def check_param_minmax(self):
''' load saved parameter from trained model and original data as no crash data
use to check parameters min-max and save param after backward '''
ws = []
bs = []
for n in self.get_netlist():
for name, param in n.named_parameters():
if 'weight' in name:
ws.append(param.view(-1))
if 'bias' in name:
bs.append(param.view(-1))
ws = torch.cat(ws)
bs = torch.cat(bs)
data_io.write_param_dist('../data/gen_by_ML/nocrash_retrain/w_b.pt',
ws, bs)
print('w max', max(ws), 'min', min(ws))
print('b max', max(bs), 'min', min(bs))
def check_grad_minmax(self):
''' load saved parameter from trained model and original data as no crash data
use to check param gradient min-max and save param grad after backward '''
gradw = []
gradb = []
for n in self.get_netlist():
for name, param in n.named_parameters():
if 'weight' in name:
gradw.append(param.grad.view(-1))
if 'bias' in name:
gradb.append(param.grad.view(-1))
gradw = torch.cat(gradw)
gradb = torch.cat(gradb)
data_io.write_param_dist(
'../data/gen_by_ML/nocrash_retrain/gradw_gradb.pt', gradw, gradb)
print('grad w max', max(gradw), 'min', min(gradw))
print('grad b max', max(gradb), 'min', min(gradb))