def main(test_mode=False):
log_fname = "logs/train.log"
if os.path.isfile(log_fname):
os.remove(log_fname)
log_hdl = logging.FileHandler(log_fname)
log_hdl.setFormatter(logging.Formatter('%(message)s'))
LOGGER.addHandler(log_hdl)
data = utils.load_data_2d(test_mode=test_mode,
valid_pct=0.1,
cropping=False)
# way to map between string labels and int labels
y_map = utils.get_y_map(data)
data['y']['train'] = utils.convert_y(data['y']['train'], y_map)
data['y']['valid'] = utils.convert_y(data['y']['valid'], y_map)
# run experiments
y_test, model, performance, optimizer = exp.resnet(data)
y_test = utils.convert_y(y_test, y_map)
utils.write_results('results/resnet.csv', y_test)
import IPython
IPython.embed()
def main(test_mode=False):
log_fname = "logs/train13.log"
if os.path.isfile(log_fname):
os.remove(log_fname)
log_hdl = logging.FileHandler(log_fname)
log_hdl.setFormatter(logging.Formatter('%(message)s'))
LOGGER.addHandler(log_hdl)
data = utils.load_data(test_mode=test_mode, cropping=True)
# way to map between string labels and int labels
y_map = utils.get_y_map(data)
data['y']['train'] = utils.convert_y(data['y']['train'], y_map)
data['y']['valid'] = utils.convert_y(data['y']['valid'], y_map)
# run experiments
lr_pred, lr_model = exp.lr_baseline(data)
lr_y_test = utils.convert_y(lr_pred['test'], y_map)
utils.write_results('results/lr_baseline.csv', lr_y_test)