def test_iterative_process_with_encoding(self):
model_fn = model_examples.LinearRegression
gather_fn = encoding_utils.build_encoded_mean_from_model(
model_fn, _test_encoder_fn('gather'))
broadcast_fn = encoding_utils.build_encoded_broadcast_from_model(
model_fn, _test_encoder_fn('simple'))
iterative_process = optimizer_utils.build_model_delta_optimizer_process(
model_fn=model_fn,
model_to_client_delta_fn=DummyClientDeltaFn,
server_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=
1.0),
stateful_delta_aggregate_fn=gather_fn,
stateful_model_broadcast_fn=broadcast_fn)
ds = tf.data.Dataset.from_tensor_slices(
collections.OrderedDict([
('x', [[1.0, 2.0], [3.0, 4.0]]),
('y', [[5.0], [6.0]]),
])).batch(2)
federated_ds = [ds] * 3
state = iterative_process.initialize()
self.assertEqual(state.model_broadcast_state.trainable[0][0], 1)
state, _ = iterative_process.next(state, federated_ds)
self.assertEqual(state.model_broadcast_state.trainable[0][0], 2)
def test_broadcast_from_model(self):
model_fn = model_examples.LinearRegression
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
broadcast_fn = encoding_utils.build_encoded_broadcast_from_model(
model_fn, _test_encoder_fn('simple'))
self.assertLen(w, 2)
self.assertIsInstance(broadcast_fn, computation_utils.StatefulBroadcastFn)
def test_iterative_process_with_encoding(self):
model_fn = model_examples.LinearRegression
gather_fn = encoding_utils.build_encoded_mean_from_model(
model_fn, _test_encoder_fn('gather'))
broadcast_fn = encoding_utils.build_encoded_broadcast_from_model(
model_fn, _test_encoder_fn('simple'))
iterative_process = optimizer_utils.build_model_delta_optimizer_process(
model_fn=model_fn,
model_to_client_delta_fn=DummyClientDeltaFn,
server_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=1.0),
stateful_delta_aggregate_fn=gather_fn,
stateful_model_broadcast_fn=broadcast_fn)
self._verify_iterative_process(iterative_process)
def test_iterative_process_with_encoding(self):
model_fn = model_examples.TrainableLinearRegression
broadcast_fn = encoding_utils.build_encoded_broadcast_from_model(
model_fn, _test_encoder_fn())
iterative_process = optimizer_utils.build_model_delta_optimizer_process(
model_fn=model_fn,
model_to_client_delta_fn=DummyClientDeltaFn,
server_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=1.0),
stateful_model_broadcast_fn=broadcast_fn)
ds = tf.data.Dataset.from_tensor_slices({
'x': [[1., 2.], [3., 4.]],
'y': [[5.], [6.]]
}).batch(2)
federated_ds = [ds] * 3
state = iterative_process.initialize()
self.assertEqual(state.model_broadcast_state.trainable.a[0], 1)
state, _ = iterative_process.next(state, federated_ds)
self.assertEqual(state.model_broadcast_state.trainable.a[0], 2)
def test_broadcast_from_model(self):
model_fn = model_examples.LinearRegression
broadcast_fn = encoding_utils.build_encoded_broadcast_from_model(
model_fn, _test_encoder_fn('simple'))
self.assertIsInstance(broadcast_fn, tff.utils.StatefulBroadcastFn)
