import data_loader import confusion_matrix import save_net_cifar10 pure_sz = int(sys.argv[1]) noise_sz = int(sys.argv[2]) back_sz = int(sys.argv[3]) # setting batch_size = 128 param_file = '/home/sainbar/fastnet-confussion-layer/config/cifar-10-18pct-confussion11x22.cfg' learning_rate = 1 image_color = 3 image_size = 32 image_shape = (image_color, image_size, image_size, batch_size) init_model = parser.parse_config_file(param_file) net = fastnet.net.FastNet(learning_rate, image_shape, init_model) # prepare data train_data, train_labels, test_data, test_labels = data_loader.load_cifar10() data_mean = train_data.mean(axis=1, keepdims=True) train_data = train_data - data_mean test_data = test_data - data_mean # noisy data noisy_data, noisy_labels = data_loader.load_noisy_labeled() noisy_data = noisy_data - data_mean noisy_labels += 11 # background noise back_data = data_loader.load_noise()
param_dict['batch_size'] = args.batch_size
param_dict['checkpoint_dir'] = args.checkpoint_dir
trainer = args.trainer
# create a checkpoint dumper
image_shape = (param_dict['image_color'], param_dict['image_size'], param_dict['image_size'], param_dict['batch_size'])
param_dict['image_shape'] = image_shape
cp_dumper = CheckpointDumper(param_dict['checkpoint_dir'], param_dict['test_id'])
param_dict['checkpoint_dumper'] = cp_dumper
# create the init_model
init_model = cp_dumper.get_checkpoint()
if init_model is None:
init_model = parse_config_file(args.param_file)
param_dict['init_model'] = init_model
# create train dataprovider and test dataprovider
dp_class = data.get_by_name(param_dict['data_provider'])
train_dp = dp_class(param_dict['data_dir'], param_dict['train_range'])
test_dp = dp_class(param_dict['data_dir'], param_dict['test_range'])
param_dict['train_dp'] = train_dp
param_dict['test_dp'] = test_dp
# get all extra information
num_batch = util.string_to_int_list(args.num_batch)
if len(num_batch) == 1:
param_dict['num_batch'] = num_batch[0]
total_correct += correct * num_case
test_error = (1. - 1.0*total_correct/total_cases)
print 'epoch:', epoch, 'train-error:', train_error, \
'test-error:', test_error
# setting
batch_size = 128
param_file = '/home/sainbar/fastnet-self-paced/config/cifar-100.cfg'
num_epoch = 10
num_epoch2 = 80
learning_rate = 1
image_color = 3
image_size = 32
image_shape = (image_color, image_size, image_size, batch_size)
init_model = parser.parse_config_file(param_file)
net = fastnet.net.FastNet(learning_rate, image_shape, init_model)
# prepare data
train_data, train_labels, test_data, test_labels = load_cifar100()
data_mean = train_data.mean(axis=1,keepdims=True)
train_data = train_data - data_mean
test_data = test_data - data_mean
# noise data
noise_sz = int(sys.argv[1])
noise_data = load_noise()
noise_data = noise_data - data_mean
noise_labels = np.zeros(noise_data.shape[1]).astype(np.float32)
for i in range(len(noise_labels)):
noise_labels[i] = np.random.randint(100)
param_dict['batch_size'] = args.batch_size
param_dict['checkpoint_dir'] = args.checkpoint_dir
trainer = args.trainer
# create a checkpoint dumper
image_shape = (param_dict['image_color'], param_dict['image_size'], param_dict['image_size'], param_dict['batch_size'])
param_dict['image_shape'] = image_shape
cp_dumper = CheckpointDumper(param_dict['checkpoint_dir'], param_dict['test_id'])
param_dict['checkpoint_dumper'] = cp_dumper
# create the init_model
init_model = cp_dumper.get_checkpoint()
if init_model is None:
init_model = parse_config_file(args.param_file)
param_dict['init_model'] = init_model
# create train dataprovider and test dataprovider
dp_class = data.get_by_name(param_dict['data_provider'])
train_dp = dp_class(param_dict['data_dir'], param_dict['train_range'])
test_dp = dp_class(param_dict['data_dir'], param_dict['test_range'], multiview = param_dict['multiview'])
param_dict['train_dp'] = train_dp
param_dict['test_dp'] = test_dp
# get all extra information
num_batch = util.string_to_int_list(args.num_batch)
if len(num_batch) == 1:
param_dict['num_batch'] = num_batch[0]
else: