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 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 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 runRBM(arg):
if type(arg) is dict:
model_config = arg
else:
model_config = load_model(arg, 'RBM')
rbm_config = load_rbm_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(rbm_config['activation'])
createDir(model_config['wdir'])
#create working dir
batch_size = model_config['batch_size']
wdir = model_config['wdir']
dbn = DBN(numpy_rng=numpy_rng,
theano_rng=theano_rng,
n_ins=model_config['n_ins'],
hidden_layers_sizes=rbm_config['hidden_layers'],
n_outs=model_config['n_outs'],
first_layer_gb=rbm_config['first_layer_gb'],
pretrainedLayers=rbm_config['pretrained_layers'],
activation=activationFn)
logger.info("Loading Pretrained network weights")
try:
# pretraining
ptr_file = model_config['input_file']
dbn.load(filename=ptr_file)
except KeyError, e:
logger.info("KeyMissing:" + str(e))
logger.info(
"Pretrained network Missing in configFile: Skipping Loading")
def runRBM(arg):
if type(arg) is dict:
model_config = arg
else :
model_config = load_model(arg,'RBM')
rbm_config = load_rbm_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(rbm_config['activation']);
createDir(model_config['wdir']);
#create working dir
batch_size = model_config['batch_size']
wdir = model_config['wdir']
dbn = DBN(numpy_rng=numpy_rng, theano_rng = theano_rng, n_ins=model_config['n_ins'],
hidden_layers_sizes=rbm_config['hidden_layers'],n_outs=model_config['n_outs'],
first_layer_gb = rbm_config['first_layer_gb'],
pretrainedLayers=rbm_config['pretrained_layers'],
activation=activationFn)
logger.info("Loading Pretrained network weights")
try:
# pretraining
ptr_file = model_config['input_file']
dbn.load(filename=ptr_file)
except KeyError, e:
logger.info("KeyMissing:"+str(e));
logger.info("Pretrained network Missing in configFile: Skipping 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 runSdA(arg):
if type(arg) is dict:
model_config = arg
else :
model_config = load_model(arg,'SDA')
sda_config = load_sda_spec(model_config['nnet_spec'])
data_spec = load_data_spec(model_config['data_spec'],model_config['batch_size']);
# numpy random generator
numpy_rng = numpy.random.RandomState(model_config['random_seed'])
#theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
#get Activation function
activationFn = parse_activation(sda_config['activation']);
createDir(model_config['wdir']);
#create working dir
logger.info('building the model')
# construct the stacked denoising autoencoder class
sda = SDA(numpy_rng=numpy_rng, n_ins=model_config['n_ins'],
hidden_layers_sizes=sda_config['hidden_layers'],
n_outs=model_config['n_outs'],activation=activationFn)
batch_size = model_config['batch_size'];
#########################
# PRETRAINING THE MODEL #
#########################
if model_config['processes']['pretraining']:
train_sets = read_dataset(data_spec['training'])
pretraining_config = model_config['pretrain_params']
corruption_levels = sda_config['corruption_levels']
preTraining(sda,train_sets,corruption_levels,pretraining_config);
del train_sets;
########################
# FINETUNING THE MODEL #
########################
if model_config['processes']['finetuning']:
fineTunning(sda,model_config,data_spec)
########################
# TESTING THE MODEL #
########################
if model_config['processes']['testing']:
testing(sda,data_spec)
##########################
## Export Features ##
##########################
if model_config['processes']['export_data']:
exportFeatures(sda,model_config,data_spec)
# save the pretrained nnet to file
logger.info('Saving model to ' + str(model_config['output_file']) + '....')
sda.save(filename=model_config['output_file'], withfinal=True)
logger.info('Saved model to ' + str(model_config['output_file']))
