keras_reuters_mlp.py

'''Trains and evaluate a simple MLP
on the Reuters newswire topic classification task.
'''

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

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

MAX_WORDS = 1000

# network and training
N_HIDDEN = 512
NB_EPOCH = 10
BATCH_SIZE = 32
VERBOSE = 1
VALIDATION_SPLIT=0.2


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

model.summary()

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=VERBOSE, validation_split=VALIDATION_SPLIT)
score = model.evaluate(X_test, Y_test,
                       batch_size=BATCH_SIZE, verbose=VERBOSE)
print('Test score:', score[0])
print('Test accuracy:', score[1])