"end_epoch" : 100000, "save_dir" : 'saved_ff_models', "ckpt_interval": 10, # for check pointing "val_interval" : 10, "verb" : 10 } # peroid for printing out losses print_dict('trainer',traindict) print_dict('loss',lossdict) print_dict('data',data) print_dict('main',maindict) data_set = my_data(data["train_file"],data["valid_file"],data["test_file"],data["n_chain"], data["train_pts"],data["vald_pts"],data["test_pts"]) loader = data_loader(data_set,data["batch_size"]) train = trainer(traindict,lossdict) train.load_models() for e in range(maindict["start_epoch"], maindict["end_epoch"]): for qpl_input,qpl_label in loader.train_loader: mydevice.load(qpl_input) q_init,p_init,q_label,p_label,l_init = pack_data(qpl_input,qpl_label) train.one_step(q_init,p_init,q_label,p_label,l_init) if e%maindict["verb"]==0: train.verbose(e+1,'train')
print('trainer dict ============== ') for key, value in traindict.items(): print(key, ':', value) print('data dict ==============') for key, value in data.items(): print(key, ':', value) print('main dict ==============') for key, value in maindict.items(): print(key, ':', value) data_set = my_data(data["train_file"], data["valid_file"], data["test_file"], data["n_chain"], data["train_pts"], data["vald_pts"], data["test_pts"]) loader = data_loader(data_set, data["batch_size"]) train = trainer(traindict) train.load_models() for e in range(maindict["start_epoch"], maindict["end_epoch"]): for qpl_input, qpl_label in loader.train_loader: mydevice.load(qpl_input) q_init, p_init, q_label, p_label, l_init = pack_data( qpl_input, qpl_label) train.one_step(q_init, p_init, q_label, p_label, l_init) if e % maindict["verb"] == 0: