/* Load libraries */
import numpy as np
from keras.datasets import imdb
from keras.preprocessing.text import Tokenizer
from keras import models
from keras import layers
from keras.callbacks import ModelCheckpoint

#/* Set random seed */
np.random.seed(0)

/* Set the number of features we want */
number_of_features = 1000

/* Load data and target vector from movie review data */
(train_data, train_target), (test_data, test_target) = imdb.load_data(num_words=number_of_features)

/* Convert movie review data to a one-hot encoded feature matrix */
tokenizer = Tokenizer(num_words=number_of_features)
train_features = tokenizer.sequences_to_matrix(train_data, mode='binary')
test_features = tokenizer.sequences_to_matrix(test_data, mode='binary')

/* Start neural network */
network = models.Sequential()

/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=16, activation='relu', input_shape=(number_of_features,)))

/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=16, activation='relu'))

/* Add fully connected layer with a sigmoid activation function */
network.add(layers.Dense(units=1, activation='sigmoid'))

/* Compile neural network */
network.compile(loss='binary_crossentropy', # Cross-entropy
                optimizer='rmsprop', # Root Mean Square Propagation
                metrics=['accuracy']) # Accuracy performance metric

/* Set callback functions to early stop training and save the best model so far */
checkpoint = [ModelCheckpoint(filepath='models.hdf5')]

/* Train neural network */
history = network.fit(train_features, # Features
                      train_target, # Target vector
                      epochs=3, # Number of epochs
                      callbacks=checkpoint, # Checkpoint
                      verbose=0, # No output
                      batch_size=100, # Number of observations per batch
                      validation_data=(test_features, test_target)) # Data for evaluation