1.tf.train.exponential_decay() 指數衰減學習率：

#tf.train.exponential_decay(learning_rate, global_steps, decay_steps, decay_rate, staircase=True/False)：#指數衰減學習率#learning_rate-學習率#global_steps-訓練輪數#decay_steps-完整的使用一遍訓練數據所需的迭代輪數；=總訓練樣本數/batch#decay_rate-衰減速度#staircase-衰減方式；=True，那就表明每decay_steps次計算學習速率變化，更新原始學習速率；=alse，那就是每一步都更新學習速率。learning_rate = tf.train.exponential_decay(initial_learning_rate = 0.001global_step = tf.Variable(0, trainable=False)decay_steps = 100decay_rate = 0.95total_loss = slim.losses.get_total_loss()learning_rate = tf.train.exponential_decay(initial_learning_rate, global_step, decay_steps, decay_rate, True, name='learning_rate')optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss, global_step)

2.tf.train.ExponentialMovingAverage(decay, steps) 滑動平均更新參數：

initial_learning_rate = 0.001global_step = tf.Variable(0, trainable=False)decay_steps = 100decay_rate = 0.95total_loss = slim.losses.get_total_loss()learning_rate = tf.train.exponential_decay(initial_learning_rate, global_step, decay_steps, decay_rate, True, name='learning_rate')optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss, global_step)ema = tf.train.ExponentialMovingAverage(decay=0.9999)#tf.trainable_variables--返回的是需要訓練的變量列表averages_op = ema.apply(tf.trainable_variables())with tf.control_dependencies([optimizer]):   train_op = tf.group(averages_op)

以上這篇有關Tensorflow梯度下降常用的優化方法分享就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持武林站長站。