def __init__(self, model_config):
super(CNNPredictor, self).__init__(model_config, 'CNN')
conv_config, conv_layer_config, mlp_config = load_conv_spec(
self.model_config['nnet_spec'], self.batch_size,
self.model_config['input_shape'])
activationFn = parse_activation(mlp_config['activation'])
if mlp_config['do_dropout'] or conv_config['do_dropout']:
self.model = DropoutCNN(
self.numpy_rng,
self.theano_rng,
conv_layer_configs=conv_layer_config,
batch_size=self.batch_size,
n_outs=self.model_config['n_outs'],
hidden_layer_configs=mlp_config,
hidden_activation=activationFn,
use_fast=conv_config['use_fast'],
l1_reg=mlp_config['l1_reg'],
l2_reg=mlp_config['l1_reg'],
max_col_norm=mlp_config['max_col_norm'],
input_dropout_factor=conv_config['input_dropout_factor'])
else:
self.model = CNN(self.numpy_rng,
self.theano_rng,
conv_layer_configs=conv_layer_config,
batch_size=batch_size,
n_outs=self.model_config['n_outs'],
hidden_layer_configs=mlp_config,
hidden_activation=activationFn,
use_fast=conv_config['use_fast'],
l1_reg=mlp_config['l1_reg'],
l2_reg=mlp_config['l1_reg'],
max_col_norm=mlp_config['max_col_norm'])
self.__load_model__(self.model_config['input_file'],
mlp_config['pretrained_layers'])
def runCNN3D(arg):
if type(arg) is dict:
model_config = arg
else:
model_config = load_model(arg, 'CNN')
conv_config, conv_layer_config, mlp_config = load_conv_spec(
model_config['nnet_spec'], model_config['batch_size'],
model_config['input_shape'])
#__debugPrintData__(conv_layer_config,'covolution config')
data_spec = load_data_spec(model_config['data_spec'],
model_config['batch_size'])
numpy_rng = numpy.random.RandomState(model_config['random_seed'])
theano_rng = RandomStreams(numpy_rng.randint(2**30))
logger.info('> ... building the model')
hidden_activation = parse_activation(mlp_config['activation'])
createDir(model_config['wdir'])
#create working dir
batch_size = model_config['batch_size']
cnn = CNN3D(numpy_rng,
theano_rng,
conv_layer_configs=conv_layer_config,
batch_size=batch_size,
n_outs=model_config['n_outs'],
hidden_layer_configs=mlp_config,
hidden_activation=hidden_activation,
l1_reg=mlp_config['l1_reg'],
l2_reg=mlp_config['l1_reg'],
max_col_norm=mlp_config['max_col_norm'])
########################
# Loading THE MODEL #
########################
try:
# pretraining
ptr_file = model_config['input_file']
pretrained_layers = mlp_config['pretrained_layers']
logger.info("Loading the pretrained network..")
cnn.load(filename=ptr_file,
max_layer_num=pretrained_layers,
withfinal=True)
except KeyError, e:
logger.warning(
"Pretrained network missing in working directory, skipping model loading"
)
def runCNN(arg):
if type(arg) is dict:
model_config = arg
else :
model_config = load_model(arg,'CNN')
conv_config,conv_layer_config,mlp_config = load_conv_spec(
model_config['nnet_spec'],
model_config['batch_size'],
model_config['input_shape'])
data_spec = load_data_spec(model_config['data_spec'],model_config['batch_size']);
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
logger.info('> ... building the model')
activationFn = parse_activation(mlp_config['activation']);
createDir(model_config['wdir']);
#create working dir
batch_size = model_config['batch_size'];
if mlp_config['do_dropout'] or conv_config['do_dropout']:
logger.info('>Initializing dropout cnn model')
cnn = DropoutCNN(numpy_rng,theano_rng,conv_layer_configs = conv_layer_config, batch_size = batch_size,
n_outs=model_config['n_outs'],hidden_layer_configs=mlp_config,
hidden_activation = activationFn,
use_fast = conv_config['use_fast'],l1_reg = mlp_config['l1_reg'],
l2_reg = mlp_config['l1_reg'],max_col_norm = mlp_config['max_col_norm'],
input_dropout_factor=conv_config['input_dropout_factor'])
else:
cnn = CNN(numpy_rng,theano_rng,conv_layer_configs = conv_layer_config, batch_size = batch_size,
n_outs=model_config['n_outs'],hidden_layer_configs=mlp_config,
hidden_activation = activationFn,
use_fast = conv_config['use_fast'],l1_reg = mlp_config['l1_reg'],
l2_reg = mlp_config['l1_reg'],max_col_norm = mlp_config['max_col_norm'])
########################
# Loading THE MODEL #
########################
try:
# pretraining
ptr_file = model_config['input_file']
pretrained_layers = mlp_config['pretrained_layers']
logger.info("Loading the pretrained network..")
cnn.load(filename=ptr_file,max_layer_num = pretrained_layers, withfinal=True)
except KeyError, e:
logger.warning("Pretrained network missing in working directory, skipping model loading")
def __init__(self,model_config): super(CNN3dPredictor, self).__init__(model_config,'CNN3d'); conv_config,conv_layer_config,mlp_config = load_conv_spec(self.model_config['nnet_spec'], self.batch_size, self.model_config['input_shape']) activationFn = parse_activation(mlp_config['activation']); self.model = CNN3D(self.numpy_rng,self.theano_rng,conv_layer_configs = conv_layer_config, batch_size = self.batch_size, n_outs= self.model_config['n_outs'], hidden_layer_configs=mlp_config,hidden_activation = hidden_activation, l1_reg = mlp_config['l1_reg'],l2_reg = mlp_config['l1_reg'], max_col_norm = mlp_config['max_col_norm']) self.__load_model__(self.model_config['input_file'],mlp_config['pretrained_layers']);
def runCNN3D(arg):
if type(arg) is dict:
model_config = arg
else :
model_config = load_model(arg,'CNN')
conv_config,conv_layer_config,mlp_config = load_conv_spec(
model_config['nnet_spec'],
model_config['batch_size'],
model_config['input_shape'])
#__debugPrintData__(conv_layer_config,'covolution config')
data_spec = load_data_spec(model_config['data_spec'],model_config['batch_size']);
numpy_rng = numpy.random.RandomState(model_config['random_seed'])
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
logger.info('> ... building the model')
hidden_activation = parse_activation(mlp_config['activation']);
createDir(model_config['wdir']);
#create working dir
batch_size = model_config['batch_size'];
cnn = CNN3D(numpy_rng,theano_rng,conv_layer_configs = conv_layer_config, batch_size = batch_size,
n_outs=model_config['n_outs'],hidden_layer_configs=mlp_config,hidden_activation = hidden_activation,
l1_reg = mlp_config['l1_reg'],l2_reg = mlp_config['l1_reg'],max_col_norm = mlp_config['max_col_norm'])
########################
# Loading THE MODEL #
########################
try:
# pretraining
ptr_file = model_config['input_file']
pretrained_layers = mlp_config['pretrained_layers']
logger.info("Loading the pretrained network..")
cnn.load(filename=ptr_file,max_layer_num = pretrained_layers, withfinal=True)
except KeyError, e:
logger.warning("Pretrained network missing in working directory, skipping model loading")
def __init__(self,model_config): super(CNNPredictor, self).__init__(model_config,'CNN'); conv_config,conv_layer_config,mlp_config = load_conv_spec(self.model_config['nnet_spec'], self.batch_size, self.model_config['input_shape']) activationFn = parse_activation(mlp_config['activation']); if mlp_config['do_dropout'] or conv_config['do_dropout']: self.model = DropoutCNN(self.numpy_rng,self.theano_rng,conv_layer_configs = conv_layer_config, batch_size = self.batch_size, n_outs=self.model_config['n_outs'], hidden_layer_configs=mlp_config, hidden_activation = activationFn, use_fast = conv_config['use_fast'],l1_reg = mlp_config['l1_reg'], l2_reg = mlp_config['l1_reg'],max_col_norm = mlp_config['max_col_norm'], input_dropout_factor=conv_config['input_dropout_factor']) else: self.model = CNN(self.numpy_rng,self.theano_rng,conv_layer_configs = conv_layer_config, batch_size = batch_size, n_outs=self.model_config['n_outs'], hidden_layer_configs=mlp_config, hidden_activation = activationFn, use_fast = conv_config['use_fast'],l1_reg = mlp_config['l1_reg'], l2_reg = mlp_config['l1_reg'],max_col_norm = mlp_config['max_col_norm']) self.__load_model__(self.model_config['input_file'],mlp_config['pretrained_layers']);
def runCNN(arg):
if type(arg) is dict:
model_config = arg
else:
model_config = load_model(arg, 'CNN')
conv_config, conv_layer_config, mlp_config = load_conv_spec(
model_config['nnet_spec'], model_config['batch_size'],
model_config['input_shape'])
data_spec = load_data_spec(model_config['data_spec'],
model_config['batch_size'])
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2**30))
logger.info('> ... building the model')
activationFn = parse_activation(mlp_config['activation'])
createDir(model_config['wdir'])
#create working dir
batch_size = model_config['batch_size']
if mlp_config['do_dropout'] or conv_config['do_dropout']:
logger.info('>Initializing dropout cnn model')
cnn = DropoutCNN(
numpy_rng,
theano_rng,
conv_layer_configs=conv_layer_config,
batch_size=batch_size,
n_outs=model_config['n_outs'],
hidden_layer_configs=mlp_config,
hidden_activation=activationFn,
use_fast=conv_config['use_fast'],
l1_reg=mlp_config['l1_reg'],
l2_reg=mlp_config['l1_reg'],
max_col_norm=mlp_config['max_col_norm'],
input_dropout_factor=conv_config['input_dropout_factor'])
else:
cnn = CNN(numpy_rng,
theano_rng,
conv_layer_configs=conv_layer_config,
batch_size=batch_size,
n_outs=model_config['n_outs'],
hidden_layer_configs=mlp_config,
hidden_activation=activationFn,
use_fast=conv_config['use_fast'],
l1_reg=mlp_config['l1_reg'],
l2_reg=mlp_config['l1_reg'],
max_col_norm=mlp_config['max_col_norm'])
########################
# Loading THE MODEL #
########################
try:
# pretraining
ptr_file = model_config['input_file']
pretrained_layers = mlp_config['pretrained_layers']
logger.info("Loading the pretrained network..")
cnn.load(filename=ptr_file,
max_layer_num=pretrained_layers,
withfinal=True)
except KeyError, e:
logger.warning(
"Pretrained network missing in working directory, skipping model loading"
)
