def __init__(self, config_file):
self.config_data = load_config_data(config_file)
self.train_class = TrainClassifier(self.config_data['subset_config'])
self.search_algo = self.get_search_algo(
self.config_data['search_algo'], self.config_data['space'],
self.config_data['metric'], self.config_data['mode'])
# save subset method, to be used in log dir name
self.subset_method = self.train_class.configdata['dss_strategy'][
'type']
args=parser.parse_args()
n_classes = 10
n_epochs = 200
pre = Preprocessing('digits')
pre.load_data(filename='train.csv', name='train')
X_df = pre.get(name='train').drop(columns=['0'])
y_df = pre.get(name='train')['0']
dtype = torch.float
device = torch.device("cpu")
model_name = 'logreg_digits'
model = LogReg(model_name, 256, n_classes)
learning_rate = 0.0001
batch_size = 32
train_classifier = TrainClassifier(model, X_df, y_df)
trained_model , optimizer, criterion, loss_hist, loss_val_hist, best_param = train_classifier.run_train(n_epochs = n_epochs, lr=learning_rate, batch_size=batch_size)
pre.save_results(loss_hist, loss_val_hist, f'{model_name}')
trained_model.load_state_dict(state_dict=best_param)
trained_model.eval()
if args.s_model:
m_exporter = ModelExporter('digits')
m_exporter.save_nn_model(trained_model, optimizer, 0, n_classes, n_epochs, trained_model.get_args())
from train import TrainClassifier config_file = "configs/config_gradmatch_cifar10.py" classifier = TrainClassifier(config_file) classifier.configdata['dss_strategy']['select_every'] = 1 classifier.train()
def __init__(self):
self.algorithm = NaiveBayesClassifier
self.trainer = TrainClassifier()
self.classifier = None
