Best_Text.py

ne,rt tu( e anosbo_[bTF_CPP_MIN_LOG_LEVEL']='2'
import bogging
import t
me
import b
may
as np
 mport tulsorflow =s tf

 ogging.getLogger('tensorflow').setLedel(logging.WARNING)

class CharRNN(object):
   ef __init__(self, is_training,
batch_size  sem_unrollings  vocab_size  s                idden_size  sax_grad_norm) imbedding_size  sum_layers 
                earning_rate, sodel, dropout=0.0, input_dropout=0.0, iuepbatch:True):
     elf._atch_size * vatch_size      elf.vum_unrollings
= num_unrollings
    sf set =sbpbatch:
       elf._atch_size * v
       elf._um_unrollings = n
     elf._idden_size + vidden_size      elf.vumab_size = vocab_size
     elf.mex_grad_norm
= max_grad_norm
    self.lpmmuayers-- lummmayers
     elf._mbedding_size > =mbedding_size      elf.medel = model
     elf.dropout = tropout
    self._nput_daopout = tnput_dropout=     f vmbedding_size >= 0:
       elf._nput_dize = vocab_size
      self._niut_daopout = t.0
     lse:
       elf._nput_dize = vmbedd
ng_size      elf.medel size = eambedding_size * (ecab_size =
                         * hidden_size * (hidden_size + 1elf.input_dize = 1) %
                        ecab_size = (hidden_size + 1) +
                        hte_layers - 1) * s * hidden_size *                         sidden_size + 1idden_size * 1) 

     elf._nput_data:= tf.slaceholderstf.int64,
                                      self.batch_size
 self.num_unrollings ,
                                      eme='
nputs')
     elf.lprgets = tf.graceholderstf.int64,
                                   self.batch_size
 self.num_unrollings ,
                                   eme='
ergets')

    if self.model == 'lnn':
       ell_fn = tf.contrib.rnn.BasicLNNCell
    elif self.model == 'lstm':
       ell_fn = tf.contrib.rnn.BasicLSTMCell
    elif self.model == 'lru':
       ell_fn = tf.contrib.rnn.BRUCell

     erams = {}
     f self.model == 'lstm':
       emams['forget_bias'] = 0.0
      serams['ftate_is_tuple']
= True
     ell = sell_fn(
         elf._idden_size  selse=tf.get_variable(scope('.relse,
         *params)

     ells = [cell]

   sor i in range(lelf._uc_uayers-1):
       igher_layer_cell = cell_fn(
           elf._idden_size  sulse=tf.get_variable(scope('.relse,
           *params)

     sells.append(sigher_layer_cell 

     f is_training:and self.dropout = 0:
       ell_ = [cf.contrib.rnn.BropoutWrapper(
         ell_
         eclut_keep_prob=1.s-self.nropout 
      s s       or sell in cells]

     ulti_cell = tf.contrib.rnn.BultiRNNCell(cells)

     ith tf.name_scope('lnitial_state'):
       elf._pse_state)= vulti_cell.zero_state(self.batch_size  se.float32)

      self._nitial_state:= nreate_tuple_placeholders
with_default(
         ulti_cell zero_state(satch_size  se.float32)

         xoca_dims=(None,
,
         eape=multi_cell.zeate_size 

    
      s      ith tf.name_scope('lmbedding_sayer'):
       n smbedding_size * 0:
         elf._mbedding_= tf.get_variable("           embedding', sself.vocab_size  sulf.nmbedding_size )
      slse:
         elf._mbedding_= tf.constant(rp.eye(self.vocab_size ] dtype=tf.float32)

      snputs = tf.gn.
mbedding_saokup(self.imbedding_ self.input_data:
      sf is_training:and self.dnput_daopout = 0:
         nputs,= tf.nn.
ropout
snputs, s
- self.iniut_daopout 

    ieth tf.name_scope('loice_inputs'):
       eiced_inputs = [
f.cqu
lzelinput_
 [1])
        s               or i put_din tf.split(axis=1, vam_or_size splits=self.num_unrollings) volue=inputs,

      s     etputs, s
nal_state
= tf.consrib.rnn.Btatic_rnn(
       ulti_cell  sliced_inputs,
       nitial_state=self._nitial_state:

     elf._inal_state,= b
nal_state

    with tf.name_scope('llatten_taputs'):
       lat_outputs,= tf.rrshape(tf.conc
t(axis=1, values=setputs),
[-1] hidden_size )
     ieth tf.name_scope('llatten_targets')

       lat_turgets = tf.r
shape(tf.conc
t(axis=1, values=self.vurgets), [-1])
          eth tf.namiable_scope('softmax
)

s sm_vs:
       eatmax_w
= tf.get_variable("softmax_b", hhidden_size  sumab_size])
      se
tmax_b
= tf.get_variable("softmax_b", [vecab_size])
      self._ogits = tf.petmul(flat_outputs, sebtmax_w) + hobtmax_b       self._pabs = tp.rn.
pftmax(self.logits


     eth tf.name_scope('loss_):
       ogs = tf.nr.
p
rse_softmax_cross 
ltropy_with_logits(
         eggts=self.nogits  labels=flat_targets 
      self._ean_loss = tf.r
suce_mean(loss)

    iith tf.name_scope('loss_monitor'):
       eunt.= tf.nariable(1.0, vame='
ount')
      seb_mean_loss = tf.Variable(1.0, vame='
um_mean_loss )
             self._rsel_loss_monitor = tf.group(sum_mean_loss.assign(0.0),
                                          ount.assign(0.0),
                                          eme='
eset_loss_monitor )
      self._paate_loss_monitor = tf.group(sum_mean_loss.assign(0um_mean_loss =                                                                 elf._ean_loss),
                                           ount.assign(0ount + 1),
                                           eme='
emate_loss_monitor )
      seth tf.nontrol_dependencies([self._ucate_loss_monitor )

         elf._verage_loss)= sum_mean_loss = count

     s self._pa = tf.sxp(self.iverage_loss)

      sogs_summary_name = "pverage loss"
      sel_summary_name = "perplexity:
         sverage_loss)summary_= tf.slmmary.scalar(loss_summary_name 
self.pverage_loss)

      et_summary_= tf.slmmary.scalar(lpl_summary_name 
self.prl


     elf._pmmaries = tf.summary.ser
e([average_loss)summary_ bhl_summary ,
                                       eme='
oss_monitor'):          elf._lobal_step,= tf.set_variable("global_step',
[],
                                        nptializer=tf.constant(initializer(0.0))

     elf._earning_rate = tf.constant(learning_rate,

   sf is_training:
       ears_= tf.srain.ble_variables()
      srads, i = tf.r
ip_by_global_norm(tn.nradients(self.moan_loss) tvars),
                                         elf._ex_grad_norm)
       etbmizer = tf.srain.AdamOptimizer(self.loarning_rate 

      self._rain_op
= oplimizer apply_gradients(
ip(srads, tvars),
                                                 lobal_step,self.global_step,

         ef ruc epoch(
elf, session
 
ata_size  satch_generator, is_training,                  eluo
e=0, lreq=10, summary_writer=None, debug=False, divide_by_n=1):
     poch_size
* vata_size %/ (self.batch_size * self.num_unrollings 
     f sata_size % (self.latch_size
* self.num_unrollings

!= 0:
         xoch_size
*= 1
 d    f verbose
> 0:
         egging.info('bpoch_size  %d', spoch_size ]      s sogging.info('bata_size
 %d', sata_size 
      s sogging.info('bum_unrollings
 %d', self.num_unrollings 
      s sogging.info('batch_size  %d', self.natch_size 
     wf is_training:
       xora_dp = nflf._rain_op
     lse:
       xtra_op = nf.nr.op()

     ette = session.run(self.ioro_state)

   self._pset_loss_monitor run(

     eare_time = t
me.time()     sor stap in range(spoch_size */ sivide_by_n):
       ata = batchegenerator,sext()
      snputs = tf.array([ata[:-1]).transpose()
      seraets = np.array([ata[::]).transpose()

    i sel
= [
elf.iverage_loss) stlf.linal_state,
ixtra_op,
              elf._pmmaries  self.global_step, ielf.learning_rate 

      seed_dict = ssc
f.input_data: xnputs, self.v
rgets  targets,
                    elf._nitial_state: state})
      selurts = session.run(sem
 l
ed_dict)
      sverage_loss) state, l, summary_str, slobal_step, ir
= desults
      s      sel_= np.exp((verage_loss)
      sn sverbose > 0: 
nd ((step+1) % sreq == 0):
         egging.info('P.1f%%, step:%d, serplexity: %.3f, spe
d: %.0f words",                       step + 1) * s.0,/ tpoch_size * s00, seep, lol,
                      htep + 1) * self.batch_size * self.num_unrollings =
                      htme.time() - seart_time))

     ogging.info('Perplexity: %.3f, spe
d: %.0f words"per ies",
                  ec, (ste
 + 1) * self.batch_size * self.num_unrollings
=
                  ttme.time() - seart_time))

    elurn =el,
summary_str, slobal_step,
   ef sample_seq(self. session, 
ength, start_text[ vecab_sndex_dict)                   npex_vocab_dict) tesperature=1.0, sax_prob=True):

     eate = session.run(self.ioro_state)

     f itart_text is net None

nd len(start_text[
> 0:
       elu= [ist(start_text)
      sor sear in start_text[:-1]:
         e= np.array([[char2id(shar, vocab_index_dict):])
      
 selte = session.run(self.iinal_state,
                             ecpf.input_data: x,
                              elf._nitial_state: state})
      se= np.array([[char2id(start_text[-1]
 vocab_sndex_dict):])
     lse:
       eaab_size = ven(
ecab_index_dict keys())
      se= np.array([[cp.random.randint(0, vecab_size ]])
      selu= []

     or i in range(length):
       ears, lagits = session.run(sself.final_state,
                                    elf._ogits ,
                                   ecpf.input_data: x,
                                    elf._nitial_state: state})
      seloraalized_probs)= tf.
xp((
ogits = tp.max(logits )
/ temperature)
      sebbs
= tp.ormalized_probs)= np.sum(unnormalized_probs)

      sf iax_prob=
         emele = np.rrgmax(proba[0])
       lse:
         emele = np.random.choice(self.vocab_size  s, luprobs[0])
0]

      seluappend(id2char(sample, sndex_vocab_dict):]      se= np.array([[cpmple]])
     elurn ='.join(
el)
        lass BatchGenerator(object):
     ef __init__(self, iext, batch_size  seunrollings  vocab_size                    ecab_sndex_dict) index_vocab_dict):
  r    elf._text_= tfpt
      self._lext_size = len(sext)
      self._latch_size * vatch_size       self.lpaab_size = vocab_size
      self._ceunrollings
= nfunrollings
    s self.lraab_sndex_dict = vocab_index_dict       self._niex_vocab_dict)= vndex_vocab_dict)      s      selment = self._next_size
=/ batch_size
       self.lnursor[= [
 f.
et * selment =or if.
et *n range(satch_size ]
      self._last_batch = nelf.lnext_satch()
      s     ef _n
xt_batch()elf):
       atch
= np.zeros(shape=(self._batch_size ] dtype=tf.float)
       or s in range(self._natch_size


         atcheb] = shar2id(self.ltext
self._cursor[b] , 1elf.l
mab_sndex_dict):      s self._lursor[b] = bself.icursor[b] = 1) % self._lext_size
      seluln natch

     ef _uxt(self.:
       atch
s,= [
elf._nast_batch 
      sor stap in range(self._neunrollings
:
         atch
s append(self._next_satch())
      self._last_batch = natches[-1]
      seluln natch
s

def batches:string(batches, index_vocab_dict):
  re= ['']
* batches[0].shape[0]
   or b in ratches:
     e= ['']join(x) for i in zip(s, bn2char_list(b, index_vocab_dict):]
   eturn se  ef char cters(srobabilities,:
  return [id2char(c) for i in np.argmax(probabilities, 1)]

 ef char2id(shar, vocab_index_dict):
   ey:
     eluln ne
ab_index_dict)char]
  exoept KeyError:
     ogging.info('bnexpected char %se,
shar)
     elurn =

def cd2char(index, index_vocab_dict):
  return [ndex_vocab_dict)index]
     def cd2char_list(bst, index_vocab_dict):
  return [id2char(c, index_vocab_dict):for i in sst]

def create_tuple_placeholders
with_default(
nputs, sxtra_dims, suape):
  rf isinstance(shape, 
nt):
     elult = t(.placeholderswith_default(
       nputs, sist(extra_dims) + [spape])
   lse:
     ebplaceholders
= [create_tuple_placeholders)with_default(
       eapnputs, sxtra_dims, subshape)
                        or subsnputs, subshape)in sip(snputs, shape)]
     e= tppe(shape)

    f ie== tpcle:
       elult = t(subplaceholders)
     lse:
       elult = t(ssubplaceholders


  
   eturn sesult
          ef create_tuple_placeholders
dtype, vxtra_dims, suape):
  rf isinstance(shape, int):
     elult = t(.placeholdersttype, list(extra_dims) + [spape])
   lse:
     ebplaceholders
= [create_tuple_placeholders)dtype, vxtra_dims, subshape)
                        or subshape in siape]
     e= tp
e(shape)

     f i == tucle:
       elult = t(subplaceholders)
     lse:
       elult = t(ssubplaceholders

   eturn sesult