def runDNN(arg):
if type(arg) is dict:
model_config = arg
else :
model_config = load_model(arg,'DNN')
dnn_config = load_dnn_spec(model_config['nnet_spec'])
data_spec = load_data_spec(model_config['data_spec'],model_config['batch_size']);
#generating Random
numpy_rng = numpy.random.RandomState(model_config['random_seed'])
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
activationFn = parse_activation(dnn_config['activation']);
#create working dir
createDir(model_config['wdir']);
batch_size = model_config['batch_size'];
n_ins = model_config['n_ins']
n_outs = model_config['n_outs']
max_col_norm = dnn_config['max_col_norm']
l1_reg = dnn_config['l1_reg']
l2_reg = dnn_config['l2_reg']
adv_activation = dnn_config['adv_activation']
hidden_layers_sizes = dnn_config['hidden_layers']
do_dropout = dnn_config['do_dropout']
logger.info('Building the model')
if do_dropout:
dropout_factor = dnn_config['dropout_factor']
input_dropout_factor = dnn_config['input_dropout_factor']
dnn = DNN_Dropout(numpy_rng=numpy_rng, theano_rng = theano_rng, n_ins=n_ins,
hidden_layers_sizes=hidden_layers_sizes, n_outs=n_outs,
activation = activationFn, dropout_factor = dropout_factor,
input_dropout_factor = input_dropout_factor, adv_activation = adv_activation,
max_col_norm = max_col_norm, l1_reg = l1_reg, l2_reg = l2_reg)
else:
dnn = DNN(numpy_rng=numpy_rng, theano_rng = theano_rng, n_ins=n_ins,
hidden_layers_sizes=hidden_layers_sizes, n_outs=n_outs,
activation = activationFn, adv_activation = adv_activation,
max_col_norm = max_col_norm, l1_reg = l1_reg, l2_reg = l2_reg)
logger.info("Loading Pretrained network weights")
try:
# pretraining
ptr_file = model_config['input_file']
pretrained_layers = dnn_config['pretrained_layers']
dnn.load(filename=ptr_file,max_layer_num = pretrained_layers, withfinal=True)
except KeyError, e:
logger.critical("KeyMissing:"+str(e));
logger.error("Pretrained network Missing in configFile")
sys.exit(2)
def __init__(self, model_config):
super(DNNPredictor, self).__init__(model_config, "DNN")
mlp_config = load_dnn_spec(self.model_config["nnet_spec"])
activationFn = parse_activation(mlp_config["activation"])
n_ins = model_config["n_ins"]
n_outs = model_config["n_outs"]
max_col_norm = mlp_config["max_col_norm"]
l1_reg = mlp_config["l1_reg"]
l2_reg = mlp_config["l2_reg"]
adv_activation = mlp_config["adv_activation"]
hidden_layers_sizes = mlp_config["hidden_layers"]
do_dropout = mlp_config["do_dropout"]
if do_dropout:
dropout_factor = dnn_config["dropout_factor"]
input_dropout_factor = dnn_config["input_dropout_factor"]
self.model = DNN_Dropout(
numpy_rng=self.numpy_rng,
theano_rng=self.theano_rng,
n_ins=n_ins,
hidden_layers_sizes=hidden_layers_sizes,
n_outs=n_outs,
activation=activationFn,
dropout_factor=dropout_factor,
input_dropout_factor=input_dropout_factor,
adv_activation=adv_activation,
max_col_norm=max_col_norm,
l1_reg=l1_reg,
l2_reg=l2_reg,
)
else:
self.model = DNN(
numpy_rng=self.numpy_rng,
theano_rng=self.theano_rng,
n_ins=n_ins,
hidden_layers_sizes=hidden_layers_sizes,
n_outs=n_outs,
activation=activationFn,
adv_activation=adv_activation,
max_col_norm=max_col_norm,
l1_reg=l1_reg,
l2_reg=l2_reg,
)
self.__load_model__(self.model_config["input_file"], mlp_config["pretrained_layers"])
def __init__(self, model_config):
super(DNNPredictor, self).__init__(model_config, 'DNN')
mlp_config = load_dnn_spec(self.model_config['nnet_spec'])
activationFn = parse_activation(mlp_config['activation'])
n_ins = model_config['n_ins']
n_outs = model_config['n_outs']
max_col_norm = mlp_config['max_col_norm']
l1_reg = mlp_config['l1_reg']
l2_reg = mlp_config['l2_reg']
adv_activation = mlp_config['adv_activation']
hidden_layers_sizes = mlp_config['hidden_layers']
do_dropout = mlp_config['do_dropout']
if do_dropout:
dropout_factor = dnn_config['dropout_factor']
input_dropout_factor = dnn_config['input_dropout_factor']
self.model = DNN_Dropout(numpy_rng=self.numpy_rng,
theano_rng=self.theano_rng,
n_ins=n_ins,
hidden_layers_sizes=hidden_layers_sizes,
n_outs=n_outs,
activation=activationFn,
dropout_factor=dropout_factor,
input_dropout_factor=input_dropout_factor,
adv_activation=adv_activation,
max_col_norm=max_col_norm,
l1_reg=l1_reg,
l2_reg=l2_reg)
else:
self.model = DNN(numpy_rng=self.numpy_rng,
theano_rng=self.theano_rng,
n_ins=n_ins,
hidden_layers_sizes=hidden_layers_sizes,
n_outs=n_outs,
activation=activationFn,
adv_activation=adv_activation,
max_col_norm=max_col_norm,
l1_reg=l1_reg,
l2_reg=l2_reg)
self.__load_model__(self.model_config['input_file'],
mlp_config['pretrained_layers'])
