mode='encoder')
if args.num_train_dec > 0:
for idx in range(args.num_train_dec):
train(epoch,
model,
dec_optimizer,
args,
use_cuda=use_cuda,
mode='decoder')
this_loss, this_ber = validate(model,
general_optimizer,
args,
use_cuda=use_cuda)
report_loss.append(this_loss)
report_ber.append(this_ber)
if args.print_test_traj == True:
print('test loss trajectory', report_loss)
print('test ber trajectory', report_ber)
print('total epoch', args.num_epoch)
#################################################
# Testing Processes
#################################################
test(model, args, use_cuda=use_cuda)
torch.save(model.state_dict(), './tmp/torch_model_' + identity + '.pt')
print('saved model', './tmp/torch_model_' + identity + '.pt')
report_loss.append(this_loss)
report_ber.append(this_ber)
report_bler.append(this_bler)
data_file = open(filename, 'a')
data_file.write(
str(epoch) + ' ' + str(this_loss) + ' ' + str(this_ber) + ' ' +
str(this_bler) + "\n")
data_file.close()
# save model per epoch
modelpath = './tmp/attention_model_' + str(epoch) + '_' + str(
args.channel) + '_lr_' + str(args.enc_lr) + '_D' + str(
args.D) + '_' + str(args.num_block) + '_' + timestamp + '.pt'
# modelpath = './tmp/model_'+str(epoch)+'_'+str(args.channel)+'_lr_'+str(args.enc_lr)+'_D'+str(args.D)+'_'+str(args.num_block)+'.pt'
torch.save(model.state_dict(), modelpath)
# try:
# # pre_modelpath = './tmp/model_'+str(epoch-1)+'_'+str(args.channel)+'_lr_'+str(args.enc_lr)+'_D'+str(args.D)+'_'+str(args.num_block)+'_'+timestamp+'.pt'
# pre_modelpath = './tmp/model_'+str(epoch-1)+'_'+str(args.channel)+'_lr_'+str(args.enc_lr)+'_D'+str(args.D)+'_'+str(args.num_block)+'.pt'
# os.system(r"rm -f {}".format(pre_modelpath))#调用系统命令行来删除文件
# except:
# pass
print('saved model', modelpath)
print("each epoch training time: {}s".format(time.time() -
epoch_start_time))
if args.print_test_traj == True:
print('test loss trajectory', report_loss)
print('test ber trajectory', report_ber)
print('test bler trajectory', report_bler)
print('total epoch', args.num_epoch)