]
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']
nnet_spec = arguments['nnet_spec']
lstm_nnet_spec = arguments['lstm_nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig()
cfg.model_type = 'ATTEND_LSTM'
cfg.parse_config_attend(arguments, nnet_spec, lstm_nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
print 'Extra dim: ' + str(cfg.extra_dim)
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2**30))
log('> ... building the model')
# setup model
dnn = PhaseATTEND_LSTM(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),
(cfg.extra_train_x), (cfg.extra_valid_x),
]
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']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig()
cfg.model_type = 'CNN'
cfg.parse_config_cnn(arguments, '10:' + nnet_spec, conv_nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
if arguments.has_key('replicate'):
cfg.replicate = int(arguments['replicate'])
# parse pre-training options
# pre-training files and layer number (how many layers are set to the pre-training parameters)
ptr_layer_number = 0
ptr_file = ''
if arguments.has_key('ptr_file') and arguments.has_key('ptr_layer_number'):
ptr_file = arguments['ptr_file']
temp = arguments['ptr_layer_number'].split(':')
if len(temp) > 1 or len(temp[0].split(',')) > 1:
arg_elements = [sys.argv[i] for i in range(1, len(sys.argv))]
arguments = parse_arguments(arg_elements)
required_arguments = ['train_data', 'valid_data', 'nnet_spec', 'conv_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']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig(); cfg.model_type = 'CNNV'
cfg.parse_config_cnn(arguments, '10:' + nnet_spec, conv_nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
# parse pre-training options
# pre-training files and layer number (how many layers are set to the pre-training parameters)
ptr_layer_number = 0; ptr_file = ''
if arguments.has_key('ptr_file') and arguments.has_key('ptr_layer_number'):
ptr_file = arguments['ptr_file']
ptr_layer_number = int(arguments['ptr_layer_number'])
# check working dir to see whether it's resuming training
resume_training = False
if os.path.exists(wdir + '/nnet.tmp') and os.path.exists(wdir + '/training_state.tmp'):
resume_training = True
cfg.lrate = _file2lrate(wdir + '/training_state.tmp')
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']
si_nnet_spec = arguments['si_nnet_spec']
si_conv_nnet_spec = arguments['si_conv_nnet_spec']
adapt_nnet_spec = arguments['adapt_nnet_spec']
wdir = arguments['wdir']
init_model_file = arguments['init_model']
# parse network configuration from arguments, and initialize data reading
cfg_si = NetworkConfig()
cfg_si.model_type = 'CNN'
cfg_si.parse_config_cnn(arguments, '10:' + si_nnet_spec, si_conv_nnet_spec)
cfg_si.init_data_reading(train_data_spec, valid_data_spec)
# parse the structure of the i-vector network
cfg_adapt = NetworkConfig()
net_split = adapt_nnet_spec.split(':')
adapt_nnet_spec = ''
for n in xrange(len(net_split) - 1):
adapt_nnet_spec += net_split[n] + ':'
cfg_adapt.parse_config_dnn(arguments, adapt_nnet_spec + '0')
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2**30))
log('> ... initializing the model')
# setup up the model
arg_elements = [sys.argv[i] for i in range(1, len(sys.argv))]
arguments = parse_arguments(arg_elements)
required_arguments = ['train_data', 'valid_data', 'nnet_spec', 'lstm_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']
nnet_spec = arguments['nnet_spec']
lstm_nnet_spec = arguments['lstm_nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig();cfg.model_type = 'ATTEND_LSTM'
cfg.parse_config_attend(arguments, 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 = ATTEND_LSTM(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)
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)
arguments = parse_arguments(arg_elements)
required_arguments = ['train_data', 'valid_data', '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']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig()
cfg.model_type = 'RNN'
cfg.parse_config_dnn(arguments, nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
print("training dataset is:", cfg.train_sets.pfile_path_list)
# parse pre-training options
# pre-training files and layer number (how many layers are set to the pre-training parameters)
ptr_layer_number = 0
ptr_file = ''
if arguments.has_key('ptr_file') and arguments.has_key('ptr_layer_number'):
ptr_file = arguments['ptr_file']
ptr_layer_number = int(arguments['ptr_layer_number'])
# check working dir to see whether it's resuming training
resume_training = False
if os.path.exists(wdir +
'/nnet.tmp') and os.path.exists(wdir +
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"]
si_nnet_spec = arguments["si_nnet_spec"]
si_conv_nnet_spec = arguments["si_conv_nnet_spec"]
adapt_nnet_spec = arguments["adapt_nnet_spec"]
wdir = arguments["wdir"]
init_model_file = arguments["init_model"]
# parse network configuration from arguments, and initialize data reading
cfg_si = NetworkConfig()
cfg_si.model_type = "CNN"
cfg_si.parse_config_cnn(arguments, "10:" + si_nnet_spec, si_conv_nnet_spec)
cfg_si.init_data_reading(train_data_spec, valid_data_spec)
# parse the structure of the i-vector network
cfg_adapt = NetworkConfig()
net_split = adapt_nnet_spec.split(":")
adapt_nnet_spec = ""
for n in xrange(len(net_split) - 1):
adapt_nnet_spec += net_split[n] + ":"
cfg_adapt.parse_config_dnn(arguments, adapt_nnet_spec + "0")
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
log("> ... initializing the model")
# setup up the model
arg_elements = [sys.argv[i] for i in range(1, len(sys.argv))]
arguments = parse_arguments(arg_elements)
required_arguments = ['train_data', 'valid_data', 'nnet_spec', 'lstm_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']
nnet_spec = arguments['nnet_spec']
lstm_nnet_spec = arguments['lstm_nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig();cfg.model_type = 'LSTMV'
cfg.parse_config_ldnn(arguments, 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 = LSTMV(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)
]
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']
nnet_spec = arguments['nnet_spec']
lstm_nnet_spec = arguments['lstm_nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig()
cfg.model_type = 'LSTMV'
cfg.parse_config_ldnn(arguments, 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 = LSTMV(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)
# check the arguments
arg_elements = [sys.argv[i] for i in range(1, len(sys.argv))]
arguments = parse_arguments(arg_elements)
required_arguments = ['train_data', 'valid_data', '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']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig();cfg.model_type = 'DNNV'
cfg.parse_config_dnn(arguments, nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
# parse pre-training options
# pre-training files and layer number (how many layers are set to the pre-training parameters)
ptr_layer_number = 0; ptr_file = ''
if arguments.has_key('ptr_file') and arguments.has_key('ptr_layer_number'):
ptr_file = arguments['ptr_file']
ptr_layer_number = int(arguments['ptr_layer_number'])
# check working dir to see whether it's resuming training
resume_training = False
if os.path.exists(wdir + '/nnet.tmp') and os.path.exists(wdir + '/training_state.tmp'):
resume_training = True
cfg.lrate = _file2lrate(wdir + '/training_state.tmp')
required_arguments = ["train_data", "valid_data", "nnet_spec", "conv_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"]
nnet_spec = arguments["nnet_spec"]
wdir = arguments["wdir"]
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig()
cfg.model_type = "CNN"
cfg.parse_config_cnn(arguments, "10:" + nnet_spec, conv_nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
# parse pre-training options
# pre-training files and layer number (how many layers are set to the pre-training parameters)
ptr_layer_number = 0
ptr_file = ""
if arguments.has_key("ptr_file") and arguments.has_key("ptr_layer_number"):
ptr_file = arguments["ptr_file"]
ptr_layer_number = int(arguments["ptr_layer_number"])
# check working dir to see whether it's resuming training
resume_training = False
if os.path.exists(wdir + "/nnet.tmp") and os.path.exists(wdir + "/training_state.tmp"):
resume_training = True
]
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']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig()
cfg.model_type = 'CRNV'
cfg.parse_config_cnn(arguments, '10:' + nnet_spec, conv_nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
# parse pre-training options
# pre-training files and layer number (how many layers are set to the pre-training parameters)
ptr_layer_number = 0
ptr_file = ''
if arguments.has_key('ptr_file') and arguments.has_key('ptr_layer_number'):
ptr_file = arguments['ptr_file']
ptr_layer_number = int(arguments['ptr_layer_number'])
# check working dir to see whether it's resuming training
resume_training = False
if os.path.exists(wdir +
'/nnet.tmp') and os.path.exists(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']
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 = 'ATTEND'
cfg.parse_config_attend(arguments, 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
nn = ATTEND(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 = nn.build_finetune_functions(
(cfg.train_x, cfg.train_y), (cfg.valid_x, cfg.valid_y),
batch_size=cfg.batch_size)
# check working dir to see whether it's resuming training
resume_training = False
if os.path.exists(wdir + '/nnet.tmp') and os.path.exists(wdir + '/training_state.tmp'):
resume_training = True
cfg.lrate = _file2lrate(wdir + '/training_state.tmp')
log('> ... found nnet.tmp and training_state.tmp, now resume training from epoch ' + str(cfg.lrate.epoch))
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
log('> ... building the model')
# setup model
if cfg.do_dropout:
dnn = DNN_Dropout(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg = cfg)
elif cfg.do_regression:
dnn = DNN_REG(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg = cfg)
cfg.model_type = 'DNN_REG'
else:
dnn = DNN(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg = cfg)
# initialize model parameters
# if not resuming training, initialized from the specified pre-training file
# if resuming training, initialized from the tmp model file
if (ptr_layer_number > 0) and (resume_training is False):
_file2nnet(dnn.layers, set_layer_num = ptr_layer_number, filename = ptr_file)
if resume_training:
_file2nnet(dnn.layers, filename = wdir + '/nnet.tmp')
# get the training, validation and testing function for the model
log('> ... getting the finetuning functions')
#TODO fix error in train_fn
train_fn, valid_fn = dnn.build_finetune_functions(
(cfg.train_x, cfg.train_y), (cfg.valid_x, cfg.valid_y),
arg_elements = [sys.argv[i] for i in range(1, len(sys.argv))]
arguments = parse_arguments(arg_elements)
required_arguments = ['train_data', 'valid_data', 'nnet_spec', 'lstm_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']
nnet_spec = arguments['nnet_spec']
lstm_nnet_spec = arguments['lstm_nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig();cfg.model_type = 'LSTMV'
cfg.parse_config_ldnn(arguments, 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 = LSTMV(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)
]
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']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig()
cfg.model_type = 'CNN_LACEA'
cfg.parse_config_cnn(arguments, '10:' + nnet_spec, conv_nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
# check working dir to see whether it's resuming training
resume_training = False
if os.path.exists(wdir + '/nnet.tmp_CNN_LACEA') and os.path.exists(
wdir + '/training_state.tmp_CNN_LACEA'):
resume_training = True
cfg.lrate = _file2lrate(wdir + '/training_state.tmp_CNN_LACEA')
log('> ... found nnet.tmp_CNN_LACEA and training_state.tmp_CNN_LACEA, now resume training from epoch '
+ str(cfg.lrate.epoch))
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2**30))
log('> ... initializing the model')
# check the arguments
arg_elements = [sys.argv[i] for i in range(1, len(sys.argv))]
arguments = parse_arguments(arg_elements)
required_arguments = ['train_data', 'valid_data', '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']
nnet_spec = arguments['nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig(); cfg.model_type = 'RNN'
cfg.parse_config_dnn(arguments, nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
print("training dataset is:", cfg.train_sets.pfile_path_list)
# parse pre-training options
# pre-training files and layer number (how many layers are set to the pre-training parameters)
ptr_layer_number = 0; ptr_file = ''
if arguments.has_key('ptr_file') and arguments.has_key('ptr_layer_number'):
ptr_file = arguments['ptr_file']
ptr_layer_number = int(arguments['ptr_layer_number'])
# check working dir to see whether it's resuming training
resume_training = False
if os.path.exists(wdir + '/nnet.tmp') and os.path.exists(wdir + '/training_state.tmp'):
resume_training = True
cfg.lrate = _file2lrate(wdir + '/training_state.tmp')
arguments = parse_arguments(arg_elements)
required_arguments = ['train_data', 'valid_data', 'si_nnet_spec', 'si_conv_nnet_spec', 'wdir', 'adapt_nnet_spec', 'init_model']
for arg in required_arguments:
if (arg in arguments) == 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']
si_nnet_spec = arguments['si_nnet_spec']
si_conv_nnet_spec = arguments['si_conv_nnet_spec']
adapt_nnet_spec = arguments['adapt_nnet_spec'];
wdir = arguments['wdir']
init_model_file = arguments['init_model']
# parse network configuration from arguments, and initialize data reading
cfg_si = NetworkConfig(); cfg_si.model_type = 'CNN'
cfg_si.parse_config_cnn(arguments, '10:' + si_nnet_spec, si_conv_nnet_spec)
cfg_si.init_data_reading(train_data_spec, valid_data_spec)
# parse the structure of the i-vector network
cfg_adapt = NetworkConfig()
net_split = adapt_nnet_spec.split(':')
adapt_nnet_spec = ''
for n in range(len(net_split) - 1):
adapt_nnet_spec += net_split[n] + ':'
cfg_adapt.parse_config_dnn(arguments, adapt_nnet_spec + '0')
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
log('> ... initializing the model')
# setup up the model
arg_elements = [sys.argv[i] for i in range(1, len(sys.argv))]
arguments = parse_arguments(arg_elements)
required_arguments = ['train_data', 'valid_data', 'nnet_spec', 'lstm_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']
nnet_spec = arguments['nnet_spec']
lstm_nnet_spec = arguments['lstm_nnet_spec']
wdir = arguments['wdir']
# parse network configuration from arguments, and initialize data reading
cfg = NetworkConfig();cfg.model_type = 'ATTEND_LSTM'
cfg.parse_config_attend(arguments, nnet_spec, lstm_nnet_spec)
cfg.init_data_reading(train_data_spec, valid_data_spec)
print 'Extra dim: '+str(cfg.extra_dim)
numpy_rng = numpy.random.RandomState(89677)
theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
log('> ... building the model')
# setup model
dnn = PhaseATTEND_LSTM(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),
(cfg.extra_train_x), (cfg.extra_valid_x),
