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 runCNN3D(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']) #__debugPrintData__(conv_layer_config,'covolution config') data_spec = load_data_spec(model_config['data_spec'], model_config['batch_size']) numpy_rng = numpy.random.RandomState(model_config['random_seed']) theano_rng = RandomStreams(numpy_rng.randint(2**30)) logger.info('> ... building the model') hidden_activation = parse_activation(mlp_config['activation']) createDir(model_config['wdir']) #create working dir batch_size = model_config['batch_size'] cnn = CNN3D(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, hidden_activation=hidden_activation, 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 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') activationFn = parse_activation(mlp_config['activation']); createDir(model_config['wdir']); #create working dir batch_size = model_config['batch_size']; if mlp_config['do_dropout'] or conv_config['do_dropout']: logger.info('>Initializing dropout cnn model') 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, hidden_activation = activationFn, 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'], input_dropout_factor=conv_config['input_dropout_factor']) 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, hidden_activation = activationFn, 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 runCNN3D(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']) #__debugPrintData__(conv_layer_config,'covolution config') data_spec = load_data_spec(model_config['data_spec'],model_config['batch_size']); numpy_rng = numpy.random.RandomState(model_config['random_seed']) theano_rng = RandomStreams(numpy_rng.randint(2 ** 30)) logger.info('> ... building the model') hidden_activation = parse_activation(mlp_config['activation']); createDir(model_config['wdir']); #create working dir batch_size = model_config['batch_size']; cnn = CNN3D(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,hidden_activation = hidden_activation, 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 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) logger.info("Loading Pretrained network weights") try: # pretraining ptr_file = model_config['input_file'] dbn.load(filename=ptr_file) except KeyError, e: logger.info("KeyMissing:" + str(e)) logger.info( "Pretrained network Missing in configFile: Skipping Loading")
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) logger.info("Loading Pretrained network weights") try: # pretraining ptr_file = model_config['input_file'] dbn.load(filename=ptr_file) except KeyError, e: logger.info("KeyMissing:"+str(e)); logger.info("Pretrained network Missing in configFile: Skipping Loading");
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') activationFn = parse_activation(mlp_config['activation']) createDir(model_config['wdir']) #create working dir batch_size = model_config['batch_size'] if mlp_config['do_dropout'] or conv_config['do_dropout']: logger.info('>Initializing dropout cnn model') 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, hidden_activation=activationFn, 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'], input_dropout_factor=conv_config['input_dropout_factor']) 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, hidden_activation=activationFn, 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 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 = read_dataset(data_spec['training']) pretraining_config = model_config['pretrain_params'] corruption_levels = sda_config['corruption_levels'] preTraining(sda,train_sets,corruption_levels,pretraining_config); del train_sets; ######################## # 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']))