def evaluate_batch(model, num_batches, eval_file, sess, data_type, handle,
str_handle):
answer_dict = {}
losses = []
for _ in tqdm(range(1, num_batches + 1)):
qa_id, loss, yp1, yp2, = sess.run(
[model.qa_id, model.loss, model.yp1, model.yp2],
feed_dict={handle: str_handle})
answer_dict_, _ = convert_tokens(eval_file, qa_id.tolist(),
yp1.tolist(), yp2.tolist())
answer_dict.update(answer_dict_)
losses.append(loss)
loss = np.mean(losses)
metrics = evaluate(eval_file, answer_dict)
metrics["loss"] = loss
loss_sum = tf.Summary(value=[
tf.Summary.Value(tag="{}/loss".format(data_type),
simple_value=metrics["loss"]),
])
f1_sum = tf.Summary(value=[
tf.Summary.Value(tag="{}/f1".format(data_type),
simple_value=metrics["f1"]),
])
em_sum = tf.Summary(value=[
tf.Summary.Value(tag="{}/em".format(data_type),
simple_value=metrics["exact_match"]),
])
return metrics, [loss_sum, f1_sum, em_sum]
def test(config):
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.char_emb_file, "r") as fh:
char_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.test_eval_file, "r") as fh:
eval_file = json.load(fh)
with open(config.test_meta, "r") as fh:
meta = json.load(fh)
total = meta["total"]
graph = tf.Graph()
print("Loading model...")
with graph.as_default() as g:
test_batch = get_dataset(config.test_record_file,
get_record_parser(config, is_test=True),
config).make_one_shot_iterator()
model = Model(config,
test_batch,
word_mat,
char_mat,
trainable=False,
graph=g)
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(config.save_dir))
if config.decay < 1.0:
sess.run(model.assign_vars)
losses = []
answer_dict = {}
remapped_dict = {}
for step in tqdm(range(total // config.batch_size + 1)):
qa_id, loss, yp1, yp2 = sess.run(
[model.qa_id, model.loss, model.yp1, model.yp2])
answer_dict_, remapped_dict_ = convert_tokens(
eval_file, qa_id.tolist(), yp1.tolist(), yp2.tolist())
answer_dict.update(answer_dict_)
remapped_dict.update(remapped_dict_)
losses.append(loss)
loss = np.mean(losses)
metrics = evaluate(eval_file, answer_dict)
with open(config.answer_file, "w") as fh:
json.dump(remapped_dict, fh)
print("Exact Match: {}, F1: {}".format(metrics['exact_match'],
metrics['f1']))
