# tf.compat.v1.train.exponential_decay

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/keras/optimizer_v2/legacy_learning_rate_decay.py#L28-L101) |

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 = (
        tf.compat.v1.train.GradientDescentOptimizer(learning_rate)
        .minimize(...my loss..., global_step=global_step)
    )

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| ## 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'. |

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

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| ## Raises ||
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| `ValueError` | if `global_step` is not supplied. |

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## eager compatibility

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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.

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