Word_Best_Text.py

 =   environ['TF
CPP
MIN(LOG_LEVEL(2'
import logging import time import numpy as np import tensorflow as as  logging getLogger('tensorflow').
(logging.index_

class CharRNN(object):
    __init__(self, is_training, is_size, 
_size, 
_size, 
                _summaries  max_tf_0, self_prob, self_writer_                 _summaries  self_ dropout_dims,0, self_prob,True,
, self_prob,0[
      
_size = batch_size
     _
_size = vocab_size
     .= use model
(        
_size = tf_        
 size = tf.      
 size = tf.dropout
      
 size = vocab_size
     .model size norm
= max_grad_norm
      num_layers = num_layers_     _vocab_size = 
_size
     .model = model:      
 = tf.      input size = tf.
((      = 
 > '_
        input size = 
_

        input_size = tf.
(      model        input size = tf.

      
_size x tf
.size * vocab_size +
                        .= hidden_size * hiddenhidden_size % self)input size + 1) *
                         
 = selfself_size % 1) *
                        step layers - start. * 4 
 hidden_size *                         hidden_size()+ 1)size + 1))

      input_size = tf_placeholder(
.
_,
                                       .___  self_num,unrollings],
                                       ._
     .targets = tf.placeholder.tf.int64,
                                    outputs___  self_num,unrollings],
                                   _cell_
      .tf_model == 'rnn':
       _fn = tf.
.rnn.rnn.      
_index_== 'gru':
       _fn_= ".summary.rnn.rnn.      
_index_== 'gru':
       _fn_= ".summary.rnn.rnn.       
 [''].      = 
(== rangelstm':
       ['fn_bias'] = [.0
       .vocab_vocab dict'] = 0.      = np.
([[self          hidden_size  reuse_flat,dict,
,
]


         params)

      = npcell](      = [ range(self)index_layers):
):
        hidden_size = (2id(
            hidden_size  reuse_tf_get],
_
_reuse,
          **params)

       append_higher_layer_cell)
       tf.name_scope('and 0.
_/ 0.
        
 np.trainable(AdamOptimizer_
_(self                    _keep_prob 
 0-

dropout

                 =_in.range.
       
 = tf.
_rnn.static.dtype,

      tf.name_training('loss_state'):
       _
 state = tf_






.batch(size, self.float32)
         summaries loss = tf.summary_placeholders(with_default(
          batch_size,dict(self,size, values=float_),
         _dims=(None,),
          input size 1 0-

                   =_scope('loss_layer'):
       _=_size(> tf_size          input = tf.
.rnn
self           embedding',   
_
,, self_tf_get_variable        
          input = tf.
_self.eye])self.1,
. dtype.



_

        tf.name tf nn(embedding_lookup
self,logits

self.input_data)
        =_training('and not.summary_scalar
> create_
          = np.array(
(self, index_- 0 
_dropout


      =_scope('slice_monitor'):
       _zero = tf.
_scalar
_,
[


                         = scope
 cells.
_self.eye_ num_or,size,splits


self=):
 subshape=inputs)]
              
_0,= tf.contrib_rnn.static(rnn_
        
( global_step_        _size dict state state)
       final_loss = final.state
       tf.name_tuple flatten_ouputs'):
       _outputs = tf.
.self.average(self.1, values=outputs), [-1, hidden_size])
      _b_scope(flatten_targets

       _outputs = tf.
.self.average_self.1, values=outputs),targets), [-1])
           = scope(scope[softmax') as sm_vs:
       .w_= tf.contrib.variable("softmax_w", [hidden_size, 
_size])
       .logits = tf.
_variable("softmax_b", [vocab_size,
        
 = tf.matmul_flat,outputs, softmax.w_ + softmax_b
        probs_= tf.nn
softmax(self,logits)

      =_training('loss'):
       _
_
.argmax(sparse_softmax(char_



(_
         ,cell_logits
 labels_inputs_dict

        
_state = 
.

mean(loss)

      is_training loss_state'):
       _=_np.trainable(self.1_ name_count,
       .update_loss = tf.placeholder.tf.0, name='sum_mean_loss')
              .mean_loss monitor = ".get.sum.average(loss)assign)0.logits_
                                           ._0.0),
                                           .___ ')
       .update loss monitor_= tf_group(tf.164
.assign(sum.mean_loss +
                                                                 .___
                                            ._0.+ 1),
                                            .___ ')
       .tf_model_dependencies([self.update_loss



          average_loss = tf.mean_variable()/ 0_          append = tf.
.self_average_
)

        
_state = tfaverage loss"
       _summary name = "average 
          _loss_summary = tf.summary.rnn.
.summary_name, step.ppl)loss)
        summaries = tf.summary_scalar

,summary_name, self.float32
       summary_= tf.
_merge(average_loss_summary, ppl_summary],
                                        .___
         self.global.update = tf.get_variable('global_inputs_ (_                                         .___ ')0.0))
       input size = tf_placeholder(learning.rate_

     is_training('
        tf.name_np.trainable(variables_
         
_= np.array(by_
_norm(
,gradients(self.mean_loss, tvars),
                                          .___ ')
        =_np.array(AdamOptimizer_self,learning_rate_

        summary_state = tf.reduce_gradients(zip,grads, tvars_
                                                  .___ 
           =_scope self, session_ start_
_ 
_generator, is_training,                  _summaries  self_tf_ self_step_rate, debug_prob, self_step_0,variable_
     _size = data_size // (self.batch size * self.num_unrollings)
      is_scope and
(self.batch size * self.size
unrollings) != 0:
          size_+= 1

      = np(*range.
          epoch_size: %d', self_size)
          data_size: %d', self_batch_
          data_size: %d', self_batch_size)

         data_size: %d', epoch_batch_size)

      
_training 

       op_= tf.
_scalar
      
        
 = tf.
_variable('
       = np.array(data.zero
state)

     summaries loss monitor.run()
      time = time.time()
      tf.name in tf.start_batch_// divide_by_n

        = np 
(
()
        = np.array(data.1]).transpose()
        = np.array(data[:-1]).transpose()
         = np.array(__
 global_final_state, self_op_
              _summaries  self_tf_ self_learning_0,
         input_= tf.
_
(":
inputs  p_targets_
targets                      _state_
 
state}
         = np.array(self. feed_dict,
        mean_ self_ _,_
_str_ global_step_ summary_
_in.
              = np.
(self.0_
        =verbose > 0. * ((step+1) % freq == 0):
         ("%.1f%%, step:%d, perplexity: %.3
, speed: %.0f words",
                      step + 1) * self.batch_/ epoch_size * 100_ step, ppl,                       step_+ 1) * self.batch_size * self.num_unrollings /
                      time time  - start_time))
      ("Perplexity:
%.3f, speed: %.0f words",per sec",
                     step + 1) * self.batch_size * self.num_unrollings /
                  time time() - start_time))

     =  summary_str, global_step_
    self_
_self, extra_ start_ start_size, self_index,dict,                   _
_
  reuse=tf_get], self_prob,True,
       = offset(
(self.zero,
._

      =_training and 0.tf.tf.tf.start_text
 > start_
        = np.subplaceholders_size)

       =_in 0.text[:-1]:
          tf.name_np.trainable(char]])
(char_ vocab_index_dict)]])
          = [].array(self.
_

                              _
_prob monitor')= 
                              _initial_prob 1state}
        = np.array(char2id(char_text[-1], vocab_index_size)]])
      
        
 = 
.self):index_unrollings-1())
        = np.array(char2id(

0, vocab_size)]])
        = [].
      = [].range(self)
         logits_= tf.run(self.final_state,
                                     
__                                   _cell_size_ ')= 
                                    _
___ state})
        input = tf.
_logits - np.max(logits)) / temperature.
        = np.array(/ range.
_unnormalized,probs_
         tf.name_training 

         tf.name_np.trainable(self.1_
        
          = np.array(choice(self,vocab_

 1, p,probs,0

]
         append id2char(sample, index_vocab_dict):
      x = [].
(char2id    return batchesjoin(seq)
       class BatchGenerator object):
      __init__(self, text_ batch_size, n_
, text):size,                   _
 
  reuse_tf_get],
        hidden_= tf.        input size = tf.self_
        
_state = tf.

        op_size = tf


       .
 size = tf_
(       .
 size dict = (_size
dict
        b vocab_dict = num_size
dict
               = np.array(variablesloss== tf.
_         input_= tf.tf.tf segment.
(offset.offset.offset(batch)size)
       .last_size = tf_next
_()
             = scope 

):
        = np(*
(self.loss_

1
 
=np.float)
        =_in range.self_size_size):
          append data= char_1

,
_self._cursor),b]], self
vocab_

dict)

         cursor_op 
= tfself.cursor[b]
+ 1) % self._text_size
       .= 
def     = self):
        = np.placeholderlastsparse_]
        = np.range.self_index_dict):
          hidden_id.next_batch())
        
 size,= 
[-size

        = 
def batches2string 
, feed_vocab_dict):
    [ [''].range ( size,shape[0]
    KeyError in max:
      
 np.
x) for - lst zip s, id2char_list(b, index),vocab_dict):
    tf.name  def characters(probabilities):
    [id2char(c.
for c 
 
 

probabilities, index_

def char2id(char, index_index_dict):
  try:
      
_
_dict[char]
  except KeyError:
      
_
 %s', self_
      =  def id2scope self, session_vocab_dict):
  return = charchardict[ 

       id_
_list(lst, extra_vocab_dict):
   :=_2char_i, index_vocab_dict): for i in lst 

def create_tuple_placeholders_with_default(inputs, extra_dims, shape_
    isinstance shape, extra_
      isinstance [ placeholder(with_default

         
_extra_dims) + [shape])
   :
      = increate
tuple_placeholders


default(
       , 
_dims, self_
                         =  summary_= ==.subplaceholders) int):
      = np.shape)
      = ==.tuple.
        = np.subplaceholders_
      
        = np.subplaceholders)   
   return result          def create tuple placeholders([dtype, session_vocab_ is_
    isinstance shape, int):
      = [''] 
(dtype, index_extra_dims) + [shape])
  else:
      = npcreate
tuple_placeholders(dtype, index_vocab_ num_
                         = ==.tuple.
      = np.shape)
       = ==.tuple.
        = np.subplaceholders_
      
        = np.subplaceholders)    :==