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')