Deep Neural Net Test with TFLearn, pickled inputs and pre-trained model.

Simple script, no tabs, but needs assets and tflearn to be functional.

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=0.001)
#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.)
# converting labels to binary vectors
#trainY = to_categorical(trainY, nb_classes=2)
testY = to_categorical(testY, nb_classes=2)

# Network building
net = tflearn.input_data([None, 100])
net = tflearn.embedding(net, input_dim=10000, output_dim=128)
net = tflearn.lstm(net, 128, dropout=0.8)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net, optimizer='adam', learning_rate=0.0001, loss='categorical_crossentropy')

# Training
model = tflearn.DNN(net, tensorboard_verbose=0), trainY, validation_set=(testX, testY), show_metric=True, batch_size=32)
print testX
pred = model.predict(testX)
print("test results? ", pred)