# tf.compat.v1.train.exponential_decay

Applies exponential decay to the learning rate.

``````tf.compat.v1.train.exponential_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 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)
)
``````

#### 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. 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'.

#### 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.