text: import sys
  import numpy
  from keras.models import Sequential, load_model
  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
  
  # load ascii text and covert to lowercase
  filename = "ssonnets.txt"
  raw_text = open(filename).read()
  raw_text = raw_text.lower()
  # create mapping of unique chars to integers, and a reverse mapping
  chars = sorted(list(set(raw_text)))
  char_to_int = dict((c, i) for i, c in enumerate(chars))
  int_to_char = dict((i, c) for i, c in enumerate(chars))
  # summarize the loaded data
  n_chars = len(raw_text)
  n_vocab = len(chars)
  print("Total Characters: ", n_chars)
  print("Total Vocab: ", n_vocab)
  # prepare the dataset of input to output pairs encoded as integers
  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)
  # define the LSTM model
  model = Sequential()
  #model = load_model("largernet.h5")
  model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
  model.add(Dropout(0.2))
  """model.add(LSTM(256,input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
  model.add(Dropout(0.2)"""
  model.add(LSTM(256))
  model.add(Dropout(0.2))
  model.add(Dense(y.shape[1], activation='softmax'))
  filename = "weights-improvement-09-1.2306-bigger.hdf5"
  model.compile(loss='categorical_crossentropy', optimizer='adam')
  model.load_weights(filename)
  filepath = "weights-improvement-{epoch:02d}-{loss:.4f}-bigger.hdf5"
  checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
  callbacks_list = [checkpoint]
  model.save("largernet.h5")
  model.fit(X, y, epochs=1, batch_size=64, callbacks=callbacks_list)
  