def test_client_tf_custom_delta_weight(self):
model = self.create_model()
dataset = self.create_dataset()
client_tf = federated_averaging._DeprecatedClientFedAvg(
model, client_weight_fn=lambda _: tf.constant(1.5))
client_outputs = client_tf(dataset, self.initial_weights())
self.assertEqual(self.evaluate(client_outputs.weights_delta_weight),
1.5)
def test_non_finite_aggregation(self, bad_value):
model = self.create_model()
dataset = self.create_dataset()
client_tf = federated_averaging._DeprecatedClientFedAvg(model)
init_weights = self.initial_weights()
init_weights.trainable[1] = bad_value
client_outputs = client_tf(dataset, init_weights)
self.assertEqual(self.evaluate(client_outputs.weights_delta_weight),
0.0)
self.assertAllClose(self.evaluate(client_outputs.weights_delta),
[[[0.0], [0.0]], 0.0])
self.assertEqual(
self.evaluate(
client_outputs.optimizer_output['has_non_finite_delta']), 1)
def test_client_tf(self):
model = self.create_model()
dataset = self.create_dataset()
client_tf = federated_averaging._DeprecatedClientFedAvg(model)
client_outputs = self.evaluate(
client_tf(dataset, self.initial_weights()))
# Both trainable parameters should have been updated,
# and we don't return the non-trainable variable.
self.assertAllGreater(
np.linalg.norm(client_outputs.weights_delta, axis=-1), 0.1)
self.assertEqual(client_outputs.weights_delta_weight, 8.0)
self.assertEqual(client_outputs.optimizer_output['num_examples'], 8)
self.assertEqual(
client_outputs.optimizer_output['has_non_finite_delta'], 0)
self.assertDictContainsSubset(
{
'num_examples': 8,
'num_examples_float': 8.0,
'num_batches': 3,
}, client_outputs.model_output)
self.assertBetween(client_outputs.model_output['loss'],
np.finfo(np.float32).eps, 10.0)