staircase_lr.py

global_step = tf.Variable(0, trainable=False)       # Variable to store number of iterations
starter_learning_rate = 0.1                         # Initial Learning rate
learning_rate = tf.train.exponential_decay(
                starter_learning_rate, global_step,  # Function applied by TF on the varible (same formula as shown above)
                100000, 0.96, staircase=True)        # make staircase=True to force an integer division and thus create a step decay
# Passing global_step to minimize() will increment it at each step.
learning_step = (
    tf.train.GradientDescentOptimizer(learning_rate)        # We create an instance of the optimizer with updated learning rate each time
    .minimize(...my loss..., global_step=global_step)       # global step (# iterations) is updated by the minimize function
)