parser.add_argument("--nb_test_samples", type=int, default=None)
+parser.add_argument("--nb_new_c_quizzes_for_train", type=int, default=None)
+
+parser.add_argument("--nb_new_c_quizzes_for_test", type=int, default=None)
+
parser.add_argument("--learning_rate", type=float, default=5e-4)
########################################
parser.add_argument("--nb_gpts", type=int, default=5)
-parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
+parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.9)
-parser.add_argument("--proba_understands", type=float, default=0.99)
+parser.add_argument("--proba_understands", type=float, default=0.9)
parser.add_argument("--proba_not_understands", type=float, default=0.5)
-parser.add_argument("--generation_temperature", type=float, default=2.0)
+parser.add_argument("--generation_temperature", type=float, default=1.0)
parser.add_argument("--dirty_debug", action="store_true", default=False)
######################################################################
+# This is the key routine that decides what generated quizzes to keep
+
+
+# token_logprobas are NxMxT where M is the number of models
+
+
+def compute_valid_quizzes_(token_logprobas):
+ warnings.warn("validation with uniform constraints", RuntimeWarning)
+ l = token_logprobas.min(dim=-1).values.sort(dim=-1).values
+ return (l[:, 0] < math.log(0.1)) & (l[:, 1] > math.log(0.5))
-def standard_validity(logproba):
- l = logproba.sort(dim=-1).values
+
+def compute_valid_quizzes(token_logprobas):
+ l = token_logprobas.sum(dim=-1).sort(dim=-1).values
return (l[:, 0] < math.log(args.proba_not_understands)) & (
l[:, 1] > math.log(args.proba_understands)
)
-def valid_quizzes_and_logprobas(recorded, criteria):
+def extract_valid_quizzes_and_logprobas(recorded):
validated_quizzes, validated_logprobas = [], []
- for q, lp in recorded:
- validated_indices = criteria(lp)
- validated_quizzes.append(q[validated_indices])
- validated_logprobas.append(lp[validated_indices])
+ for quizzes, token_logprobas in recorded:
+ validated_indices = compute_valid_quizzes(token_logprobas)
+ validated_quizzes.append(quizzes[validated_indices])
+ validated_logprobas.append(token_logprobas[validated_indices])
if len(validated_quizzes) > 0:
return torch.cat(validated_quizzes, dim=0), torch.cat(
c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
if c_quizzes.size(0) > 0:
- logproba = quiz_machine.logproba_of_solutions(models, c_quizzes)
- recorded_quizzes_logprobas.append((c_quizzes, logproba))
+ token_logproba = quiz_machine.solution_token_logprobas(models, c_quizzes)
+ recorded_quizzes_logprobas.append((c_quizzes, token_logproba))
- validated_quizzes, validated_logprobas = valid_quizzes_and_logprobas(
- recorded_quizzes_logprobas, standard_validity
- )
+ (
+ validated_quizzes,
+ validated_logprobas,
+ ) = extract_valid_quizzes_and_logprobas(recorded_quizzes_logprobas)
if validated_quizzes is not None:
nb_validated = validated_quizzes.size(0)
validated_quizzes[nb_for_train:nb_to_create], for_train=False
)
- ######################################################################
- # save the log probas
-
- file_name = os.path.join(
- args.result_dir, f"culture_c_quiz_all_{n_epoch:04d}_logp.dat"
- )
-
- with open(file_name, "w") as logp_file:
- for _, ll in recorded_quizzes_logprobas:
- for l in ll:
- s = " ".join([str(x.item()) for x in l])
- logp_file.write(s + "\n")
-
######################################################################
# save images with their logprobas
if vq.size(0) > 0:
prefix = f"culture_c_quiz_{n_epoch:04d}"
-
- file_name = os.path.join(args.result_dir, prefix + "_logp.dat")
- with open(file_name, "w") as logp_file:
- for l in vl:
- s = " ".join([str(x.item()) for x in l])
- logp_file.write(s + "\n")
+ filename = os.path.join(args.result_dir, prefix + "_logp.pth")
+ torch.save(vl, filename)
+ # with open(file_name, "w") as logp_file:
+ # for l in vl:
+ # s = " ".join([str(x.item()) for x in l])
+ # logp_file.write(s + "\n")
quiz_machine.save_quiz_illustrations(args.result_dir, prefix, vq)
######################################################################
-nb_new_c_quizzes_for_train = args.nb_train_samples // 50
-nb_new_c_quizzes_for_test = args.nb_test_samples // 50
+if args.nb_new_c_quizzes_for_train is None:
+ args.nb_new_c_quizzes_for_train = args.nb_train_samples // 50
+
+if args.nb_new_c_quizzes_for_test is None:
+ args.nb_new_c_quizzes_for_test = args.nb_test_samples // 50
log_string(
- f"nb_new_c_quizzes_for_train {nb_new_c_quizzes_for_train} nb_new_c_quizzes_for_test {nb_new_c_quizzes_for_test}"
+ f"nb_new_c_quizzes_for_train {args.nb_new_c_quizzes_for_train} nb_new_c_quizzes_for_test {args.nb_new_c_quizzes_for_test}"
)
######################################################################
if args.dirty_debug:
args.accuracy_to_make_c_quizzes = 0.0
args.nb_gpts = 2
- nb_new_c_quizzes_for_train = 100
- nb_new_c_quizzes_for_test = 10
-
- def standard_validity(logproba):
- l = logproba.sort(dim=-1).values
- return l[:, 0] < math.log(0.5)
+ args.nb_new_c_quizzes_for_train = 100
+ args.nb_new_c_quizzes_for_test = 10
######################################################################
cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
log_string(f"current_test_accuracies {cta}")
+ ##################################################
+ # If all the models are good enough, generate new quizzes and
+ # re-compute the test errors
+
+ if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
+ create_c_quizzes(
+ models,
+ quiz_machine,
+ nb_for_train=args.nb_new_c_quizzes_for_train,
+ nb_for_test=args.nb_new_c_quizzes_for_test,
+ )
+
+ filename = "c_quizzes.pth"
+ quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename))
+ log_string(f"wrote {filename}")
+
+ # Force one epoch of training
+ for model in models:
+ model.main_test_accuracy = 0.0
+
##################################################
# Select, improve, and eval the worst model
for model in weakest_models:
quiz_machine.renew_w_quizzes(model, args.nb_train_samples)
- ##################################################
- # If all the models are good enough, generate new quizzes and
- # re-compute the test errors
-
- if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
- create_c_quizzes(
- models,
- quiz_machine,
- nb_for_train=nb_new_c_quizzes_for_train,
- nb_for_test=nb_new_c_quizzes_for_test,
- )
-
- filename = "c_quizzes.pth"
- quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename))
- log_string(f"wrote {filename}")
######################################################################
projects / culture.git / blobdiff