def test_ops_with_var_and_adadelta(self):
var_list = [
deo.get_variable('sp_var', initializer=0.0, dim=2),
]
opt_list = [
adadelta.AdadeltaOptimizer(),
]
self.common_run_context(var_list, opt_list, name='adadelta_test')
def testMinimizeSparseResourceVariable(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
loss = pred * pred
sgd_op = adadelta.AdadeltaOptimizer(
1.0, 1.0, 1.0).minimize(loss)
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllCloseAccordingToType([[1.0, 2.0]], self.evaluate(var0))
# Run 1 step of sgd
sgd_op.run()
# Validate updated params
self.assertAllCloseAccordingToType([[-111, -138]], self.evaluate(var0))
def doTestBasic(self, use_resource=False, use_callable_params=False):
num_updates = 4 # number of ADADELTA steps to perform
for dtype in [dtypes.half, dtypes.float32]:
for grad in [0.2, 0.1, 0.01]:
for lr in [1.0, 0.5, 0.1]:
var0_init = [1.0, 2.0]
var1_init = [3.0, 4.0]
if use_resource:
var0 = resource_variable_ops.ResourceVariable(
var0_init, dtype=dtype)
var1 = resource_variable_ops.ResourceVariable(
var1_init, dtype=dtype)
else:
var0 = variables.Variable(var0_init, dtype=dtype)
var1 = variables.Variable(var1_init, dtype=dtype)
grads = constant_op.constant([grad, grad], dtype=dtype)
accum = 0.0
accum_update = 0.0
# ADADELTA gradient optimizer
rho = 0.95
epsilon = 1e-8
if use_callable_params:
adadelta_opt = adadelta.AdadeltaOptimizer(
learning_rate=lambda: lr, # pylint: disable=cell-var-from-loop
rho=lambda: rho, # pylint: disable=cell-var-from-loop
epsilon=lambda: epsilon) # pylint: disable=cell-var-from-loop
else:
adadelta_opt = adadelta.AdadeltaOptimizer(
learning_rate=lr, rho=rho, epsilon=epsilon)
if not context.executing_eagerly():
adadelta_update = adadelta_opt.apply_gradients(
zip([grads, grads], [var0, var1]))
self.evaluate(variables.global_variables_initializer())
# TODO(lxuechen): This is hard to test in eager mode,
# since the optimizer is not fully initialized until the first
# call to `apply_gradients`
opt_vars = adadelta_opt.variables()
self.assertStartsWith(opt_vars[0].name,
var0._shared_name)
self.assertStartsWith(opt_vars[1].name,
var0._shared_name)
self.assertStartsWith(opt_vars[2].name,
var1._shared_name)
self.assertStartsWith(opt_vars[3].name,
var1._shared_name)
self.assertEqual(4, len(opt_vars))
# Assign slots
slot = [None] * 2
slot_update = [None] * 2
self.assertEqual(["accum", "accum_update"],
adadelta_opt.get_slot_names())
slot[0] = adadelta_opt.get_slot(var0, "accum")
self.assertEquals(slot[0].get_shape(),
var0.get_shape())
self.assertFalse(
slot[0] in variables.trainable_variables())
slot_update[0] = adadelta_opt.get_slot(
var0, "accum_update")
self.assertEquals(slot_update[0].get_shape(),
var0.get_shape())
self.assertFalse(
slot_update[0] in variables.trainable_variables())
slot[1] = adadelta_opt.get_slot(var1, "accum")
self.assertEquals(slot[1].get_shape(),
var1.get_shape())
self.assertFalse(
slot[1] in variables.trainable_variables())
slot_update[1] = adadelta_opt.get_slot(
var1, "accum_update")
self.assertEquals(slot_update[1].get_shape(),
var1.get_shape())
self.assertFalse(
slot_update[1] in variables.trainable_variables())
# Fetch params to validate initial values
self.assertAllClose(var0_init, self.evaluate(var0))
self.assertAllClose(var1_init, self.evaluate(var1))
update = [None] * num_updates
tot_update = 0
for step in range(num_updates):
# Run adadelta update for comparison
if not context.executing_eagerly():
self.evaluate(adadelta_update)
else:
adadelta_opt.apply_gradients(
zip([grads, grads], [var0, var1]))
# Perform initial update without previous accum values
accum = accum * rho + (grad**2) * (1 - rho)
update[step] = (np.sqrt(accum_update + epsilon) *
(1. / np.sqrt(accum + epsilon)) * grad)
accum_update = (accum_update * rho +
(update[step]**2) * (1.0 - rho))
tot_update += update[step] * lr
if not context.executing_eagerly():
# Check that the accumulators have been updated
# TODO(lxuechen): This is hard to test in eager mode
for slot_idx in range(2):
self.assertAllCloseAccordingToType(
np.array([accum, accum],
dtype=dtype.as_numpy_dtype()),
self.evaluate(slot[slot_idx]),
rtol=1e-5)
self.assertAllCloseAccordingToType(
np.array([accum_update, accum_update],
dtype=dtype.as_numpy_dtype()),
self.evaluate(slot_update[slot_idx]),
rtol=1e-5)
# Check that the parameters have been updated
self.assertAllCloseAccordingToType(
np.array([
var0_init[0] - tot_update,
var0_init[1] - tot_update
],
dtype=dtype.as_numpy_dtype()),
self.evaluate(var0),
rtol=1e-5)
self.assertAllCloseAccordingToType(
np.array([
var1_init[0] - tot_update,
var1_init[1] - tot_update
],
dtype=dtype.as_numpy_dtype()),
self.evaluate(var1),
rtol=1e-5)
def doTestBasic(self, use_resource=False):
num_updates = 4 # number of ADADELTA steps to perform
for dtype in [dtypes.half, dtypes.float32]:
for grad in [0.2, 0.1, 0.01]:
for lr in [1.0, 0.5, 0.1]:
with self.test_session():
var0_init = [1.0, 2.0]
var1_init = [3.0, 4.0]
if use_resource:
var0 = resource_variable_ops.ResourceVariable(
var0_init, dtype=dtype)
var1 = resource_variable_ops.ResourceVariable(
var1_init, dtype=dtype)
else:
var0 = variables.Variable(var0_init, dtype=dtype)
var1 = variables.Variable(var1_init, dtype=dtype)
grads = constant_op.constant([grad, grad], dtype=dtype)
accum = 0.0
accum_update = 0.0
# ADADELTA gradient optimizer
rho = 0.95
epsilon = 1e-8
adadelta_opt = adadelta.AdadeltaOptimizer(
lr, rho, epsilon)
adadelta_update = adadelta_opt.apply_gradients(
zip([grads, grads], [var0, var1]))
variables.global_variables_initializer().run()
# Assign slots
slot = [None] * 2
slot_update = [None] * 2
self.assertEqual(["accum", "accum_update"],
adadelta_opt.get_slot_names())
slot[0] = adadelta_opt.get_slot(var0, "accum")
self.assertEquals(slot[0].get_shape(),
var0.get_shape())
self.assertFalse(
slot[0] in variables.trainable_variables())
slot_update[0] = adadelta_opt.get_slot(
var0, "accum_update")
self.assertEquals(slot_update[0].get_shape(),
var0.get_shape())
self.assertFalse(
slot_update[0] in variables.trainable_variables())
slot[1] = adadelta_opt.get_slot(var1, "accum")
self.assertEquals(slot[1].get_shape(),
var1.get_shape())
self.assertFalse(
slot[1] in variables.trainable_variables())
slot_update[1] = adadelta_opt.get_slot(
var1, "accum_update")
self.assertEquals(slot_update[1].get_shape(),
var1.get_shape())
self.assertFalse(
slot_update[1] in variables.trainable_variables())
# Fetch params to validate initial values
self.assertAllClose(var0_init, var0.eval())
self.assertAllClose(var1_init, var1.eval())
update = [None] * num_updates
tot_update = 0
for step in range(num_updates):
# Run adadelta update for comparison
adadelta_update.run()
# Perform initial update without previous accum values
accum = accum * rho + (grad**2) * (1 - rho)
update[step] = (np.sqrt(accum_update + epsilon) *
(1. / np.sqrt(accum + epsilon)) *
grad)
accum_update = (accum_update * rho +
(update[step]**2) * (1.0 - rho))
tot_update += update[step] * lr
# Check that the accumulators have been updated
for slot_idx in range(2):
self.assertAllCloseAccordingToType(
np.array([accum, accum],
dtype=dtype.as_numpy_dtype()),
slot[slot_idx].eval(),
rtol=1e-3)
self.assertAllCloseAccordingToType(
np.array([accum_update, accum_update],
dtype=dtype.as_numpy_dtype()),
slot_update[slot_idx].eval(),
rtol=1e-3)
# Check that the parameters have been updated
self.assertAllCloseAccordingToType(np.array(
[
var0_init[0] - tot_update,
var0_init[1] - tot_update
],
dtype=dtype.as_numpy_dtype()),
var0.eval(),
rtol=1e-3)
self.assertAllCloseAccordingToType(np.array(
[
var1_init[0] - tot_update,
var1_init[1] - tot_update
],
dtype=dtype.as_numpy_dtype()),
var1.eval(),
rtol=1e-3)
def test_adadelta_apply_restriction(self):
opt = adadelta.AdadeltaOptimizer()
self.commonly_apply_restriction_verify(opt)
def testBasic(self):
num_updates = 4 # number of ADADELTA steps to perform
if "CPU" in self.device:
# To avoid timeout on CPU.
all_grad = [0.2, 0.01]
all_lr = [1.0, 0.1]
else:
all_grad = [0.2, 0.1, 0.01]
all_lr = [1.0, 0.5, 0.1]
for dtype in self.float_types | self.complex_types:
with self.session(), self.test_scope():
for grad in all_grad:
for lr in all_lr:
var0_init = [1.0, 2.0]
var1_init = [3.0, 4.0]
var0 = resource_variable_ops.ResourceVariable(
var0_init, dtype=dtype)
var1 = resource_variable_ops.ResourceVariable(
var1_init, dtype=dtype)
grads = constant_op.constant([grad, grad], dtype=dtype)
accum = 0.0
accum_update = 0.0
# ADADELTA gradient optimizer
rho = 0.95
epsilon = 1e-8
adadelta_opt = adadelta.AdadeltaOptimizer(
learning_rate=lr, rho=rho, epsilon=epsilon)
adadelta_update = adadelta_opt.apply_gradients(
zip([grads, grads], [var0, var1]))
self.evaluate(variables.global_variables_initializer())
opt_vars = adadelta_opt.variables()
self.assertStartsWith(opt_vars[0].name,
var0._shared_name)
self.assertStartsWith(opt_vars[1].name,
var0._shared_name)
self.assertStartsWith(opt_vars[2].name,
var1._shared_name)
self.assertStartsWith(opt_vars[3].name,
var1._shared_name)
self.assertEqual(4, len(opt_vars))
# Assign slots
slot = [None] * 2
slot_update = [None] * 2
self.assertEqual(["accum", "accum_update"],
adadelta_opt.get_slot_names())
slot[0] = adadelta_opt.get_slot(var0, "accum")
self.assertEqual(slot[0].get_shape(), var0.get_shape())
self.assertNotIn(slot[0],
variables.trainable_variables())
slot_update[0] = adadelta_opt.get_slot(
var0, "accum_update")
self.assertEqual(slot_update[0].get_shape(),
var0.get_shape())
self.assertNotIn(slot_update[0],
variables.trainable_variables())
slot[1] = adadelta_opt.get_slot(var1, "accum")
self.assertEqual(slot[1].get_shape(), var1.get_shape())
self.assertNotIn(slot[1],
variables.trainable_variables())
slot_update[1] = adadelta_opt.get_slot(
var1, "accum_update")
self.assertEqual(slot_update[1].get_shape(),
var1.get_shape())
self.assertNotIn(slot_update[1],
variables.trainable_variables())
# Fetch params to validate initial values
self.assertAllClose(var0_init, self.evaluate(var0))
self.assertAllClose(var1_init, self.evaluate(var1))
update = [None] * num_updates
tot_update = 0
for step in range(num_updates):
# Run adadelta update for comparison
self.evaluate(adadelta_update)
# Perform initial update without previous accum values
accum = accum * rho + (grad**2) * (1 - rho)
update[step] = (np.sqrt(accum_update + epsilon) *
(1. / np.sqrt(accum + epsilon)) * grad)
accum_update = (accum_update * rho +
(update[step]**2) * (1.0 - rho))
tot_update += update[step] * lr
# Check that the accumulators have been updated
for slot_idx in range(2):
self.assertAllCloseAccordingToType(
np.array([accum, accum], dtype=dtype),
self.evaluate(slot[slot_idx]),
rtol=1e-5)
self.assertAllCloseAccordingToType(
np.array([accum_update, accum_update],
dtype=dtype),
self.evaluate(slot_update[slot_idx]),
rtol=1e-5)
# Check that the parameters have been updated
self.assertAllCloseAccordingToType(np.array(
[
var0_init[0] - tot_update,
var0_init[1] - tot_update
],
dtype=dtype),
self.evaluate(var0),
rtol=1e-5)
self.assertAllCloseAccordingToType(np.array(
[
var1_init[0] - tot_update,
var1_init[1] - tot_update
],
dtype=dtype),
self.evaluate(var1),
rtol=1e-5)
def test_adadelta_restrict_on_policy(self):
opt = adadelta.AdadeltaOptimizer()
self.common_single_step_restrict_verification(opt)
def test_adadelta_restrictor_update(self):
opt = adadelta.AdadeltaOptimizer()
self.common_single_step_update_verification(opt)
def test_adadelta_minimize_trainable(self):
base_opt = adadelta.AdadeltaOptimizer(1.0)
test_opt = adadelta.AdadeltaOptimizer(1.0)
self.common_minimize_trainable(base_opt, test_opt, name="adadelta")
