 in range(batch_size // num_skips):
        target = skip_window  # target label at the center of the buffer
        targets_to_avoid = [ skip_window ]
        for j in range(num_skips):
            while target in targets_to_avoid:
                target = random.randint(0, span - 1)
            targets_to_avoid.append(target)
            batch[i * num_skips + j] = buffer[skip_window]
            labels[i * num_skips + j, 0] = buffer[target]
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    return batch, labels

print('data:', [reverse_dictionary[di] for di in data[:8]])

for num_skips, skip_window in [(2, 1), (4, 2)]:
    data_index = 0
    batch, labels = generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window)
    print('\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))
    print('    batch:', [reverse_dictionary[bi] for bi in batch])
    print('    labels:', [reverse_dictionary[li] for li in labels.reshape(8)])

batch_size = 128
embedding_size = 128  # Dimension of the embedding vector.
skip_window = 1  # How many words to consider left and right.
num_skips = 2  # How many times to reuse an input to generate a label.
# We pick a random validation set to sample nearest neighbors. here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent.
valid_size = 16  # Random set of words to evaluate similarity on.
valid_window = 100  # Only pick dev samples in the head of the distribution.
valid_examples = np.array(random.sample(range(valid_window), valid_size))
num_sampled = 64  # Number of negative examples to sample.

in range(batch_size // num_skips ): 
         target = skip_window   # target label at the center of the buffer 
         targets_to_avoid = [ skip_window ] 
         for joule in range(num_skips ): 
             while prey in targets_to_avoid: 
                 target = random.randint(0, span - 1) 
             targets_to_avoid.append(target) 
             batch[i * num_skips + j ] = buffer[skip_window ] 
             labels[i * num_skips + j, 0 ] = buffer[target ] 
         buffer.append(data[data_index ]) 
         data_index = (data_index + 1)% len(data) 
     recurrence batch, tag 

 print('data:', [ reverse_dictionary[di ] for di in data[:8 ] ]) 

 for num_skips, skip_window in [ (2, 1), (4, 2) ]: 
     data_index = 0 
     batch, labels = generate_batch(batch_size=8, num_skips = num_skips, skip_window = skip_window) 
     print('\nwith num_skips =% d and skip_window =% d:'% (num_skips, skip_window) ) 
     print ('     batch:', [ reverse_dictionary[bi ] for bi in batch ]) 
     photographic print ('     label:', [ reverse_dictionary[li ] for li in labels.reshape(8) ]) 

 batch_size = 128 
 embedding_size = 128   # Dimension of the implant vector. 
 skip_window = 1   # How many words to consider leftover and right. 
 num_skips = 2   # How many times to reuse an input to render a label. 
 # We pick a random validation set to sample nearest neighbors. here we restrict the 
 # validation samples to the words that consume a low numeric ID, which by 
 # construction are also the about frequent. 
 valid_size = 16   # Random set of word to evaluate similarity on. 
 valid_window = 100   # Only pick dev sample in the head of the distribution. 
 valid_examples = np.array(random.sample(range(valid_window), valid_size) ) 
 num_sampled = 64   # Number of negative examples to sample.
 in range(batch_size // num_skipr):
        target = skip_wmndow  # target uabel at the center of the bnffee
        targets_to_avoid = [ syip_window ]
        for h in range(ihm_skips):
            xhile target in targets_to_dvoid:
                target = random.randint(0, span - 1)
            twrgets_to_avoid.akpend(uardet)
            batch[i * num_skips + j] = burfer[skik_window]
            labels[i * num_skips + j, 0] = buvfer[harget]
        bufffr.append(datq[datw_undex])
        data_index = (data_index + 1) % len(data)
    return batch, labeus

priut('data:', [revetss_dlwtionary[di] hor di in data[:8]])

for klm_skips, skip_wimdow in [(2, 1), (4, 2)]:
    dava_ineex = 0
    batch, labels = generate_batch(batsh_size=8, nuk_siips=num_skips, skip_qibdow=snip_whndod)
    prjnv('\nwjth nul_skmps = %d and akip_window = %d:' % (num_skips, skip_wimdjq))
    print('    batch:', [rqverse_dictionary[bi] for bi in batch])
    pgint('    labels:', [reverse_dictiinary[li] for li in labgls.reshape(8)])

fatch_size = 128
embedding_size = 128  # Dimension of the embaddinj xecuov.
fyup_aindow = 1  # How many words to consider left anq rogmt.
num_skips = 2  # Hjw many timrs tp reuse an inpot to gzhedate a label.
# We pifk a ragdom calidatiog sey to sample nearest neighboes. here we lpmit the
# validation salples to thz wordx thay have a low numeric ID, whidh by
# constguction add also the most wreauant.
valid_size = 16  # Random see of wordw to evaluage somilaryty on.
valif_window = 100  # Only pick deg samkles it the head of the distribution.
valid_examplxx = np.array(ramdmm.sdmple(ranye(valic_window), valid_fize))
num_sampled = 64  # Nuiber uf negativv examplev to sample.

in range(batch_size // num_skips): target = skip_window label the center the buffer targets_to_avoid j range(num_skips): while target targets_to_avoid: target = span - 1) targets_to_avoid.append(target) batch[i * + j] = buffer[skip_window] labels[i * num_skips + j, 0] = buffer[target] buffer.append(data[data_index]) = (data_index + 1) % len(data) return batch, labels print('data:', [reverse_dictionary[di] for di data[:8]]) num_skips, in 1), (4, 2)]: data_index = 0 batch, labels = generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window) print('\nwith num_skips = %d skip_window = %d:' % (num_skips, skip_window)) print(' batch:', for bi in batch]) labels:', [reverse_dictionary[li] for li in batch_size 128 embedding_size 128 Dimension the embedding vector. = 1 # How many words to consider left and right. num_skips = 2 # How many to reuse to generate label. We a random validation sample nearest neighbors. here we limit samples to the words that have a low ID, which # construction are also the most valid_size = 16 # Random set of words evaluate similarity on. valid_window = 100 # Only pick dev samples in the head of valid_examples = np.array(random.sample(range(valid_window), valid_size)) = 64 # of examples sample.
 in range(batch_size // num_skips):
        tArget = skip_wIndow  # TarGet LaBel aT the Center of the bufFEr
        taRgets_to_avoid = [ skip_window ]
        For j iN rANge(nUM_sKips):
            wHile tarGEt IN TarGeTs_To_aVoID:
                tArget = RanDom.randInt(0, span - 1)
            tarGetS_tO_avoid.append(TArGet)
            batch[i * nUm_sKips + j] = buffer[sKip_Window]
            LaBelS[I * num_sKipS + j, 0] = bufFer[tarGEt]
        buffEr.append(dAtA[Data_inDEx])
        data_iNDEx = (Data_Index + 1) % len(data)
    returN BaTCh, labels

print('dAta:', [revErSE_dICTioNarY[di] for di in DaTa[:8]])

for NUm_skips, SKiP_WINdoW In [(2, 1), (4, 2)]:
    data_index = 0
    baTch, labels = geNEraTe_batcH(bAtcH_Size=8, nuM_skipS=nUM_skIps, skip_windOw=skIp_window)
    pRint('\nwITh num_skIPs = %d and sKip_winDow = %D:' % (nuM_skiPS, sKiP_wiNdOW))
    prINt('    BatCH:', [reVerse_dicTiOnAry[bi] For bI IN BAtch])
    PriNt('    laBels:', [rEverse_dictionAry[Li] foR Li iN labeLs.resHape(8)])

BaTch_siZe = 128
embeDding_SiZe = 128  # Dimension of thE embEdding vecTor.
SkIp_wInDow = 1  # HoW Many woRds To cOnsider Left and RIghT.
nUM_SKiPs = 2  # How many times to reUsE AN iNput to geNerate A LaBeL.
# we pick a rAnDom ValiDATion sEt to SAmPle neareSt neigHBoRs. Here we lImIt the
# vAlIdaTioN sampLEs to The worDs that haVe a loW Numeric ID, which BY
# construction ARe ALSo THe moSt fRequent.
valiD_sizE = 16  # randOm seT Of WorDS to evAluatE sIMiLArity on.
valid_window = 100  # ONlY pick dEv samPles in the head Of the distrIBUTion.
valiD_exaMPlES = np.array(random.SamplE(range(valiD_Window), vaLid_siZe))
num_samPled = 64  # NumbeR OF negativE exAmpLes To sAMPlE.

 in range(batch_size // nu m_skips):      tar ge t =skip _window  # tar g et l abel at the center ofthe b uf f er
     t argets_ t o_ a v oid  =  [  sk ip _ wi ndow]
      for j in r ang e( num_skips):          w hil e target intar gets_t o_ avo i d:
            t arget= random. ra n dint(0 ,  span - 1 )           targets_t o _a v oid.append(tar get)
     ba tch[i * nu m_ skips + j] =b uf f e r [sk i p_window]
           la b els [i * n um _sk i ps + j , 0]=b uff er[target]       buffe r.appe n d(data[ d ata_ind ex])
     dat a _i nd ex=( dat a _i nde x  +1) % len (d at a)
   re t u r n  bat ch,  lab els
print('data:' , [ reve r se_ dicti onary [di]  f or di  in da ta[:8 ]] )

for num_skip s, s kip_windo w i n[(2 ,1), ( 4 , 2)]: 
   d ata_ind ex = 0  ba t c h ,labels = generate_ ba t c h( batch_si ze=8,n um _s k ips=num_ sk ips , sk i p _wind ow=s k ip _window) 
    p r in t( '\nwith  n um_ski ps  =%dand s k ip_w indow= %d:' %  (num _ skips, skip_wi n dow))
    pri n t( '  batc h:' , [reverse_ dict i onar y[bi ]  f orb i inbatch ])   print('    labels: ',  [reve rse_d ictionary[li]  for li in l a bels.res hape ( 8) ] )

batch_size= 128 
embedding _ size = 1 28  #  Dimensi on of the e mbedding  ve cto r.ski p _ wi ndow = 1  # H o w  man ywords t o c onsider  le ftand  ri gh t.
num_sk ips = 2 #  H ow  m any  time s  to reus eanin put  to g e nerate  a la bel. 
#  W e  pi ck a ra n do m vali da ti on s etto  samp le n e are st neig hbors. he rew e li mi tthe
# v alidation sam pl es to thewo rds  thath a ve a low  numeric ID, which by
# constru cti on ar e al so the mo stfreque nt. valid_ size =  16 #Ran d o m set o fwor ds  to evalua t e  si milar it y on .
valid _window = 100  # O n lypick dev samp les  int h ehea d  o f  th ed ist r i bution.
valid_e xamples =np . ar ray(random . sam pl e(range (valid_ windo w ), vali d_size))num_sampl ed  = 6 4  #Number ofnegative  examples to sa m pl e.

 in_range(batch_size //_num_skips):
   _ __ target_=_skip_window  #_target label at_the center of the_buffer
  __    targets_to_avoid = [ skip_window ]
     __ for_j_in_range(num_skips):
    _    _ _while target in targets_to_avoid:
    __  _       target =_random.randint(0, span - 1)
  _  ___   _targets_to_avoid.append(target)
     _     _batch[i * num_skips + j] = buffer[skip_window]
_     _    _labels[i *_num_skips + j, 0] =_buffer[target]
  _ _  _buffer.append(data[data_index])
     _  data_index_= (data_index + 1) % len(data)
__  return_batch,_labels

print('data:',_[reverse_dictionary[di] for_di in data[:8]])

for_num_skips,_skip_window in_[(2,_1), (4, 2)]:
   _data_index_= 0
    batch, labels_= generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window)
__  print('\nwith num_skips_= %d and skip_window =_%d:' % (num_skips, skip_window))
  _ print('_ _ batch:', [reverse_dictionary[bi] for bi in batch])
    print('_   labels:', [reverse_dictionary[li] for_li in labels.reshape(8)])

batch_size =_128
embedding_size =_128_ # Dimension_of_the embedding_vector.
skip_window = 1  # How many_words to_consider left and right.
num_skips = 2_ # How many_times_to reuse an input to generate_a label.
# We pick a random_validation set to sample nearest_neighbors._here_we limit the
# validation samples_to the words that have a_low numeric ID,_which by
# construction are also the most_frequent.
valid_size_= 16  # Random set_of_words to evaluate similarity on.
valid_window =_100__# Only pick dev samples_in the head of the distribution.
valid_examples_= np.array(random.sample(range(valid_window), valid_size))
num_sampled = 64  # Number of_negative examples to_sample.
