def testConstructAdadeltaWithLR(self):
opt = adadelta.Adadelta(lr=1.0, rho=0.9, epsilon=1.)
opt_2 = adadelta.Adadelta(learning_rate=0.1, rho=0.9, epsilon=1., lr=1.0)
opt_3 = adadelta.Adadelta(learning_rate=0.1, rho=0.9, epsilon=1.)
self.assertIsInstance(opt.lr, tf.Variable)
self.assertIsInstance(opt_2.lr, tf.Variable)
self.assertIsInstance(opt_3.lr, tf.Variable)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllClose(self.evaluate(opt.lr), (1.0))
self.assertAllClose(self.evaluate(opt_2.lr), (1.0))
self.assertAllClose(self.evaluate(opt_3.lr), (0.1))
def testMinimizeSparseResourceVariable(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
with tf.Graph().as_default():
for dtype in _DATA_TYPES:
var0 = tf.Variable([[1.0, 2.0]], dtype=dtype)
x = tf.constant([[4.0], [5.0]], dtype=dtype)
def loss():
pred = tf.matmul(
tf.compat.v1.nn.embedding_lookup([var0], [0]), x
)
return pred * pred
sgd_op = adadelta.Adadelta(1.0, 1.0, 1.0).minimize(
loss, var_list=[var0]
)
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllCloseAccordingToType(
[[1.0, 2.0]], self.evaluate(var0)
)
# Run 1 step of sgd
self.evaluate(sgd_op)
# Validate updated params
self.assertAllCloseAccordingToType(
[[-111, -138]], self.evaluate(var0)
)
def testAdadelta(self):
self._compare_numerical(adadelta_old.Adadelta(),
adadelta_new.Adadelta())
"FtrlV1", lambda: tf.compat.v1.train.FtrlOptimizer(0.001)
)
rmsprop_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
"RmsPropV1", lambda: tf.compat.v1.train.RMSPropOptimizer(0.001)
)
# TODO(shiningsun): consider adding the other v1 optimizers
optimizers_v1 = [
gradient_descent_optimizer_v1_fn,
adagrad_optimizer_v1_fn,
ftrl_optimizer_v1_fn,
rmsprop_optimizer_v1_fn,
]
adadelta_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"AdadeltaKerasV2", lambda: adadelta_keras_v2.Adadelta(0.001)
)
adagrad_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"AdagradKerasV2", lambda: adagrad_keras_v2.Adagrad(0.001)
)
adam_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"AdamKerasV2", lambda: adam_keras_v2.Adam(0.001, epsilon=1.0)
)
adam_experimental_fn = tf.__internal__.test.combinations.NamedObject(
"AdamExperimental", lambda: adam_experimental.Adam(0.001)
)
adamax_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"AdamaxKerasV2", lambda: adamax_keras_v2.Adamax(0.001, epsilon=1.0)
)
nadam_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"NadamKerasV2", lambda: nadam_keras_v2.Nadam(0.001, epsilon=1.0)
def doTestBasic(self, use_resource=False, use_callable_params=False):
num_updates = 4 # number of ADADELTA steps to perform
for dtype in _DATA_TYPES:
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 = tf.Variable(var0_init, dtype=dtype)
var1 = tf.Variable(var1_init, dtype=dtype)
else:
var0 = tf.Variable(var0_init, dtype=dtype)
var1 = tf.Variable(var1_init, dtype=dtype)
grads = tf.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.Adadelta(
learning_rate=lambda: lr, # pylint: disable=cell-var-from-loop
rho=lambda: rho, # pylint: disable=cell-var-from-loop
epsilon=epsilon,
) # pylint: disable=cell-var-from-loop
else:
adadelta_opt = adadelta.Adadelta(learning_rate=lr,
rho=rho,
epsilon=epsilon)
if not tf.executing_eagerly():
adadelta_update = adadelta_opt.apply_gradients(
zip([grads, grads], [var0, var1]))
self.evaluate(
tf.compat.v1.global_variables_initializer())
# Assign slots
slot = [None] * 2
slot_update = [None] * 2
slot[0] = adadelta_opt.get_slot(var0, "accum_grad")
self.assertEqual(slot[0].shape, var0.shape)
slot_update[0] = adadelta_opt.get_slot(
var0, "accum_var")
self.assertEqual(slot_update[0].shape, var0.shape)
slot[1] = adadelta_opt.get_slot(var1, "accum_grad")
self.assertEqual(slot[1].shape, var1.shape)
slot_update[1] = adadelta_opt.get_slot(
var1, "accum_var")
self.assertEqual(slot_update[1].shape, var1.shape)
# 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 tf.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) *
(1.0 / np.sqrt(accum + epsilon)) *
grad)
accum_update = accum_update * rho + (update[step]**
2) * (1.0 - rho)
tot_update += update[step] * lr
if not tf.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(0),
),
self.evaluate(slot[slot_idx]),
rtol=1e-5,
)
self.assertAllCloseAccordingToType(
np.array(
[accum_update, accum_update],
dtype=dtype.as_numpy_dtype(0),
),
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(0),
),
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(0),
),
self.evaluate(var1),
rtol=1e-5,
)
def testConstructAdadeltaWithEpsilonValues(self):
opt = adadelta.Adadelta(epsilon=None)
self.assertEqual(opt.epsilon, 1e-7)
opt = adadelta.Adadelta(epsilon=1e-8)
self.assertEqual(opt.epsilon, 1e-8)
def testAdadeltaCompatibility(self): opt_v1 = optimizer_v1.Adadelta(lr=0.01) opt_v2 = adadelta.Adadelta(learning_rate=0.01) self._testOptimizersCompatibility(opt_v1, opt_v2)
