def test_mnist_training_round_trip(self):
it = canonical_form_utils.get_iterative_process_for_canonical_form(
test_utils.get_mnist_training_example())
cf = canonical_form_utils.get_canonical_form_for_iterative_process(it)
new_it = canonical_form_utils.get_iterative_process_for_canonical_form(cf)
state1 = it.initialize()
state2 = new_it.initialize()
self.assertEqual(str(state1), str(state2))
dummy_x = np.array([[0.5] * 784], dtype=np.float32)
dummy_y = np.array([1], dtype=np.int32)
client_data = [collections.OrderedDict(x=dummy_x, y=dummy_y)]
round_1 = it.next(state1, [client_data])
state = round_1[0]
metrics = round_1[1]
alt_round_1 = new_it.next(state2, [client_data])
alt_state = alt_round_1[0]
alt_metrics = alt_round_1[1]
self.assertAllEqual(
anonymous_tuple.name_list(state), anonymous_tuple.name_list(alt_state))
self.assertAllEqual(
anonymous_tuple.name_list(metrics),
anonymous_tuple.name_list(alt_metrics))
self.assertAllClose(state, alt_state)
self.assertAllClose(metrics, alt_metrics)
self.assertEqual(
tree_analysis.count_tensorflow_variables_under(
test_utils.computation_to_building_block(it.next)),
tree_analysis.count_tensorflow_variables_under(
test_utils.computation_to_building_block(new_it.next)))
def test_mnist_training_round_trip(self):
it = canonical_form_utils.get_iterative_process_for_canonical_form(
test_utils.get_mnist_training_example())
cf = canonical_form_utils.get_canonical_form_for_iterative_process(it)
new_it = canonical_form_utils.get_iterative_process_for_canonical_form(
cf)
state1 = it.initialize()
state2 = new_it.initialize()
self.assertEqual(str(state1), str(state2))
dummy_x = np.array([[0.5] * 784], dtype=np.float32)
dummy_y = np.array([1], dtype=np.int32)
client_data = [
collections.OrderedDict([('x', dummy_x), ('y', dummy_y)])
]
round_1 = it.next(state1, [client_data])
state = round_1[0]
metrics = round_1[1]
alt_round_1 = new_it.next(state2, [client_data])
alt_state = alt_round_1[0]
alt_metrics = alt_round_1[1]
self.assertEqual(str(round_1), str(alt_round_1))
self.assertTrue(
np.array_equal(state.model.weights, state.model.weights))
self.assertTrue(np.array_equal(state.model.bias, alt_state.model.bias))
self.assertTrue(np.array_equal(state.num_rounds, alt_state.num_rounds))
self.assertTrue(
np.array_equal(metrics.num_rounds, alt_metrics.num_rounds))
self.assertTrue(
np.array_equal(metrics.num_examples, alt_metrics.num_examples))
self.assertTrue(np.array_equal(metrics.loss, alt_metrics.loss))
def test_mnist_training_round_trip(self):
it = form_utils.get_iterative_process_for_map_reduce_form(
mapreduce_test_utils.get_mnist_training_example())
mrf = form_utils.get_map_reduce_form_for_iterative_process(it)
new_it = form_utils.get_iterative_process_for_map_reduce_form(mrf)
state1 = it.initialize()
state2 = new_it.initialize()
self.assertAllClose(state1, state2)
whimsy_x = np.array([[0.5] * 784], dtype=np.float32)
whimsy_y = np.array([1], dtype=np.int32)
client_data = [collections.OrderedDict(x=whimsy_x, y=whimsy_y)]
round_1 = it.next(state1, [client_data])
state = round_1[0]
metrics = round_1[1]
alt_round_1 = new_it.next(state2, [client_data])
alt_state = alt_round_1[0]
self.assertAllClose(state, alt_state)
alt_metrics = alt_round_1[1]
self.assertAllClose(metrics, alt_metrics)
self.assertEqual(
tree_analysis.count_tensorflow_variables_under(
it.next.to_building_block()),
tree_analysis.count_tensorflow_variables_under(
new_it.next.to_building_block()))
def test_constructs_canonical_form_from_mnist_training_example(self):
it = canonical_form_utils.get_iterative_process_for_canonical_form(
test_utils.get_mnist_training_example())
cf = canonical_form_utils.get_canonical_form_for_iterative_process(it)
self.assertIsInstance(cf, canonical_form.CanonicalForm)
def test_constructs_map_reduce_form_from_mnist_training_example(self):
it = form_utils.get_iterative_process_for_map_reduce_form(
mapreduce_test_utils.get_mnist_training_example())
mrf = form_utils.get_map_reduce_form_for_iterative_process(it)
self.assertIsInstance(mrf, forms.MapReduceForm)
