tf.train.exponential_decay( learning_rate, global_step, decay_steps, decay_rate, staircase=False, name=None )
Applies exponential decay to the 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 a
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 * decay_rate ^ (global_step / decay_steps)
If the argument
global_step / decay_steps is an
integer division and the decayed learning rate follows a staircase function.
Example: decay every 100000 steps with a base of 0.96:
... global_step = tf.Variable(0, trainable=False) starter_learning_rate = 0.1 learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step, 100000, 0.96, staircase=True) # 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) )
learning_rate: A scalar
Tensoror a Python number. The initial learning rate.
global_step: A scalar
Tensoror a Python number. Global step to use for the decay computation. Must not be negative.
decay_steps: A scalar
Tensoror a Python number. Must be positive. See the decay computation above.
decay_rate: A scalar
Tensoror a Python number. The decay rate.
staircase: Boolean. If
Truedecay the learning rate at discrete intervals
name: String. Optional name of the operation. Defaults to 'ExponentialDecay'.
Tensor of the same type as
learning_rate. The decayed
global_stepis not supplied.
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.