def test_qgen(sess, testset, tokenizer, qgen, cpu_pool, batch_size, logger):
qgen_sources = qgen.get_sources(sess)
qgen_evaluator = Evaluator(qgen_sources, qgen.scope_name, network=qgen, tokenizer=tokenizer)
qgen_batchifier = QuestionerBatchifier(tokenizer, qgen_sources, status=('success',))
qgen_iterator = Iterator(testset, pool=cpu_pool,
batch_size=batch_size,
batchifier=qgen_batchifier)
[qgen_loss] = qgen_evaluator.process(sess, qgen_iterator, outputs=[qgen.ml_loss])
logger.info("QGen test loss: {}".format(qgen_loss))
def test_guesser(sess, testset, tokenizer, guesser, cpu_pool, batch_size, logger):
guesser_sources = guesser.get_sources(sess)
guesser_evaluator = Evaluator(guesser_sources, guesser.scope_name, network=guesser, tokenizer=tokenizer)
guesser_batchifier = QuestionerBatchifier(tokenizer, guesser_sources, status=('success',))
guesser_iterator = Iterator(testset, pool=cpu_pool,
batch_size=batch_size,
batchifier=guesser_batchifier)
[guesser_loss, guesser_error] = guesser_evaluator.process(sess, guesser_iterator, [guesser.loss, guesser.error])
logger.info("Guesser test loss: {}".format(guesser_loss))
logger.info("Guesser test error: {}".format(guesser_error))
class SingleGameIterator(object):
""" Single game iterator """
def __init__(self, tokenizer, game):
self.n_examples = 1
#self.batchifier = LooperBatchifier(tokenizer, generate_new_games=False)
self.batchifier = QuestionerBatchifier(tokenizer, None)
self.game = game
def __iter__(self):
""" Applies batching (and transforms into dict) """
# Futurely consider yielding games from the queue for the same looper
return iter([self.batchifier.apply([self.game])])
sources = network.get_sources(sess)
logger.info("Sources: " + ', '.join(sources))
sess.run(tf.global_variables_initializer())
start_epoch = load_checkpoint(sess, saver, args, save_path)
best_val_err = 0
best_train_err = None
# create training tools
evaluator = Evaluator(sources,
network.scope_name,
network=network,
tokenizer=tokenizer)
batchifier = QuestionerBatchifier(tokenizer,
sources,
status=('success', ))
for t in range(start_epoch, no_epoch):
logger.info('Epoch {}..'.format(t + 1))
train_iterator = Iterator(trainset,
batch_size=batch_size,
pool=cpu_pool,
batchifier=batchifier,
shuffle=True)
train_loss, train_accuracy = evaluator.process(sess,
train_iterator,
outputs=outputs +
[optimizer])
def __init__(self, tokenizer, game):
self.n_examples = 1
#self.batchifier = LooperBatchifier(tokenizer, generate_new_games=False)
self.batchifier = QuestionerBatchifier(tokenizer, None)
self.game = game