_start = time.time()
if args.update_num % args.log_valid_every_n == 0:
valid_accumulator = Accumulator(accs, disps)
_start = time.time()
for v_i, v_batch in enumerate(valid_batches[:4]):
dynet.renew_cg()
result = lm.process_batch(v_batch, training=False)
valid_accumulator.update(result)
print "[[ Validation %6d | " % args.update_num, valid_accumulator.pp(
), "| Time: %4f ]]" % (time.time() - _start),
if args.output:
print "(logging to %s)" % args.output,
with open(args.output, "a") as outfile:
outfile.write("\n%d," % args.update_num +
valid_accumulator.lp())
if args.save:
if best_score is None or best_score > valid_accumulator.values[
"loss"]:
print "new best...saving to", args.save,
best_score = valid_accumulator.values["loss"]
model.save(args.save)
trainer.status()
if "samples" in result: print "\n" + vocab.pp(result["samples"][0])
print "\n"
_start = time.time()
# trainer.update_epoch()
# Test evaluation
test_data = util.load_data(args.test_data, DATA_VIEW, args.start_token,
