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

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

See the guide: Training > Decaying the learning rate

Applies natural exponential 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 exponential 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 * exp(-decay_rate * global_step)

Example: decay exponentially with a base of 0.96:

...
global_step = tf.Variable(0, trainable=False)
learning_rate = 0.1
k = 0.5
learning_rate = tf.train.exponential_time_decay(learning_rate, global_step, k)

# 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 'ExponentialTimeDecay'.

Returns:

A scalar Tensor of the same type as learning_rate. The decayed learning rate.

Raises:

• ValueError: if global_step is not supplied.

Defined in tensorflow/python/training/learning_rate_decay.py.