def process(class_id):
print("class", class_id)
d = CifarDataManager()
model = Model(learning_rate=args.learning_rate,
L1_loss_penalty=args.l1_loss_penalty,
threshold=args.threshold)
# print(class_id, args.number, args.learning_rate, args.l1_loss_penalty, args.threshold)
train_images, train_labels = d.train.generateSpecializedData(
class_id, args.number)
model.encode_class_data(class_id, train_images)
if len(line) < 2:
continue
label = int(float(line[1]))
cluster = int(float(line[0]))
if label in classid_cluster:
classid_cluster[label].append(cluster)
else:
classid_cluster[label] = [cluster]
class_counter = {}
class_clusters = {}
for class_id in range(args.start_class, args.end_class + 1):
class_counter[class_id] = 0
class_clusters[class_id] = []
data_loader = CifarDataManager()
batch_size = 100
clusters = []
for _ in range(500 * 100 // batch_size):
train_images, train_labels = data_loader.train.next_batch_without_onehot(
batch_size)
for i in range(batch_size):
label = train_labels[i]
if label < args.start_class or label > args.end_class:
continue
clusters.append(classid_cluster[label][class_counter[label]])
class_clusters[label].append(
classid_cluster[label][class_counter[label]])
class_counter[label] += 1
from vggTrimmedModel import TrimmedModel
from CIFAR_DataLoader import CifarDataManager
import numpy as np
d = CifarDataManager()
model = TrimmedModel(target_class_id= np.random.randint(100,size=10),
multiPruning=True)
'''
Todo List:
1. Modify the accuracy in trimmed network (Done)
'''
for _ in range(50):
test_images, test_labels = d.test.next_batch(200)
model.test_accuracy_pretrim(test_images, test_labels)
model.assign_weight()
model.test_accuracy(test_images, test_labels)
break
def test_cluster(centroid, test_images, test_labels):
d = CifarDataManager()
model = TrimmedModel(centroid)
model.assign_weight()
return model.test_cluster(test_images, test_labels)
default=True,
type=bool_string)
parser.add_argument("--save_folder",
help="folder to save results",
default=None,
type=str)
parser.add_argument("--mode",
help="'train' or 'test' data",
default="train",
type=str)
args = parser.parse_args()
assert args.dataset in ['imagenet', 'cifar'
], "--dataset can only be 'imagenet' or 'cifar'."
d = CifarDataManager(load_cluster=False)
model = Model(learning_rate=args.learning_rate,
L1_loss_penalty=args.l1_loss_penalty,
threshold=args.threshold,
entropy_penalty=args.entropy_penalty)
for i in range(args.begin_class, args.end_class + 1):
print("Current class: {}".format(i))
print("Gnerating data...")
if args.mode == "train":
train_images, train_labels = d.train.generateSpecializedData(
class_id=i, count=args.max_samples)
else:
train_images, train_labels = d.test.generateSpecializedData(
class_id=i, count=args.max_samples)
print("Encoding data...")
default="cluster",
type=str)
parser.add_argument("--save_dir",
help="directory to save trained classifiers",
default="classifiers",
type=str)
parser.add_argument("--cluster_dir",
help="directory saving clusters",
default="../data",
type=str)
args = parser.parse_args()
mode = args.mode
assert mode in ["class", "cluster"]
data_loader = CifarDataManager(cluster_dir=args.cluster_dir)
# cluster_ids = [3]
# class_ids = [0]
corre = [([3], [0]), ([4], [3]), ([1], [1]), ([2], [2]), ([6], [4]),
([0], [4])]
# corre = [([2],[2,4,3]),([6],[0]),([1],[0]),([0],[3,4,1]),([4],[3,4]),([3],[1]),([5],[1])]
# corre = [([3],[0]),([4],[3])]
# test_images, test_labels = data_loader.test.next_batch_balance_without_onehot(200,class_ids)
for item in corre:
cluster_ids = item[0]
class_ids = item[1]
print(cluster_ids, class_ids)
if mode == "class":
target_ids = class_ids
else:
