import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.datasets import reuters
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.utils import np_utils
from keras.preprocessing.text import Tokenizer

if __name__ == '__main__':
    max_words = 1000
    batch_size = 32
    nb_epoch = 5

    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')

    exit()

    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(512, input_shape=(max_words,)))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])

    history = model.fit(X_train, Y_train,
                        nb_epoch=nb_epoch, batch_size=batch_size,
                        verbose=1, validation_split=0.1)
    score = model.evaluate(X_test, Y_test,
                           batch_size=batch_size, verbose=1)
    print('Test score:', score[0])
    print('Test accuracy:', score[1])

Add reuters.py