model_ops.py 文件源码

def cyclic_learning_rate(
        learning_rate_min,
        learning_rate_max,
        step_size,
        global_step,
        mode='triangular',
        scope=None):
    with tf.variable_scope(scope, 'CyclicLearningRate'):
        cycle = tf.floor(1 + tf.to_float(global_step) / (2 * step_size))

        if mode == 'triangular':
            scale = 1
        elif mode == 'triangular2':
            scale = 2**(cycle - 1)
        else:
            raise ValueError('Unrecognized mode: {}'.format(mode))

        x = tf.abs(tf.to_float(global_step) / step_size - 2 * cycle + 1)
        lr = learning_rate_min + (learning_rate_max - learning_rate_min) * \
            tf.maximum(0.0, 1 - x) / scale

        return lr