def test_self_contained_example(self):
client_data = create_client_data()
model = MnistTrainableModel()
losses = []
for _ in range(2):
outputs = simple_fedavg.client_update(model, client_data(),
simple_fedavg._get_weights(model))
losses.append(outputs.model_output['loss'].numpy())
self.assertAllEqual(outputs.optimizer_output['num_examples'].numpy(), 2)
self.assertLess(losses[1], losses[0])
def server_init(model, optimizer):
"""Returns initial `tff.learning.framework.ServerState`.
Args:
model: A `tff.learning.Model`.
optimizer: A `tf.train.Optimizer`.
Returns:
A `tff.learning.framework.ServerState` namedtuple.
"""
optimizer_vars = simple_fedavg._create_optimizer_vars(model, optimizer)
return (simple_fedavg.ServerState(
model=simple_fedavg._get_weights(model),
optimizer_state=optimizer_vars), optimizer_vars)
def _assert_server_update_with_all_ones(self, model_fn):
optimizer_fn = lambda: tf.keras.optimizers.SGD(learning_rate=0.1)
model = model_fn()
optimizer = optimizer_fn()
state, optimizer_vars = server_init(model, optimizer)
weights_delta = tf.nest.map_structure(
tf.ones_like,
simple_fedavg._get_weights(model).trainable)
for _ in range(2):
state = simple_fedavg.server_update(model, optimizer, optimizer_vars,
state, weights_delta)
model_vars = self.evaluate(state.model)
train_vars = model_vars.trainable
self.assertLen(train_vars, 2)
# weights are initialized with all-zeros, weights_delta is all ones,
# SGD learning rate is 0.1. Updating server for 2 steps.
self.assertAllClose(
train_vars, {k: np.ones_like(v) * 0.2 for k, v in train_vars.items()})
