# loop over different models for model in models: print('model: ' + model) model_path = helper.make_directory(sstype_path, model) # loop over different RNA binding proteins results = [] for rbp_index, experiment in enumerate(experiments): print('Analyzing: ' + experiment) tf.reset_default_graph() # reset any tensorflow graphs np.random.seed(247) # for reproducibilitjy tf.set_random_seed(247) # for reproducibility # load rbp dataset train, valid, test = helper.load_dataset_hdf5(data_path, ss_type=ss_type, rbp_index=rbp_index) # process rbp dataset train, valid, test = helper.process_data(train, valid, test, method=normalize_method) # get shapes input_shape = list(train['inputs'].shape) input_shape[0] = None output_shape = train['targets'].shape # load model genome_model = helper.import_model(model)
# directory to save parameters of saliency classifier_path = helper.make_directory(results_path, normalize_method+'_'+ss_type) classifier_path = helper.make_directory(classifier_path, 'ensemble') # path to pre-trained model parameters best_path = os.path.join(trained_path, 'mixed_'+normalize_method+'_'+ss_type) # loop over different RNA binding proteins for rbp_name in rbp_names: print('Analyzing: '+ rbp_name) tf.reset_default_graph() # reset any tensorflow graphs np.random.seed(247) # for reproducibilitjy tf.set_random_seed(247) # for reproducibility # load rbp dataset train, valid, test = helper.load_dataset_hdf5(data_path, dataset_name=rbp_name, ss_type=ss_type) # process rbp dataset train, valid, test = helper.process_data(train, valid, test, method=normalize_method) # get shapes input_shape = list(train['inputs'].shape) input_shape[0] = None output_shape = train['targets'].shape # get ensemble model predictions ensemble_predictions, predictions = helper.ensemble_predictions(train, rbp_name, models, input_shape, output_shape, best_path, use_scope=True) max_index = np.argsort(ensemble_predictions)[::-1] # directories to store classifier model and motifs
dataset_file_path = os.path.join(dataset_path, experiment_name + '_' + str(window) + '.h5') # set results path results_path = helper.make_directory('../../results', 'encode_eclip') # model results path model_path = helper.make_directory(results_path, model_name) # path to save model experiment_name = rbp_name + '_' + cell_name model_save_path = os.path.join(model_path, experiment_name) #----------------------------------------------------------------------------------------- # load dataset train, valid, test = helper.load_dataset_hdf5(dataset_file_path, ss_type=ss_type) # get shapes of data input_shape = list(train['inputs'].shape) input_shape[0] = None output_shape = [None, train['targets'].shape[1]] # build model genome_model = helper.import_model(model_name) model_layers, optimization = genome_model(input_shape, output_shape) # build neural network class nnmodel = nn.NeuralNet(seed=247) nnmodel.build_layers(model_layers, optimization) nnmodel.inspect_layers()