"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: