def work(): config_dict = yaml.load(open(sys.argv[1], 'r')) print config_dict if config_dict['working_mode'] == 'train_new': 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) main_loop.run() elif config_dict['working_mode'] == 'train_resume': # TODO pass
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)
from data import build_datasets from model import build_model, build_algorithm from monitor import build_extensions from blocks.main_loop import MainLoop from blocks.model import Model from blocks.extensions.saveload import Load 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 = {}