def __init__(self, config_dict): print config_dict train, valid, alphabet = build_datasets(config_dict) generator, cost = build_model(len(alphabet), config_dict) algorithm = build_algorithm(generator, cost, config_dict) extensions = build_extensions(cost, algorithm, valid, config_dict) main_loop = MainLoop(algorithm=algorithm, data_stream=train, model=Model(cost), extensions=extensions) ml = Load(config_dict['checkpoint_path'], load_log=True) ml.load_to(main_loop) generator = main_loop.model.get_top_bricks()[-1] self.numbers_from_text = pickle.load(open(config_dict['dict_path'])) x = tensor.lmatrix('sample') cost_cg = generator.cost(x) self.cost_f = theano.function([x], cost_cg)
import cPickle as pickle from blocks.graph import ComputationGraph config_dict = yaml.load(open(sys.argv[1], 'r')) print config_dict train, valid, alphabet = build_datasets(config_dict) generator, cost = build_model(len(alphabet), config_dict) algorithm = build_algorithm(generator, cost, config_dict) extensions = build_extensions(cost, algorithm, valid, config_dict) main_loop = MainLoop(algorithm=algorithm, data_stream=train, model=Model(cost), extensions=extensions) ml = Load(config_dict['checkpoint_path'], load_log=True) print dir(ml) ml.load_to(main_loop) generator = main_loop.model.get_top_bricks()[-1] sampler = ComputationGraph(generator.generate( n_steps=1000, batch_size=10, iterate=True)).get_theano_function() samples = sampler() outputs = samples[-2] charset = pickle.load(open(config_dict['dict_path'])) new_charset = {} for v in charset: new_charset[charset[v]] = v charset = new_charset print charset for i in xrange(outputs.shape[1]): print "Sample number ", i, ": ",