# 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') 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) log('> ... finetuning the model') while (cfg.lrate.get_rate() != 0): # one epoch of sgd training train_error = train_sgd(train_fn, cfg) log('> epoch %d, training error %f ' % (cfg.lrate.epoch, 100*numpy.mean(train_error)) + '(%)') # validation valid_error = validate_by_minibatch(valid_fn, cfg) log('> epoch %d, lrate %f, validation error %f ' % (cfg.lrate.epoch, cfg.lrate.get_rate(), 100*numpy.mean(valid_error)) + '(%)') cfg.lrate.get_next_rate(current_error = 100*numpy.mean(valid_error)) # output nnet parameters and lrate, for training resume if cfg.lrate.epoch % cfg.model_save_step == 0: _nnet2file(dnn.layers, filename=wdir + '/nnet.tmp') _lrate2file(cfg.lrate, wdir + '/training_state.tmp') # save the model and network configuration if cfg.param_output_file != '': _nnet2file(dnn.layers, filename=cfg.param_output_file, input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor) log('> ... the final PDNN model parameter is ' + cfg.param_output_file) if cfg.cfg_output_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') 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) log('> ... finetuning the model') while (cfg.lrate.get_rate() != 0): # one epoch of sgd training train_error, pred, labels = train_sgd(train_fn, cfg) n_data_train = len(pred) log("-->> Epoch %d, \n " % cfg.lrate.epoch) log("- Training:\n") if multi_label: format_results(train_error, pred, labels, multi_label, cfg) else: format_results(train_error, pred, cfg.train_sets.label_vec, multi_label, cfg) # validation valid_error, pred2, labels = validate_by_minibatch(valid_fn, cfg) n_data_valid = len(pred2) log("- Validation:\n") if multi_label: format_results(valid_error, pred2, labels, multi_label, cfg) else: format_results(valid_error, pred2, cfg.valid_sets.label_vec, multi_label, cfg)
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) log('> ... finetuning the model') while (cfg.lrate.get_rate() != 0): # one epoch of sgd training train_error = train_sgd(train_fn, cfg) log('> epoch %d, training error %0.4f ' % (cfg.lrate.epoch, numpy.mean(train_error))) # validation valid_error = validate_by_minibatch(valid_fn, cfg) log('> epoch %d, lrate %f, validation error %0.4f ' % (cfg.lrate.epoch, cfg.lrate.get_rate(), numpy.mean(valid_error))) cfg.lrate.get_next_rate(current_error=numpy.mean(valid_error)) # output nnet parameters and lrate, for training resume if cfg.lrate.epoch % cfg.model_save_step == 0: _nnet2file(dnn.layers, filename=wdir + '/nnet.tmp') _lrate2file(cfg.lrate, wdir + '/training_state.tmp') # save the model and network configuration if cfg.param_output_file != '': _nnet2file(dnn.layers,
# 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') 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) log('> ... finetuning the model') while (cfg.lrate.get_rate() != 0): # one epoch of sgd training train_error, pred = train_sgd(train_fn, cfg) labels = cfg.train_sets.label_vec correct_number = 0.0 confusion_matrix = numpy.zeros((cfg.n_outs, cfg.n_outs)) class_occurrence = numpy.zeros((1,cfg.n_outs)) for i in range(len(pred)): p_sorted = pred[i] if p_sorted == labels[i]: correct_number += 1 confusion_matrix[labels[i], labels[i]] += 1 else: confusion_matrix[labels[i], p_sorted] += 1 class_occurrence[0, labels[i]] += 1 confusion_matrix = 100 * confusion_matrix / class_occurrence.T log('-->> Epoch %d, training error %f ' % (cfg.lrate.epoch, 100 * numpy.mean(train_error)) + '(%)') log('Confusion Matrix is \n\n ' + str(numpy.around(confusion_matrix, 2)) + ' (%)\n')
# initialize model parameters if resume_training: _file2nnet(dnn, filename = wdir + '/nnet.tmp_DNN') # 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) log('> ... finetuning the model') count = 0 while (cfg.lrate.get_rate() != 0): # one epoch of sgd training train_acc, train_err = train_sgd(train_fn, cfg) log('> epoch %d, lrate %f, training accuracy %f' % (cfg.lrate.epoch, cfg.lrate.get_rate(), 100*numpy.mean(train_acc)) + '(%)') # validation valid_acc, valid_err = validate_by_minibatch(valid_fn, cfg) log('> epoch %d, lrate %f, validate accuracy %f' % (cfg.lrate.epoch, cfg.lrate.get_rate(), 100*numpy.mean(valid_acc)) + '(%)') cfg.lrate.get_next_rate(current_error = 100*numpy.mean(valid_err)) # output nnet parameters and lrate, for training resume if cfg.lrate.epoch % cfg.model_save_step == 0: _nnet2file(dnn, filename=wdir + '/nnet.tmp_DNN') _lrate2file(cfg.lrate, wdir + '/training_state.tmp_DNN') # save the model and network configuration if cfg.param_output_file != '': _nnet2file(dnn, filename=cfg.param_output_file, input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor) log('> ... the final PDNN model parameter is ' + cfg.param_output_file)
def dnn_run(arguments): 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) train_data_spec = arguments['train_data'] valid_data_spec = arguments['valid_data'] nnet_spec = arguments['nnet_spec'] wdir = arguments['wdir'] cfg = NetworkConfig() 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') 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) 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') 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) log('> ... finetuning the model') while (cfg.lrate.get_rate() != 0): # one epoch of sgd training train_error = train_sgd(train_fn, cfg) log('> epoch %d, training error %f ' % (cfg.lrate.epoch, 100*numpy.mean(train_error)) + '(%)') # validation valid_error = validate_by_minibatch(valid_fn, cfg) log('> epoch %d, lrate %f, validation error %f ' % (cfg.lrate.epoch, cfg.lrate.get_rate(), 100*numpy.mean(valid_error)) + '(%)') cfg.lrate.get_next_rate(current_error = 100*numpy.mean(valid_error)) # output nnet parameters and lrate, for training resume if cfg.lrate.epoch % cfg.model_save_step == 0: _nnet2file(dnn.layers, filename=wdir + '/nnet.tmp') _lrate2file(cfg.lrate, wdir + '/training_state.tmp') # save the model and network configuration if cfg.param_output_file != '': _nnet2file(dnn.layers, filename=cfg.param_output_file, input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor) log('> ... the final PDNN model parameter is ' + cfg.param_output_file) if cfg.cfg_output_file != '': _cfg2file(dnn.cfg, filename=cfg.cfg_output_file) log('> ... the final PDNN model config is ' + cfg.cfg_output_file)
def dnn_run(arguments): 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) train_data_spec = arguments['train_data'] valid_data_spec = arguments['valid_data'] nnet_spec = arguments['nnet_spec'] wdir = arguments['wdir'] cfg = NetworkConfig() 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') 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) 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') 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) log('> ... finetuning the model') while (cfg.lrate.get_rate() != 0): # one epoch of sgd training train_error = train_sgd(train_fn, cfg) log('> epoch %d, training error %f ' % (cfg.lrate.epoch, 100 * numpy.mean(train_error)) + '(%)') # validation valid_error = validate_by_minibatch(valid_fn, cfg) log('> epoch %d, lrate %f, validation error %f ' % (cfg.lrate.epoch, cfg.lrate.get_rate(), 100 * numpy.mean(valid_error)) + '(%)') cfg.lrate.get_next_rate(current_error=100 * numpy.mean(valid_error)) # output nnet parameters and lrate, for training resume if cfg.lrate.epoch % cfg.model_save_step == 0: _nnet2file(dnn.layers, filename=wdir + '/nnet.tmp') _lrate2file(cfg.lrate, wdir + '/training_state.tmp') # save the model and network configuration if cfg.param_output_file != '': _nnet2file(dnn.layers, filename=cfg.param_output_file, input_factor=cfg.input_dropout_factor, factor=cfg.dropout_factor) log('> ... the final PDNN model parameter is ' + cfg.param_output_file) if cfg.cfg_output_file != '': _cfg2file(dnn.cfg, filename=cfg.cfg_output_file) log('> ... the final PDNN model config is ' + cfg.cfg_output_file)