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_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
