def polyak()

in optim.py [0:0]

• 15 lines of code
• 2 McCabe index (conditional complexity)

def polyak(params, beta):
    #params = tf.trainable_variables()
    ema = tf.train.ExponentialMovingAverage(decay=beta, zero_debias=True)
    avg_op = tf.group(ema.apply(params))
    # Swapping op
    updates = []
    for i in range(len(params)):
        p = params[i]
        avg = ema.average(p)
        tmp = 0. + avg * 1.
        with tf.control_dependencies([tmp]):
            update1 = avg.assign(p)
            with tf.control_dependencies([update1]):
                update2 = p.assign(tmp)
                updates += [update1, update2]
    swap_op = tf.group(*updates)
    return avg_op, swap_op, ema