import tflearn
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb

#IMDB dataset loading
train, test, _ = imdb.load_data(path='imdb.pkl', n_words='10000', valid_portion=0.1)
trainX, trainY = train
testX, testY = test

'''Data preprocessing'''
#Sequence padding
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)

#Convert labels to binary vectors
trainY = to_categorical(trainY, nb_classes=2)
testY = to_categorical(testY, nb_classes=2)


'''Network model'''
#Input layer
net = tflearn.input_data([None, 100])

#Hidden layer
net = tflearn.embedding(net, input_dim=10000, output_dim=128)

#Output layer
net = tflearn.lstm(net, 128, dropout=0.8)
net = tflearn.fully_connected(net, 2, activation='softmax')

#Training model
net = tflearn.regression(net, optimizer='adam', learning_rate=0.0001,
                                        loss='categorical_crossentropy')

'''Training'''
model = tflearn.DNN(net, tensorboard_verbose=0)
model.fit(trainX, trainY, validation_set(testX, testY), show_metric=True,
                                                            batch_size=32)
