Linkify feature flags - Add feature flag detection for Kotlin files

What is this PR for?

Implement FeatureFlagExtractor to parse and identify enums annotated with @FeatureFlag in *Features.kt files.
Add FeatureFlagFileListener to trigger PSI analysis upon opening files matching the *Features.kt pattern.

PR is ready for a review again

Are you planning to add linkifying at all in this PR? Or at least maybe look at a test to make sure the PSI loading is correct?

Testing PR is in progress and linkifying will also be handled in a followup PRs

let's make this configurable

Sounds good. Will be added in followup PR as well instead of this PR.


Revert "history service should do event reordering making sure corres…

…ponding … (#601)"
This reverts commit 6164b73da4a2d57fd78f3351033e53c93f886cb7.
This revert is done because a patch should be added to the release

Coverage decreased (-0.1%) to 64.071% when pulling dc5991c9ce731a04aeeac09b68c3587acc9b41e4 on revert into 1f1d16ffefb7699aaad76f199a3d8f7db899c38c on master.


Incorrect type casting in lisa.datautils.series_convert()

Describe the bug
Converting a series with values higher than 2^63 can lead to a negative value if a signed type is tried first due to Series.astype() behavior:
import pandas as pd
import numpy as np

series = pd.Series([18446743558487338888])
print(series.astype('int64'))

print(series_convert(series, dtype='int64'))

This lambda in lisa.trace needs to be changed so that invalid casts raise an exception:
    astype = lambda dtype: lambda x: x.astype(dtype, copy=False)

Closing as we are migrating the repo to GitLab. New messages on this conversation should be posted at https://gitlab.arm.com/tooling/lisa/-/issues/2159
Note that you can login with your GitHub account on gitlab.arm.com so it should be painless.


ArgumentError: second must not be greater than 29 on valid zip files

Hi I'm getting the above error for zip files, that can be unzipped normally using command line tools.
Just as an aside, I'm curious why seconds can not be greater than 29 whereas all the other time measurements seem to follow their corresponding max values, 59 for minutes, 24 for hours etc

This is a limitation of the DOS time stamps.  As I understood the spec when I originally read it, the seconds field of the structure really only counts every other second in order to conserve 1 bit in the structure.  That maximum value of 29 actually corresponds second 58 in the minute, a value of 15 is second 30, and so on.
While a larger value can be set in that structure, it's technically invalid since it would mean you're counting second 60 or more of a minute, which is nonsensical.  In order to loosen the restriction imposed by this library, a new decision would have to be made regarding how to handle these excessively large values of seconds.  The current decision is to error out, but it's also possible to cap excessive values to the maximum valid value.  Probably the thing to do would be to allow support for sloppy zip files when specifically requested.

@aniruddhaborah, can you tell me more about how you ended up with ZIP files that contain invalid time stamps?

The zip files were created on iOS using https://github.com/pixelglow/ZipZap
A small percentage of zip files end up with these timestamps

Any plans to add support to skip the time validations for these zip files?

What does InfoZip's unzip tool do with respect to those invalid time stamps?  Does it set the seconds to 0 or 59, or does it do something else.  Much of the logic in this library was built using the unzip tool as a reference, so I would be willing to make it follow the behavior of that tool here.

https://github.com/LuaDist/zip/blob/f6cfe48f6bc5bf2d505a0e0eb265ce4cb238db89/fileio.c#L1099
It looks like my interpretation of how infozip encodes DOS time was incorrect.  The link above shows how unix time is converted, and seconds is first incremented by 1 before dropping the least significant bit.


React-Table.css parse error

Dear ,
I created new react js project and i imported this module. It was work any problem. But when I imported in my project  I get error. Could you help me please ?
Here below ,
ERROR in ./~/react-table/react-table.css
Module parse failed: C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\react-table\react-table.css Unexpected token (1:0)
You may need an appropriate loader to handle this file type.
SyntaxError: Unexpected token (1:0)
at Parser.pp$4.raise (C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\webpack\node_modules\acorn\dist\acorn.js:2222:15)
at Parser.pp.unexpected (C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\webpack\node_modules\acorn\dist\acorn.js:604:10)
at Parser.pp$3.parseExprAtom (C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\webpack\node_modules\acorn\dist\acorn.js:1823:14)
at Parser.pp$3.parseExprSubscripts (C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\webpack\node_modules\acorn\dist\acorn.js:1716:21)
at Parser.pp$3.parseMaybeUnary (C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\webpack\node_modules\acorn\dist\acorn.js:1693:19)
at Parser.pp$3.parseExprOps (C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\webpack\node_modules\acorn\dist\acorn.js:1638:21)
at Parser.pp$3.parseMaybeConditional (C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\webpack\node_modules\acorn\dist\acorn.js:1621:21)
at Parser.pp$3.parseMaybeAssign (C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\webpack\node_modules\acorn\dist\acorn.js:1598:21)
at Parser.pp$3.parseExpression (C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\webpack\node_modules\acorn\dist\acorn.js:1574:21)
at Parser.pp$1.parseStatement (C:\Users\IDARA\Desktop\VsCode\VisionForm_Grid\node_modules\webpack\node_modules\acorn\dist\acorn.js:728:49)

This looks environment related to me. This question is better asked on the #react-table Slack channel (where a wider range of users may have experienced this problem and have a solution for it).  You can find a badge at the top of the README in the doco.  The github issues are reserved for bugs or feature requests with the code.


Update python-password-generator.py

Description
A short summary of what is included in your Pull Request.
Fixes #(issue_no)
Replace issue_no in the above line, with the issue related to this PR.
Type of change
Choosing one or more options from the following as per the nature of your Pull request.

[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[x] Documentation Update

Checklist:
Please tick all the boxes that are fulfilled by your Pull Request.

[x] I have named my files and folder, according to this project's guidelines.
[x] My code follows the style guidelines of this project.
[x] My Pull Request has a descriptive title. (not a vague title like Update index.md)
[x] I have commented on my code, particularly in hard-to-understand areas.
[x] I have created a helpful and easy to understand README.md, according to the given README_TEMPLATE.
[x] I have included a requirements.txt file (if external libraries are required.)
[x] My changes do not produce any warnings.
[ ] I have added a working sample/screenshot of the script.

I'd done some changes
please do consider my pull request and allow me do this project


feat(angular): impl menu that allows devs to pass their own content

Exploratory PR to work out the right API

@vitale232 Over in the discussion here I mentioned I like this: {{ item }} 😅😅 projection approach. Did you want to give that a stab? Also, lmk if you're going to do that here or want to close this out and do on a separate branch 🙏

@roblevintennis sorry to go a little MIA... More over in the original PR discussion, but I've been trying to push out a work project. Between that and the weather getting nicer, I haven't had a chance to work on this much.
The plan is very much to clean up the code a little bit, get the Storybook integration working with the new ag-menu API, and make a PR back into Agnostic UI. More to come soon 😅 😅

No worries I understand we all need to deal with work and take advantage of good weather IRL when possible. thanks for the update 👍🏽


Unexpected Key(s) error on loading model

I am unable to load the model because of the following error:
/site-packages/bela/task/joint_el_task.py
           self.encoder.load_state_dict(encoder_state)
/site-packages/torch/nn/modules/module.py", line 2153, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for HFEncoder:
        Unexpected key(s) in state_dict: "transformer.embeddings.position_ids".

I have tried downgrading transformers.
I am using pytorch 2.2.0, cuda 12.1 and transformers 4.37.0.

Had to downgrade the transformers to 4.29.2 to resolve the above error.


Add support for flags

@phadej I started from 087395f, but end up refactoring it quite a bit.

You made it earn about unknown fields. Great. LGTM


Support for Android Firefox

Hello,
is it planned to support firefox on Android?
Currently the firefox extension cannot be used on firefox Android (unable to install it with the newest version, with an older firefox version I'm able to isntall it but the settings seem not to be saved so whenever I click the addon it redirects me to the "getting started" website).
The bookmarklet does not work on firefox Android either (which is probably a bug in firefox).

@elmodor For the extension, please report issues to the repo of the extension itself: https://github.com/sissbruecker/linkding-extension. Please add an issue there and close this one if you want support specifically for the extension, or clarify this issue if it's about the bookmarklet.
Not using Android, so my motivation for working on this would be extremely low. However we have now have a how-to describing how to get the bookmarklet working on Android/Chrome, maybe something similar can be achieved with Firefox? Also check the iOS workaround in the same doc, there might be a similar app for Android that allows to create share actions that could then open the add bookmark form with the URL from the browser tab.

I have opened https://github.com/sissbruecker/linkding-extension/issues/6 there. Not sure if it is fixable with a low effort, I just know that firefox changed something a while ago regarding their addons and that broke all "old" plugins that did not got updated.
Anyways, the bookmarklet used not to work on android firefox. However, while checking and giving you the official link to the bug ticket, 9 days ago someone said that it is working now with the latest firefox version that just got released. So I tried it as well, and voilá! It works. Awesome.
It is described there how it works:
https://github.com/mozilla-mobile/fenix/issues/2871#issuecomment-831852154
I think it is really similar (if not identical) to the solution posted in the how-to for chrome.
So maybe you can remove the
Note that this only works with Chrome and not with other browsers on Android.
and mention that it should work on Android Firefox as well with the latest Android Firefox version (>88.1.2)

Closing as issue has been moved.


Can't get this to work in any browser other than FF

Creating a map to run from the computer. Base map is pulled in from the web with pins overlaid from KML files located in the same folder.
This works perfectly on my computer in firefox but in Chrome, Ie and Safari it does not. The base map loads but 'fit bounds' does not occur and the pins are not displayed. Any ideas?
*edit to add, i can't paste the code, the "insert" code button above does not seem to work?!

Could you provide a jsfiddle in order i help you ?

Thanks for your reply Brunob.  I am not having much luck getting jsfiddle to work. It doesnt like the way I am calling external files.  The js should be visible though.
https://jsfiddle.net/qvzfmgbf/

Maybe you can provide an online version of your problematic file ?

I had been running this on my computer from a folder on the desktop. I have now uploaded it online to show you and it seems to be working fine when viewed with different browsers. Any idea why chrome/IE/safari don't like the files when they are hosted on the computer?
http://pixelcriminal.co.uk/map/eyts.html

These browser doesn't like to play with ajax load on local files, that was your bug, you have to use a local server or simply dev with firefox ;)


Enable Lean builds in CI

This is taken from https://github.com/eric-wieser/lean-graded-rings/blob/master/.github/workflows/lean_build.yml, which has a few nice features on top of the usual script (notably, not rebuilding from scratch).
To see this in action, you might need to click a button below the PR to enable builds.
Alternatively, you might need to go to the "actions" tab.

Cool, many thanks!
I was planning on enabling some github workflow anyhow...


Set up circleci npm autopublishing

Based on the existing config from https://github.com/resin-io-modules/resin-pine.
Change-Type: patch

@pimterry , I just want to make sure I understand, because I'm not that familiar with Circleci. Do you test it with node-8 and with node-6 but deploy it with node-6?

@MoranF yes, exactly.
We have to deploy from one, and it shouldn't make any difference in principle, but we deploy node 6 just because it's probably safer if there is a difference (code that works on node 6 is more likely to work on node 8 than the other way around).
Nobody is very familar with CircleCI yet, so no need to worry about that. It is definitely good to spend some time reading through it to get a general idea of what's going on though, because the plan is to move everything on Travis to Circle instead, so the more people who understand it the better!

I don't have a way to test it but looks good

Neither do I :smiley:. It's passing on this PR though, and the deployment part works in resin-pine, so lets hope it works!


Add support for removing Debian packages

I couldn't come up with a name better than nopackage(s) for the require part. I'm open to suggestions.
This pull request assumes pull request #10.

Not sure about the best API for requiring resources not to be present.
Let's keep it that way right now, and revisit it later if needed. Thanks!


[Bug]: timestamp columns parse default value set as Fragment

No duplicates 🥲.

[X] I have searched for a similar issue in our bug tracker and didn't find any solutions.

Describe the bug
Trying to execute migration with default value set as Fragment
$table
        ->addColumn('ttl', 'timestamp', [
            'nullable' => true,
            'default' => new Fragment('ADDTIME(`Date`, \'2:00:00\')'),
        ]);

causes exception DateTimeImmutable::__construct(): Failed to parse time string (addtime(Date,'2:00:00')) at position 0 (a): The timezone could not be found in the database.
Full backtrace:
Exception: DateTimeImmutable::__construct(): Failed to parse time string (addtime(`Date`,'2:00:00')) at position 0 (a): The timezone could not be found in the database in ~/vendor/cycle/database/src/Schema/AbstractColumn.php:754
Stack trace:
#0 ~/vendor/cycle/database/src/Schema/AbstractColumn.php(754): DateTimeImmutable->__construct()
#1 ~/vendor/cycle/database/src/Driver/MySQL/Schema/MySQLColumn.php(372): Cycle\Database\Schema\AbstractColumn->formatDatetime()
#2 ~/vendor/cycle/database/src/Schema/AbstractColumn.php(370): Cycle\Database\Driver\MySQL\Schema\MySQLColumn->formatDatetime()
#3 ~/vendor/cycle/database/src/Schema/AbstractColumn.php(666): Cycle\Database\Schema\AbstractColumn->getDefaultValue()
#4 ~/vendor/cycle/database/src/Driver/MySQL/Schema/MySQLColumn.php(299): Cycle\Database\Schema\AbstractColumn->compare()
#5 ~/vendor/cycle/database/src/Schema/Comparator.php(108): Cycle\Database\Driver\MySQL\Schema\MySQLColumn->compare()
#6 ~/vendor/cycle/database/src/Schema/AbstractTable.php(705): Cycle\Database\Schema\Comparator->alteredColumns()
#7 ~/vendor/cycle/database/src/Schema/AbstractTable.php(619): Cycle\Database\Schema\AbstractTable->normalizeSchema()
#8 ~/vendor/cycle/migrations/src/Operation/Table/Update.php(28): Cycle\Database\Schema\AbstractTable->save()
#9 ~/vendor/cycle/migrations/src/Capsule.php(69): Cycle\Migrations\Operation\Table\Update->execute()
#10 ~/vendor/cycle/migrations/src/TableBlueprint.php(243): Cycle\Migrations\Capsule->execute()
#11 ~/vendor/cycle/migrations/src/TableBlueprint.php(197): Cycle\Migrations\TableBlueprint->execute()
#12 ~/schema/inv/20231013.155347_0_inv.ip_ipmi_role.php(32): Cycle\Migrations\TableBlueprint->update()
#13 ~/dev/seeding-executor.php(73): ~\Migrations\inv\migrationclass->up()
#14 ~/vendor/cycle/database/src/Database.php(188): ~\Migrations\{closure}()
#15 ~/dev/seeding-executor.php(74): Cycle\Database\Database->transaction()
#16 {main}

Is this a regression?
Not that I know.
To Reproduce

class MigrationClass extends Migration {

    /**
     * Create tables, add columns or insert data here
     */
    public function up() {
        $this->table('table_a')
                ->addColumn('ttl', 'timestamp', [
                    'nullable' => true,
                    'default' => new Fragment('ADDTIME(`Date`, \'2:00:00\')'),
                ])
                ->update();
    }

    /**
     * Drop created, columns and etc here
     */
    public function down() {
        $this->table('table_a')
            ->dropColumn('ttl')
            ->update();
    }
}

Expected behaviour
A column should be added with default value set to +2 hours from time of row creation.
Media prove
No response
Database
MySQL
Your environment

OS: CentOs Stream
PHP 8.0.30
cycle/migrations 3.1.0
cycle/database 2.7.0

Additional context
No response

After some additional investigation, it turned out the issue arise when Cycle tries to understand the database structure.
I.e.:
Step#1: Creating database with such column: it's fine.
Step#2: Reading the table from database (f.e. to further modify it) in the next migration: fails.


Users/kaadhina/x unit reader

Hi @kaadhina, I'm your friendly neighborhood Microsoft Pull Request Bot (You can call me MSBOT). Thanks for your contribution!

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looks fine


error while launching with annotation

Dear Earthwings!
i am trying to launch your code of annotation, the way you described but it prompts error, the error is following while launching with demo.launch.
[ WARN] [1646230664.838619041]: Failed to open /workspace/kitti/annotate3.yaml for writing. Annotations will not be saved.
looking forward to your solution.
thank you in advance

I want to know your solution, sir. I am facing the same problem, and could not get over it. Thank you.

Have the same question.

The path does not exist.  You can select an existing path as shown in the following image, and then click Save Config of RViz.


Exception traces shown on first upload of new artifact

The first time an artifact is deployed, the download of existing metadata obviously fails. In this case, an exception is however shown to the user, which is confusing (the deploy however succeeds). This might be just be a case of adjusting the log levels for the messages to DEBUG rather than the current WARN and SEVERE.

This shouldn't be an issue anymore with the new version as it uses the official S3 client and is much less noisy.


Issues with the DocBook conversion to HTML

[ ] title should be inside section
[ ] literalLayout instead of literal
[ ] literalLayout should be inside para
[ ] itemizedlist should have para inside

purpose type should be changed to TextType, I've created a PR in the eml-profile project https://github.com/gbif/eml-profile/pull/8

Some of the issues now covered with better error messages


fast vs. best

https://github.com/tesseract-ocr/tesseract/issues/943#issuecomment-303239929
theraysmith commented on May 23, 2017

Far greater performance improvements can be made by making the network smaller. As I already indicated, I have had some very good results in this area, with a network 3x faster than the legacy code (for English) and much faster than the legacy code for complex scripts.

https://github.com/tesseract-ocr/tesseract/issues/995#issuecomment-314609036
theraysmith commented on Jul 12, 2017

2 parallel sets of tessdata. "best" and "fast". "Fast" will exceed the speed of legacy Tesseract in real time, provided you have the required parallelism components, and in total CPU only slightly slower for English. Way faster for most non-latin languages, while being <5% worse than "best".

https://github.com/tesseract-ocr/tessdata_best/pull/17#issuecomment-374455979
@theraysmith commented on Mar 20, 2018

How does fast relate to best:
Best is what is says it is. For languages where we have eval data, it is the network configuration that yielded best results on the eval data.
Fast is a speed/accuracy compromise, based on my own judgement, as to what offered the best "value for money" in speed vs accuracy. For some languages, this is still best, but for most not.
The "best value for money" network configuration was then integerized for further speed.
If you want best to run faster, it is easy to integerize "best" at the cost of a small loss in accuracy.
It seemed pointless to add to the confusopoly of langdatas further by providing the integerized best.
For languages that have no eval data, both best and fast are a guess, based on using a configuration that worked well for the most closely related language.

Still unsolved: How to build tessdata_fast from tessdata_best. We know how to replace the float data in tessdata_best by integer data, but we don't know how the network was made smaller.

Fast maybe using a diff network spec. Eg. Ray's das 2016 slides show a diff network string. It is possible that Ray has not posted some of those methods to github.

Yes, I also think that tessdata_fast uses a different network spec. Ray said that one step was making the network smaller. So which spec was used for each language / script, and how was the conversion done?

My guess is that fast and best were trained independently.
First, fast is trained with a spec that produce a smaller net than best.
Then, the float->int conversion is done.

The network configuration is stored in the lstm data in the traineddata.
With a small change to combine_tessdata, I produced the attached.
On Tue, Mar 20, 2018 at 9:29 AM jbreiden<EMAIL_ADDRESS>wrote:

@amitdo https://github.com/amitdo is exactly right. You cannot derive
fast from best. I'm sorry that I said the opposite earlier; I was confused
and wrong.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/tesseract-ocr/tesseract/issues/1404#issuecomment-374664028,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AL056ZhY9kmzuLFh9vSCr-5L0PlcQokCks5tgS5XgaJpZM4SxopC
.

--
Ray.
Version string:4.00.00alpha:afr:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=286700, sample_iteration=286724, null_char=95, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:amh:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=6112200, sample_iteration=6112270, null_char=284, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Arabic:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=5524000, sample_iteration=5532770, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ara:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=5521100, sample_iteration=5544718, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Armenian:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2036600, sample_iteration=2037181, null_char=191, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:asm:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx512O1c1], flags=41, iteration=2758500, sample_iteration=2758570, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:aze_cyrl:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=7665300, sample_iteration=7666563, null_char=88, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:aze:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=6760000, sample_iteration=6760858, null_char=88, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:bel:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=3105300, sample_iteration=3105747, null_char=95, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Bengali:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=1111400, sample_iteration=1111452, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ben:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=807100, sample_iteration=807306, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:bod:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3513900, sample_iteration=3524222, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:bos:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=3258400, sample_iteration=3258771, null_char=95, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:bre:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx512O1c1], flags=41, iteration=14000, sample_iteration=14000, null_char=124, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:bul:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=1801400, sample_iteration=1802032, null_char=92, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Canadian_Aboriginal:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=1949300, sample_iteration=1949421, null_char=652, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:cat:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=2427200, sample_iteration=2427351, null_char=88, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ceb:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=6718000, sample_iteration=6772655, null_char=73, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ces:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=2058000, sample_iteration=2058141, null_char=121, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Cherokee:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=7542100, sample_iteration=7542435, null_char=191, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:chi_sim:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=1971300, sample_iteration=1976813, null_char=223, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:chi_sim_vert:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3656700, sample_iteration=3679197, null_char=223, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:chi_tra:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=1988000, sample_iteration=1992889, null_char=226, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:chi_tra_vert:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3846900, sample_iteration=3902087, null_char=226, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:chr:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=7847200, sample_iteration=7847486, null_char=102, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:cos:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=5120400, sample_iteration=5120693, null_char=104, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:cym:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=4212000, sample_iteration=4212285, null_char=108, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Cyrillic:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=3401300, sample_iteration=3402083, null_char=202, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:dan:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=4983400, sample_iteration=4983829, null_char=100, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:deu:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=4860000, sample_iteration=4860533, null_char=114, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Devanagari:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=5059200, sample_iteration=5060646, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:div:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=3249400, sample_iteration=3256730, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:dzo:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=4258800, sample_iteration=4335799, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ell:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=3725500, sample_iteration=3725577, null_char=213, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:eng:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=6352400, sample_iteration=6352704, null_char=110, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:enm:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=2720200, sample_iteration=2721910, null_char=104, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:epo:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=6842600, sample_iteration=6843069, null_char=101, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:est:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=7906300, sample_iteration=7907020, null_char=103, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Ethiopic:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=149100, sample_iteration=149101, null_char=385, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:eus:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=15200, sample_iteration=15204, null_char=89, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fao:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2812800, sample_iteration=2812950, null_char=126, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fas:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=2762200, sample_iteration=2773866, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fil:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=7435100, sample_iteration=7435587, null_char=81, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fin:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3741900, sample_iteration=3742321, null_char=94, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Fraktur:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3739900, sample_iteration=3740440, null_char=166, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fra:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=2330100, sample_iteration=2330256, null_char=139, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:frk:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3692700, sample_iteration=3692967, null_char=98, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:frm:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=176600, sample_iteration=176697, null_char=140, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fry:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3246500, sample_iteration=3246617, null_char=106, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Georgian:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2308600, sample_iteration=2310386, null_char=225, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:gla:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3365500, sample_iteration=3365659, null_char=104, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:gle:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=2457900, sample_iteration=2458041, null_char=91, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:glg:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=2711300, sample_iteration=2711543, null_char=97, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:grc:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=3843500, sample_iteration=3843646, null_char=211, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Greek:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=8909600, sample_iteration=8909880, null_char=236, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Gujarati:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=6433600, sample_iteration=6434873, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:guj:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=1609800, sample_iteration=1609975, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Gurmukhi:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=7659800, sample_iteration=7660645, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Hangul:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=2103800, sample_iteration=2103883, null_char=210, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Hangul_vert:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=5218300, sample_iteration=5220005, null_char=213, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:HanS:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=2009700, sample_iteration=2013398, null_char=238, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:HanS_vert:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2966600, sample_iteration=2977655, null_char=238, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:HanT:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2342300, sample_iteration=2346092, null_char=240, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:HanT_vert:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=4202000, sample_iteration=4241091, null_char=240, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:hat:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=2653800, sample_iteration=2653890, null_char=96, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Hebrew:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=4137300, sample_iteration=4140116, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:heb:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=3399600, sample_iteration=3405090, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:hin:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2736900, sample_iteration=2737076, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:hrv:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=2723200, sample_iteration=2723414, null_char=105, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:hun:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=3443300, sample_iteration=3443765, null_char=97, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:hye:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=5222400, sample_iteration=5224599, null_char=104, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:iku:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=3862400, sample_iteration=3862672, null_char=595, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ind:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=4643300, sample_iteration=4643534, null_char=84, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:isl:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=1463300, sample_iteration=1463419, null_char=110, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ita_old:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2975300, sample_iteration=2980533, null_char=117, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ita:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=5146400, sample_iteration=5146726, null_char=118, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Japanese:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2937400, sample_iteration=2940965, null_char=429, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Japanese_vert:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=2510500, sample_iteration=2514591, null_char=429, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:jav:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx384O1c1], flags=41, iteration=449600, sample_iteration=449630, null_char=92, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:jpn:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3261700, sample_iteration=3267677, null_char=414, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:jpn_vert:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=1874000, sample_iteration=1878622, null_char=414, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Kannada:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=882600, sample_iteration=882910, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:kan:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2217600, sample_iteration=2219122, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:kat_old:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=6695800, sample_iteration=6695849, null_char=84, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:kat:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=5277000, sample_iteration=5286853, null_char=78, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:kaz:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=3952100, sample_iteration=3952887, null_char=116, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Khmer:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=8292700, sample_iteration=8393977, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:khm:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2208100, sample_iteration=2250468, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:kir:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=5271100, sample_iteration=5274339, null_char=142, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:kor:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=1458300, sample_iteration=1458333, null_char=143, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:kor_vert:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=1357200, sample_iteration=1359703, null_char=147, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:kur_ara:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2729500, sample_iteration=2731777, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:lao:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=1887800, sample_iteration=1891645, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Lao:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=6606700, sample_iteration=6614442, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Latin:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=3353000, sample_iteration=3353210, null_char=301, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:lat:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2768500, sample_iteration=2768698, null_char=99, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:lav:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=6526700, sample_iteration=6527341, null_char=113, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:lit:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=1190000, sample_iteration=1190081, null_char=107, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ltz:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=16600, sample_iteration=16602, null_char=86, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Malayalam:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=3192300, sample_iteration=3194140, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:mal:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=591500, sample_iteration=592095, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:mar:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=3278000, sample_iteration=3278243, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:mkd:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=2341900, sample_iteration=2342274, null_char=83, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:mlt:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=4769500, sample_iteration=4770242, null_char=84, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:mon:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3913100, sample_iteration=3913523, null_char=96, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:mri:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=6812500, sample_iteration=6812705, null_char=81, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:msa:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=1919100, sample_iteration=1919220, null_char=86, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Myanmar:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3179300, sample_iteration=3184457, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:mya:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=2609700, sample_iteration=2617837, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:nep:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=2913000, sample_iteration=2913772, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:nld:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=1233000, sample_iteration=1233084, null_char=149, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:nor:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2363800, sample_iteration=2363942, null_char=103, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:oci:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3059700, sample_iteration=3059833, null_char=128, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ori:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=4023900, sample_iteration=4026843, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Oriya:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=1636900, sample_iteration=1637404, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:osd:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=3724000, sample_iteration=3777456, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:pan:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=1331400, sample_iteration=1331667, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:pol:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=4037800, sample_iteration=4038310, null_char=112, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:por:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=1850300, sample_iteration=1850422, null_char=118, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:pus:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=2355400, sample_iteration=2358432, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:que:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx384O1c1], flags=41, iteration=3950200, sample_iteration=3950594, null_char=122, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ron:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=916200, sample_iteration=916276, null_char=109, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:rus:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=6081800, sample_iteration=6084626, null_char=123, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:san:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=200200, sample_iteration=200279, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Sinhala:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=244600, sample_iteration=244610, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:sin:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=4268500, sample_iteration=4268756, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:slk:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=5441500, sample_iteration=5442289, null_char=118, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:slv:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=5751300, sample_iteration=5751826, null_char=91, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:snd:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=2550300, sample_iteration=2555328, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:spa_old:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=1844900, sample_iteration=1845568, null_char=123, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:spa:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=6514100, sample_iteration=6514491, null_char=107, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:sqi:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=6242300, sample_iteration=6242829, null_char=78, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:srp_latn:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3226700, sample_iteration=3226895, null_char=111, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:srp:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=2332000, sample_iteration=2332252, null_char=101, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:sun:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=5572000, sample_iteration=5572210, null_char=93, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:swa:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=2668900, sample_iteration=2669094, null_char=81, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:swe:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=1803900, sample_iteration=1804028, null_char=98, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Syriac:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx512O1c1], flags=41, iteration=3512900, sample_iteration=3516598, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:syr:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=2731600, sample_iteration=2738621, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Tamil:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=1497800, sample_iteration=1497936, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:tam:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=2116900, sample_iteration=2117148, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:tat:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3975700, sample_iteration=3975926, null_char=97, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:tel:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=705200, sample_iteration=705553, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Telugu:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=2673100, sample_iteration=2673794, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:tgk:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=4505600, sample_iteration=4505867, null_char=120, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Thaana:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3214500, sample_iteration=3217590, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Thai:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=5339000, sample_iteration=5343543, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:tha:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=1895200, sample_iteration=1897798, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Tibetan:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3447300, sample_iteration=3450348, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:tir:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=10498000, sample_iteration=10498000, null_char=267, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ton:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=4730800, sample_iteration=4730909, null_char=107, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:tur:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=41, iteration=2465600, sample_iteration=2465932, null_char=102, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:uig:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=2731200, sample_iteration=2737916, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ukr:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=3513800, sample_iteration=3514620, null_char=90, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:urd:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3927800, sample_iteration=3938775, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:uzb_cyrl:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=4993600, sample_iteration=4995772, null_char=92, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:uzb:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=3363200, sample_iteration=3363853, null_char=86, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Vietnamese:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=41, iteration=1874300, sample_iteration=1874317, null_char=268, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:vie:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=41, iteration=2528700, sample_iteration=2528724, null_char=268, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:yid:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=41, iteration=2008300, sample_iteration=2009307, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:yor:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=4201700, sample_iteration=4201736, null_char=100, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:afr:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=286700, sample_iteration=286724, null_char=95, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:amh:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=40, iteration=6112200, sample_iteration=6112270, null_char=284, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Arabic:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2417900, sample_iteration=2421715, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ara:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=2111400, sample_iteration=2120404, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Armenian:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=1999200, sample_iteration=1999769, null_char=191, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:asm:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx512O1c1], flags=40, iteration=2758500, sample_iteration=2758570, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:aze_cyrl:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=7665300, sample_iteration=7666563, null_char=88, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:aze:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=6760000, sample_iteration=6760858, null_char=88, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:bel:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2014700, sample_iteration=2014972, null_char=95, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Bengali:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=1118100, sample_iteration=1118153, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ben:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx512O1c1], flags=40, iteration=1534600, sample_iteration=1534685, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:bod:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3513900, sample_iteration=3524222, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:bos:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=3258400, sample_iteration=3258771, null_char=95, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:bre:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx512O1c1], flags=40, iteration=14000, sample_iteration=14000, null_char=124, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:bul:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=887500, sample_iteration=887807, null_char=92, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Canadian_Aboriginal:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=1949300, sample_iteration=1949421, null_char=652, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:cat:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=40, iteration=2427200, sample_iteration=2427351, null_char=88, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ceb:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=6718000, sample_iteration=6772655, null_char=73, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ces:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3211200, sample_iteration=3211404, null_char=121, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Cherokee:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=7542100, sample_iteration=7542435, null_char=191, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:chi_sim:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=1971300, sample_iteration=1976813, null_char=223, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:chi_sim_vert:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=2335900, sample_iteration=2350219, null_char=223, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:chi_tra:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=1988000, sample_iteration=1992889, null_char=226, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:chi_tra_vert:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=3521300, sample_iteration=3571821, null_char=226, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:chr:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=40, iteration=7847200, sample_iteration=7847486, null_char=102, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:cos:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=5120400, sample_iteration=5120693, null_char=104, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:cym:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=4212000, sample_iteration=4212285, null_char=108, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Cyrillic:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3121900, sample_iteration=3122619, null_char=202, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:dan:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2547300, sample_iteration=2547538, null_char=100, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:deu:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3543300, sample_iteration=3543653, null_char=114, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Devanagari:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx512O1c1], flags=40, iteration=2475100, sample_iteration=2475272, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:div:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=3249400, sample_iteration=3256730, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:dzo:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=4258800, sample_iteration=4335799, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ell:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3335500, sample_iteration=3335569, null_char=213, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:eng:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=814100, sample_iteration=814136, null_char=110, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:enm:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=2720200, sample_iteration=2721910, null_char=104, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:epo:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=40, iteration=6842600, sample_iteration=6843069, null_char=101, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:est:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=1637300, sample_iteration=1637435, null_char=103, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Ethiopic:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx128O1c1], flags=40, iteration=149100, sample_iteration=149101, null_char=385, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:eus:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=15200, sample_iteration=15204, null_char=89, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fao:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2812800, sample_iteration=2812950, null_char=126, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fas:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=896400, sample_iteration=897843, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fil:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3854100, sample_iteration=3854348, null_char=81, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fin:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3741900, sample_iteration=3742321, null_char=94, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Fraktur:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3739900, sample_iteration=3740440, null_char=166, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fra:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=40, iteration=3486400, sample_iteration=3486632, null_char=139, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:frk:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3692700, sample_iteration=3692967, null_char=98, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:frm:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=40, iteration=176600, sample_iteration=176697, null_char=140, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:fry:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3246500, sample_iteration=3246617, null_char=106, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Georgian:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2308600, sample_iteration=2310386, null_char=225, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:gla:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3365500, sample_iteration=3365659, null_char=104, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:gle:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=2457900, sample_iteration=2458041, null_char=91, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:glg:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=2711300, sample_iteration=2711543, null_char=97, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:grc:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=3843500, sample_iteration=3843646, null_char=211, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Greek:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=1727400, sample_iteration=1727456, null_char=236, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Gujarati:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx128O1c1], flags=40, iteration=4569600, sample_iteration=4569934, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:guj:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=1447700, sample_iteration=1448272, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Gurmukhi:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=3177000, sample_iteration=3177347, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Hangul:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2510300, sample_iteration=2510401, null_char=210, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Hangul_vert:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=881800, sample_iteration=882047, null_char=213, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:HanS:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=2009700, sample_iteration=2013398, null_char=238, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:HanS_vert:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2966600, sample_iteration=2977655, null_char=238, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:HanT:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2342300, sample_iteration=2346092, null_char=240, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:HanT_vert:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2664100, sample_iteration=2686122, null_char=240, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:hat:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=2653800, sample_iteration=2653890, null_char=96, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Hebrew:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=1902800, sample_iteration=1903513, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:heb:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=40, iteration=1568000, sample_iteration=1570525, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:hin:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=803600, sample_iteration=803651, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:hrv:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=4712800, sample_iteration=4713183, null_char=105, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:hun:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2046400, sample_iteration=2046674, null_char=97, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:hye:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=40, iteration=1590800, sample_iteration=1591465, null_char=104, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:iku:synth20170629
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1], flags=40, iteration=3862400, sample_iteration=3862672, null_char=595, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ind:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=876700, sample_iteration=876737, null_char=84, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:isl:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2355200, sample_iteration=2355391, null_char=110, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ita_old:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=40, iteration=2975300, sample_iteration=2980533, null_char=117, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:ita:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx384O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx384O1c1], flags=40, iteration=1645500, sample_iteration=1645594, null_char=118, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Japanese:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=1688700, sample_iteration=1690739, null_char=429, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Japanese_vert:synth20170629:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=2510500, sample_iteration=2514591, null_char=429, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:jav:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx64Lrx64Lfx384O1c1], flags=40, iteration=449600, sample_iteration=449630, null_char=92, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:jpn:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=2162500, sample_iteration=2166492, null_char=414, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:jpn_vert:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=1874000, sample_iteration=1878622, null_char=414, learning_rate=0.001, momentum=0.5, adam_beta=0.999
Version string:4.00.00alpha:Kannada:synth20170629:[1,48,0,1Ct3,3,16Mp

Hi.... My name is june
I want to really communicate with you
But, I have no way .. I send email, upload article in google gruops
NO reply ... ㅜㅜ
Please Could you give me the answer ..
I really hope ...
@theraysmith @Shreeshrii Fast data and best data is not related langdata ?
And I have a question Currenlty, Uploaded langdata is trained for 3.XX??
There is yet no langdata for tesseract 4.00?

The network configuration is stored in the lstm data in the traineddata.
With a small change to combine_tessdata, I produced the attached.

Ray,
Some of the languages have two entries, eg. for Hindi
Version string:4.00.00alpha:hin:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2736900, sample_iteration=2737076, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999

and
Version string:4.00.00alpha:hin:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=803600, sample_iteration=803651, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999

I see flags=41 and flags=40. What is the difference between the two?
I don't see any ref to these flags in https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00#lstmtraining-command-line

@Shreeshrii Hi shreeshrii please would you give me the answer? ...my questiosn in mail 2 days ago.

@godofcheerup, don't nag people to answer your questions,

@amitdo Okay Sorry..

You seem to have combined fast and best. The two hin entries come from the
two files.
The flags are TrainingFlags from lstmrecognizer.h. 0x40 is compress
unicharset and 1 is integer mode.
On Tue, Mar 20, 2018 at 10:11 PM Shreeshrii<EMAIL_ADDRESS>wrote:

The network configuration is stored in the lstm data in the traineddata.
With a small change to combine_tessdata, I produced the attached.
Ray,
Some of the languages have two entries, eg. for Hindi
Version string:4.00.00alpha:hin:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx384O1c1], flags=41, iteration=2736900, sample_iteration=2737076, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
This one is from fast: flags 41 = compress + integer mode, and the last
LSTM is of size 384.

and
Version string:4.00.00alpha:hin:synth20170629:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1], flags=40, iteration=803600, sample_iteration=803651, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999
This one is from best with flags 40 = compress + not integer mode, and the
last LSTM is of size 512

I see flags=41 and flags=40. What is the difference between the two?
I don't see any ref to these flags in
https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00#lstmtraining-command-line
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/tesseract-ocr/tesseract/issues/1404#issuecomment-374835410,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AL056V1DLITCeDWHDkO9TWFBnfDmnzi5ks5tgeD5gaJpZM4SxopC
.

--
Ray.

On Wed, Mar 21, 2018 at 11:06 PM, theraysmith<EMAIL_ADDRESS>wrote:

You seem to have combined fast and best. The two hin entries come from the
two files.

I was using the info from the attachment from your post as it came in
email, I thought it might be for fast and best but wasn't sure as the
second set was not complete - probably msg got truncated.

The flags are TrainingFlags from lstmrecognizer.h. 0x40 is compress
unicharset and 1 is integer mode.

This one is from fast: flags 41 = compress + integer mode, and the last
LSTM is of size 384.

This one is from best with flags 40 = compress + not integer mode, and
the
last LSTM is of size 512

Will you be uploading the new version of combine_tessdata which provides
this information?

@theraysmith, your best list was ended in 'Kannada'.

Then, the float->int conversion is done, which further reduces the size of the model and makes it even faster if your CPU supports AVX2.

I wonder if the int net will be faster than the float net with just avx/sse.

I wonder whether Tesseract could generate the integer net on the fly from a best net right after starting an OCR process.

@Shreeshrii have you figured out how to generate Best or Fast manually?

@christophered What do you mean by 'manually'?
They can be generated using tesstrain.sh, lstmtraining etc as described in the wiki page.
Same checkpoint can be used for creating both 'best' and 'fast' formats i.e. float and integer models.
  lstmtraining \
  --stop_training \
  --continue_from $train_output_dir/plusdeva_checkpoint \
  --traineddata $train_output_dir/$Lang/$Lang.traineddata \
  --model_output $best_trained_data_file
##
    lstmtraining \
  --stop_training \
  --convert_to_int \
  --continue_from $train_output_dir/plusdeva_checkpoint \
  --traineddata $train_output_dir/$Lang/$Lang.traineddata \
  --model_output $fast_trained_data_file

An existing 'best' float model can be converted to 'fast' integer using combine_tessdata.

@Shreeshrii I meant what are the original specifications and settings to generate a training model similar to Best or Fast from scratch, not fine-tuning.

To duplicate Ray's training we will need the same langdata, font list etc. That info is not available.
The network spec is listed in the version string for best. For fast, I have added the info from Ray on a page in wiki.

@theraysmith Can you share the base data that you used for training Arabic? ( configs, prohibited characters, word-list, etc..). Also how many lines & fonts did you use? Because I am thinking of seriously improving the Tesseract Arabic model for all. Waiting for your reply Ray

Network spec for 'tessdata_fast' are available on wiki page - https://github.com/tesseract-ocr/tesseract/wiki/Data-Files-in-tessdata_fast#version-string--40000alpha--network-specification
sample
Version string:4.00.00alpha:ara:synth20170629
LSTM training info:Network str:[1,48,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx192O1c1], flags=41, iteration=5521100, sample_iteration=5544718, null_char=2, learning_rate=0.001, momentum=0.5, adam_beta=0.999

fontlists and langdata for LSTM training is available in the new repository https://github.com/tesseract-ocr/langdata_lstm
eg. Arabic files are in https://github.com/tesseract-ocr/langdata_lstm/tree/master/ara

Sorry the last few comments are a little conflicting. Is it true that simply converting best to int generates fast, or do they have different net specs?
From the wiki page, eng_best uses
[1,36,0,1Ct3,3,16Mp3,3Lfys64Lfx96Lrx96Lfx512O1c1]
whereas eng_fast uses:
[1,36,0,1Ct3,3,16Mp3,3Lfys48Lfx96Lrx96Lfx192O1c1]
So they are each trained separately with different configuration, and it's not possible to convert best into fast?
At this point, are there script allowing us, using all the available repos such as langdata_lstm, to exactly generate tessdata_fast/tessdata_best (assuming we have the fonts, as those maybe can't be distributed)? I know all the data for tesstrain.sh flags are in langdata_lstm, but it would be nice if we could also add a file maybe such as train_<lang>_<version>.sh which looks like
tesstrain.sh
 --flag1=value1
 --flag2=value2

With the exact command used to generate that tessdata

Is it true that simply converting best to int generates fast,

Not 'fast' as the one from the fast repo, but still much faster than best.

or do they have different net specs?

best(double)->int won't change the spec.

From the wiki page....
So they are each trained separately with different configuration, and it's not possible to convert best into fast?

You got that right.

converting best to int generates fast

converting tessdata_best to int generates the int version of best model, which is faster than best model and has similar accuracy.
The models in tessdata_fast are int models and may have been trained with a different network spec.

That makes sense. So if I

take all the files from langadata_lstm/eng
use the net_spec from version strings above
feed it all to tesstrain.sh
run it for ~800k iterations (best) or 6m iterations (fast) as the version string says

then I should in theory get back the same model? Am I missing any other "unknown" secret sauce I'm missing? btw, is it really trained with almost 5000 fonts as listed in okfonts.txt? Wow.

Your last question is off-topic.
Try your luck in the forum.


move file manager to dynamic lists

WIP
How valuable are the imgui thingies?

I find them quite useful particularly when there are multiple files with similar or difficult to pronounce names. Showing which file is which index is very useful in that context.

Note for my future self: dynamic lists are currently a beta only feature, so pausing this effort.


Microsoft Update setting in Install Wizard?

The installation wizard asks whether or not to use Windows Update.
What does this mean? Does it ask whether the agent should be kept up to date by using Windows Update? Or does this setting change how my server uses Windos Update?

Document Details
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ID: 00a00a3e-a0df-7ee5-4733-7231bd7a024f
Version Independent ID: 94361683-e1e7-7e55-6993-86d3c2486a14
Content: Connect Windows computers to Azure Monitor - Azure Monitor
Content Source: articles/azure-monitor/platform/agent-windows.md
Service: azure-monitor
Sub-service: logs
GitHub Login: @bwren
Microsoft Alias: bwren

Thanks for your comment. We are actively investigating and will get back to you shortly. Thanks for your patience.

Good question, @Borkason. AFAIK, this Windows Update pop-up is specific to Microsoft Monitoring Agent setup. The simply means the agent will be updated via Windows Update. This shouldn't change any pre-existing update preferences on your server(s). Doesn't hurt to double-check your Windows Update settings once the agent installation has been successfully completed. :)

We will now close this issue. If there are further questions regarding this matter, please reply and we will gladly continue the discussion.