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
dnn = CNN_SAT(numpy_rng=numpy_rng, theano_rng = theano_rng, cfg_si = cfg_si, cfg_adapt = cfg_adapt)
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')
log('> ... found nnet.tmp and training_state.tmp, now resume training from epoch ' + str(cfg.lrate.epoch))
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')
# construct the cnn architecture
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
dnn = CNN_SAT(numpy_rng=numpy_rng,
