text: import numpy
  from keras.models import Sequential
  from keras.layers import Dense
  from keras.layers import Dropout
  from keras.layers import LSTM
  from keras.callbacks import ModelCheckpoint
  from keras.utils import np_utils
  
  filename = "text.txt"
  raw_text = open(filename).read()
  raw_text = raw_text.lower()
  
  chars = sorted(list(set(raw_text)))
  char_to_int = dict((c, i) for i, c in enumerate(chars))
  
  
  n_chars = len(raw_text)
  n_vocab = len(chars)
  print("Total Characters: ", n_chars)
  print("Total Vocab: ", n_vocab)
  
  seq_length = 100
  seq_length = 100
  dataX = []
  dataY = []
  for i in range(0, n_chars - seq_length, 1):
      seq_in = raw_text[i:i + seq_length]
      seq_out = raw_text[i + seq_length]
      dataX.append([char_to_int[char] for char in seq_in])
      dataY.append(char_to_int[seq_out])
  n_patterns = len(dataX)
  print("Total Patterns: ", n_patterns)
  
  # reshape X to be [samples, time steps, features]
  X = numpy.reshape(dataX, (n_patterns, seq_length, 1))
  # normalize
  X = X / float(n_vocab)
  # one hot encode the output variable
  y = np_utils.to_categorical(dataY)
   
  model = Sequential()
  model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2])))
  model.add(Dropout(0.2))
  model.add(Dense(y.shape[1], activation='softmax'))
  model.compile(loss='categorical_crossentropy', optimizer='adam')
  
  # define the checkpoint
  filepath="weights-improvement-{epoch:02d}-{loss:.4f}.hdf5"
  checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
  callbacks_list = [checkpoint]
  
  model.fit(X, y, epochs=20, batch_size=128, callbacks=callbacks_list)
  