] 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),