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(BatchNormalization(input_shape=(256,))) # try without batch normalization (doesn't work as well!)
model.add(Dropout(0.5))
model.add(Dense(256, nb_classes, init='normal'))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adadelta')

print "Training..."
model.fit(X_train, Y_train, nb_epoch=5, batch_size=batch_size)
score = model.evaluate(X_test, Y_test, batch_size=batch_size)
print 'Test score:', score

classes = model.predict_classes(X_test, batch_size=batch_size)
acc = np_utils.accuracy(classes, y_test)
print 'Test accuracy:', acc

