def retrain(args):
game = sys.argv[1]
gen_prefix = sys.argv[2]
gen_prefix_next = sys.argv[3]
configs = Configs()
train_config = getattr(configs, game)(gen_prefix)
generation_descr = templates.default_generation_desc(train_config.game,
multiple_policy_heads=True,
num_previous_states=0)
# create a transformer
man = get_manager()
transformer = man.get_transformer(train_config.game, generation_descr)
# create the manager
trainer = train.TrainManager(train_config, transformer)
trainer.update_config(train_config, next_generation_prefix=gen_prefix_next)
nn_model_config = get_nn_model(train_config.game, transformer)
#nn_model_config = templates.nn_model_config_template(train_config.game, "small", transformer)
trainer.get_network(nn_model_config, generation_descr)
data = trainer.gather_data()
trainer.do_epochs(data)
trainer.save()
def do_training(game,
gen_prefix,
next_step,
starting_step,
num_previous_states,
gen_prefix_next,
do_data_augmentation=False):
man = get_manager()
# create a transformer
generation_descr = templates.default_generation_desc(
game,
multiple_policy_heads=True,
num_previous_states=num_previous_states)
transformer = man.get_transformer(game, generation_descr)
# create train_config
train_config = get_train_config(game, gen_prefix, next_step, starting_step)
trainer = train.TrainManager(train_config,
transformer,
do_data_augmentation=do_data_augmentation)
trainer.update_config(train_config, next_generation_prefix=gen_prefix_next)
# get the nn model and set on trainer
nn_model_config = get_nn_model(train_config.game, transformer)
trainer.get_network(nn_model_config, generation_descr)
trainer.do_epochs()
trainer.save()
def speed_test():
ITERATIONS = 3
man = get_manager()
# get data
train_config = config()
# get nn to test speed on
transformer = man.get_transformer(train_config.game)
trainer = train.TrainManager(train_config, transformer)
nn_model_config = templates.nn_model_config_template(train_config.game, "small", transformer)
generation_descr = templates.default_generation_desc(train_config.game)
trainer.get_network(nn_model_config, generation_descr)
data = trainer.gather_data()
res = []
batch_size = 4096
sample_count = len(data.inputs)
keras_model = trainer.nn.get_model()
# warm up
for i in range(2):
idx, end_idx = i * batch_size, (i + 1) * batch_size
print i, idx, end_idx
inputs = np.array(data.inputs[idx:end_idx])
res.append(keras_model.predict(inputs, batch_size=batch_size))
print res[0]
for _ in range(ITERATIONS):
res = []
times = []
gc.collect()
print 'Starting speed run'
num_batches = sample_count / batch_size + 1
print "batches %s, batch_size %s, inputs: %s" % (num_batches,
batch_size,
len(data.inputs))
for i in range(num_batches):
idx, end_idx = i * batch_size, (i + 1) * batch_size
inputs = np.array(data.inputs[idx:end_idx])
print "inputs", len(inputs)
s = time.time()
Y = keras_model.predict(inputs, batch_size=batch_size)
times.append(time.time() - s)
print "outputs", len(Y[0])
print "times taken", times
print "total_time taken", sum(times)
print "predictions per second", sample_count / float(sum(times))
def go():
ITERATIONS = 3
man = get_manager()
# get data
train_config = config()
# get nn to test speed on
transformer = man.get_transformer(train_config.game)
trainer = train.TrainManager(train_config, transformer)
nn_model_config = templates.nn_model_config_template(train_config.game, "small", transformer)
generation_descr = templates.default_generation_desc(train_config.game)
trainer.get_network(nn_model_config, generation_descr)
data = trainer.gather_data()
r = Runner(trainer.gather_data(), trainer.nn.get_model())
r.warmup()
def test_trainer_update_config():
# we need the data for this test
conf = get_conf_reversi()
# create a transformer
man = get_manager()
generation_descr = templates.default_generation_desc(conf.game)
generation_descr.num_previous_states = 2
generation_descr.multiple_policy_heads = True
transformer = man.get_transformer(conf.game, generation_descr)
# create the manager
trainer = train.TrainManager(conf, transformer, next_generation_prefix="x2test")
nn_model_config = templates.nn_model_config_template(conf.game, "tiny", transformer)
trainer.get_network(nn_model_config, generation_descr)
data = trainer.gather_data()
print data
trainer.do_epochs(data)
trainer.save()