def testBasicWithLearningRateInverseTimeDecaySerializeAndDeserialize(self):
for dtype in [tf.half, tf.float32, tf.float64]:
learning_rate = learning_rate_schedule.InverseTimeDecay(
3.0, decay_steps=1.0, decay_rate=0.5)
sgd = gradient_descent.SGD(learning_rate=learning_rate)
sgd = gradient_descent.SGD.from_config(sgd.get_config())
self._test_basic_sgd_with_learning_rate_decay(sgd, dtype)
def testConfigWithLearningRateDecay(self):
with test_utils.use_gpu():
var0 = tf.Variable([[1.0], [2.0]], dtype=tf.float32)
for decay_schedule in [
learning_rate_schedule.InverseTimeDecay(
0.5, decay_steps=1.0, decay_rate=0.1),
learning_rate_schedule.PiecewiseConstantDecay(
[5], [1., .5])
]:
step = 10
opt = gradient_descent.SGD(decay_schedule)
config = opt.get_config()
opt2 = gradient_descent.SGD.from_config(config)
# assert both are equal float values.
self.assertAllEqual(
decay_schedule(step),
opt._get_hyper('learning_rate')(step))
self.assertAllEqual(
decay_schedule(step),
opt2._get_hyper('learning_rate')(step))
loss = lambda: 3 * var0
# learning rate variable is created when calling minimize.
opt.minimize(loss, [var0])
self.evaluate(tf.compat.v1.global_variables_initializer())
config = opt.get_config()
opt3 = gradient_descent.SGD.from_config(config)
self.assertAllEqual(
self.evaluate(opt._get_hyper('learning_rate')(step)),
opt3._get_hyper('learning_rate')(step))
def testStaircase(self, serialize):
initial_lr = 0.1
k = 10
decay_rate = 0.96
step = tf.Variable(0)
decayed_lr = learning_rate_schedule.InverseTimeDecay(
initial_lr, k, decay_rate, staircase=True)
decayed_lr = _maybe_serialized(decayed_lr, serialize)
self.evaluate(tf.compat.v1.global_variables_initializer())
for i in range(k + 1):
expected = initial_lr / (1 + decay_rate * (i // k))
self.assertAllClose(self.evaluate(decayed_lr(step)), expected, 1e-6)
self.evaluate(step.assign_add(1))
def testBasicWithLearningRateInverseTimeDecay(self):
for dtype in _DATA_TYPES:
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = tf.Variable(var0_np)
var1 = tf.Variable(var1_np)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
learning_rate = 3.0
decay = 0.5
lr_schedule = learning_rate_schedule.InverseTimeDecay(
learning_rate, decay_steps=1.0, decay_rate=decay)
ada_opt = adagrad.Adagrad(lr_schedule)
accum0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
accum1_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
if not tf.executing_eagerly():
ada_update = ada_opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
v0_val, v1_val = self.evaluate([var0, var1])
self.assertAllClose([1.0, 2.0], v0_val)
self.assertAllClose([3.0, 4.0], v1_val)
# Run 3 steps of adagrad
for t in range(3):
if not tf.executing_eagerly():
self.evaluate(ada_update)
else:
ada_opt.apply_gradients(zip([grads0, grads1],
[var0, var1]))
lr_np = learning_rate / (1 + decay * t)
var0_np, accum0_np = adagrad_update_numpy(
var0_np, accum0_np, grads0_np, lr_np)
var1_np, accum1_np = adagrad_update_numpy(
var1_np, accum1_np, grads1_np, lr_np)
self.assertAllCloseAccordingToType(var0_np,
self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np,
self.evaluate(var1))
def testBasicWithLearningRateInverseTimeDecay(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
for i, dtype in enumerate([tf.half, tf.float32, tf.float64]):
with tf.Graph().as_default(), self.cached_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = tf.Variable(var0_np, name="var0_%d" % i)
var1 = tf.Variable(var1_np, name="var1_%d" % i)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
learning_rate = 0.001
decay = 0.5
lr_schedule = learning_rate_schedule.InverseTimeDecay(
learning_rate, decay_steps=1.0, decay_rate=decay)
beta_1 = 0.9
beta_2 = 0.999
epsilon = 1e-7
opt = adam.NonFusedAdam(
learning_rate=lr_schedule,
beta_1=beta_1,
beta_2=beta_2,
epsilon=epsilon)
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Run 3 steps of NonFusedAdam
for t in range(3):
self.evaluate(update)
lr_np = learning_rate / (1 + decay * t)
var0_np, m0, v0 = adam_update_numpy(
var0_np, grads0_np, t, m0, v0, lr=lr_np)
var1_np, m1, v1 = adam_update_numpy(
var1_np, grads1_np, t, m1, v1, lr=lr_np)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
def testAdaptiveLearningRate(self):
for dtype in _DATA_TYPES:
with self.test_session():
var0 = tf.Variable([1.0, 2.0], dtype=dtype)
var1 = tf.Variable([3.0, 4.0], dtype=dtype)
def loss():
return 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop
sgd = gradient_descent.SGD(1.0)
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Run 1 step of sgd through optimizer
opt_op = sgd.minimize(loss, [var0, var1])
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(opt_op)
# Validate updated params
# var0 = [1., 2.] - 1.0 * [5, 5]
self.assertAllClose([-4., -3.], self.evaluate(var0))
# var1 = [3., 4.] - 1.0 * [3, 3]
self.assertAllClose([0., 1.], self.evaluate(var1))
sgd.learning_rate = 0.5
if tf.executing_eagerly():
sgd.minimize(loss, [var0, var1])
else:
self.evaluate(opt_op)
# Validate updated params
# var0 = [-4., -3.] - 0.5 * [5, 5]
self.assertAllClose([-6.5, -5.5], self.evaluate(var0))
# var1 = [0., 1.] - 0.5 * [3, 3]
self.assertAllClose([-1.5, -0.5], self.evaluate(var1))
sgd.learning_rate = learning_rate_schedule.InverseTimeDecay(
0.5, decay_steps=1.0, decay_rate=0.5)
if tf.executing_eagerly():
sgd.minimize(loss, [var0, var1])
else:
self.evaluate(opt_op)
def testLearningRateDecayUsedInTwoFunctions(self):
a = tf.Variable([1., 2.], name='var')
b = tf.Variable([1.], name='var')
learning_rate_decay = learning_rate_schedule.InverseTimeDecay(
0.5, decay_steps=1.0, decay_rate=0.5)
opt = adam.Adam(learning_rate=learning_rate_decay)
loss_a = lambda: 3 * a
loss_b = lambda: 2 * b
@tf.function
def fn_a():
opt.minimize(loss_a, [a])
return a
@tf.function
def fn_b():
opt.minimize(loss_b, [b])
return b
fn_a()
fn_b()
def inverse_time_decay(learning_rate,
global_step,
decay_steps,
decay_rate,
staircase=False,
name=None):
"""Applies inverse time decay to the initial learning rate.
When training a model, it is often recommended to lower the learning rate as
the training progresses. This function applies an inverse decay function
to a provided initial learning rate. It requires an `global_step` value to
compute the decayed learning rate. You can just pass a TensorFlow variable
that you increment at each training step.
The function returns the decayed learning rate. It is computed as:
```python
decayed_learning_rate = learning_rate / (1 + decay_rate * global_step /
decay_step)
```
or, if `staircase` is `True`, as:
```python
decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step /
decay_step))
```
Example: decay 1/t with a rate of 0.5:
```python
...
global_step = tf.Variable(0, trainable=False)
learning_rate = 0.1
decay_steps = 1.0
decay_rate = 0.5
learning_rate = tf.compat.v1.train.inverse_time_decay(learning_rate,
global_step,
decay_steps, decay_rate)
# Passing global_step to minimize() will increment it at each step.
learning_step = (
tf.compat.v1.train.GradientDescentOptimizer(learning_rate)
.minimize(...my loss..., global_step=global_step)
)
```
Args:
learning_rate: A scalar `float32` or `float64` `Tensor` or a Python number.
The initial learning rate.
global_step: A Python number. Global step to use for the decay computation.
Must not be negative.
decay_steps: How often to apply decay.
decay_rate: A Python number. The decay rate.
staircase: Whether to apply decay in a discrete staircase, as opposed to
continuous, fashion.
name: String. Optional name of the operation. Defaults to
'InverseTimeDecay'.
Returns:
A scalar `Tensor` of the same type as `learning_rate`. The decayed
learning rate.
Raises:
ValueError: if `global_step` is not supplied.
@compatibility(eager)
When eager execution is enabled, this function returns a function which in
turn returns the decayed learning rate Tensor. This can be useful for changing
the learning rate value across different invocations of optimizer functions.
@end_compatibility
"""
decayed_lr = learning_rate_schedule.InverseTimeDecay(learning_rate,
decay_steps,
decay_rate,
staircase=staircase,
name=name)
if not tf.executing_eagerly():
decayed_lr = decayed_lr(global_step)
else:
decayed_lr = functools.partial(decayed_lr, global_step)
return decayed_lr
