if arguments.has_key(arg) == False:
print "Error: the argument %s has to be specified" % (arg)
exit(1)
# mandatory arguments
train_data_spec = arguments['train_data']
valid_data_spec = arguments['valid_data']
conv_nnet_spec = arguments['conv_nnet_spec']
lstm_nnet_spec = arguments['lstm_nnet_spec']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig()
cfg.model_type = 'CLDNNV'
cfg.parse_config_cldnn(arguments, nnet_spec, conv_nnet_spec,
lstm_nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2**30))
log('> ... building the model')
# setup model
dnn = CLDNNV(numpy_rng=numpy_rng, theano_rng=theano_rng, cfg=cfg)
# get the training, validation and testing function for the model
log('> ... getting the finetuning functions')
train_fn, valid_fn = dnn.build_finetune_functions(
(cfg.train_x, cfg.train_y), (cfg.valid_x, cfg.valid_y),
batch_size=cfg.batch_size)
log('> ... finetuning the model')
required_arguments = ['train_data', 'valid_data','conv_nnet_spec','lstm_nnet_spec', 'nnet_spec', 'wdir']
for arg in required_arguments:
if arguments.has_key(arg) == False:
print "Error: the argument %s has to be specified" % (arg); exit(1)
# mandatory arguments
train_data_spec = arguments['train_data']
valid_data_spec = arguments['valid_data']
conv_nnet_spec = arguments['conv_nnet_spec']
lstm_nnet_spec = arguments['lstm_nnet_spec']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig();cfg.model_type = 'CLDNNV'
cfg.parse_config_cldnn(arguments, nnet_spec, conv_nnet_spec, lstm_nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
log('> ... building the model')
# setup model
dnn = CLDNNV(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg = cfg)
# get the training, validation and testing function for the model
log('> ... getting the finetuning functions')
train_fn, valid_fn = dnn.build_finetune_functions(
(cfg.train_x, cfg.train_y), (cfg.valid_x, cfg.valid_y),
batch_size=cfg.batch_size)
log('> ... finetuning the model')
