def test_mnist(alg):
'''
Test if the algorithm can learn to classify MNIST digits.
Uses CNN policy.
'''
learn_kwargs = learn_args[alg]
learn_kwargs.update(common_kwargs)
learn = get_learn_function(alg)
learn_fn = lambda e: learn(env=e, **learn_kwargs)
env_fn = lambda: MnistEnv(seed=0, episode_len=100)
simple_test(env_fn, learn_fn, 0.6)
def test_serialization(learn_fn, network_fn):
'''
Test if the trained model can be serialized
'''
if network_fn.endswith('lstm') and learn_fn in [
'acktr', 'trpo_mpi', 'deepq'
]:
# TODO make acktr work with recurrent policies
# and test
# github issue: https://github.com/openai/baselines/issues/194
return
env = DummyVecEnv([lambda: MnistEnv(10, episode_len=100)])
ob = env.reset().copy()
learn = get_learn_function(learn_fn)
kwargs = {}
kwargs.update(network_kwargs[network_fn])
kwargs.update(learn_kwargs[learn_fn])
learn = partial(learn, env=env, network=network_fn, seed=0, **kwargs)
with tempfile.TemporaryDirectory() as td:
model_path = os.path.join(td, 'serialization_test_model')
with tf.Graph().as_default(), make_session().as_default():
model = learn(total_timesteps=100)
model.save(model_path)
mean1, std1 = _get_action_stats(model, ob)
variables_dict1 = _serialize_variables()
with tf.Graph().as_default(), make_session().as_default():
model = learn(total_timesteps=0, load_path=model_path)
mean2, std2 = _get_action_stats(model, ob)
variables_dict2 = _serialize_variables()
for k, v in variables_dict1.items():
np.testing.assert_allclose(
v,
variables_dict2[k],
atol=0.01,
err_msg='saved and loaded variable {} value mismatch'.format(
k))
np.testing.assert_allclose(mean1, mean2, atol=0.5)
np.testing.assert_allclose(std1, std2, atol=0.5)
def make_env():
env = MnistEnv(episode_len=100)
env.seed(10)
return env
def make_env():
env = MnistEnv(episode_len=100)
env.seed(10)
return env