登录
View source on GitHub
|
Applies inverse time decay to the initial learning rate.
tf.compat.v1.train.inverse_time_decay(
learning_rate, global_step, decay_steps, decay_rate, staircase=False, name=None
)
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:
decayed_learning_rate = learning_rate / (1 + decay_rate * global_step /
decay_step)
or, if staircase is True, as:
decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step /
decay_step))
Example: decay 1/t with a rate of 0.5:
...
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.
|
Eager Compatibility
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.