def main(): global_config.assign_config() if args['device'] == "cpu": os.environ['CUDA_VISIBLE_DEVICES'] = '' else: os.environ['CUDA_VISIBLE_DEVICES'] = '0' rm_file("./data/log/*log.txt") if args['mode'] == "train": Train().run() elif args['mode'] == "new_train": print("Clean chceck point: train_dir ") clean_folder(global_config.global_config.train_dir) print("Clean log directory") clean_folder(global_config.global_config.log_dir) Train().run() elif args['mode'] == "eval": Evaluate().run()
if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) package_path = osp.join(this_dir, '..') add_path(package_path) import tensorflow as tf import numpy as np from pprint import pprint as pp from graph.forward.Iforward import IForward from utils.config import global_config global_config.assign_config() iforward = IForward('train', None) # define variable logit_input = tf.placeholder(shape=[None, None], dtype=tf.float32) label_input = tf.placeholder(shape=[None], dtype=tf.int64) # build graph def get_class_softmax_loss(logit_input, label_input): ''' tf.nn.sparse_softmax_cross_entropy_with_logits will apply: softmax to logit_input, one_hot encoded to label_input then compute cross-entropy between (above) two value to get losses
def run(self): # set evn to use CPU only global_config.assign_config() self.build_computation_graph()
def run(self): global_config.assign_config() # self.prepared_dataset = PrepareDataset() self.build_computation_graph() self.run_training()