# tf.train.exponential_decay

Applies exponential 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 an exponential 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:

``````decayed_learning_rate = learning_rate *
decay_rate ^ (global_step / decay_steps)
``````

If the argument `staircase` is `True`, then `global_step / decay_steps` is an integer division and the decayed learning rate follows a staircase function.

Example: decay every 100000 steps with a base of 0.96:

``````...
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
learning_rate = tf.compat.v1.train.exponential_decay(starter_learning_rate,
global_step,
100000, 0.96, staircase=True)
# Passing global_step to minimize() will increment it at each step.
learning_step = (
.minimize(...my loss..., global_step=global_step)
)
``````

`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. Must not be negative.
`decay_steps` A scalar `int32` or `int64` `Tensor` or a Python number. Must be positive. See the decay computation above.
`decay_rate` A scalar `float32` or `float64` `Tensor` or a Python number. The decay rate.
`staircase` Boolean. If `True` decay the learning rate at discrete intervals
`name` String. Optional name of the operation. Defaults to 'ExponentialDecay'.

A scalar `Tensor` of the same type as `learning_rate`. The decayed learning rate.

`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.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]