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')
conv_activation = parse_activation(conv_config['activation']);
hidden_activation = parse_activation(mlp_config['activation']);
createDir(model_config['wdir']);
#create working dir
batch_size = model_config['batch_size'];
if mlp_config['do_dropout']:
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,
conv_activation = conv_activation,hidden_activation = hidden_activation,
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'])
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,
conv_activation = conv_activation,hidden_activation = hidden_activation,
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 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 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)
#########################
# PRETRAINING THE MODEL #
#########################
if model_config['processes']['pretraining']:
train_sets, train_xy, train_x, train_y = read_dataset(data_spec['training'])
preTraining(dbn,train_sets,train_xy,train_x,model_config['pretrain_params'])
########################
# FINETUNING THE MODEL #
########################
if model_config['processes']['finetuning']:
fineTunning(dbn,model_config,data_spec)
########################
# TESTING THE MODEL #
########################
if model_config['processes']['testing']:
testing(dbn,data_spec)
##########################
## Export Features ##
##########################
if model_config['processes']['export_data']:
exportFeatures(dbn,model_config,data_spec)
logger.info('Saving model to ' + str(model_config['output_file']) + ' ....')
dbn.save(filename=model_config['output_file'], withfinal=True);
logger.info('Saved model to ' + str(model_config['output_file']))
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, train_xy, train_x, train_y = read_dataset(data_spec['training'])
pretraining_config= model_config['pretrain_params']
corruption_levels =sda_config['corruption_levels']
preTraining(sda,train_sets,train_xy,train_x,corruption_levels,pretraining_config);
########################
# 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']))
momentum = float(arguments['momentum'])
nnet_layers = nnet_spec.split(":")
n_ins = int(nnet_layers[0])
hidden_layers_sizes = []
for i in range(1, len(nnet_layers)-1):
hidden_layers_sizes.append(int(nnet_layers[i]))
n_outs = int(nnet_layers[-1])
activation = T.nnet.sigmoid
do_maxout = False
pool_size = 1
do_pnorm = False
pnorm_order = 1
if arguments.has_key('activation'):
activation = parse_activation(arguments['activation'])
if arguments['activation'].startswith('maxout'):
do_maxout = True
pool_size = int(arguments['activation'].replace('maxout:',''))
elif arguments['activation'].startswith('pnorm'):
do_pnorm = True
pool_size, pnorm_order = parse_two_integers(arguments['activation'])
# deal with dropout
do_dropout = False
dropout_factor = [0.0]
input_dropout_factor = 0.0
if arguments.has_key('dropout_factor'):
do_dropout = True
if arguments.has_key('input_dropout_factor'):
input_dropout_factor = float(arguments['input_dropout_factor'])
# full layer configurations
nnet_layers = full_nnet_spec.split(":")
hidden_layers_sizes = []
for i in range(0, len(nnet_layers)-1):
hidden_layers_sizes.append(int(nnet_layers[i]))
n_outs = int(nnet_layers[-1])
# ivec layer configurations
nnet_layers = ivec_nnet_spec.split(":")
ivec_layers_sizes = []
for i in xrange(len(nnet_layers)):
ivec_layers_sizes.append(int(nnet_layers[i]))
conv_activation = T.nnet.sigmoid
full_activation = T.nnet.sigmoid
if arguments.has_key('conv_activation'):
conv_activation = parse_activation(arguments['conv_activation'])
if arguments.has_key('full_activation'):
full_activation = parse_activation(arguments['full_activation'])
# which part of the network to be updated
update_part = []
for part in arguments['update_part'].split(':'):
update_part.append(int(part))
# the dimension of the ivectors
ivec_dim = int(arguments['ivec_dim'])
# whether to use the fast version of CNN with pylearn2
use_fast = False
if arguments.has_key('use_fast'):
use_fast = string_2_bool(arguments['use_fast'])
conv_layer_number = int(arguments['conv_layer_number'])
wdir = arguments['wdir']
output_file_prefix = arguments['output_file_prefix']
conv_net_file = arguments['conv_net_file']
# network structure
conv_configs = []
for i in xrange(conv_layer_number):
config_path = wdir + '/conv.config.' + str(i)
if os.path.exists(config_path) == False:
print "Error: config files for convolution layers do not exist."
exit(1)
else:
with open(config_path, 'rb') as fp:
conv_configs.append(json.load(fp))
# convert string activaton to theano
conv_configs[i]['activation'] = parse_activation(conv_configs[i]['activation'])
# whether to use the fast mode
use_fast = False
if arguments.has_key('use_fast'):
use_fast = string_2_bool(arguments['use_fast'])
# paths for output files
output_scp = output_file_prefix + '.scp'
output_ark = output_file_prefix + '.ark'
start_time = time.clock()
feat_rows = []
feat_mats_np = []
uttIDs = []
n_ins = int(nnet_layers[0])
hidden_layers_sizes = []
for i in range(1, len(nnet_layers)-1):
hidden_layers_sizes.append(int(nnet_layers[i]))
n_outs = int(nnet_layers[-1])
ptr_layer_number = len(hidden_layers_sizes)
if arguments.has_key('ptr_layer_number'):
ptr_layer_number = int(arguments['ptr_layer_number'])
hidden_activation = T.nnet.sigmoid
do_maxout = False
pool_size = 1
first_reconstruct_activation = T.nnet.sigmoid
if arguments.has_key('hidden_activation'):
hidden_activation = parse_activation(arguments['hidden_activation'])
if arguments['hidden_activation'].startswith('maxout'):
do_maxout = True
pool_size = int(arguments['hidden_activation'].replace('maxout:',''))
if arguments.has_key('first_reconstruct_activation'):
first_reconstruct_activation = parse_activation(arguments['first_reconstruct_activation'])
# if initialized with current
keep_layer_num=0
current_nnet = wdir + 'nnet.ptr.current'
train_sets, train_xy, train_x, train_y = read_dataset(dataset, dataset_args)
# numpy random generator
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
