def load_network_fixme(self, game):
import glob
import datetime
p = os.path.join(self.data_path, game, "models")
os.chdir(p)
gens = glob.glob("%s_*" % game)
for g in gens:
print "doing", game, g
generation = os.path.splitext(g)[0]
new_style_gen = generation.replace(game + "_", "")
print generation, new_style_gen
# dummy generation_descr
generation_descr = templates.default_generation_desc(game)
json_str = open(self.model_path(game, generation)).read()
keras_model = keras_models.model_from_json(json_str)
keras_model.load_weights(self.weights_path(game, generation))
transformer = self.get_transformer(game, generation_descr)
print transformer, keras_model, generation_descr
nn = NeuralNetwork(transformer, keras_model, generation_descr)
generation_descr.name = new_style_gen
generation_descr.trained_losses = "unknown"
generation_descr.trained_validation_losses = "unknown"
generation_descr.trained_policy_accuracy = "unknown"
generation_descr.trained_value_accuracy = "unknown"
ctime = os.stat(self.model_path(game, generation)).st_ctime
generation_descr.date_created = datetime.datetime.fromtimestamp(
ctime).strftime("%Y/%m/%d %H:%M")
print generation_descr
self.save_network(nn)
def load_network(self, game, generation_name):
json_str = open(self.generation_path(game, generation_name)).read()
generation_descr = attrutil.json_to_attr(json_str)
json_str = open(self.model_path(game, generation_name)).read()
keras_model = keras_models.model_from_json(json_str)
keras_model.load_weights(self.weights_path(game, generation_name))
transformer = self.get_transformer(game, generation_descr)
return NeuralNetwork(transformer, keras_model, generation_descr)
def test_nn_model_config_template():
game = "breakthrough"
gen_prefix = "x1"
prev_states = 1
gen_desc = templates.default_generation_desc(
game,
gen_prefix,
multiple_policy_heads=True,
num_previous_states=prev_states)
transformer = man.get_transformer(game, gen_desc)
model = templates.nn_model_config_template("breakthrough", "small",
transformer)
attrutil.pprint(model)
keras_model = get_network_model(model, gen_desc)
network = NeuralNetwork(transformer, keras_model, gen_desc)
print network
network.summary()
def create_new_network(self, game, nn_model_conf=None, generation_descr=None):
if generation_descr is None:
generation_descr = templates.default_generation_desc(game)
transformer = self.get_transformer(game, generation_descr)
if isinstance(nn_model_conf, str):
nn_model_conf = templates.nn_model_config_template(game,
network_size_hint=nn_model_conf,
transformer=transformer)
elif nn_model_conf is None:
nn_model_conf = templates.nn_model_config_template(game,
network_size_hint="small",
transformer=transformer)
assert isinstance(nn_model_conf, confs.NNModelConfig)
assert isinstance(generation_descr, datadesc.GenerationDescription)
keras_model = get_network_model(nn_model_conf, generation_descr)
return NeuralNetwork(transformer, keras_model, generation_descr)