# mandatory arguments
train_data_spec = arguments['train_data']
valid_data_spec = arguments['valid_data']
conv_nnet_spec = arguments['conv_nnet_spec']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig()
cfg.model_type = 'CNN'
cfg.parse_config_cnn(arguments, '10:' + nnet_spec, conv_nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
if arguments.has_key('replicate'):
cfg.replicate = int(arguments['replicate'])
# parse pre-training options
# pre-training files and layer number (how many layers are set to the pre-training parameters)
ptr_layer_number = 0
ptr_file = ''
if arguments.has_key('ptr_file') and arguments.has_key('ptr_layer_number'):
ptr_file = arguments['ptr_file']
temp = arguments['ptr_layer_number'].split(':')
if len(temp) > 1 or len(temp[0].split(',')) > 1:
ptr_layer_number = [map(int, i.split(',')) for i in temp]
else:
ptr_layer_number = int(temp[0])
# check working dir to see whether it's resuming training
