def testFloat64InitLearningRate(self, serialize):
num_training_steps = 1000
initial_lr = np.float64(1.0)
for step in range(0, 1500, 250):
decayed_lr = learning_rate_schedule.CosineDecay(initial_lr,
num_training_steps)
decayed_lr = _maybe_serialized(decayed_lr, serialize)
expected = self.np_cosine_decay(step, num_training_steps)
self.assertAllClose(self.evaluate(decayed_lr(step)), expected, 1e-6)
def testAlpha(self, serialize):
num_training_steps = 1000
initial_lr = 1.0
alpha = 0.1
for step in range(0, 1500, 250):
decayed_lr = learning_rate_schedule.CosineDecay(
initial_lr, num_training_steps, alpha)
decayed_lr = _maybe_serialized(decayed_lr, serialize)
expected = self.np_cosine_decay(step, num_training_steps, alpha)
self.assertAllClose(self.evaluate(decayed_lr(step)), expected,
1e-6)
def cosine_decay(learning_rate,
global_step,
decay_steps,
alpha=0.0,
name=None):
"""Applies cosine decay to the learning rate.
When training a model, it is often recommended to lower the learning rate as
the training progresses. This function applies a cosine decay function
to a provided initial learning rate. It requires a `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
global_step = min(global_step, decay_steps)
cosine_decay = 0.5 * (1 + cos(pi * global_step / decay_steps))
decayed = (1 - alpha) * cosine_decay + alpha
decayed_learning_rate = learning_rate * decayed
```
Example usage:
```python
decay_steps = 1000
lr_decayed = cosine_decay(learning_rate, global_step, decay_steps)
```
Args:
learning_rate: A scalar `float32` or `float64` Tensor or a Python number.
The initial learning rate.
global_step: A scalar `int32` or `int64` `Tensor` or a Python number. Global
step to use for the decay computation.
decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. Number
of steps to decay over.
alpha: A scalar `float32` or `float64` Tensor or a Python number. Minimum
learning rate value as a fraction of learning_rate.
name: String. Optional name of the operation. Defaults to 'CosineDecay'.
Returns:
A scalar `Tensor` of the same type as `learning_rate`. The decayed
learning rate.
Raises:
ValueError: if `global_step` is not supplied.
References:
Stochastic Gradient Descent with Warm Restarts:
[Loshchilov et al., 2017]
(https://openreview.net/forum?id=Skq89Scxx¬eId=Skq89Scxx)
([pdf](https://openreview.net/pdf?id=Skq89Scxx))
@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.CosineDecay(learning_rate,
decay_steps,
alpha=alpha,
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
