def foo1(x):
val = intrinsics.sequence_reduce(x, 0, add_numbers)
self.assertIsInstance(val, value_base.Value)
return val
self.assert_type(foo1, '(int32* -> int32)')
@computations.federated_computation(
computation_types.FederatedType(
computation_types.SequenceType(tf.int32), placements.SERVER))
def foo2(x):
val = intrinsics.sequence_reduce(x, 0, add_numbers)
self.assertIsInstance(val, value_base.Value)
return val
self.assert_type(foo2, '(int32*@SERVER -> int32@SERVER)')
@computations.federated_computation(
computation_types.FederatedType(
computation_types.SequenceType(tf.int32), placements.CLIENTS))
def foo3(x):
val = intrinsics.sequence_reduce(x, 0, add_numbers)
self.assertIsInstance(val, value_base.Value)
return val
self.assert_type(foo3, '({int32*}@CLIENTS -> {int32}@CLIENTS)')
if __name__ == '__main__':
common_test.main()
server_state, metrics = iterative_process.next(server_state, federated_ds)
self.assertLess(metrics.loss, prev_loss)
prev_loss = metrics.loss
def test_execute_empty_data(self):
iterative_process = federated_averaging.build_federated_averaging_process(
model_fn=model_examples.TrainableLinearRegression)
# Results in empty dataset with correct types and shapes.
ds = tf.data.Dataset.from_tensor_slices({
'x': [[1., 2.]],
'y': [[5.]]
}).batch(
5, drop_remainder=True)
federated_ds = [ds] * 2
server_state = iterative_process.initialize()
first_state, metric_outputs = iterative_process.next(
server_state, federated_ds)
self.assertEqual(
self.evaluate(tf.reduce_sum(first_state.model.trainable.a)) +
self.evaluate(tf.reduce_sum(first_state.model.trainable.b)), 0)
self.assertEqual(metric_outputs.num_examples, 0)
self.assertTrue(tf.is_nan(metric_outputs.loss))
if __name__ == '__main__':
test.main()
