def train_orientation(output_path): from jpcnn.core import JPCNN_Network, JPCNN_Data print('[orientation] Loading the Orientation training data') data_list, label_list = load_orientation() print('[orientation] Loading the data into a JPCNN_Data') data = JPCNN_Data() data.set_data_list(data_list) data.set_label_list(label_list) print('[orientation] Create the JPCNN_Model used for training') model = Orientation_Model() print('[orientation] Create the JPCNN_network and start training') net = JPCNN_Network(model, data) net.train( output_path, train_learning_rate=0.01, train_batch_size=128, train_max_epochs=100, )
def train_classifier(output_path, **kwargs): print('[classifier] Loading the classifier training data') data_list, label_list = load_classifier(**kwargs) print('[classifier] Loading the data into a JPCNN_Data') data = JPCNN_Data() data.set_data_list(data_list) data.set_label_list(label_list) print('[classifier] Create the JPCNN_Model used for training') model = Classifier_Model() print('[classifier] Create the JPCNN_network and start training') net = JPCNN_Network(model, data) model_path = net.train( output_path, train_learning_rate=0.01, train_batch_size=64, train_max_epochs=40, train_mini_batch_augment=False, ) return model_path
def train_labeler(output_path, **kwargs): print('[labeler] Loading the labeler training data') data_list, label_list = load_labeler(**kwargs) print('[labeler] Loading the data into a JPCNN_Data') data = JPCNN_Data() data.set_data_list(data_list) data.set_label_list(label_list) print('[labeler] Create the JPCNN_Model used for training') model = Labeler_Model() print('[labeler] Create the JPCNN_network and start training') net = JPCNN_Network(model, data) model_path = net.train( output_path, train_learning_rate=0.01, train_batch_size=64, train_max_epochs=5, train_mini_batch_augment=False, ) return model_path