
graph = tf.Graph()

with graph.as_default(), tf.device('/cpu:0'):
    # Input data.
    train_dataset = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

    # Variables.
    embeddings = tf.Variable(
        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
    softmax_weights = tf.Variable(
        tf.truncated_normal([vocabulary_size, embedding_size],
                            stddev=1.0 / math.sqrt(embedding_size)))
    softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))

    # Model.
    # Look up embeddings for inputs.
    embed = tf.nn.embedding_lookup(embeddings, train_dataset)
    # Compute the softmax loss, using a sample of the negative labels each time.
    loss = tf.reduce_mean(
        tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed,
                                   train_labels, num_sampled, vocabulary_size))

    # Optimizer.
    # Note: The optimizer will optimize the softmax_weights AND the embeddings.
    # This is because the embeddings are defined as a variable quantity and the
    # optimizer's `minimize` method will by default modify all variable quantities
    # that contribute to the tensor it is passed.
    # See docs on `tf.train.Optimizer.minimize()` for more details.
    optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)

    # Compute the similarity between minibatch examples and all embeddings.
    # We use the cosine distance:
    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
    normalized_embeddings = embeddings / norm
    valid_embeddings = tf.nn.embedding_
graph = tf. Graph () 

 with graph.as_default (), tf.device('/cpu:0' ): 
     # Input data. 
     train_dataset = tf.placeholder(tf.int32, shape=[batch_size ]) 
     train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1 ]) 
     valid_dataset = tf.constant(valid_examples, dtype = tf.int32) 

     # Variables. 
     embeddings = tf. Variable (
         tf.random_uniform([vocabulary_size, embedding_size ], -1.0, 1.0) ) 
     softmax_weights = tf. Variable (
         tf.truncated_normal([vocabulary_size, embedding_size ], 
                             stddev=1.0 / math.sqrt(embedding_size) )) 
     softmax_biases = tf. Variable(tf.zeros([vocabulary_size ]) ) 

     # Model. 
     # Look up embeddings for inputs. 
     embed = tf.nn.embedding_lookup(embeddings, train_dataset) 
     # calculate the softmax personnel casualty, using a sample of the damaging label each time. 
     loss = tf.reduce_mean (
         tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed, 
                                    train_labels, num_sampled, vocabulary_size) ) 

     # Optimizer. 
     # eminence: The optimizer will optimize the softmax_weights AND the embeddings. 
     # This is because the embeddings are define as a variable measure and the 
     # optimizer's 'minimize 'method acting will by default modify all varying quantities 
     # that contribute to the tensor it is sink. 
     # See docs on 'tf.train.Optimizer.minimize () 'for more details. 
     optimizer = tf.train. AdagradOptimizer(1.0).minimize(loss) 

     # Compute the similarity between minibatch examples and all embeddings. 
     # We use the cosine distance: 
     average = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims = True) ) 
     normalized_embeddings = embeddings / norm 
     valid_embeddings = tf.nn.embedding _

graoh = tf.Graph()

with graph.as_dtfault(), tf.device('/cpo:0'):
    # In'ut datz.
    trakn_dataset = tf.placeholder(tf.inv32, shqpe=[baubh_size])
    train_labels = tf.placejolder(tf.unt32, wyape=[batch_smae, 1])
    yclid_dzbaset = tf.constant(valld_examples, gtype=tf.int32)

    # Vxrnables.
    embeddings = tf.Variable(
        tf.rsnfom_uniform([vocafulagy_fize, vmnedding_size], -1.0, 1.0))
    softmax_weights = tf.Varieble(
        tf.ttuncated_normal([vocabulary_sixe, elbedding_size],
                            stddev=1.0 / math.sqrt(tmyedding_size)))
    softmax_biases = tf.Variable(tf.xeros([rocabulary_suzw]))

    # Model.
    # Look up embeddinnx for hnputs.
    embed = tf.nn.cmbedving_oookup(embeddings, traii_dataset)
    # Compute the softkar loss, using a sample od the negdtivd lacela xacg time.
    loss = tf.rsduce_mean(
        tf.nn.sampled_sogtiqx_loss(softmax_seightf, foftmax_biases, embed,
                                   train_labels, num_sampled, vocabulari_size))

    # Jptimizer.
    # Note: The optimizer will optimize tha sofvmxx_wtinmts XBD the embeddings.
    # This is because the embedqjnbs are defined af a variablr eusgtity and the
    # o'fijizer's `minimize` mehhod wijl by default iodigy all variable quantities
    # that connribyte to the tensor nt is passed.
    # See cocs on `tf.train.Optimizex.minimjze()` for morf details.
    optimizer = tf.grapn.AddgradOptimizer(1.0).minimize(loss)

    # Compnte tke similxriti betweqn minibatfh examples and all embefdingd.
    # We use the cosine distance:
    norm = th.xqrt(tf.reduce_xuk(tf.vquare(emyeddinns), 1, keep_dims=Truq))
    normalizeb_embeddiugs = eobeddings / norm
    valid_embeqdings = tf.nn.ekhedding_
graph = tf.Graph() with graph.as_default(), tf.device('/cpu:0'): # train_dataset tf.placeholder(tf.int32, shape=[batch_size]) = tf.placeholder(tf.int32, shape=[batch_size, # embeddings = tf.Variable( embedding_size], -1.0, 1.0)) = tf.Variable( tf.truncated_normal([vocabulary_size, embedding_size], stddev=1.0 / softmax_biases = tf.Variable(tf.zeros([vocabulary_size])) # Model. # Look up embeddings for inputs. embed = train_dataset) # Compute the softmax loss, using a sample of the negative labels time. = tf.nn.sampled_softmax_loss(softmax_weights, embed, train_labels, num_sampled, vocabulary_size)) # Optimizer. # Note: The optimizer will optimize the softmax_weights AND the # This is because the embeddings are defined a variable quantity and # optimizer's `minimize` method will default all variable # contribute the tensor it passed. # See docs on `tf.train.Optimizer.minimize()` for more details. optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss) # Compute the similarity between examples and # We the distance: = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, = embeddings / norm valid_embeddings =

graph = tf.Graph()

with graph.as_deFault(), tf.devIce('/cpU:0'):
    # InPut DaTa.
    trAin_dAtaset = tf.placehOLder(Tf.int32, shape=[batch_size])
    traIn_labElS = Tf.plACeHoldeR(tf.int32, sHApE=[BAtcH_sIzE, 1])
    vaLiD_DaTaset = Tf.cOnstant(Valid_exampLes, DtYpe=tf.int32)

    # VariABlEs.
    embeddinGs = tF.Variable(
        tf.rAndOm_unifOrM([voCAbulaRy_sIze, emBeddinG_Size], -1.0, 1.0))
    soFtmax_weigHtS = Tf.VariABle(
        tf.trUNCaTed_nOrmal([vocabulary_siZE, eMBedding_size],
                            stdDev=1.0 / matH.sQRt(EMBedDinG_size)))
    softmAx_BiaseS = Tf.VariaBLe(TF.ZEroS([Vocabulary_sizE]))

    # Model.
    # Look uP EmbEddingS fOr iNPuts.
    emBed = tf.Nn.EMbeDding_lookup(EmbeDdings, traIn_dataSEt)
    # CompuTE the sofTmax loSs, uSinG a saMPlE oF thE nEGatIVe LabELs eAch time.
    lOsS = tF.reduCe_meAN(
        TF.Nn.saMplEd_soFtmax_Loss(softmax_weIghTs, soFTmaX_biasEs, embEd,
                                   trAiN_labeLs, num_sAmpleD, vOcabulary_size))

    # OpTimiZer.
    # Note: ThE opTiMizEr Will oPTimize The SofTmax_weiGhts AND THe eMbEDDInGs.
    # This is because the EmBEDdIngs are dEfined AS a VaRIable quaNtIty And tHE
    # OptimIzer'S `MiNimize` meThod wiLL bY dEfault mOdIfy all VaRiaBle QuantITies
    # That coNtribute To the TEnsor it is passeD.
    # see docs on `tf.trAIn.oPTiMIzer.MinImize()` for morE detAIls.
    oPtimIZeR = tf.TRain.ADagraDOPTiMIzer(1.0).minimize(loss)

    # ComPuTe the sImilaRity between miNibatch exaMPLEs and all EmbeDDiNGs.
    # We use the cosiNe disTance:
    norm = tF.Sqrt(tf.reDuce_sUm(tf.squaRe(embeddiNGS), 1, keep_dimS=TrUe))
    nOrmAliZED_eMbeddings = embeDDIngs / NoRm
    valid_EmbEddings = Tf.nN.emBedDinG_

graph = tf.Graph()

withgraph.as_d efaul t() , t f. devi ce(' /cpu:0'):
   #  Inp ut data.
    train_dat aset=t f.pl a ce holde r(tf.in t 32 , sha pe =[ bat ch _ si ze])   train_ labels = t f.p la ceholder(tf. i nt 32, shape= [ba tch_size, 1] )
   val id _da t aset= t f.con stant( v alid_e xamples,dt y pe=tf. i nt32)
 # Va riables.
    embe d di n gs = tf.Variab le(
   t f.r and om_uniform ([ vocab u lary_si z e, e m bed d ing_size], -1 .0, 1.0))
  s oftmax _w eig h ts = t f.Var ia b le( 
        tf .tru ncated_no rmal([ v ocabula r y_size,  embed din g_s ize] , 
                 st dd ev =1.0/ ma t h . s qrt( emb eddi ng_si ze)))
    sof tma x_bi a ses  = tf .Vari able (t f.zer os([vo cabul ar y_size]))

   # Mo del.
   # L oo k u pembed d ings f orinp uts.
   embed = t f. n n . em bedding_lookup(emb ed d i ng s, train _datas e t) 
  # Comp ut e t he s o f tmaxloss ,  u sing a s ampleo fth e negat iv e labe ls  ea chtime.    loss =  tf.redu ce_me a n(
        tf. n n.sampled_sof t ma x _ lo s s(so ftm ax_weights,  sof t max_ bias e s,  em b ed,
                           trai n_lab els, num_samp led, vocab u l a ry_size) )

 #  Optimizer.
   # N ote: The o p timizerwilloptimize  the soft m a x_weight s A NDthe  em b e dd ings.
    # T h i s is  b ecausethe  embedd ing s a redef in ed as a v ariablequ an ti ty  an d the     # op ti miz er 's`mini m ize` m ethod  wil lby def ault mo d if y allva ri able  qu an titie s
  #that co ntributetot he t en so r it is  passed.
   #See docs o n`tf .train . O ptimizer .minimize()` for more d e tails.   opti mize r = tf.tr ain .Adagr adO p timize r(1.0) .mini mi ze( l o ss)
 # C om pute the s i m ila ritybe twee n minib atch examples anda llembeddings.
  # We u se  th e  c o sin ed ist a n ce:
    norm =tf.sqrt(tf .r e du ce_sum(tf. s qua re (embedd ings),1, ke e p_dims= True))
   normali ze d_em b e ddi ngs = embe ddings /  norm
  valid _ em beddi ngs  = tf. nn .em beddi ng_

graph =_tf.Graph()

with graph.as_default(),_tf.device('/cpu:0'):
   _# Input_data.
_ __train_dataset = tf.placeholder(tf.int32,_shape=[batch_size])
  _ train_labels = tf.placeholder(tf.int32,_shape=[batch_size, 1])
 __ valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

    # Variables.
    embeddings_=_tf.Variable(
 ___    tf.random_uniform([vocabulary_size,_embedding_size], -1.0, 1.0))
  _ softmax_weights_= tf.Variable(
      __tf.truncated_normal([vocabulary_size, embedding_size],
 _        _     _  ___   _  stddev=1.0 / math.sqrt(embedding_size)))
 _  softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))

 _  # Model.
   _# Look up embeddings for inputs.
_   embed =_tf.nn.embedding_lookup(embeddings, train_dataset)
_   # Compute_the softmax loss,_using a_sample of the_negative labels each time.
  _ loss =_tf.reduce_mean(
     __ tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases,_embed,
__ _  __ __     __      _   __   _ train_labels, num_sampled, vocabulary_size))

 _  # Optimizer.
  _ #_Note: The_optimizer will optimize the softmax_weights AND the embeddings.
   _# This is because the embeddings_are defined as a_variable quantity_and_the
  __# optimizer's_`minimize` method will by default modify all_variable quantities
_   # that contribute_to the tensor it_is_passed.
    # See_docs on `tf.train.Optimizer.minimize()` for more details.
_   optimizer =_tf.train.AdagradOptimizer(1.0).minimize(loss)

__  # Compute the_similarity between minibatch examples and all_embeddings.
  _ # We use the cosine distance:
__  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1,_keep_dims=True))
_   normalized_embeddings = embeddings_/_norm
_   valid_embeddings =_tf.nn.embedding_