new_contents: #! /usr/bin/env python
  from __future__ import division
  import sys
  import os
  import time
  import numpy as np
  from utils import *
  from datetime import datetime
  from gru_theano import GRUTheano
  from preprocess2 import load_data_from_json2, compute_f1, write_output, testing
  import math
  
  LEARNING_RATE = float(os.environ.get("LEARNING_RATE", "0.001"))
  VOCABULARY_SIZE = int(os.environ.get("VOCABULARY_SIZE", "8000"))
  EMBEDDING_DIM = int(os.environ.get("EMBEDDING_DIM", "50"))
  HIDDEN_DIM = int(os.environ.get("HIDDEN_DIM", "100"))
  NEPOCH = int(os.environ.get("NEPOCH", "20"))
  MODEL_OUTPUT_FILE = os.environ.get("MODEL_OUTPUT_FILE")
  INPUT_DATA_FILE = os.environ.get("INPUT_DATA_FILE", '../Data/Filippova/compression-data.json')
  PRINT_EVERY = int(os.environ.get("PRINT_EVERY", "1000"))
  
  ts = datetime.now().strftime("%Y-%m-%d-%H-%M")
  if not MODEL_OUTPUT_FILE:
    MODEL_OUTPUT_FILE = "GRU-%s-%s-%s-%s.dat" % (ts, VOCABULARY_SIZE, EMBEDDING_DIM, HIDDEN_DIM)
  
  # Load data
  #x_train, y_train, word_to_index, index_to_word = load_data(INPUT_DATA_FILE, VOCABULARY_SIZE)
  (X_train, y_train, len_sent_train, sample_weight_train), (X_test, y_test, len_sent_test, sample_weight_test), (original_sentence_text, compression_sentence_text) = load_data_from_json2(INPUT_DATA_FILE,  0.2, VOCABULARY_SIZE)
  
  '''
  # Build model
  print '\nBuild model'
  model = GRUTheano(VOCABULARY_SIZE, hidden_dim=HIDDEN_DIM, bptt_truncate=-1)
  #model = load_model_parameters_theano('GRU-2016-08-05-13-48-2000-50-100.dat.npz')
  
  #Print SGD step time
  print 'S2q'
  t1 = time.time()
  model.sgd_step(X_train[10], y_train[10], LEARNING_RATE)
  c = model.ce_error(X_train[10], y_train[10])
  print ('loss x[10]: %f'%c)
  t2 = time.time()
  print "SGD Step time: %f milliseconds" % ((t2 - t1) * 1000.)
  sys.stdout.flush()
  
  # We do this every few examples to understand what's going on
  def sgd_callback(model, num_examples_seen):
    dt = datetime.now().isoformat()
    loss = model.calculate_loss(X_train[:1000], y_train[:1000])
    print("\n%s (%d)" % (dt, num_examples_seen))
    print("--------------------------------------------------")
    print("Loss: %f" % loss)
    save_model_parameters_theano(model, MODEL_OUTPUT_FILE)
    print("\n")
    sys.stdout.flush()
  
  t3 = time.time()
  for epoch in range(NEPOCH):
    print('Epoch %d: ' % epoch)
    train_with_sgd(model, X_train, y_train, learning_rate=LEARNING_RATE, nepoch=1, decay=0.9,
      callback_every=PRINT_EVERY, callback=sgd_callback)
  t4 = time.time()
  print "SGD Train time: %f" % ((t4 - t3))
  sys.stdout.flush()
  #
  print 'Testing...'
  predict_test = testing(model, X_test)
  np.save("%s.predict" % (MODEL_OUTPUT_FILE), predict_test)
  '''
  
  def compute_f1_except0(y_test, predict_test):
      f1=[0,0,0,0,0,[]]
      num_error=0
      num_f1_0 = 0
      for i in range(len(y_test)):
          num_True = 0
          num_predict = 0
          num_test = 0
          for y in range(1,len(y_test[i])):
              if y_test[i][y] == predict_test[i][y] and y_test[i][y] == 1:
                  num_True+=1
              if y_test[i][y] ==1:
                  num_test+=1
              if predict_test[i][y] ==1:
                  num_predict+=1
          f1[0]+=num_True
          f1[1]+=num_predict
          f1[2]+=num_test
          if num_True ==0:
              f1_sent = 0
              num_f1_0+=1
          else:
              precision = num_True/num_predict
              recall = num_True/num_test
              f1_sent= 2*(precision*recall)/(precision+recall)
          if math.isnan(f1_sent):
              f1_sent=0
              num_error+=1
          f1[3]+=f1_sent
  
          f1[4]+=len(y_test[i])
          f1[5].append((i, f1_sent, predict_test[i], y_test[i]))
      f1[3] = f1[3] / (len(y_test) - num_error-num_f1_0)
      if (len(y_test)-num_error) ==0:
          print ('----- All predicts are zero -----')
      else:
          print ('Total: %d - Error sentences: %d = %d'%(len(y_test), num_error, len(y_test)-num_error))
          print ('No. sent F1 is 0: %d'%num_f1_0)
          print (f1[3])
          return f1
  
  predict_test=np.load("/home/nhitt/Dropbox/Compression_lstm/rnn-tutorial-gru-lstm/Output_2ndAttention_theano/AttentionV2-GRU-2016-08-13-10-12-8000-50-100.dat.predict.npy")
  print 'Compute f1:...'
  f1 = compute_f1_except0(y_test, predict_test)
  #write_output('./Output_seq2seq_theano', f1[5], original_sentence_text, compression_sentence_text, 0.6)
  
  
subject: add early stop