 cultural memory. So Ptolemy's epicyclics converted into a piece of pure geometry is still alive today, and so is Euclidean geometry, their revision as physical theories notwithstanding (Resnik calls such conversion "Euclidean rescue").
What about unicorns? They are also a that-which, that which is like a live horse with a horn. Does this make them akin to numbers in their artifactual role? Indeed it does. The distinction, if any, is to be found in our use of them as tools, on which their being supervenes. Unicorns are loose, poetic tools, their being is accordingly ephemeral, numbers, on the other hand, supervene on facts of a highly regimented practice, now supported by symbolic formalisms of decimals, Peano arithmetic, etc. Hence, facts about them have a much firmer hold on reality, although the difference is of degree and not of kind. And of course numbers do also have physical base of supervenience from which they were abstracted, while unicorns, so far as we know, do not.
Meinong goes even futher than Peirce in diluting being, he allows even inconsistent objects (e.g. round squares) to "subsist", if not to "exist", see SEP's Nonexistent Objects.

 Q:

How can I return the same batch twice from a tensorflow dataset iterator?

I am converting some legacy code to use the Dataset API - this code uses feed_dict to feed one batch to the train operation (actually three times) and then recalculates the losses for display using the same batch. So I need to have an iterator that returns the exact same batch two (or several) times. Unfortunately, I can't seem to find a way of doing it with tensorflow datasets - is it possible?

A:

You can repeat individual elements of a Dataset using Dataset.flat_map(), Dataset.from_tensors() and Dataset.repeat() together. For example, to repeat elements twice:
NUM_REPEATS = 2
dataset = tf.data.Dataset.range(10)  #...or the output of `.batch()`, etc.

# Repeat each element of `dataset` NUM_REPEATS times.
dataset = dataset.flat_map(
    lambda x: tf.data.Dataset.from_
cultural memory. So Ptolemy's epicyclics converted into a piece of pure geometry is however alert today, and so is Euclidean geometry, their revision as forcible theories notwithstanding (Resnik calls such conversion " Euclidean rescue "). 
 What about unicorn? They are besides a that - which, that which is like a live horse with a horn. Does this make them akin to numbers in their artifactual function? Indeed it does. The distinction, if any, is to be establish in our use of them as tools, on which their being supervenes. unicorn are loose, poetic tools, their being is consequently ephemeral, numbers, on the other hand, supervene on facts of a highly regimented exercise, now hold by symbolic formalisms of decimals, Peano arithmetic, etc. therefore, facts about them have a much firmer hold on reality, although the remainder is of degree and not of kind. And of course numbers do also have physical base of supervenience from which they were abstracted, while unicorns, so far as we acknowledge, do not. 
 Meinong goes even futher than Peirce in load being, he allow even inconsistent objects (e.g. orotund squares) to " subsist ", if not to " exist ", determine SEP's Nonexistent Objects. 

  Q: 

 How can I return the same batch doubly from a tensorflow dataset iterator? 

 I am converting some legacy code to use the Dataset API - this code uses feed_dict to feed one batch to the train operation (actually three times) and then recalculates the losses for display using the same batch. therefore I need to get an iterator that return the exact same batch two (or several) time. Unfortunately, I can't seem to find a way of do it with tensorflow datasets - is it possible? 

 A: 

 You can repeat individual elements of a Dataset use Dataset.flat_map (), Dataset.from_tensors () and Dataset.repeat () together. For exemplar, to repeat elements doubly: 
 NUM_REPEATS = 2 
 dataset = tf.data. Dataset.range(10)   #... or the output of '.batch () `, etc. 

 # Repeat each element of 'dataset 'NUM_REPEATS times. 
 dataset = dataset.flat_map (
     lambda x: tf.data.Dataset.from _
 cuptural memory. So Ptolemy'r epicyclics coucerted into z piece uf pure geometry is still almve roday, and so is Euclidean gdometry, tjeir revusioi as physical thxkries notwithabandiug (Resnik calls xuch convession "Euclideat fedcue").
What about unicorns? They are alfo a thst-ahich, that whish ix liks a live horse with a horn. Does thjs make them akin to numbers in their artifactkal gole? Indeed it doed. The distibctijb, if any, is go be found in our use of them as tools, on which theif beiug supervengs. Ujhcorns are ooose, poetic tools, their teing ix accordingly cphemxral, numbers, on the other hand, supervene on facts of a highly regimentee practhce, tow ryppurttd uy aymbollc hormalisms kf decimals, Peano arithmetic, euc. Rvmce, facts abkut thqm have a much firmer hold on reality, altvoufh the difference is of degree and not of kijd. And of course numbers do also have physical base of suparvenmevce fvom dyifh they were abstracted, while unicorns, so far zs wv know, do not.
Meinjng goes evrn fijher than Peirze in bjlhting being, he alllws eveg incinsistent objrcts (e.g. round squares) to "suvsist", if not ro "exist", see SEP's Uonexistent Ubjevts.

 Q:

Now can I return the saoe bztch twice vrom a tehrorflow dataset ktegatos?

I am converting some legasy code ti usz the Dagasej API - ehis code kses naed_dict to feed onf bateh to the train operation (actually three times) and then recakcglanes the ljsses for display tsing the same batch. Fo I veed to hane an itecator that rqturns the exdft same batci two (or feveeal) rimes. Uvwortunately, I van't seem to find a way of doing it wlth tgnaorflow datasetw - us it possible?

A:

Uou cag gepxat iggividual elekentr ow a Dagaset using Aataxet.flat_map(), Dataset.frmm_tehsors() and Dataset.rrpcat() togetyer. For qxample, to releat elements twict:
NUM_RE'EATS = 2
dataxet = tf.data.Dataset.range(10)  #...or the ohtput of `.hatgh()`, etc.

# Repeat eacm elgment of `dacaset` NUM_REPEATS times.
dataset = dataset.flet_map(
    lambda x: tf.daja.Dataset.from_
cultural memory. So Ptolemy's epicyclics converted into of geometry is alive today, and revision physical theories notwithstanding calls such conversion rescue"). What about unicorns? They are a that-which, that which is like a live horse with a horn. Does make them akin to numbers in their artifactual role? Indeed it does. The if is be in our use of them as tools, on which their being supervenes. Unicorns are loose, poetic their being is accordingly ephemeral, numbers, on the hand, supervene on facts a highly regimented practice, now by formalisms of Peano etc. facts about them a much firmer hold on reality, although the difference is of degree and not of kind. And course numbers have physical of from they were abstracted, so far as we know, do even futher than Peirce in diluting being, he even inconsistent (e.g. round squares) to "subsist", if to "exist", see SEP's Nonexistent Objects. Q: How I return the same batch twice from a tensorflow dataset iterator? I am converting some to use the Dataset - this code feed_dict feed batch the train (actually three times) and then recalculates the losses for display using same batch. So I need to have an iterator that exact batch two (or times. Unfortunately, I can't to a way of doing tensorflow - A: can individual elements of a using Dataset.flat_map(), Dataset.from_tensors() and Dataset.repeat() For example, to repeat dataset = tf.data.Dataset.range(10) #...or the output of `.batch()`, # Repeat each element of `dataset` NUM_REPEATS dataset = dataset.flat_map( lambda x: tf.data.Dataset.from_
 cultural memory. So Ptolemy's ePicyclics cOnverTed IntO a PiecE of pUre geometry is sTIll aLive today, and so is EuclidEan geOmETry, tHEiR reviSion as pHYsICAl tHeOrIes NoTWiThstaNdiNg (ResniK calls such ConVeRsion "EuclideAN rEscue").
What aBouT unicorns? TheY arE also a ThAt-wHIch, thAt wHich iS like a LIve horSe with a hoRn. dOes thiS Make theM AKiN to nUmbers in their artiFAcTUal role? Indeed iT does. THe DIsTINctIon, If any, is to bE fOund iN Our use oF ThEM AS toOLs, on which theiR being superVEneS. UnicoRnS arE Loose, pOetic ToOLs, tHeir being is AccoRdingly epHemeraL, Numbers, ON the othEr hand, SupErvEne oN FaCtS of A hIGhlY ReGimENteD practicE, nOw SuppoRted BY SYMbolIc fOrmaLisms Of decimals, PeaNo aRithMEtiC, etc. HEnce, fActs AbOut thEm have A much FiRmer hold on realiTy, alThough the DifFeRenCe Is of dEGree anD noT of Kind. And Of coursE NumBeRS DO aLso have physical basE oF SUpErvenienCe from WHiCh THey were aBsTraCted, WHIle unIcorNS, sO far as we Know, do NOt.
meInong goEs Even fuThEr tHan peircE In diLuting Being, he aLlows EVen inconsistenT Objects (e.g. rounD SqUAReS) To "suBsiSt", if not to "exIst", sEE SEP'S NonEXiSteNT ObjeCts.

 Q:

HOw CAn i Return the same batch tWiCe from A tensOrflow dataset Iterator?

I aM CONverting Some LEgACy code to use the datasEt API - this cODe uses feEd_dicT to feed oNe batch to THE train opEraTioN (acTuaLLY tHree times) and tHEN recAlCulates The Losses fOr dIspLay UsiNg The same baTch. So I neEd To HaVe An iTeratOR that retUrNs tHe ExaCt samE Batch tWo (or sEverAl) TiMEs. UNfortunATeLY, i can'T sEeM to fInd A wAy of dOing IT wiTh tensoRflow dataSetS - Is it PoSsIble?

A:

YoU can repeat indIvIdual elemeNtS of A DatasET Using DatAset.flat_map(), Dataset.from_tENsors() anD DaTaset.RepeAt() togetheR. FoR exampLe, tO Repeat ElemenTs twiCe:
nUM_repEATS = 2
DATaSet = Tf.Data.DataseT.RAngE(10)  #...or thE oUtpuT of `.batcH()`, etc.

# Repeat each elemENt oF `dataset` NUM_REpEAtS tiMES.
dAtaSEt = DAtaSeT.FlaT_MAp(
    lambda x: tf.data.dataset.froM_
 cultural memory. So Ptole my's epicy clics  co nve rt ed i ntoa piece of pur e  geo metry is still alive t oday,  a n d so is  Eucl idean g e om e t ry,  t he irre v is ion a s p hysical  theoriesnot wi thstanding ( R es nik callssuc h conversion  "E uclide an  re s cue") .
W hat a bout u n icorns ? They ar ea lso at hat-whi c h ,that  which is like al iv e  horse with ahorn.Do e st h ismak e them aki nto nu m bers in th e i r  ar t ifactual role ? Indeed it doe s. The  d ist i nction , ifan y , i s to be fou nd i n our use  of th e m as to o ls, onwhichthe irbein g  s up erv en e s.U ni cor n s a re loose ,po etictool s , t heir  be ingis ac cordingly eph eme ral, num bers,  on t he o th er ha nd, su perve ne  on facts of ahigh ly regime nte dpra ct ice,n ow sup por ted  by sym bolic f o rma li s m s  o f decimals, Peanoar i t hm etic, et c. Hen c e,  f a cts abou tthe m ha v e  a mu ch f i rm er holdon rea l it y,  althou gh  the d if fer enc e iso f de gree a nd not o f kin d . And of cours e  numbers do a l so h av e  phy sic al base ofsupe r veni ence fr omw hichtheywe r ea bstracted, while un ic orns,so fa r as we know,  do not.
M e i n ong goes  eve n  f u ther than Peir ce in  dilutingb eing, he  allo ws eveninconsist e n t object s ( e.g . r oun d sq uares) to "su b s ist" ,if notto"exist" , s eeSEP 'sNo nexistent  Objects .
Q: 
How  canI  returnth e s am e b atcht wice f rom a  ten so rf l owdataset it e r ator ?
I  amcon ve rting  som e  le gacy co de to use  th e  Dat as et  API -this code use sfeed_dictto  fe ed one b atch tothe train operation (ac t ually t hre e tim es)and thenrec alcula tes the lo sses f or di sp lay u singt h esam ebatch. SoI nee d toha ve a n itera tor that returns t h e e xact same bat chtwo( o rsev e ra l ) t im e s.U n fortunately, Ican't seem  t o  f ind a wayo f d oi ng it w ith ten sorfl o w datas ets - isit possib le ?

A : 
Yo u can repe at indiv idual ele m entso fa Dat ase t usin gDat aset. flat_m a p() , Dat aset.f ro m_tens ors()  a nd Datas et.repeat() together. F or exa mple,  to  repeat e lem e nts  twice:
N UM_R EPEATS = 2 
da tas et =tf. d ata.D atas e t. ran g e(10)   #. . .or the o u tp uto f  ` .batch()`,e t c .
# Rep eat each e leme nt of `dataset` N U M_REPEATS time s.
d a t ase t = data se t.flat_map(
   l am b d a x: tf. da ta.Dataset. from_
 cultural_memory. So_Ptolemy's epicyclics converted into_a piece_of_pure geometry_is_still alive today,_and so is_Euclidean geometry, their revision_as physical theories_notwithstanding_(Resnik calls such conversion "Euclidean rescue").
What about unicorns? They are also a that-which, that_which_is like_a_live_horse with a horn. Does_this make them akin to_numbers in_their artifactual role? Indeed it does. The distinction,_if_any, is to_be found in our use of them as tools,_on which their being supervenes. Unicorns_are loose, poetic_tools,_their_being is accordingly ephemeral,_numbers, on the other hand, supervene_on facts of a highly regimented_practice, now supported by symbolic formalisms of_decimals, Peano arithmetic, etc. Hence, facts_about them have a much_firmer hold_on reality, although the difference_is of degree_and not_of kind. And_of course numbers do also have_physical base of_supervenience from which they were abstracted,_while_unicorns, so far_as_we_know, do_not.
Meinong goes even_futher_than Peirce_in_diluting being, he allows even inconsistent_objects_(e.g. round squares) to "subsist", if not_to "exist", see SEP's_Nonexistent_Objects.

 Q:

How can I_return the same batch twice_from a tensorflow dataset iterator?

I am_converting some_legacy code_to use the Dataset API - this code uses feed_dict to_feed one batch to the train_operation (actually three times)_and then_recalculates_the losses for_display_using the_same batch. So I need to have_an iterator_that returns the exact same batch_two (or several) times._Unfortunately,_I can't seem to find a_way of doing it with tensorflow_datasets - is it possible?

A:

You_can_repeat_individual elements of a Dataset_using Dataset.flat_map(), Dataset.from_tensors() and Dataset.repeat() together._For example, to_repeat elements twice:
NUM_REPEATS = 2
dataset = tf.data.Dataset.range(10)__#...or the output of `.batch()`, etc.

#_Repeat_each element of `dataset` NUM_REPEATS times.
dataset_=_dataset.flat_map(
_   lambda x:_tf.data.Dataset.from_