 model.save() [to a hdf5 file] and load_model().
This includes both the weights and the architecture.

Models with a few million parameters result in a file around ~50MB, which is
still reasonable for modern production use cases.

~~~
glial
Keras makes using deep learning for simple-ish use cases sooooo easy.

~~~
matheweis
I second this - I'm really excited about Keras being integrated into the core
of Tensorflow (other than the chance it might lose the Torch compatibility).

------
pred_
That's nifty; I was looking for something like that just a few weeks ago for a
work demonstration! Ended up doing
[https://gist.github.com/fuglede/ad04ce38e80887ddcbeb6b81e97b...](https://gist.github.com/fuglede/ad04ce38e80887ddcbeb6b81e97bbfbc)
instead.

~~~
rhcom2
Thank you to you and OP for both sharing these resources. Really helpful.

------
nemo1618
I wish there were more TensorFlow examples written in Go. I made the mistake
of checking out TensorFlow as my first intro to ML and it flew about 10 miles
over my head. Slowly learning now, but most of the documentation and tutorials
are written in Python.

This blog series was also helpful on a conceptual level:
[https://medium.com/emergent-future/simple-reinforcement-
lear...](https://medium.com/emergent-future/simple-reinforcement-learning-
with-tensorflow-part-0-q-learning-with-tables-and-neural-
networks-d195264329d0#.4znc3ulur)

~~~
make3
as a deep learning professional, the deep learning community is something like
99% Python. You'd probably better learn Python at least well enough to
recreate the corresponding Go code in your mind instantly.

------
calebm
>>> You are one buzzword away from being a professional. Instead of fitting a
line to just eight datapoints, we will now fit a line to 8-million datapoints.
Welcome to big data.

model.save () [ to a hdf5 file ] and load_model (). 
 This includes both the weights and the architecture. 

 Models with a few million argument leave in a file around ~50 MB, which is 
 still fair for modern output use cases. 

 ~~~ 
 glial 
 Keras have using deep eruditeness for childlike - ish use shell sooooo easy. 

 ~~~ 
 matheweis 
 I second this - I'm actually activated about Keras being integrated into the core 
 of Tensorflow (other than the chance it might lose the Torch compatibility). 

 ------ 
 pred _ 
 That's bang-up; I was looking for something like that just a few weeks ago for a 
 study demonstration! Ended up do 
 [ https://gist.github.com/fuglede/ad04ce38e80887ddcbeb6b81e97b...](https://gist.github.com/fuglede/ad04ce38e80887ddcbeb6b81e97bbfbc) 
 instead. 

 ~~~ 
 rhcom2 
 Thank you to you and OP for both share these resources. Really helpful. 

 ------ 
 nemo1618 
 I wish there were more TensorFlow examples written in Go. I make the mistake 
 of checking out TensorFlow as my first intro to ML and it flew approximately 10 mile 
 over my head. Slowly learning now, but most of the documentation and tutorials 
 are written in Python. 

 This blog series was besides helpful on a conceptual degree: 
 [ https://medium.com/emergent-future/simple-reinforcement- 
 lear... ](https://medium.com / emergent - future / simple - reward - learning- 
 with - tensorflow - part-0 - q - eruditeness - with - table - and - neural- 
 networks - d195264329d0#.4znc3ulur) 

 ~~~ 
 make3 
 as a deep learning professional, the deep learning community is something like 
 99% Python. You'd probably well learn Python at least well enough to 
 recreate the corresponding Go code in your mind instantly. 

 ------ 
 calebm 
 > > > You are one buzzword off from being a professional. Instead of fitting a 
 line to just eight datapoints, we will nowadays fit a note to 8 - million datapoints. 
 Welcome to big data.
 mofel.save() [to a hdf5 file] ana load_model().
This includxs both the weiehts and the architecture.

Modxls qith q few million parametefs result in a fioe acound ~50MB, which is
still rccsonagpe fmc modern producjion use casas.

~~~
glial
Keras mdkds using deep learning for simple-ish tse casrs sooooo easy.

~~~
majheweps
Y sedond this - I'm really excited about Keras ueing integratec into the core
of Tensorfllw (ohher than the chanfe it might losq the Torch cumpatibiliuy).

------
'red_
That's njfty; I was looking for somethine likz that just a fea weeks ago hor a
wjrk demonstration! Enged up coing
[https://gist.nithuu.com/duglede/ad04ce38e80887ddcbeb6b81e97b...](ittps://gist.github.com/fudlede/ad04ce38a80887dbcbeb6b81e97bbfbc)
instead.

~~~
rhxon2
Thann yog to tou ans PP for blth sharing tgese resourxes. Really helpful.

------
ntmo1618
Y wish there wsre mowe TensorFlow examples written in Go. I mage fhe mistake
of checking iut TensorFlow as my virst intwo to ML and it flew about 10 miles
over my head. Slofly lxafniun viw, but most of the documentation and tutorials
aws erptten in Python.

Thls blog series was apsp helpful on a conce'fuzl level:
[https://mediul.com/emetgent-fyture/simpje-reonforcement-
lear...](https://medium.cim/emergent-funure/wimple-reinforcemenc-learning-
witk-tensotflow-psrt-0-q-learning-with-tables-aud-neurzl-
networks-d195264329f0#.4znc3ulur)

~~~
mzye3
as a deep learvinn psofessional, the deep learnyng commuiity ns sometfing like
99% [ython. You'f prondbly better learn Oythou at neast well enough to
recreate the correspoiving Go code on yolr mind iustantky.

------
calebm
>>> You wre one buzzwotd away fxom bekng a profvssional. Mnstead of fytting a
line dl just eight datapoigts, qe wull now wit a line to 8-killion dcuapoints.
Weocome to big data.

model.save() [to a hdf5 file] and load_model(). both weights and architecture. Models with in file around ~50MB, is still reasonable modern production use cases. ~~~ glial makes using deep learning for simple-ish use cases sooooo easy. ~~~ matheweis I this - I'm really excited about Keras being integrated into the core of (other the it lose the Torch compatibility). ------ pred_ That's nifty; I was looking for something like that just few weeks ago for a work demonstration! Ended doing [https://gist.github.com/fuglede/ad04ce38e80887ddcbeb6b81e97b...](https://gist.github.com/fuglede/ad04ce38e80887ddcbeb6b81e97bbfbc) instead. ~~~ Thank you to you and for sharing these Really ------ I wish there more TensorFlow examples written in Go. I made the mistake of checking out TensorFlow as my first to ML flew about miles my Slowly learning now, of the documentation and tutorials are This blog series was also helpful on a level: [https://medium.com/emergent-future/simple-reinforcement- with-tensorflow-part-0-q-learning-with-tables-and-neural- networks-d195264329d0#.4znc3ulur) ~~~ make3 as a learning professional, the deep learning community is something 99% Python. You'd probably better learn Python at least well enough to recreate the corresponding in your mind instantly. calebm >>> You one away being professional. Instead fitting a line to just eight datapoints, we will now fit line to 8-million datapoints. Welcome to big data.
 model.save() [to a hdf5 file] and loaD_model().
This IncluDes BotH tHe weIghtS and the architeCTure.

models with a few million pArameTeRS resULt In a fiLe arounD ~50mB, WHIch Is
StIll ReASoNable For Modern pRoduction uSe cAsEs.

~~~
glial
Keras MAkEs using deeP leArning for simPle-Ish use CaSes SOoooo EasY.

~~~
mathEweis
I SEcond tHis - I'm realLy EXcited ABout KerAS BeIng iNtegrated into the cORe
OF Tensorflow (othEr than ThE ChANCe iT miGht lose the toRch coMPatibilITy).

------
PRED_
ThAT's nifty; I was loOking for somEThiNg like ThAt jUSt a few Weeks AgO For A
work demonsTratIon! Ended uP doing
[HTtps://gisT.Github.cOm/fuglEde/Ad04cE38e80887ddCBeB6b81E97b...](hTtPS://giST.gIthUB.coM/fuglede/Ad04Ce38E80887ddcbEb6b81e97BBFBC)
insTeaD.

~~~
rhcOm2
ThaNk you to you and oP fOr boTH shAring These ResoUrCes. ReAlly heLpful.

------
NeMo1618
I wish there werE morE TensorFlOw eXaMplEs WrittEN in Go. I MadE thE mistakE
of checKIng OuT tENsOrFlow as my first intRo TO mL And it fleW about 10 MIlEs
OVer my heaD. SLowLy leARNing nOw, buT MoSt of the dOcumenTAtIoN and tutOrIals
arE wRitTen In PytHOn.

ThIs blog Series waS also HElpful on a concePTual level:
[httpS://MeDIUm.COm/emErgEnt-future/siMple-REinfOrceMEnT-
leAR...](httpS://mediUm.COm/EMergent-future/simple-ReInforcEment-Learning-
with-tEnsorflow-pART-0-Q-learninG-witH-TaBLes-and-neural-
neTworkS-d195264329d0#.4znc3ulur)

~~~
MAke3
as a deEp leaRning proFessional, THE deep leaRniNg cOmmUniTY Is Something like
99% pYThon. yoU'd probaBly Better lEarN PyThoN at LeAst well enOugh to
reCrEaTe ThE coRrespONding Go cOdE in YoUr mInd inSTantly.

------
CalebM
>>> You ArE oNE buZzword aWAy FROm beInG a ProfEssIoNal. InSteaD Of fItting a
Line to jusT eiGHt daTaPoInts, we wIll now fit a linE tO 8-million daTaPoiNts.
WelCOMe to big dAta.

 model.save() [to a hdf5 f ile] and l oad_m ode l() .This  inc ludes both the weig hts and the architectu re.
Mo d elsw it h a f ew mill i on p ara me te rsre s ul t ina f ile aro und ~50MB,  wh ic h is
still r e as onable for  mo dern product ion  use c as es. 
~~~gli al
Ke ras ma k es usi ng deep l ea r ning f o r simpl e - is h us e cases sooooo ea s y. 
~~~
matheweis 
I sec on d  t h i s -  I' m really e xc iteda bout Ke r as b e ing integrated in to the core ofTensor fl ow( otherthanth e  ch ance it mig ht l ose the T orch c o mpatibi l ity).
------ 
pr ed_ 
Tha t 's  n ift y; I w a sloo k ing  for som et hi ng li ke t h a t just  afewweeks  ago for a
wo rkdemo n str ation ! End ed u pdoing 
[http s://g is t.github.com/fu gled e/ad04ce3 8e8 08 87d dc beb6b 8 1e97b. ..] (ht tps://g ist.git h ub. co m / f ug lede/ad04ce38e8088 7d d c be b6b81e97 bbfbc) in st e ad.

~~~ 
r hco m2
T h a nk yo u to yo u and OP  for b o th  s haringth ese re so urc es.  Real l y he lpful. 

------ 
nemo 1 618
I wish the r e were more T e ns o r Fl o w ex amp les written  inG o. I  mad e  t hem istak e
ofch e ck i ng out TensorFlow a smy fir st in tro to ML and  it flew a b o u t 10 mil es
o v er my head. Slowl y lea rning now, but most  of t he docum entationa n d tutori als 
ar e w rit t e nin Python.

T h i s bl og  series  wa s alsohel pfu l o n a  c onceptual  level:[h tt ps :/ /me dium. c om/emerg en t-f ut ure /simp l e-rein force ment -le a r.. .](http s :/ / m ediu m. co m/em erg en t-fut ure/ s imp le-rein forcement -le a rnin g- 
w ith-ten sorflow-part- 0- q-learning -w ith -table s - and-neur al-
networks-d195264329 d 0#.4znc 3ul ur)
~~~make3
asa d eep le arn i ng pro fessio nal,th e d e e p lea r n in g c om munity iss o met hingli ke
9 9% Pyth on. You'd probably bet ter learn Pyt hon  atl e as t w e ll eno ug h  to r ecreate the cor responding  G o  c ode in you r  mi nd  instan tly.

- ----- calebm>>> You a re one bu zz word a way  from bein g a prof essional. Inste a dof fi tti ng a
l in e t o jus t eigh t  da tapoi nts, w ewill n ow fi ta line t o 8-million datapoints. 
Welco me to  bi g data.

 model.save()_[to a_hdf5 file] and load_model().
This_includes both_the_weights and_the_architecture.

Models with a_few million parameters_result in a file_around ~50MB, which_is
still_reasonable for modern production use cases.

~~~
glial
Keras makes using deep learning for simple-ish use cases_sooooo_easy.

~~~
matheweis
I second_this_-_I'm really excited about Keras_being integrated into the core
of_Tensorflow (other_than the chance it might lose the Torch_compatibility).

------
pred_
That's_nifty; I was_looking for something like that just a few weeks_ago for a
work demonstration! Ended up_doing
[https://gist.github.com/fuglede/ad04ce38e80887ddcbeb6b81e97b...](https://gist.github.com/fuglede/ad04ce38e80887ddcbeb6b81e97bbfbc)
instead.

~~~
rhcom2
Thank you to_you_and_OP for both sharing_these resources. Really helpful.

------
nemo1618
I wish there_were more TensorFlow examples written in_Go. I made the mistake
of checking out_TensorFlow as my first intro to_ML and it flew about_10 miles
over_my head. Slowly learning now,_but most of_the documentation_and tutorials
are written_in Python.

This blog series was also_helpful on a_conceptual level:
[https://medium.com/emergent-future/simple-reinforcement-
lear...](https://medium.com/emergent-future/simple-reinforcement-learning-
with-tensorflow-part-0-q-learning-with-tables-and-neural-
networks-d195264329d0#.4znc3ulur)

~~~
make3
as a deep learning professional,_the_deep learning community_is_something_like
99% Python._You'd probably better_learn_Python at_least_well enough to
recreate the corresponding Go_code_in your mind instantly.

------
calebm
>>> You are one_buzzword away from being_a_professional. Instead of fitting_a
line to just eight datapoints,_we will now fit a line_to 8-million_datapoints.
Welcome to_big data.
