old_contents: from __future__ import absolute_import
  from __future__ import print_function
  import numpy as np
  
  from keras.datasets import reuters
  from keras.models import Sequential
  from keras.layers.core import Dense, Dropout, Activation
  from keras.layers.normalization import BatchNormalization
  from keras.utils import np_utils
  from keras.preprocessing.text import Tokenizer
  
  '''
      Train and evaluate a simple MLP on the Reuters newswire topic classification task.
      GPU run command:
          THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python examples/reuters_mlp.py
      CPU run command:
          python examples/reuters_mlp.py
  '''
  
  max_words = 10000
  batch_size = 16
  
  print("Loading data...")
  (X_train, y_train), (X_test, y_test) = reuters.load_data(nb_words=max_words, test_split=0.2)
  print(len(X_train), 'train sequences')
  print(len(X_test), 'test sequences')
  
  nb_classes = np.max(y_train)+1
  print(nb_classes, 'classes')
  
  print("Vectorizing sequence data...")
  tokenizer = Tokenizer(nb_words=max_words)
  X_train = tokenizer.sequences_to_matrix(X_train, mode="binary")
  X_test = tokenizer.sequences_to_matrix(X_test, mode="binary")
  print('X_train shape:', X_train.shape)
  print('X_test shape:', X_test.shape)
  
  print("Convert class vector to binary class matrix (for use with categorical_crossentropy)")
  Y_train = np_utils.to_categorical(y_train, nb_classes)
  Y_test = np_utils.to_categorical(y_test, nb_classes)
  print('Y_train shape:', Y_train.shape)
  print('Y_test shape:', Y_test.shape)
  
  print("Building model...")
  model = Sequential()
  model.add(Dense(max_words, 256, init='normal'))
  model.add(Activation('relu'))
  model.add(Dropout(0.5))
  model.add(Dense(256, nb_classes, init='normal'))
  model.add(Activation('softmax'))
  
  model.compile(loss='categorical_crossentropy', optimizer='adam')
  
  model.fit(X_train, Y_train, nb_epoch=4, batch_size=batch_size, verbose=1, show_accuracy=True, validation_split=0.1)
  score = model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=1, show_accuracy=True)
  print('Test score:', score)
  
  classes = model.predict_classes(X_test, batch_size=batch_size, verbose=0)
  acc = np_utils.accuracy(classes, y_test)
  print('Test accuracy:', acc)
  
new_contents: from __future__ import absolute_import
  from __future__ import print_function
  import numpy as np
  
  from keras.datasets import reuters
  from keras.models import Sequential
  from keras.layers.core import Dense, Dropout, Activation
  from keras.layers.normalization import BatchNormalization
  from keras.utils import np_utils
  from keras.preprocessing.text import Tokenizer
  
  '''
      Train and evaluate a simple MLP on the Reuters newswire topic classification task.
      GPU run command:
          THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python examples/reuters_mlp.py
      CPU run command:
          python examples/reuters_mlp.py
  '''
  
  max_words = 10000
  batch_size = 16
  
  print("Loading data...")
  (X_train, y_train), (X_test, y_test) = reuters.load_data(nb_words=max_words, test_split=0.2)
  print(len(X_train), 'train sequences')
  print(len(X_test), 'test sequences')
  
  nb_classes = np.max(y_train)+1
  print(nb_classes, 'classes')
  
  print("Vectorizing sequence data...")
  tokenizer = Tokenizer(nb_words=max_words)
  X_train = tokenizer.sequences_to_matrix(X_train, mode="binary")
  X_test = tokenizer.sequences_to_matrix(X_test, mode="binary")
  print('X_train shape:', X_train.shape)
  print('X_test shape:', X_test.shape)
  
  print("Convert class vector to binary class matrix (for use with categorical_crossentropy)")
  Y_train = np_utils.to_categorical(y_train, nb_classes)
  Y_test = np_utils.to_categorical(y_test, nb_classes)
  print('Y_train shape:', Y_train.shape)
  print('Y_test shape:', Y_test.shape)
  
  print("Building model...")
  model = Sequential()
  model.add(Dense(max_words, 256, init='normal'))
  model.add(Activation('relu'))
  model.add(Dropout(0.5))
  model.add(Dense(256, nb_classes, init='normal'))
  model.add(Activation('softmax'))
  
  model.compile(loss='categorical_crossentropy', optimizer='adam')
  
  model.fit(X_train, Y_train, nb_epoch=4, batch_size=batch_size, verbose=1, show_accuracy=True, validation_split=0.1)
  score = model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=1, show_accuracy=True)
  print('Test score:', score[0])
  print('Test accuracy:', score[1])
  
subject: Fix Reuters example