from __future__ import print_function

import numpy as np

import keras

from keras.datasets import reuters

from keras.models import Sequential

from keras.layers import Dense, Dropout, Activation

from keras.preprocessing.text import Tokenizer

from keras.optimizers import SGD

word_index = reuters.get_word_index(path="reuters_word_index.json")

idx_to_word = dict(zip(word_index.values(), word_index.keys()))

max_words = 20706

batch_size = 32

epochs = 5

print('Loading data...')

#(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_words, test_split=0.2)

(x_train, y_train), (x_test, y_test) = reuters.load_data(test_split=0.2)

print(len(x_train), 'train sequences')

print(len(x_test), 'test sequences')

#print(x_train[1], y_train[1])

#print("words")

#words = [idx_to_word[i] for i in x_train[1]]

#print(" ".join(words))

num_classes = np.max(y_train) + 1

print(num_classes, 'classes')

print('Vectorizing sequence data...')

tokenizer = Tokenizer(num_words=max_words)

x_train = tokenizer.sequences_to_matrix(x_train, mode='binary')

#print(x_train[0], y_train[0])

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 = keras.utils.to_categorical(y_train, num_classes)

y_test = keras.utils.to_categorical(y_test, num_classes)

print('y_train shape:', y_train.shape)

print('y_test shape:', y_test.shape)

print('Building model...')

model = Sequential()

model.add(Dense(int(.5*max_words), input_shape=(max_words,)))

model.add(Activation('sigmoid'))

model.add(Dropout(0.5))

model.add(Dense(num_classes))

model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',

optimizer='adam',

metrics=['accuracy'])

history = model.fit(x_train, y_train,

validation_split=0.1)

score = model.evaluate(x_test, y_test,

batch_size=batch_size, verbose=1)

"""

model = Sequential()

# Dense(64) is a fully-connected layer with 64 hidden units.

# in the first layer, you must specify the expected input data shape:

# here, 20-dimensional vectors.

model.add(Dense(int(.25*max_words), activation='relu', input_dim=max_words))

model.add(Dropout(0.5))

model.add(Dense(int(.25*max_words), activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(num_classes, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss='categorical_crossentropy',

optimizer=sgd,

metrics=['accuracy'])

model.fit(x_train, y_train,

epochs=20,

batch_size=128)

score = model.evaluate(x_test, y_test, batch_size=128)

"""

with open('results-keras-1h-sig' + str(max_words) + '.txt', 'wt') as f:

print('Test score:', score[0])

print('Test accuracy:', score[1])

f.write("Test score " + str(score[0]) + '\n')

f.write("Test acc " + str(score[1]) + '\n')