def main():
rounds = 5
n_aug_sample_points = [1, 10, 50, 100, 250, 500, 750, 1000]
n_train = 1000
n_jobs = 1
cv = 1
use_GPU = True
batch_size = 512
CNN_extractor_max_iter = 40
use_loss = False
# Can use multiple valus of C for cross-validation
# logistic_reg__Cs = [[10], [100], [1000], [1e6]]
logistic_reg__Cs = [[10]]
classes_datasets = [
((3, 8), dataset_loaders.Dataset.MNIST),
]
selected_augmentations = [
(augmentations.Image_Transformation.translate, {
"mag_aug": 2
}),
(augmentations.Image_Transformation.rotate, {
"mag_aug": 30,
"n_rotations": 15
}),
(augmentations.Image_Transformation.crop, {
"mag_augs": [1, 2, 3, 4, 5, 6]
}),
]
experiment_configs = [
("baseline", False, False),
("random_proportional", False, False),
("random_proportional", False, True),
("random_proportional", True, False),
("random_proportional", True, True),
("random_inverse_proportional", False, False),
# ("random_inverse_proportional", True, False),
# ("random_softmax_proportional", False, False),
# ("random_softmax_proportional", False, True),
# ("random_softmax_proportional", True, False),
# ("random_softmax_proportional", True, True),
# ("random_inverse_softmax_proportional", False, False),
# ("random_inverse_softmax_proportional", True, False),
("deterministic_proportional", False, False),
("deterministic_proportional", False, True),
("deterministic_proportional", True, False),
("deterministic_proportional", True, True),
("deterministic_inverse_proportional", False, False),
("deterministic_inverse_proportional", True, False),
]
for logistic_reg__C in logistic_reg__Cs:
for classes, dataset in classes_datasets:
for aug_transformation, aug_kw_args in selected_augmentations:
dataset_class_str = experiments_util.classes_to_class_str(
classes)
print("Class types: {}".format(dataset_class_str))
reg_str = "-".join(list(map(str, logistic_reg__C)))
results_filename = "aug_results_margin_{}_{}_{}_{}{}".format(
dataset.name,
dataset_class_str,
aug_transformation.name,
reg_str,
"_loss" if use_loss else "",
)
experiments.run_test_margin(
classes,
rounds,
n_aug_sample_points,
n_train,
n_jobs,
cv,
use_GPU,
batch_size,
dataset,
aug_transformation,
aug_kw_args,
logistic_reg__C,
CNN_extractor_max_iter,
use_loss,
experiment_configs,
results_filename,
)
def main():
rounds = 5
#n_aug_sample_points = [1, 10, 50, 100, 250, 500, 750, 1000]
#n_aug_sample_points = [1, 10, 30, 50, 70, 100, 150, 250, 500]
n_aug_sample_points = [1, 10, 30, 50, 70, 100, 150, 200, 250]
#n_aug_sample_points = [x for x in range(1, 1001)]
n_train = 1000
n_jobs = 1
cv = 1
use_GPU = True
batch_size = 128
CNN_extractor_max_iter = 40
use_loss = False
dpp_normalize_features = True
dpp_phi_scale = 1e3
dpp_weight_by_scores_opts = [True, False]
# Can use multiple valus of C for cross-validation
logistic_reg__Cs = [[10]]
classes_datasets = [
((0, 1), dataset_loaders.Dataset.CIFAR10),
]
selected_augmentations = [
(augmentations.Image_Transformation.translate, {
"mag_aug": 3
}),
(augmentations.Image_Transformation.rotate, {
"mag_aug": 5,
"n_rotations": 4
}),
(augmentations.Image_Transformation.crop, {
"mag_augs": [2]
}),
]
experiment_configs = [
# ("baseline", False, False),
("random_proportional", False, False),
]
for dpp_weight_by_scores in dpp_weight_by_scores_opts:
for logistic_reg__C in logistic_reg__Cs:
for classes, dataset in classes_datasets:
for aug_transformation, aug_kw_args in selected_augmentations:
dataset_class_str = experiments_util.classes_to_class_str(
classes)
print("Class types: {}".format(dataset_class_str))
reg_str = "-".join(list(map(str, logistic_reg__C)))
if not dpp_weight_by_scores:
results_filename = "aug_results_dpp_raw_norm_{}_{}_{}_{}{}".format(
dataset.name,
dataset_class_str,
aug_transformation.name,
reg_str,
"_loss" if use_loss else "",
)
else:
results_filename = "aug_results_dpp_norm_{}_{}_{}_{}{}".format(
dataset.name,
dataset_class_str,
aug_transformation.name,
reg_str,
"_loss" if use_loss else "",
)
if dataset == dataset_loaders.Dataset.CIFAR10:
model_filename = "models/cifar10_ResNet56v2_model.h5"
elif dataset == dataset_loaders.Dataset.NORB:
if (aug_transformation ==
augmentations.Image_Transformation.translate):
model_filename = \
"models/norb_small_ResNet56v2_model_translate.h5"
else:
model_filename = \
"models/norb_small_ResNet56v2_model_rotate_crop.h5"
else:
raise RuntimeError("Unknown model for configuration")
run_test_dpp(
classes,
rounds,
n_aug_sample_points,
n_train,
n_jobs,
cv,
use_GPU,
batch_size,
dataset,
aug_transformation,
aug_kw_args,
logistic_reg__C,
CNN_extractor_max_iter,
use_loss,
experiment_configs,
results_filename,
model_filename,
dpp_weight_by_scores,
dpp_normalize_features,
dpp_phi_scale,
)
def main():
rounds = 5
n_aug_sample_points = [1, 10, 50, 100, 250, 500, 750, 1000]
n_train = 1000
n_jobs = 1
cv = 1
use_GPU = True
batch_size = 128
CNN_extractor_max_iter = 40
use_loss = False
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
K.set_session(sess)
# Can use multiple valus of C for cross-validation
logistic_reg__Cs = [[10]]
classes_datasets = [
((3, 8), dataset_loaders.Dataset.MNIST),
]
selected_augmentations = [
(augmentations.Image_Transformation.translate, {
"mag_aug": 2
}),
(augmentations.Image_Transformation.rotate, {
"mag_aug": 30,
"n_rotations": 15
}),
(augmentations.Image_Transformation.crop, {
"mag_augs": [1, 2, 3, 4, 5, 6]
}),
]
experiment_configs = [
("baseline", False, False),
("random_proportional", False, False),
("random_proportional", False, True),
("random_proportional", True, False),
("random_proportional", True, True),
("random_inverse_proportional", False, False),
# ("random_inverse_proportional", True, False),
# ("random_softmax_proportional", False, False),
# ("random_softmax_proportional", False, True),
# ("random_softmax_proportional", True, False),
# ("random_softmax_proportional", True, True),
# ("random_inverse_softmax_proportional", False, False),
# ("random_inverse_softmax_proportional", True, False),
("deterministic_proportional", False, False),
("deterministic_proportional", False, True),
("deterministic_proportional", True, False),
("deterministic_proportional", True, True),
("deterministic_inverse_proportional", False, False),
("deterministic_inverse_proportional", True, False),
]
for logistic_reg__C in logistic_reg__Cs:
for classes, dataset in classes_datasets:
for aug_transformation, aug_kw_args in selected_augmentations:
dataset_class_str = experiments_util.classes_to_class_str(
classes)
print("Class types: {}".format(dataset_class_str))
reg_str = "-".join(list(map(str, logistic_reg__C)))
results_filename = "aug_results_{}_{}_{}_{}{}".format(
dataset.name,
dataset_class_str,
aug_transformation.name,
reg_str,
"_loss" if use_loss else "",
)
all_results = experiments.run_test_clustered_sweep(
classes,
rounds,
n_aug_sample_points,
n_train,
n_jobs,
cv,
use_GPU,
batch_size,
dataset,
aug_transformation,
aug_kw_args,
logistic_reg__C,
CNN_extractor_max_iter,
use_loss,
experiment_configs,
results_filename,
)
all_results_df = (
pd.DataFrame(all_results).set_index("n_clusters"))
print("all_results", all_results_df)
all_results_df.to_csv(results_filename + "_sweep.csv")
def main():
rounds = 5
n_aug_sample_points = [1, 10, 50, 100, 250, 500, 750, 1000]
n_train = 1000
n_jobs = 1
cv = 1
use_GPU = True
batch_size = 128
CNN_extractor_max_iter = 40
use_loss = False
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
K.set_session(sess)
# Can use multiple valus of C for cross-validation
logistic_reg__Cs = [[10]]
classes_datasets = [
((0, 1), dataset_loaders.Dataset.NORB),
]
selected_augmentations = [
(augmentations.Image_Transformation.translate, {
"mag_aug": 6
}),
(augmentations.Image_Transformation.rotate, {
"mag_aug": 5,
"n_rotations": 4
}),
(augmentations.Image_Transformation.crop, {
"mag_augs": [2]
}),
]
experiment_configs = [
("baseline", False, False),
("random_proportional", False, False),
("random_proportional", False, True),
("random_proportional", True, False),
("random_proportional", True, True),
("random_inverse_proportional", False, False),
# ("random_inverse_proportional", True, False),
# ("random_softmax_proportional", False, False),
# ("random_softmax_proportional", False, True),
# ("random_softmax_proportional", True, False),
# ("random_softmax_proportional", True, True),
# ("random_inverse_softmax_proportional", False, False),
# ("random_inverse_softmax_proportional", True, False),
("deterministic_proportional", False, False),
("deterministic_proportional", False, True),
("deterministic_proportional", True, False),
("deterministic_proportional", True, True),
("deterministic_inverse_proportional", False, False),
("deterministic_inverse_proportional", True, False),
]
for logistic_reg__C in logistic_reg__Cs:
for classes, dataset in classes_datasets:
for aug_transformation, aug_kw_args in selected_augmentations:
dataset_class_str = experiments_util.classes_to_class_str(
classes)
print("Class types: {}".format(dataset_class_str))
reg_str = "-".join(list(map(str, logistic_reg__C)))
results_filename = "aug_results_{}_{}_{}_{}{}".format(
dataset.name,
dataset_class_str,
aug_transformation.name,
reg_str,
"_loss" if use_loss else "",
)
if dataset == dataset_loaders.Dataset.CIFAR10:
model_filename = "models/cifar10_ResNet56v2_model.h5"
elif dataset == dataset_loaders.Dataset.NORB:
if (aug_transformation ==
augmentations.Image_Transformation.translate):
model_filename = "models/norb_small_ResNet56v2_model_translate.h5"
else:
model_filename = \
"models/norb_small_ResNet56v2_model_rotate_crop.h5"
else:
raise RuntimeError("Unknown model for configuration")
all_results = pretrained_experiments.run_test_clustered_sweep(
classes,
rounds,
n_aug_sample_points,
n_train,
n_jobs,
cv,
use_GPU,
batch_size,
dataset,
aug_transformation,
aug_kw_args,
logistic_reg__C,
CNN_extractor_max_iter,
use_loss,
experiment_configs,
results_filename,
model_filename,
)
all_results_df = (
pd.DataFrame(all_results).set_index("n_clusters"))
print("all_results", all_results_df)
all_results_df.to_csv(results_filename + "_sweep.csv")