#!/usr/bin/env python3

from keras.datasets import reuters
from keras import models
from keras import layers
from keras.utils.np_utils import to_categorical
import numpy as np

(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)

print("Length training data", len(train_data))
print("Length training labels", len(train_labels))
print("Length test data", len(test_data))
print("Length test labels", len(test_labels))

word_index = reuters.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
decoded_newswire = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])

def vectorize_sequences(sequences, dimension=10000):
	results = np.zeros((len(sequences), dimension))
	for i, sequence in enumerate(sequences):
		results[i, sequence] = 1.
	return results

x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)

def to_one_hot(labels, dimension=46):
	results = np.zeros((len(labels), dimension))
	for i, label in enumerate(labels):
		results[i, label] = 1.
	return results

one_hot_train_labels = to_one_hot(train_labels)
one_hot_test_labels = to_one_hot(test_labels)

one_hot_train_labels = to_categorical(train_labels)
one_hot_test_labels = to_categorical(test_labels)

x_val = x_train[:1000]
partial_x_train = x_train[1000:]

y_val = one_hot_train_labels[:1000]
partial_y_train = one_hot_train_labels[1000:]

model = models.Sequential()
model.add(layers.Dense(32, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(46, activation='softmax'))

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

model.fit(partial_x_train,
	partial_y_train,
	epochs=9,
	batch_size=512,
	validation_data=(x_val, y_val))

results = model.evaluate(x_test, one_hot_test_labels)

print(results)
