def test_param_values(self): box_env_spec = GarageEnv(DummyBoxEnv(obs_dim=(2, ))).spec cmb = ContinuousMLPBaseline(env_spec=box_env_spec) new_cmb = ContinuousMLPBaseline(env_spec=box_env_spec, name='ContinuousMLPBaseline2') # Manual change the parameter of ContinuousMLPBaseline with tf.compat.v1.variable_scope('ContinuousMLPBaseline', reuse=True): bias = tf.compat.v1.get_variable('mlp/hidden_0/bias') bias.load(tf.ones_like(bias).eval()) old_param_values = cmb.get_param_values() new_param_values = new_cmb.get_param_values() assert not np.array_equal(old_param_values, new_param_values) new_cmb.set_param_values(old_param_values) new_param_values = new_cmb.get_param_values() assert np.array_equal(old_param_values, new_param_values)
def test_param_values(self, obs_dim): box_env = GarageEnv(DummyBoxEnv(obs_dim=obs_dim)) with mock.patch(('garage.tf.baselines.' 'continuous_mlp_baseline.' 'ContinuousMLPRegressor'), new=SimpleMLPRegressor): cmb = ContinuousMLPBaseline(env_spec=box_env.spec) new_cmb = ContinuousMLPBaseline(env_spec=box_env.spec, name='ContinuousMLPBaseline2') # Manual change the parameter of ContinuousMLPBaseline with tf.compat.v1.variable_scope('ContinuousMLPBaseline2', reuse=True): return_var = tf.compat.v1.get_variable('SimpleMLPModel/return_var') return_var.load(1.0) old_param_values = cmb.get_param_values() new_param_values = new_cmb.get_param_values() assert not np.array_equal(old_param_values, new_param_values) new_cmb.set_param_values(old_param_values) new_param_values = new_cmb.get_param_values() assert np.array_equal(old_param_values, new_param_values)