def test_param_values(self): gcb = GaussianCNNBaseline(env_spec=test_env_spec, filters=((3, (3, 3)), (6, (3, 3))), strides=(1, 1), padding='SAME', hidden_sizes=(32, ), adaptive_std=False, use_trust_region=False) new_gcb = GaussianCNNBaseline(env_spec=test_env_spec, filters=((3, (3, 3)), (6, (3, 3))), strides=(1, 1), padding='SAME', hidden_sizes=(32, ), adaptive_std=False, use_trust_region=False, name='GaussianCNNBaseline2') # Manual change the parameter of GaussianCNNBaseline with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True): bias_var = tf.compat.v1.get_variable( 'dist_params/mean_network/hidden_0/bias') bias_var.load(tf.ones_like(bias_var).eval()) old_param_values = gcb.get_param_values() new_param_values = new_gcb.get_param_values() assert not np.array_equal(old_param_values, new_param_values) new_gcb.set_param_values(old_param_values) new_param_values = new_gcb.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.' 'gaussian_cnn_baseline.' 'GaussianCNNRegressor'), new=SimpleGaussianCNNRegressor): gcb = GaussianCNNBaseline(env_spec=box_env.spec) new_gcb = GaussianCNNBaseline(env_spec=box_env.spec, name='GaussianCNNBaseline2') # Manual change the parameter of GaussianCNNBaseline with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True): return_var = tf.compat.v1.get_variable( 'SimpleGaussianCNNModel/return_var') return_var.load(1.0) old_param_values = gcb.get_param_values() new_param_values = new_gcb.get_param_values() assert not np.array_equal(old_param_values, new_param_values) new_gcb.set_param_values(old_param_values) new_param_values = new_gcb.get_param_values() assert np.array_equal(old_param_values, new_param_values)