def test_test_problem(self):
weights, fn, grad_fn = optimizer_test_utils.test_quadratic_problem()
# Function value in initial point is not negligible and gradient non-zero.
self.assertGreater(fn(weights), 5.0)
self.assertGreater(tf.linalg.norm(grad_fn(weights)), 0.01)
# All-zeros is optimum with function value of 0.0.
self.assertAllClose(0.0, fn(tf.zeros_like(weights))[0, 0])
self.assertAllClose(tf.zeros_like(weights),
grad_fn(tf.zeros_like(weights)))
def test_convergence(self, momentum):
weights, fn, grad_fn = optimizer_test_utils.test_quadratic_problem()
self.assertGreater(fn(weights), 5.0)
optimizer = sgdm.SGD(0.1, momentum=momentum)
state = optimizer.initialize(tf.TensorSpec(weights.shape, weights.dtype))
for _ in range(100):
gradients = grad_fn(weights)
state, weights = optimizer.next(state, weights, gradients)
self.assertLess(fn(weights), 0.005)
def test_disjoint_init_and_next_true(self, momentum):
"""Tests behavior expected as 'TFF server optimizer'.
This test creates two `tff.tf_computation`s, which would correspond to parts
of the two arguments for creation of a `tff.templates.IterativeProcess`.
The `KerasOptimizers` is instantiated in both of these computations, and
only one of its `initialize` and `next` methods is invoked in each of them.
The state which optimizers need is exposed by the `KerasOptimizer` and needs
to be carried between the invocations of the created `tff.Computation`s.
Note that even though it is expected that `variables` passed to the
`single_step` method are expected to be `tf.Variable` instances, the code
can still be written in a functional manner.
Args:
momentum: Momentum parameter to be used in tf.keras.optimizers.SGD.
"""
init_w, fn, grad_fn = optimizer_test_utils.test_quadratic_problem()
weights = init_w()
self.assertGreater(fn(weights), 5.0)
optimizer_fn = lambda: tf.keras.optimizers.SGD(0.1, momentum=momentum)
@tensorflow_computation.tf_computation()
def initialize_fn():
variables = tf.Variable(tf.zeros([5, 1]))
optimizer = keras_optimizer.KerasOptimizer(
optimizer_fn, variables, disjoint_init_and_next=True)
return optimizer.initialize(
tf.TensorSpec(variables.shape, variables.dtype))
@tf.function
def single_step(optimizer, state, variables):
gradients = grad_fn(variables)
new_state, updated_weights = optimizer.next(
state, variables, gradients)
return new_state, updated_weights
@tensorflow_computation.tf_computation()
def next_fn(state, initial_weights):
variables = tf.Variable(initial_weights)
optimizer = keras_optimizer.KerasOptimizer(
optimizer_fn, variables, disjoint_init_and_next=True)
return single_step(optimizer, state, variables)
state = initialize_fn()
for _ in range(100):
state, weights = next_fn(state, weights)
self.assertLess(fn(weights), 0.005)
# The optimizer variables are exposed by the KerasOptimizer. First variable
# of a keras optimzier is the number of steps taken.
self.assertEqual(100, state[0])
def test_convergence(self):
init_w, fn, grad_fn = optimizer_test_utils.test_quadratic_problem()
weights = init_w()
self.assertGreater(fn(weights), 5.0)
optimizer = adagrad.build_adagrad(0.5)
state = optimizer.initialize(tf.TensorSpec(weights.shape, weights.dtype))
for _ in range(100):
gradients = grad_fn(weights)
state, weights = optimizer.next(state, weights, gradients)
self.assertLess(fn(weights), 0.005)
def test_disjoint_init_and_next_false(self, momentum):
"""Tests behavior expected as 'TFF client optimizer'.
The main part of this test is `training_loop` method, which works with
already instantiated `KerasOptimizer` and uses both of its `initialize` and
`next` methods to perform a number of training steps. This is the behavior
expected to happen during local training at clients.
Note that even though it is expected that `variables` passed to the
`training_loop` method are expected to be `tf.Variable` instances, the code
can still be written in a functional manner.
Args:
momentum: Momentum parameter to be used in tf.keras.optimizers.SGD.
"""
init_w, fn, grad_fn = optimizer_test_utils.test_quadratic_problem()
weights = init_w()
self.assertGreater(fn(weights), 5.0)
optimizer_fn = lambda: tf.keras.optimizers.SGD(0.1, momentum=momentum)
@tf.function
def training_loop(optimizer, variables):
state = optimizer.initialize(
tf.TensorSpec(variables.shape, variables.dtype))
for _ in range(100):
gradients = grad_fn(variables)
state, variables = optimizer.next(state, variables, gradients)
return state, variables
@tensorflow_computation.tf_computation()
def local_training(initial_weights):
variables = tf.Variable(initial_weights)
optimizer = keras_optimizer.KerasOptimizer(
optimizer_fn, variables, disjoint_init_and_next=False)
return training_loop(optimizer, variables)
state, optimized_weights = local_training(weights)
self.assertLess(fn(optimized_weights), 0.005)
# The optimizer variables are handled internally in the KerasOptimizer.
self.assertEmpty(state)