Training running

This commit is contained in:
Si11ium 2020-06-08 10:23:12 +02:00
parent 27ae8467fc
commit 821b2d1961
3 changed files with 11 additions and 8 deletions

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@ -31,7 +31,7 @@ main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=
# Training
main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
main_arg_parser.add_argument("--train_epochs", type=int, default=500, help="")
main_arg_parser.add_argument("--train_epochs", type=int, default=250, help="")
main_arg_parser.add_argument("--train_batch_size", type=int, default=10, help="")
main_arg_parser.add_argument("--train_lr", type=float, default=1e-3, help="")
main_arg_parser.add_argument("--train_weight_decay", type=float, default=1e-8, help="")

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@ -1,26 +1,27 @@
import warnings
from _templates.new_project.utils.project_config import Config
warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=UserWarning)
# Imports
# =============================================================================
from _templates.new_project.main import run_lightning_loop, args
from main import run_lightning_loop
from utils.project_config import ThisConfig
from _parameters import args
if __name__ == '__main__':
# Model Settings
config = Config().read_namespace(args)
config = ThisConfig().read_namespace(args)
# bias, activation, model, norm, max_epochs
cnn_classifier = dict(train_epochs=10, model_use_bias=True, model_use_norm=True, data_batchsize=512)
pn2 = dict(model_type='PN2',model_use_bias=True, model_use_norm=True, data_batchsize=250)
p2g = dict(model_type='P2G', model_use_bias=True, model_use_norm=True, data_batchsize=250)
# bias, activation, model, norm, max_epochs
for arg_dict in [cnn_classifier]:
for seed in range(5):
for arg_dict in [p2g]:
for seed in range(10):
arg_dict.update(main_seed=seed)
config = config.update(arg_dict)

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@ -213,6 +213,8 @@ class BaseValMixin:
disp.plot(include_values=True)
self.logger.log_image('Confusion Matrix', image=disp.figure_, step=self.current_epoch)
plt.close('all')
return summary_dict