def test_rerouted_function(self, fn, args):
"""Tests rerouted function.
Tests that a method of the internal optimizer is correctly routed from
the BoltOn instance to the internal optimizer instance (TestOptimizer,
here).
Args:
fn: fn to test
args: arguments to that fn
"""
loss = TestLoss(1, 1, 1)
optimizer = TestOptimizer()
bolton = opt.BoltOn(optimizer, loss)
model = TestModel(3)
model.compile(optimizer, loss)
model.layers[0].kernel = model.layers[0].kernel_initializer(
(model.layer_input_shape[0], model.n_outputs))
model.layers[0].kernel = model.layers[0].kernel_initializer(
(model.layer_input_shape[0], model.n_outputs))
bolton._is_init = True # pylint: disable=protected-access
bolton.layers = model.layers
bolton.epsilon = 2
bolton.noise_distribution = 'laplace'
bolton.n_outputs = 1
bolton.n_samples = 1
self.assertEqual(
getattr(bolton, fn, lambda: 'fn not found')(*args), 'test')
def test_fn(self, fn, args, result, test_attr):
"""test that a fn of BoltOn optimizer is working as expected.
Args:
fn: method of Optimizer to test
args: args to optimizer fn
result: the expected result
test_attr: None if the fn returns the test result. Otherwise, this is the
attribute of BoltOn to check against result with.
"""
tf.random.set_seed(1)
loss = TestLoss(1, 1, 1)
bolton = opt.BoltOn(TestOptimizer(), loss)
model = TestModel(1)
model.layers[0].kernel = model.layers[0].kernel_initializer(
(model.layer_input_shape[0], model.n_outputs))
bolton._is_init = True # pylint: disable=protected-access
bolton.layers = model.layers
bolton.epsilon = 2
bolton.noise_distribution = 'laplace'
bolton.n_outputs = 1
bolton.n_samples = 1
res = getattr(bolton, fn, None)(*args)
if test_attr is not None:
res = getattr(bolton, test_attr, None)
if hasattr(res, 'numpy') and hasattr(result,
'numpy'): # both tensors/not
res = res.numpy()
result = result.numpy()
self.assertEqual(res, result)
def test_run(fn, args):
loss = TestLoss(1, 1, 1)
bolton = opt.BoltOn(TestOptimizer(), loss)
model = TestModel(1, (1, ), 1)
model.compile(bolton, loss)
model.layers[0].kernel = model.layers[0].kernel_initializer(
(model.layer_input_shape[0], model.n_outputs))
getattr(bolton, fn)(*args)
def test_run(noise, epsilon):
loss = TestLoss(1, 1, 1)
bolton = opt.BoltOn(TestOptimizer(), loss)
model = TestModel(1, (1, ), 1)
model.compile(bolton, loss)
model.layers[0].kernel = model.layers[0].kernel_initializer(
(model.layer_input_shape[0], model.n_outputs))
with bolton(noise, epsilon, model.layers, 1, 1, 1) as _:
pass
def test_run():
loss = TestLoss(1, 1, 1)
bolton = opt.BoltOn(TestOptimizer(), loss)
model = TestModel(1, (1, ), 1)
model.compile(bolton, loss)
model.layers[0].kernel = model.layers[0].kernel_initializer(
(model.layer_input_shape[0], model.n_outputs))
with bolton(noise, epsilon, model.layers, class_weights, 1,
1) as _:
pass
return _ops.convert_to_tensor_v2(bolton.epsilon, dtype=tf.float32)
def test_attribute_error(self, attr):
"""Test rerouting of attributes.
Test that attribute of internal optimizer is correctly rerouted to the
internal optimizer
Args:
attr: attribute to test
"""
loss = TestLoss(1, 1, 1)
internal_optimizer = TestOptimizer()
optimizer = opt.BoltOn(internal_optimizer, loss)
with self.assertRaises(AttributeError):
getattr(optimizer, attr)
def test_reroute_attr(self, attr):
"""Test a function is rerouted.
Test that attribute of internal optimizer is correctly rerouted to the
internal optimizer.
Args:
attr: attribute to test
"""
loss = TestLoss(1, 1, 1)
internal_optimizer = TestOptimizer()
optimizer = opt.BoltOn(internal_optimizer, loss)
self.assertEqual(getattr(optimizer, attr),
getattr(internal_optimizer, attr))
def project_fn(r):
loss = TestLoss(1, 1, r)
bolton = opt.BoltOn(TestOptimizer(), loss)
model = TestModel(n_out, shape, init_value)
model.compile(bolton, loss)
model.layers[0].kernel = model.layers[0].kernel_initializer(
(model.layer_input_shape[0], model.n_outputs))
bolton._is_init = True # pylint: disable=protected-access
bolton.layers = model.layers
bolton.epsilon = 2
bolton.noise_distribution = 'laplace'
bolton.n_outputs = 1
bolton.n_samples = 1
bolton.project_weights_to_r()
return _ops.convert_to_tensor_v2(bolton.layers[0].kernel,
tf.float32)
def test_run(fn, args):
loss = TestLoss(1, 1, 1)
bolton = opt.BoltOn(TestOptimizer(), loss)
model = TestModel(1, (1, ), 1)
model.compile(bolton, loss)
model.layers[0].kernel = model.layers[0].kernel_initializer(
(model.layer_input_shape[0], model.n_outputs))
bolton._is_init = True # pylint: disable=protected-access
bolton.noise_distribution = 'laplace'
bolton.epsilon = 1
bolton.layers = model.layers
bolton.class_weights = 1
bolton.n_samples = 1
bolton.batch_size = 1
bolton.n_outputs = 1
res = getattr(bolton, fn, lambda: 'test')(*args)
if res != 'test':
res = 1
else:
res = 0
return _ops.convert_to_tensor_v2(res, dtype=tf.float32)
