def train_RNet(base_dir, prefix, log_dir, model_checkpoint, end_epoch, display, lr, optimizer='adam'): """ train PNet :param dataset_dir: tfrecord path :param prefix: :param end_epoch: :param display: :param lr: :return: """ net_factory = R_Net train(net_factory, prefix, end_epoch, base_dir, log_dir, display=display, base_lr=lr, ckpt=model_checkpoint, optimizer=optimizer)
def train_PNet(base_dir, prefix, end_epoch, display, lr, restore): """ :param base_dir: '../../DATA/imglists/PNet' :param prefix: '../ckpt/%s_model/PNet_landmark/PNet' % model_name :param end_epoch: :param display: :param lr: :return: """ net_factory = P_Net train(net_factory, prefix, end_epoch, base_dir, display=display, base_lr=lr, restore=restore)
def train_PNet(base_dir, prefix, end_epoch, display, lr): """ train PNet :param dataset_dir: tfrecord path :param prefix: :param end_epoch: max epoch for training :param display: :param lr: learning rate :return: """ net_factory = P_Net train(net_factory,prefix, end_epoch, base_dir, display=display, base_lr=lr)
def train_ONet(base_dir, prefix, end_epoch, display, lr): """ train PNet :param dataset_dir: tfrecord path :param prefix: :param end_epoch: :param display: :param lr: :return: """ net_factory = O_Net train(net_factory, prefix, end_epoch, base_dir, display=display, base_lr=lr)
def train_PNet(tfrecord_path, model_save_path, max_epoch, display, lr): """ train PNet :param tfrecord_path: tfrecord训练数据路径 :param model_save_path: 模型保存路径 :param max_epoch: 最大训练epoch :param display: 日志打印 :param lr: learning rate :return: """ net_factory = P_Net train(net_factory, model_save_path, max_epoch, tfrecord_path, display=display, base_lr=lr)
def train_PNet(base_dir, prefix, end_epoch, display, lr): """ train PNet Parameters: --------------- base_dir: dataset_dir: tfrecord路径 prefix: model路径 end_epoch: 训练最大轮次数 display: 每训练display个step输出训练状态 lr: 学习率 """ net_factory = P_Net train(net_factory, prefix, end_epoch, base_dir, display=display, base_lr=lr)