添加自己的参数和网络

1. add network

from .net import Net
from .my_net import MyNet

def factory(engine=None):

  Logger()('Creating mnist network...')

  if Options()['model']['network']['name'] == 'net':
      network = Net()

	# 要设置好网络的名字
	#model:
    # 	name: simple
    # 	network:
    # 		import: mnist.models.networks.factory
    #		name: my_net
    #		mul: 2
    #		drop: 0.2
  elif Options()['model']['network']['name'] == 'my_net':
      opt = Options()['model.network']
      network = MyNet(
          mul=opt['mul'],
          drop=opt['drop']
      )

  else:
      raise ValueError()

  if Options()['misc']['cuda'] and len(utils.available_gpu_ids()) >= 2:
          network = DataParallel(network)

  return network

2. add options

exp:
  dir: logs/mnist/my_net
  resume: # last, best_[...], or empty (from scratch)
dataset:
  import: mnist.datasets.factory
  name: mnist
  dir: data/mnist
  train_split: train
  eval_split: val
  nb_threads: 4
  batch_size: 64
model:
  name: simple
  network:
    import: mnist.models.networks.factory
    name: my_net
    mul: 2
    drop: 0.2
  criterion:
    name: nll
  metric:
    name: accuracy
    topk: [1,5]
optimizer:
  name: sgd
  lr: 0.01
  momentum: 0.5
engine:
  name: default
  debug: False
  nb_epochs: 10
  print_freq: 10
  saving_criteria:
  - loss:min          # save when new_best < best
  - accuracy_top1:max # save when new_best > best
  - accuracy_top5:max # save when new_best > best
misc:
  cuda: False
  seed: 1337
view:
- logs:train_epoch.loss+logs:eval_epoch.loss
- logs:train_batch.loss
- logs:train_epoch.accuracy_top1+logs:eval_epoch.accuracy_top1
- logs:train_epoch.accuracy_top5+logs:eval_epoch.accuracy_top5