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tesseract-ocr / tesseract

Tesseract Open Source OCR Engine (main repository)
https://tesseract-ocr.github.io/
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fast vs. best #1404

Closed amitdo closed 3 years ago

amitdo commented 6 years ago

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.

stweil commented 6 years ago

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. See also the discussion on Google Groups.

Shreeshrii commented 6 years ago

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.

stweil commented 6 years ago

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? Maybe @jbreiden can find that out.

amitdo commented 6 years ago

My guess is that fast and best were trained independently.

First, fast is trained with a spec that produces a smaller net than best. As a result of smaller model, the prediction will be faster. 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.

jbreiden commented 6 years ago

@amitdo is exactly right. You cannot derive fast from best. I'm sorry that I said the opposite earlier; I was confused and wrong.

theraysmith commented 6 years ago

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 notifications@github.com 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.

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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

Shreeshrii commented 6 years ago

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

godofcheerup commented 6 years ago

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

amitdo commented 6 years ago

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

godofcheerup commented 6 years ago

@amitdo Okay Sorry..

theraysmith commented 6 years ago

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 notifications@github.com 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.

amitdo commented 6 years ago

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

amitdo commented 6 years ago

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.

stweil commented 6 years ago

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

ghost commented 6 years ago

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

Shreeshrii commented 6 years ago

@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.

ghost commented 6 years ago

@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.

Shreeshrii commented 6 years ago

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.

ghost commented 6 years ago

@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? How much time did it take to train? etc.. Because I am thinking of seriously improving the Tesseract Arabic model for all. Waiting for your reply Ray

Shreeshrii commented 5 years ago

Network spec for 'tessdata_fast' for all languages and scripts 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

EhsanKia commented 4 years ago

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 langdatalstm, but it would be nice if we could also add a file maybe such as `train_.sh` which looks like

tesstrain.sh
 --flag1=value1
 --flag2=value2
 --net_spec=....

With the exact command used to generate that tessdata

amitdo commented 4 years ago

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.

Shreeshrii commented 4 years ago

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.

EhsanKia commented 4 years ago

That makes sense. So if I

  1. take all the files from langadata_lstm/eng
  2. use the net_spec from version strings above
  3. feed it all to tesstrain.sh
  4. 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? Is there any other secret sauce I'm missing?

Btw, is it really trained with almost 5000 fonts as listed in okfonts.txt? Wow!

amitdo commented 4 years ago

@EhsanKia,

Your last question is off-topic.

Try your luck in the forum.