def save(self):
# XXX set generation attributes
man = get_manager()
man.save_network(self.nn, generation_name=self.next_generation)
self.do_callbacks()
###############################################################################
# save a previous model for next time
if self.controller.retrain_best is None:
log.warning("No retraining network")
return
log.info("Saving retraining network with val_policy_acc: %.4f" %
(self.controller.retrain_best_val_policy_acc))
# there is an undocumented keras clone function, but this is sure to work (albeit slow and evil)
from ggpzero.util.keras import keras_models
for_next_generation = "%s_prev" % self.next_generation
prev_model = keras_models.model_from_json(
self.nn.keras_model.to_json())
prev_model.set_weights(self.controller.retrain_best)
prev_generation_descr = attrutil.clone(self.nn.generation_descr)
prev_generation_descr.name = for_next_generation
prev_nn = network.NeuralNetwork(self.nn.gdl_bases_transformer,
prev_model, prev_generation_descr)
man.save_network(prev_nn, for_next_generation)
self.do_callbacks()
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)