tf.train.inverse_time_decay

Applies inverse time decay to the initial learning rate.

Aliases:

``````tf.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 = (
.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.