new_contents: from keras.models import Sequential
  from keras.layers.core import Dense, Activation
  from keras.layers.recurrent import LSTM
  from keras.datasets.data_utils import get_file
  import numpy as np
  import random, sys
  
  '''
      Example script to generate text from Nietzsche's writings.
  
      At least 20 epochs are required before the generated text
      starts sounding coherent.
  
      It is recommended to run this script on GPU, as recurrent
      networks are quite computationally intensive.
  
      If you try this script on new data, make sure your corpus 
      has at least ~100k characters. ~1M is better.
  '''
  
  path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
  text = open(path).read().lower()
  print('corpus length:', len(text))
  
  chars = set(text)
  print('total chars:', len(chars))
  char_indices = dict((c, i) for i, c in enumerate(chars))
  indices_char = dict((i, c) for i, c in enumerate(chars))
  
  # cut the text in semi-redundant sequences of 30 characters
  maxlen = 30
  step = 5
  sentences = []
  next_chars = []
  for i in range(0, len(text) - maxlen, step):
      sentences.append(text[i : i + maxlen])
      next_chars.append(text[i + maxlen])
  print('nb sequences:', len(sentences))
  
  print('Vectorization...')
  X = np.zeros((len(sentences), maxlen, len(chars)))
  y = np.zeros((len(sentences), len(chars)))
  for i, sentence in enumerate(sentences):
      for t, char in enumerate(sentence):
          X[i, t, char_indices[char]] = 1.
      y[i, char_indices[next_chars[i]]] = 1.
  
  
  # build the model: 2 stacked LSTM
  print('Build model...')
  model = Sequential()
  model.add(LSTM(len(chars), 512, return_sequences=True))
  model.add(LSTM(512, 512, return_sequences=False))
  model.add(Dense(512, len(chars)))
  model.add(Activation('softmax'))
  
  model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
  
  # helper function to sample an index from a probability array
  def sample(a, diversity=0.75):
      if random.random() > diversity:
          return np.argmax(a)
      while 1:
          i = random.randint(0, len(a)-1)
          if a[i] > random.random():
              return i
  
  # train the model, output generated text after each epoch
  for iteration in range(1, 30):
      print()
      print('-' * 50)
      print('Iteration', iteration)
      model.fit(X, y, batch_size=128, nb_epoch=1)
  
      start_index = random.randint(0, len(text) - maxlen - 1)
  
      for diversity in [0.4, 0.7, 1.]:
          print()
          print('----- diversity:', diversity)
  
          generated = ''
          sentence = text[start_index : start_index + maxlen]
          generated += sentence
          print('----- Generating with seed: "' + sentence + '"')
          sys.stdout.write(generated)
  
          for iteration in range(400):
              x = np.zeros((1, maxlen, len(chars)))
              for t, char in enumerate(sentence):
                  x[0, t, char_indices[char]] = 1.
  
              preds = model.predict(x, verbose=0)[0]
              next_index = sample(preds, diversity)
              next_char = indices_char[next_index]
  
              generated += next_char
              sentence = sentence[1:] + next_char
  
              sys.stdout.write(next_char)
              sys.stdout.flush()
          print()
  
  
  
subject: Add LSTM text generation example