def test_client_tf(self):
model = self.model()
dataset = self.dataset()
client_tf = federated_averaging.ClientFedAvg(model)
init_op = tf.group(
model_utils.model_initializer(model),
tf.compat.v1.initializers.variables(client_tf.variables),
name='fedavg_initializer')
client_outputs = client_tf(dataset, self.initial_weights())
tf.compat.v1.get_default_graph().finalize()
with self.session() as sess:
sess.run(init_op)
out = sess.run(client_outputs)
# Both trainable parameters should have been updated,
# and we don't return the non-trainable 'c'.
self.assertCountEqual(['a', 'b'], list(out.weights_delta.keys()))
self.assertGreater(np.linalg.norm(out.weights_delta['a']), 0.1)
self.assertGreater(np.linalg.norm(out.weights_delta['b']), 0.1)
self.assertEqual(out.weights_delta_weight, 8.0)
self.assertEqual(out.optimizer_output['num_examples'], 8)
self.assertEqual(out.optimizer_output['has_non_finite_delta'], 0)
self.assertEqual(out.model_output['num_examples'], 8)
self.assertEqual(out.model_output['num_batches'], 3)
self.assertBetween(out.model_output['loss'],
np.finfo(np.float32).eps, 10.0)
def test_client_tf(self):
model = self.model()
dataset = self.dataset()
client_tf = federated_sgd.ClientSgd(model)
init_op = tf.group(model_utils.model_initializer(model),
tf.compat.v1.initializers.variables(
client_tf.variables),
name='fedsgd_initializer')
client_outputs = client_tf(dataset, self.initial_weights())
tf.compat.v1.get_default_graph().finalize()
with self.session() as sess:
sess.run(init_op)
out = sess.run(client_outputs)
# Both trainable parameters should have gradients,
# and we don't return the non-trainable 'c'.
self.assertCountEqual(['a', 'b'], list(out.weights_delta.keys()))
# Model deltas for squared error.
self.assertAllClose(out.weights_delta['a'], [[1.0], [0.0]])
self.assertAllClose(out.weights_delta['b'], 1.0)
self.assertAllClose(out.weights_delta_weight, 8.0)
self.assertEqual(out.model_output['num_examples'], 8)
self.assertEqual(out.model_output['num_batches'], 3)
self.assertAlmostEqual(out.model_output['loss'], 0.5)
self.assertEqual(out.optimizer_output['client_weight'], 8.0)
self.assertEqual(out.optimizer_output['has_non_finite_delta'], 0)
def test_model_initializer(self):
with tf.Graph().as_default() as g:
model = model_utils.enhance(model_examples.LinearRegression(2))
init = model_utils.model_initializer(model)
with self.session(graph=g) as sess:
sess.run(init)
# Make sure we can read all the variables
try:
sess.run(model.local_variables)
sess.run(model.weights)
except tf.errors.FailedPreconditionError:
self.fail('Expected variables to be initialized, but got '
'tf.errors.FailedPreconditionError')
