.Phrases

Unfortunately, the mechanism behind this cap is very crude & non-intuitive. Whenever the tally of all known keys in they survey-dict (which includes both unigrams & bigrams) hits this threshold (default 40,000,000), a prune operation is performed that discards all token counts (unigrams & bigrams) at low-frequencies until the total unique-keys is under the threshold. And, it sets the low-frequency floor for future prunes to be at least as high as was necessary for this prune.

For example, the 1st time this is hit, it might need to discard all the 1-count tokens. And due to the typical Zipfian distribution of word-frequencies, that step along might not just get the total count of known tokens slightly under the threshold, but massively under the threshold. And, any subsequent prune will start by eliminated at least everything with fewer than 2 occurrences.

This results in the sawtooth counts you're seeing. When the model can't fit in max_vocab_size, it overshrinks. It may do this many times in the course of processing a very-large corpus. As a result, final counts of lower-frequency words/bigrams can also be serious undercounts - depending somewhat arbitrarily on whether a key's counts survived the various prune-thresholds. (That's also influenced by where in the corpus a token appears. A token that only appears in the corpus after the last prune will still have a precise count, even if it only appears once! Although rare tokens that appeared any number of times could be severely undercounted, if they were always below the cutoff at each prior prune.)

The best solution would be to use a precise count that uses/correlates some spillover storage on-disk, to only prune (if at all) at the very end, ensuring only the truly-least-frequent keys are discarded. Unfortunately, Gensim's never implemented that option.

The next-best, for many cases, could be to use a memory-efficient approximate counting algorithm, that vaguely maintains the right magnitudes of counts for a much-larger number of keys. There's been a litte work in Gensim on this in the past, but not yet integrated with the Phrases functionality.

That leaves you with the only practical workaround in the short term: change the max_vocab_size parameter to be larger.

You could try setting it to math.inf (might risk lower performance due to int-vs-float comparisons) or sys.maxsize – essentially turning off the pruning entirely, to see if your survey can complete without exhausting your RAM. But, you might run out of memory anyway.

You could also try a larger-but-not-essentially-infinite cap – whatever fits in your RAM – so that far less pruning is done. But you'll still see the non-intuitive decreases in total counts, sometimes, if in fact the threshold is ever enforced. Per the docs, a very rough (perhaps outdated) estimate is that the default max_vocab_size=40000000 consumes about 3.6GB at peak saturation. So if you've got a 64GB machine, you could possibly try a max_vocab_size thats 10-14x larger than the default, etc.

I think the problem you are facing could be related to Dart type promotion that does not always work as you could expect. It is thoroughly explained here.

However, how I do handle this with freezed is by using the generated union methods. When rendering the UI, you could use them like this:

Unsupported feature at the moment.

Source: Issue #616

There are a number of factors to consider here. Some ideas:

• how fast can the table be read? What indexing / materialized views are in place? Is there any contention at the database level to rule out?
• Recommendation: ensure database is set up for fast read on the table you are exporting
• as you are on-premises, what is the local network card setup and throughput?
• Recommendation: ensure local network setup is as fast as possible
• as you are on-premises, you must be using a Self-hosted Integration Runtime (SHIR). What is the spec of this machine? eg 8GB RAM, SSD for spooling etc as per the minimum specification. Where is this located? eg 'near' the datasource (in the same on-premises network) or in the cloud. It is possible to scale out SHIRs by having up to four nodes but you should ensure via the metrics available to you that this is a bottleneck before scaling out.
• Recommendation: consider locating the SHIR 'close' to the datasource (ie in the same network)
• is the SHIR software version up-to-date? This gets updated occasionally so it's good practice to keep it updated.
• Recommendation: keep the SHIR software up-to-date
• do you have Express Route or going across the internet? ER would probably be faster
• Recommendation: consider Express Route. Alternately consider Data Box for a large one-off export.
• you should almost certainly land directly to ADLS Gen 2 or blob storage. Going straight into the database could result in contention there and you are dealing with Synapse concepts such as transaction logging, DWU, resource class and queuing contention among others. View the metrics for the storage in the Azure portal to determine it is under stress. If it is under stress (which I think unlikely), consider multiple storage accounts
• Recommendation: load data to ADLS2. Although this might seem like an extra step, it provides a recovery point and avoids contention issues by attempting to do the extract and load all at the same time. I would only load directly to the database if you can prove it goes faster and you definitely don't need the recovery point
• what format are you landing in the lake? Converting to parquet is quite compute intensive for example. Landing to the lake does leave an audit trail and give you a position to recover from if things go wrong
• Recommendation: use parquet for a compressed format. You may need to optimise the file size.
• ultimately the best thing to do would be one big bulk load (say taking the weekend) and then do incremental upserts using a CDC mechanism. This would allow you to meet your 2 hour window.
• Recommendation: consider a one-off big bulk load and CDC / incremental loads to stay within the timeline

In summary, it's probably your network but you have a lot of investigation to do first, and then a number of options I've listed above to work through.

Well a few things. For training set you say you have 16,0000 images. However with a batch size of 32 and steps_per_epoch= 100 then for any given epoch you are only training on 3,200 images. Similarly you have 2000 validation images, but with a batch size of 32 and validation_steps = 5 you are only validating on 5 X 32 = 160 images. Now Vgg is an OK model but I don't use it because it is very large which increases the training time significantly and there are other models out there for transfer learning that are smaller and even more accurate. I suggest you try using EfficientNetB3. Use the code

The Code A will not block the UI thread, because the launch method does not block the current thread.

As the documentation says:

Launches a new coroutine without blocking the current thread and returns a reference to the coroutine as a [Job].

If the context does not have any dispatcher nor any other [ContinuationInterceptor], then [Dispatchers.Default] is used.

So in your case, CodeA uses the Dispatches.Default under the hood, while CodeB uses the Dispatchers.IO

More on coroutines here

The first example

I have a solution/workaround for this case.
Let's make an (for example) AuthEvent.onUserDataUpdated(User) event, in the stream listener you have to call add() with this event and create a handler for it (on<...>(...)) to emit new AuthState.

You're probably sending/getting Null value from your api call and it's not matching with your type string. or the field name is not same. Please check the value and field name.