tf.train.inverse_time_decay

Applies inverse time decay to the initial learning rate.

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tf.compat.v1.train.inverse_time_decay

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 = (
    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.