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Applies natural exponential decay to the initial learning rate.
Compat aliases for migration
See Migration guide for more details.
tf.train.natural_exp_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 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 / decay_step)
decayed_learning_rate = learning_rate * exp(-decay_rate * floor(global_step / decay_step))
Example: decay exponentially with a base of 0.96:
... global_step = tf.Variable(0, trainable=False) learning_rate = 0.1 decay_steps = 5 k = 0.5 learning_rate = tf.compat.v1.train.natural_exp_decay(learning_rate, global_step, decay_steps, k) # 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) )
||A Python number. Global step to use for the decay computation. Must not be negative.|
||How often to apply decay.|
||A Python number. The decay rate.|
||Whether to apply decay in a discrete staircase, as opposed to continuous, fashion.|
||String. Optional name of the operation. Defaults to 'ExponentialTimeDecay'.|
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.