tf.train.inverse_time_decay

tf.train.inverse_time_decay(
learning_rate,
global_step,
decay_steps,
decay_rate,
staircase=False,
name=None
)


See the guide: Training > Decaying the learning rate

Applies inverse time decay to the initial learning rate.

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.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.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.