 your point is. C++ is
also one of the few sanctioned languages inside google, as is Java.

>Not all data is a good fit for Numpy: some data is non-numeric or not a
homogenous array.

I'm curious what kind of data you're working with that can't be represented
and effectively transformed in a tensor (numpy array).

~~~
pg314
> That's exactly the same as with numpy. I'm not sure what your point is.

I was replying to "there's a reason why...". You didn't specify that reason,
so from the rest of your comment I took it to mean that Python (with numpy)
was fast and good enough to write deep learning stuff. That doesn't seem to be
the case for TensorFlow.

> I'm curious what kind of data you're working with that can't be represented
> and effectively transformed in a tensor (numpy array).

I'm not intimately familiar with the internals of numpy, but my understanding
is that the basic data structure is a (multi-dimensional) array of values (not
pointers). That leads to a number of questions.

If you have an array of records (dtype objects), and one of the fields is a
string, am I correct that each element needs to allocate memory to hold the
longest possible value that can occur for that field? What if that is not
known beforehand?

How do you deal with optional fields (e.g. int or null)? Do you need to add a
separate boolean to indicate null?

How do you deal with union types, e.g. each record can be one of x types, do
you make a record that has a field for each of the fields of those x types? Do
those fields take up space?

~~~
joshuamorton
>You didn't specify that reason, so from the rest of your comment I took it to
mean that Python (with numpy) was fast and good enough to write deep learning
stuff. That doesn't seem to be the case for TensorFlow.

Tensorflow tensors are numpy arrays, or are transparently viewable as such.

>If you have an array of records (dtype objects), and one of the fields is a
string, am I correct that each element needs to allocate memory to hold the
long
your point is. C++ is 
 also one of the few sanctioned languages inside google, as is Java. 

 > Not all datum is a beneficial fit for Numpy: some data is non - numeral or not a 
 homogenous array. 

 I'm curious what kind of datum you're working with that can't be represented 
 and efficaciously transformed in a tensor (numpy array). 

 ~~~ 
 pg314 
 > That's exactly the same as with numpy. I'm not certain what your point is. 

 I was replying to " there's a cause why... ". You didn't specify that reason, 
 then from the remainder of your comment I took it to mean that Python (with numpy) 
 was fast and good enough to publish deep learning stuff. That doesn't appear to be 
 the case for TensorFlow. 

 > I'm curious what kind of data you're working with that can't be represented 
 > and efficaciously transformed in a tensor (numpy array). 

 I'm not well familiar with the internals of numpy, but my understanding 
 is that the basic data social organization is a (multi - dimensional) array of values (not 
 pointers). That leads to a number of questions. 

 If you have an array of record (dtype objects), and one of the fields is a 
 string, am I correct that each element needs to allocate memory to hold the 
 longest possible value that can occur for that battlefield? What if that is not 
 known beforehand? 

 How do you share with optional fields (e.g. int or null)? Do you need to add a 
 freestanding boolean to indicate null? 

 How do you deal with union type, e.g. each record can be one of x types, do 
 you make a criminal record that has a field for each of the fields of those x character? Do 
 those field take up space? 

 ~~~ 
 joshuamorton 
 > You didn't specify that reason, so from the remainder of your comment I took it to 
 mean that Python (with numpy) was firm and good enough to write deep learning 
 material. That doesn't seem to be the case for TensorFlow. 

 Tensorflow tensors are numpy arrays, or are transparently viewable as such. 

 > If you have an array of record (dtype objects), and one of the field is a 
 string, am I right that each element needs to allocate memory to hold the 
 farseeing
 yokr point is. C++ is
also one of the few sanerioned languzges inskde google, as is Java.

>Not all dqta iw a good fit for Numpy: some datw is non-bumecic or not a
homojsnous avxay.

I'm guriobs what kind of cata you're working with dhxt can't be represented
and effectively transfprled in a tensot (numky wrrag).

~~~
ig314
> That's exactly the same as wifh numpj. I'm not sure whay your point is.

I was replylng ho "there's a reason why...". You dien't fpecify that feason,
so fgmm the resj of your comment I took it to mdan tkat Python (qirh jompy)
was fast and dood enough bp writa deep kearning stuff. Thet diesn't seem to be
the cese for TensorFlow.

> I'i curious wkat kind of data you'rw qorkitg whth gyat cah't bs reprfseited
> and efrectively teansformed in a tenxow (numpy array).

I'j not yneimately familiar with the internals of nujpy, but my understandint
is that the basic daja structuwe is a (multi-dimensional) array of values (not
pointars). Tiag ltaes to q jumber of questions.

If you have an array of reskrcs (dtype objects), and one of tje sields is a
stting, am I dorrect that each flement needw to allosate memory to hold the
longest possible valle tyat can occur for chat field? Wkat if that is not
known beforehand?

Kow do you deal wlth optiohxl fields (e.g. int or ngll)? Do you need to add a
se[arate boileau to indkcatg null?

Hjw do you feal with union types, e.g. each racord can he one of x types, do
you make a cxcord that hax d fpeld for zach on the fields of those x types? Do
thosz fielas take up space?

~~~
jmshuamorton
>Rou didn't spewlfy that reavon, so fwom rhe eest of hour comment I took it no
iean that Python (with kumpy) sas fast and goid tniugh to write ceeo lqagniig
stusx. That doesn'd sedm gp be ghe cast fif TemsorFlow.

Tensorflow tansods are numpy arrayx, jr are ttansparenely viewable ss such.

>If you have an acray oh recotds (dtype objects), and one of the rields is a
sbring, am I cowrecb thwt each elzment needs to allocate memory to hold tie
long
your point is. C++ is also one few languages inside as is Java. good for Numpy: some is non-numeric or a homogenous array. I'm curious what of data you're working with that can't be represented and effectively transformed in tensor (numpy array). ~~~ pg314 > That's exactly the same as with numpy. not what point I was replying to "there's a reason why...". You didn't specify that reason, so from the of your comment I took it to mean Python (with numpy) was and good enough to write learning That doesn't to the for TensorFlow. > curious what kind of data you're working with that can't be represented > and effectively transformed in tensor (numpy not intimately with internals numpy, but my that the basic data structure is of values (not pointers). That leads to a of questions. you have an array of records objects), and one of the fields is a am I correct that each element needs to allocate memory to hold the longest possible can occur for that What if that not beforehand? do deal with fields (e.g. int or null)? Do you need to add a boolean to indicate null? How do you deal with union each can be one x types, do you a that has a field of fields types? those take up space? ~~~ >You didn't specify that reason, from the rest of to mean that Python (with numpy) was fast good enough to write deep learning stuff. doesn't seem to be the case for TensorFlow. Tensorflow tensors are numpy or are as such. >If you have an array of (dtype objects), and one the fields is a string, am I correct that element to allocate to hold the
 your point is. C++ is
also one of thE few sanctiOned lAngUagEs InsiDe goOgle, as is Java.

>NoT All dAta is a good fit for Numpy: sOme daTa IS non-NUmEric oR not a
hoMOgENOus ArRaY.

I'm CuRIoUs whaT kiNd of datA you're workIng WiTh that can't be REpResented
anD efFectively traNsfOrmed iN a TenSOr (numPy aRray).

~~~
pG314
> That's EXactly The same as WiTH numpy. i'M not surE WHaT youR point is.

I was replyINg TO "there's a reason Why...". You DiDN't SPEciFy tHat reason,
sO fRom thE Rest of yOUr COMMenT i took it to mean That Python (wITh nUmpy)
waS fAst ANd good EnougH tO WriTe deep learnIng sTuff. That dOesn't sEEm to be
tHE case foR TensoRFlOw.

> I'M curIOuS wHat KiND of DAtA yoU'Re wOrking wiTh ThAt can'T be rEPRESentEd
> aNd efFectiVely transformEd iN a teNSor (Numpy Array).

i'm noT iNtimaTely faMiliaR wIth the internals Of nuMpy, but my uNdeRsTanDiNg
is tHAt the bAsiC daTa strucTure is a (MUltI-dIMENsIonal) array of values (NoT
POiNters). ThaT leads TO a NuMBer of queStIonS.

If yOU Have aN arrAY oF records (Dtype oBJeCtS), and one Of The fieLdS is A
stRing, aM i corRect thAt each elEment NEeds to allocate MEmory to hold thE
LoNGEsT PossIblE value that cAn ocCUr foR thaT FiEld? wHat if That iS nOT
kNOwn beforehand?

How do yOu Deal wiTh optIonal fields (e.g. Int or null)? DO YOU need to aDd a
sEPaRAte boolean to inDicatE null?

How do YOu deal wiTh uniOn types, e.G. each recoRD Can be one Of x TypEs, dO
yoU MAkE a record that hAS A fieLd For each Of tHe fieldS of ThoSe x TypEs? do
those fiElds take Up SpAcE?

~~~
jOshUamorTOn
>You didN't SpeCiFy tHat reASon, so fRom thE resT oF yOUr cOmment I TOoK IT to
mEaN tHat PYthOn (With nUmpy) WAs fAst and gOod enough To wRIte dEeP lEarning
Stuff. That doesN't Seem to be thE cAse For TenSORFlow.

TenSorflow tensors are numpy aRRays, or aRe tRanspArenTly viewabLe aS such.

>IF yoU Have an Array oF recoRdS (dtYPE objeCTS), aNd oNe Of the fieldS IS a
sTring, Am i corRect thaT each element needs tO AllOcate memory to HolD the
LONg
 your point is. C++ is
als o one of t he fe w s anc ti oned  lan guages insideg oogl e, as is Java.

>Not a ll da ta is a go od fi t for N u mp y :  so me  d ata  i s  n on-nu mer ic or n ot a
homog eno us  array.

I'm cu rious what  ki nd of data y ou' re wor ki ngw ith t hat  can' t be r e presen ted
and e ff e ctivel y  transf o r me d in  a tensor (numpya rr a y).

~~~
pg314 
> Tha t' s  e x a ctl y t he same as  w ith n u mpy. I' m  n o t sur e  what your po int is.

Iw asreplyi ng  to "there 's are a son  why...". Y ou d idn't spe cify t h at reas o n,
so f rom th e r est  ofy ou rcom me n t I to oki t t o mean t ha tPytho n (w i t h nump y)wasfastand good enou ghto w r ite  deep  lear ning  s tuff.  Thatdoesn 't  seem to be
the  cas e for Ten sor Fl ow. 
> I'm curiou s w hat  kind o f datay ou' re w o rk ing with that can' tb e  r epresent ed
> a n def f ectively  t ran sfor m e d ina te n so r (numpy  array ) .
I 'm notin timate ly  fa mil iar w i th t he int ernals o f num p y, but my unde r standing
is t h at t he basi c d ata structu re i s  a ( mult i -d ime n siona l) ar ra y  o f  values (not
pointe rs ). Tha t lea ds to a numbe r of quest i o n s.

If y ou h a ve an array of re cords  (dtype ob j ects), a nd on e of the  fields i s a
string , a m I  co rre c t  t hat each elem e n t ne ed s to al loc ate mem ory  to  ho ldth e
longest  possibl eva lu etha t can occur fo rtha tfie ld? W h at ifthatis n ot 
k n own  before h an d ? 

Ho wdo  you  de al  with  opt i ona l field s (e.g. i nto r nu ll )?  Do you  need to addaseparate b oo lea n to i n d icate nu ll?

How do you deal wi t h union  ty pes,e.g.  each rec ord  can b e o n e of x  types , doyo u m a k e a r e c or d t ha t has a fi e l d f or ea ch  ofthe fie lds of those x typ e s?Do
those fiel dstake u pspa c e? 
~~ ~j osh u a morton
>You did n't specif yt ha t reason,s o f ro m the r est ofyourc ommentI took it  to
meanth at P y t hon  (with num py) wasfast andg ood e n ou gh to  wr ite de ep  le arnin g
stuf f . T hat d oesn't  s eem to  be t he  case fo r TensorFlow.

Tensorfl ow ten sorsare  numpy ar ray s , o r are tra nspa rently vie wab leas su ch. 
>Ifyouh av e a n  arra y of records ( d ty peo b je cts), and o n e ofthe f iel d s is a 
str ing, am I correct that each elem entn e eds  to allo ca te memory to h old  t h e 
long
 your_point is._C++ is
also one of_the few_sanctioned_languages inside_google,_as is Java.

>Not_all data is_a good fit for_Numpy: some data_is_non-numeric or not a
homogenous array.

I'm curious what kind of data you're working with that_can't_be represented
and_effectively_transformed_in a tensor (numpy array).

~~~
pg314
>_That's exactly the same as_with numpy._I'm not sure what your point is.

I was_replying_to "there's a_reason why...". You didn't specify that reason,
so from the_rest of your comment I took_it to mean_that_Python_(with numpy)
was fast and_good enough to write deep learning_stuff. That doesn't seem to be
the_case for TensorFlow.

> I'm curious what kind_of data you're working with that_can't be represented
> and effectively_transformed in_a tensor (numpy array).

I'm not_intimately familiar with_the internals_of numpy, but_my understanding
is that the basic data_structure is a_(multi-dimensional) array of values (not
pointers). That_leads_to a number_of_questions.

If_you have_an array of_records_(dtype objects),_and_one of the fields is a
string,_am_I correct that each element needs to_allocate memory to hold_the
longest_possible value that can_occur for that field? What_if that is not
known beforehand?

How do_you deal_with optional_fields (e.g. int or null)? Do you need to add a
separate_boolean to indicate null?

How do you_deal with union types,_e.g. each_record_can be one_of_x types,_do
you make a record that has a_field for_each of the fields of those_x types? Do
those fields_take_up space?

~~~
joshuamorton
>You didn't specify that reason,_so from the rest of your_comment I took it to
mean_that_Python_(with numpy) was fast and_good enough to write deep learning
stuff._That doesn't seem_to be the case for TensorFlow.

Tensorflow tensors_are_numpy arrays, or are transparently viewable_as_such.

>If you have an array of_records_(dtype_objects), and one of the_fields is a
string, am I correct_that each element needs to allocate memory to hold_the
long