predicting.py

import numpy
example_input = "I loved the way that the actors were cast, also, It is clear that they've put a huge effort in post-production."
#example_input = "Try your movie review here!"
# Steps explained: 
# 1st: tokenize input. We cast it to an iterator to fake a generator.
input_iter = iter([example_input])
input_tokens = data.tokenize(input_iter, vocab_file='en_8k.subword')
# 2nd: cast the results to a list and get the first value (the tokens, not the label or anything else)
tokenized_input = list(input_tokens)[0]
# 3rd: Add fake batch dimmension
tokenized_with_batch = tokenized_input[None, :]
# 4th: input it to the model and get the logprobs
sentiment_log_probs = sentiment_analysis_model(tokenized_with_batch)
# 5th: get the values to between 0 and 1 by exponentiating the logprobs.
norm_log_probs = numpy.exp(sentiment_log_probs)
# 6th: get if it's either true or false by checking the position of the greatest values at norm_log_probs dimmension 0 (that's what argmax does)
sentiment = numpy.argmax(norm_log_probs[0])
print('Input review:\n"{}"\nThe sentiment is: {}'.format(example_input, "Positive" if sentiment else "Negative"))