def parse_dkf_args(runme_path, conf_path, weight_path): # DKF uses arguments like "-vm LR -infm structured", etc. This script # loads those arguments. new_argv = get_args(runme_path) new_argv.extend(['-reload', weight_path, '-params', conf_path]) from parse_args_dkf import parse params = parse(new_argv) return params
import os, time, sys """ Add the higher level directory to PYTHONPATH to be able to access the models """ sys.path.append('../') """ Change this to modify the loadDataset function """ from load import loadDataset """ This will contain a hashmap where the parameters correspond to the default ones modified by any command line options given to this script """ from parse_args_dkf import parse params = parse() """ Some utility functions from theanomodels """ from utils.misc import removeIfExists, createIfAbsent, mapPrint, saveHDF5, displayTime """ Load the dataset into a hashmap. See load.py for details """ dataset = loadDataset() params['savedir'] += '-template' createIfAbsent(params['savedir']) """ Add dataset and NADE parameters to "params" which will become part of the model """ for k in ['dim_observations', 'data_type']: params[k] = dataset[k] mapPrint('Options: ', params) if params['use_nade']: params['data_type'] = 'binary_nade' """ import DKF + learn/evaluate functions """ start_time = time.time() from stinfmodel.dkf import DKF
import os,time,sys sys.path.append('../') import numpy as np from datasets.load import loadDataset from parse_args_dkf import parse; params = parse() from utils.misc import removeIfExists,createIfAbsent,mapPrint,saveHDF5,displayTime params['dim_stochastic'] = 1 if params['dataset']=='': params['dataset']='synthetic9' dataset = loadDataset(params['dataset']) params['savedir']+='-'+params['dataset'] createIfAbsent(params['savedir']) #Saving/loading for k in ['dim_observations','dim_actions','data_type']: params[k] = dataset[k] mapPrint('Options: ',params) #Setup VAE Model (or reload from existing savefile) start_time = time.time() from stinfmodel.dkf import DKF import stinfmodel.evaluate as DKF_evaluate import stinfmodel.learning as DKF_learn displayTime('import DKF',start_time, time.time()) dkf = None #Remove from params start_time = time.time()