train_log_mode='TRAIN_LOSS_ONLY',
test_log_mode='TEST_LOSS_ONLY')
# Configure GPU Device
if args.gpu >= 0:
cuda.check_cuda_available()
xp = cuda.cupy if args.gpu >= 0 else np
# loading dataset
dataset = mnist.load()
dim = dataset['train']['data'][0].size
N_train = len(dataset['train']['target'])
N_test = len(dataset['test']['target'])
train_data_dict = {'data':dataset['train']['data'].reshape(N_train, dim).astype(np.float32)}
test_data_dict = {'data':dataset['test']['data'].reshape(N_test, dim).astype(np.float32)}
train_data = DataFeeder(train_data_dict, batchsize=args.batch)
test_data = DataFeeder(test_data_dict, batchsize=args.valbatch)
train_data.hook_preprocess(mnist_preprocess)
test_data.hook_preprocess(mnist_preprocess)
# Model Setup
h_units = 1200
model = models.AutoencoderModel(
Perceptrons(train_data['data'][0].size, h_units, activation=F.relu),
Perceptrons(h_units, train_data['data'][0].size)
)
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
}
# making labeled data
lplo = cross_validation.LeavePLabelOut(labels=six.moves.range(N_train),
p=args.slabeled)
fold = 1
for i in six.moves.range(fold):
train_idx, test_idx = next(iter(lplo))
labeled_data_dict = {
'data': unlabeled_data_dict['data'][test_idx].astype(np.float32),
'target': dataset['train']['target'][test_idx].astype(np.int32)
}
#
labeled_data = datafeeders.SiameseFeeder(labeled_data_dict,
batchsize=args.lbatch)
unlabeled_data = DataFeeder(unlabeled_data_dict, batchsize=args.ubatch)
test_data = datafeeders.SiameseFeeder(test_data_dict, batchsize=args.valbatch)
labeled_data.hook_preprocess(mnist_preprocess)
unlabeled_data.hook_preprocess(mnist_preprocess_u)
test_data.hook_preprocess(mnist_preprocess)
# Model Setup
h_units = 1200
model = models.SiameseModel(
Mlp(labeled_data['data'][0].size, h_units, h_units,
np.max(labeled_data['target']) + 1))
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
dataset = mnist.load()
dim = dataset['train']['data'][0].size
N_train = len(dataset['train']['target'])
N_test = len(dataset['test']['target'])
test_data_dict = {'data':dataset['test']['data'].reshape(N_test, dim).astype(np.float32),
'target':dataset['test']['target'].astype(np.int32)}
unlabeled_data_dict = {'data':dataset['train']['data'].reshape(N_train, dim).astype(np.float32)}
# making labeld data
lplo = cross_validation.LeavePLabelOut(labels=six.moves.range(N_train), p=args.slabeled)
fold = 1
for i in six.moves.range(fold):
train_idx, test_idx = next(iter(lplo))
labeled_data_dict = {'data':unlabeled_data_dict['data'][test_idx].astype(np.float32),
'target':dataset['train']['target'][test_idx].astype(np.int32)}
labeled_data = DataFeeder(labeled_data_dict, batchsize=args.lbatch)
unlabeled_data = DataFeeder(unlabeled_data_dict, batchsize=args.ubatch)
test_data = DataFeeder(test_data_dict, batchsize=args.valbatch)
labeled_data.hook_preprocess(mnist_preprocess)
unlabeled_data.hook_preprocess(mnist_preprocess)
test_data.hook_preprocess(mnist_preprocess)
# Model Setup
h_units = 1200
model = models.ClassifierModel(Mlp(labeled_data['data'][0].size, h_units, h_units, np.max(labeled_data['target'])+1))
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
}
unlabeled_data_dict = {
'data': dataset['train']['data'].reshape(N_train, dim).astype(np.float32)
}
# making labeld data
lplo = cross_validation.LeavePLabelOut(labels=six.moves.range(N_train),
p=args.slabeled)
fold = 1
for i in six.moves.range(fold):
train_idx, test_idx = next(iter(lplo))
labeled_data_dict = {
'data': unlabeled_data_dict['data'][test_idx].astype(np.float32),
'target': dataset['train']['target'][test_idx].astype(np.int32)
}
labeled_data = DataFeeder(labeled_data_dict, batchsize=args.lbatch)
unlabeled_data = DataFeeder(unlabeled_data_dict, batchsize=args.ubatch)
test_data = DataFeeder(test_data_dict, batchsize=args.valbatch)
labeled_data.hook_preprocess(mnist_preprocess)
unlabeled_data.hook_preprocess(mnist_preprocess)
test_data.hook_preprocess(mnist_preprocess)
# Model Setup
h_units = 1200
model = models.ClassifierModel(
Mlp(labeled_data['data'][0].size, h_units, h_units,
np.max(labeled_data['target']) + 1))
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
N_train = len(dataset['train']['target'])
N_test = len(dataset['test']['target'])
test_data_dict = {'data':dataset['test']['data'][:1000].reshape(1000, dim).astype(np.float32),
'target':dataset['test']['target'][:1000].astype(np.int32)}
unlabeled_data_dict = {'data':dataset['train']['data'].reshape(N_train, dim).astype(np.float32)}
# making labeled data
lplo = cross_validation.LeavePLabelOut(labels=six.moves.range(N_train), p=args.slabeled)
fold = 1
for i in six.moves.range(fold):
train_idx, test_idx = next(iter(lplo))
labeled_data_dict = {'data':unlabeled_data_dict['data'][test_idx].astype(np.float32),
'target':dataset['train']['target'][test_idx].astype(np.int32)}
labeled_data = datafeeders.TripletFeeder(labeled_data_dict, batchsize=args.lbatch)
unlabeled_data = DataFeeder(unlabeled_data_dict, batchsize=args.ubatch)
test_data = datafeeders.TripletFeeder(test_data_dict, batchsize=args.valbatch)
labeled_data.hook_preprocess(mnist_preprocess)
unlabeled_data.hook_preprocess(mnist_preprocess_u)
test_data.hook_preprocess(mnist_preprocess)
# Model Setup
h_units = 1200
model = models.TripletModel(Mlp(labeled_data['data'][0].size, h_units, h_units, np.max(labeled_data['target'])+1))
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
# loading dataset
dataset = cifar10.load()
dim = dataset["train"]["data"][0].size
N_train = len(dataset["train"]["target"])
N_test = len(dataset["test"]["target"])
train_data_dict = {
"data": dataset["train"]["data"].astype(np.float32),
"target": dataset["train"]["target"].astype(np.int32),
}
test_data_dict = {
"data": dataset["test"]["data"].astype(np.float32),
"target": dataset["test"]["target"].astype(np.int32),
}
train_data = DataFeeder(train_data_dict, batchsize=args.batch)
test_data = DataFeeder(test_data_dict, batchsize=args.valbatch)
train_data.hook_preprocess(cifar_preprocess)
test_data.hook_preprocess(cifar_preprocess)
# Model Setup
model = models.ClassifierModel(AllConvNet())
# model = models.ClassifierModel(AllConvNetBN())
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
# Opimizer Setup
