def evaluate_network(network, corpus):
scores = []
for instance in corpus:
process = FeedForwardProcess(network, instance)
process.run_to_end()
scores.append(process.calc_score())
return np.mean(scores)
logging.info("Generating corpus")
corpus = NestedList.generate_corpus(settings.CORPUS_SIZE)
logging.info("Evaluating untrained network")
mean_f1_score = evaluate_network(network, corpus)
logging.info("Mean score: %f", mean_f1_score)
logging.info("Training the network")
for i in range(settings.NUM_EPOCHS):
logging.info("Epoch %d", i)
process = FeedForwardProcess(network)
logging.debug("Initial input: %s", [process.input_struct])
process.run_to_end()
logging.debug("Initial output: %s", process.output_struct.root)
score = process.calc_score()
logging.debug("Initial score: %f", score)
if score != 1:
fork_score = 0
fork_count = 0
while fork_score <= score and fork_count < settings.MAX_FORK_ATTEMPTS:
logging.debug("Fork attempt %d", fork_count)
fork, fork_step_index = process.get_random_fork()
logging.debug("Running feed forward")
