def test_load_and_save():
env = gym.make('CartPole-v0')
M = Model(env, build_graph, build_update_feed_dict)
M.update([0, 0, 0, 0], 2)
weights = M.G._session.run(M['vars'])
M.save('test_load_and_save_model')
M.update([0, 0, 0, 0], 2)
del M
M = Model.load(path='test_load_and_save_model')
weights2 = M.G._session.run(M['vars'])
M.build_update_feed([0, 1, 0, 1], 2)
M.update([0, 0, 0, 0], 2)
shutil.rmtree('test_load_and_save_model')
assert np.allclose(weights, weights2)
def test_gradient_ops():
env = gym.make('CartPole-v0')
M = Model(env, build_graph, build_update_feed_dict)
feed_dict = M.build_update_feed([0, 1, 0, 1], 2)
weights_before = M.get_weights()[0]
M.apply_gradient_ops(M.get_loss().name, feed_dict)
weights_after = M.get_weights()[0]
assert weights_after[0] == pytest.approx(weights_before[0], 1e-7)
assert weights_after[2] == pytest.approx(weights_before[2], 1e-7)
assert weights_after[1] != weights_before[1]
assert weights_after[3] != weights_before[3]