import matplotlib.pyplot as plt
import al
from al.dataset import mnist
from al.model.model_zoo.simple_cnn import ConvModel
from al.model.mnist import MnistLearner
from al.dataset.mnist import MnistDataset
from al.train.active_train import ActiveTrain
from al.helpers.experiment import set_up_experiment, load_config
from al.experiments import set_up_learner
DATASET = 'mnist'
FOLDER_PATH = os.path.dirname(__file__)
OUTPUT_DIR, FIGURE_DIR, logger, logger_name = set_up_experiment(__file__,
FOLDER_PATH,
logging_lvl=20)
config = load_config(FOLDER_PATH, DATASET)
setupper = set_up_learner(DATASET)
config['active_learning']['output_dir'] = OUTPUT_DIR
config['experiment']['logger_name'] = logger_name
model_name = config['experiment']['model']
dataset, learner = setupper(config, OUTPUT_DIR, logger)
queried = os.path.join(os.path.dirname(__file__), 'results',
'queries-margin_sampling-0-simplenet.txt')
df = pd.read_csv(queried, header=0, skiprows=1)
# print(df)
query_step = 0
plot_size = 32
from al.train.active_train import ActiveTrain
from al.helpers.experiment import set_up_experiment, load_config
from al.experiments import set_up_learner
EXPERIMENT_NAME = 'initial_training_data'
FOLDER_PATH = os.path.expanduser(
f'~/Documents/active-learning/experiments/{EXPERIMENT_NAME}')
DATASETS = ['mnist', 'cifar', 'pascalvoc_detection']
dataset_to_initsizes = {
'mnist': [10, 30, 100, 300, 1000],
'cifar': [100, 300, 1000, 3000, 10000],
'pascalvoc_detection': [100, 300, 500, 1000]
}
REPEATS = 2
OUTPUT_DIR, FIGURE_DIR, logger, logger_name = set_up_experiment(
EXPERIMENT_NAME)
def run_single_experiment(dataset_name, init_size):
logger.info(f'INITIAL SIZE : {init_size}')
config = load_config(FOLDER_PATH, dataset_name)
setupper = set_up_learner(dataset_name)
config['active_learning']['output_dir'] = OUTPUT_DIR
config['active_learning']['init_size'] = init_size
config['experiment']['logger_name'] = logger_name
logger.debug('Getting dataset and learner')
dataset, learner = setupper(config, OUTPUT_DIR, logger)
logger.debug('Getting trainer')
trainer = ActiveTrain(learner, dataset, config['experiment']['strategy'],
logger_name)
logger.debug('Training...')
