file: tensorflow/python/training/learning_rate_decay.py

参考：tensorflow中常用学习率更新策略

神经网络中通过超参数 learning rate，来控制每次参数更新的幅度。学习率太小会降低网络优化的速度，增加训练时间；学习率太大则可能导致可能导致参数在局部最优解两侧来回振荡，网络不能收敛。

tensorflow 定义了很多的 学习率衰减方式：

指数衰减 tf.train.exponential_decay()

指数衰减是比较常用的衰减方法，学习率是跟当前的训练轮次指数相关的。

tf.train.exponential_decay(

    learning_rate,     # 初始学习率

    global_step,       # 当前训练轮次

    decay_steps,       # 衰减周期

    decay_rate,        # 衰减率系数

    staircase=False,   # 定义是否是阶梯型衰减，还是连续衰减，默认是 False

    name=None

)

'''

decayed_learning_rate = learning_rate *

                      decay_rate ^ (global_step / decay_steps)

'''

示例代码：

import tensorflow as tf

import matplotlib.pyplot as plt

style1 = []

style2 = []

N = 200

with tf.Session() as sess:

  sess.run(tf.global_variables_initializer())

  for step in range(N):

    # 标准指数型衰减

    learing_rate1 = tf.train.exponential_decay(

      learning_rate=0.5, global_step=step, decay_steps=10, decay_rate=0.9, staircase=False)

    # 阶梯型衰减

    learing_rate2 = tf.train.exponential_decay(

      learning_rate=0.5, global_step=step, decay_steps=10, decay_rate=0.9, staircase=True)

    lr1 = sess.run([learing_rate1])

    lr2 = sess.run([learing_rate2])

    style1.append(lr1)

    style2.append(lr2)

step = range(N)

plt.plot(step, style1, 'g-', linewidth=2, label='exponential_decay')

plt.plot(step, style2, 'r--', linewidth=1, label='exponential_decay_staircase')

plt.title('exponential_decay')

plt.xlabel('step')

plt.ylabel('learing rate')

plt.legend(loc='upper right')

plt.tight_layout()

plt.show()

分段常数衰减 tf.train.piecewise_constant()

tf.train.piecewise_constant_decay(

    x,                # 当前训练轮次

    boundaries,       # 学习率应用区间

    values,           # 学习率常数列表

    name=None

)

'''

learning_rate value is `values[0]` when `x <= boundaries[0]`,

    `values[1]` when `x > boundaries[0]` and `x <= boundaries[1]`, ...,

    and values[-1] when `x > boundaries[-1]`.

'''

示例代码：

import tensorflow as tf

import matplotlib.pyplot as plt

boundaries = [10, 20, 30]

learing_rates = [0.1, 0.07, 0.025, 0.0125]

style = []

N = 40

with tf.Session() as sess:

  sess.run(tf.global_variables_initializer())

  for step in range(N):

    learing_rate = tf.train.piecewise_constant(step, boundaries=boundaries, values=learing_rates)

    lr = sess.run([learing_rate])

    style.append(lr)

step = range(N)

plt.plot(step, style, 'r-', linewidth=2)

plt.title('piecewise_constant')

plt.xlabel('step')

plt.ylabel('learing rate')

plt.tight_layout()

plt.show()

多项式衰减 tf.train.polynomial_decay()

tf.train.polynomial_decay(

    learning_rate,               # 初始学习率

    global_step,                 # 当前训练轮次

    decay_steps,                 # 大衰减周期

    end_learning_rate=0.0001,    # 最小的学习率

    power=1.0,                   # 多项式的幂

    cycle=False,                 # 学习率是否循环

    name=None)

'''

global_step = min(global_step, decay_steps)

decayed_learning_rate = (learning_rate - end_learning_rate) *

                        (1 - global_step / decay_steps) ^ (power) +

                         end_learning_rate

'''

示例代码：

import tensorflow as tf

import matplotlib.pyplot as plt

style1 = []

style2 = []

N = 200

with tf.Session() as sess:

  sess.run(tf.global_variables_initializer())

  for step in range(N):

    # cycle=False

    learing_rate1 = tf.train.polynomial_decay(

      learning_rate=0.1, global_step=step, decay_steps=50,

      end_learning_rate=0.01, power=0.5, cycle=False)

    # cycle=True

    learing_rate2 = tf.train.polynomial_decay(

      learning_rate=0.1, global_step=step, decay_steps=50,

      end_learning_rate=0.01, power=0.5, cycle=True)

    lr1 = sess.run([learing_rate1])

    lr2 = sess.run([learing_rate2])

    style1.append(lr1)

    style2.append(lr2)

steps = range(N)

plt.plot(steps, style1, 'g-', linewidth=2, label='polynomial_decay')

plt.plot(steps, style2, 'r--', linewidth=1, label='polynomial_decay_cycle')

plt.title('polynomial_decay')

plt.xlabel('step')

plt.ylabel('learing rate')

plt.legend(loc='upper right')

plt.tight_layout()

plt.show()

自然指数衰减 tf.train.natural_exp_decay()

tf.train.natural_exp_decay(

    learning_rate,        # 初始学习率

    global_step,          # 当前训练轮次

    decay_steps,          # 衰减周期

    decay_rate,           # 衰减率系数

    staircase=False,      # 定义是否是阶梯型衰减，还是连续衰减，默认是 False

    name=None

)

'''

decayed_learning_rate = learning_rate * exp(-decay_rate * global_step)

'''

示例代码：

import tensorflow as tf

import matplotlib.pyplot as plt

style1 = []

style2 = []

N = 200

with tf.Session() as sess:

  sess.run(tf.global_variables_initializer())

  for step in range(N):

    # 标准指数型衰减

    learing_rate1 = tf.train.natural_exp_decay(

      learning_rate=0.5, global_step=step, decay_steps=10, decay_rate=0.9, staircase=False)

    # 阶梯型衰减

    learing_rate2 = tf.train.natural_exp_decay(

      learning_rate=0.5, global_step=step, decay_steps=10, decay_rate=0.9, staircase=True)

    lr1 = sess.run([learing_rate1])

    lr2 = sess.run([learing_rate2])

    style1.append(lr1)

    style2.append(lr2)

step = range(N)

plt.plot(step, style1, 'g-', linewidth=2, label='natural_exp_decay')

plt.plot(step, style2, 'r--', linewidth=1, label='natural_exp_decay_staircase')

plt.title('natural_exp_decay')

plt.xlabel('step')

plt.ylabel('learing rate')

plt.legend(loc='upper right')

plt.tight_layout()

plt.show()

倒数衰减 tf.train.inverse_time_decay()

tf.train.inverse_time_decay(

    learning_rate,     # 初始学习率

    global_step,       # 当前训练轮次

    decay_steps,       # 衰减周期

    decay_rate,        # 衰减率系数

    staircase=False,   # 定义是否是阶梯型衰减，还是连续衰减，默认是 False

    name=None

)

'''

decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)

'''

示例代码：

import tensorflow as tf

import matplotlib.pyplot as plt

style1 = []

style2 = []

N = 200

with tf.Session() as sess:

  sess.run(tf.global_variables_initializer())

  for step in range(N):

    # 标准指数型衰减

    learing_rate1 = tf.train.inverse_time_decay(

      learning_rate=0.5, global_step=step, decay_steps=20, decay_rate=0.2, staircase=False)

    # 阶梯型衰减

    learing_rate2 = tf.train.inverse_time_decay(

      learning_rate=0.5, global_step=step, decay_steps=20, decay_rate=0.2, staircase=True)

    lr1 = sess.run([learing_rate1])

    lr2 = sess.run([learing_rate2])

    style1.append(lr1)

    style2.append(lr2)

step = range(N)

plt.plot(step, style1, 'g-', linewidth=2, label='inverse_time_decay')

plt.plot(step, style2, 'r--', linewidth=1, label='inverse_time_decay_staircase')

plt.title('inverse_time_decay')

plt.xlabel('step')

plt.ylabel('learing rate')

plt.legend(loc='upper right')

plt.tight_layout()

plt.show()

余弦衰减 tf.train.cosine_decay()

tf.train.cosine_decay(

    learning_rate,   # 初始学习率

    global_step,     # 当前训练轮次

    decay_steps,     # 衰减周期

    alpha=0.0,       # 最小的学习率

    name=None

)

'''

global_step = min(global_step, decay_steps)

cosine_decay = 0.5 * (1 + cos(pi * global_step / decay_steps))

decayed = (1 - alpha) * cosine_decay + alpha

decayed_learning_rate = learning_rate * decayed

'''

改进的余弦衰减方法还有：
线性余弦衰减，对应函数 tf.train.linear_cosine_decay()
噪声线性余弦衰减，对应函数 tf.train.noisy_linear_cosine_decay()

示例代码：

import tensorflow as tf

import matplotlib.pyplot as plt

style1 = []

style2 = []

style3 = []

N = 200

with tf.Session() as sess:

  sess.run(tf.global_variables_initializer())

  for step in range(N):

    # 余弦衰减

    learing_rate1 = tf.train.cosine_decay(

      learning_rate=0.1, global_step=step, decay_steps=50)

    # 线性余弦衰减

    learing_rate2 = tf.train.linear_cosine_decay(

      learning_rate=0.1, global_step=step, decay_steps=50)

    # 噪声线性余弦衰减

    learing_rate3 = tf.train.noisy_linear_cosine_decay(

      learning_rate=0.1, global_step=step, decay_steps=50,

      initial_variance=0.01, variance_decay=0.1, num_periods=0.2, alpha=0.5, beta=0.2)

    lr1 = sess.run([learing_rate1])

    lr2 = sess.run([learing_rate2])

    lr3 = sess.run([learing_rate3])

    style1.append(lr1)

    style2.append(lr2)

    style3.append(lr3)

step = range(N)

plt.plot(step, style1, 'g-', linewidth=2, label='cosine_decay')

plt.plot(step, style2, 'r--', linewidth=1, label='linear_cosine_decay')

plt.plot(step, style3, 'b--', linewidth=1, label='linear_cosine_decay')

plt.title('cosine_decay')

plt.xlabel('step')

plt.ylabel('learing rate')

plt.legend(loc='upper right')

plt.tight_layout()

plt.show()

循环余弦衰减 tf.train.cosine_decay_restarts()

这是在 fast.ai 中强推的衰减方式

tf.train.cosine_decay_restarts(

    learning_rate,      # 初始学习率

    global_step,        # 当前训练轮次

    first_decay_steps,  # 首次衰减周期

    t_mul=2.0,          # 随后每次衰减周期倍数

    m_mul=1.0,          # 随后每次初始学习率倍数

    alpha=0.0,          # 最小的学习率=alpha*learning_rate

    name=None

)

'''

See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent

with Warm Restarts. https://arxiv.org/abs/1608.03983

The learning rate multiplier first decays

from 1 to `alpha` for `first_decay_steps` steps. Then, a warm

restart is performed. Each new warm restart runs for `t_mul` times more steps

and with `m_mul` times smaller initial learning rate.

'''

示例代码：

import tensorflow as tf

import matplotlib.pyplot as plt

style1 = []

style2 = []

N = 200

with tf.Session() as sess:

  sess.run(tf.global_variables_initializer())

  for step in range(N):

    # 循环余弦衰减

    learing_rate1 = tf.train.cosine_decay_restarts(

      learning_rate=0.1, global_step=step, first_decay_steps=50,

      )

    # 余弦衰减

    learing_rate2 = tf.train.cosine_decay(

      learning_rate=0.1, global_step=step, decay_steps=50)

    lr1 = sess.run([learing_rate1])

    lr2 = sess.run([learing_rate2])

    style1.append(lr1)

    style2.append(lr2)

step = range(N)

plt.plot(step, style1, 'g-', linewidth=2, label='cosine_decay_restarts')

plt.plot(step, style2, 'r--', linewidth=1, label='cosine_decay')

plt.title('cosine_decay_restarts')

plt.xlabel('step')

plt.ylabel('learing rate')

plt.legend(loc='upper right')

plt.tight_layout()

plt.show()

调用例子

import tensorflow as tf

def Swish(features):

    return features*tf.nn.sigmoid(features)

# 1. create data

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('../MNIST_data', one_hot=True)

X = tf.placeholder(tf.float32, shape=(None, 784), name='X')

y = tf.placeholder(tf.int32, shape=(None), name='y')

is_training = tf.placeholder(tf.bool, None, name='is_training')

# 2. define network

he_init = tf.contrib.layers.variance_scaling_initializer()

with tf.name_scope('dnn'):

    hidden1 = tf.layers.dense(X, 300, kernel_initializer=he_init, name='hidden1')

    hidden1 = tf.layers.batch_normalization(hidden1, momentum=0.9)

    hidden1 = tf.nn.relu(hidden1)

    hidden2 = tf.layers.dense(hidden1, 100, kernel_initializer=he_init, name='hidden2')

    hidden2 = tf.layers.batch_normalization(hidden2, training=is_training, momentum=0.9)

    hidden2 = tf.nn.relu(hidden2)

    logits = tf.layers.dense(hidden2, 10, kernel_initializer=he_init, name='output')

    # prob = tf.layers.dense(hidden2, 10, tf.nn.softmax, name='prob')

# 3. define loss

with tf.name_scope('loss'):

    # tf.losses.sparse_softmax_cross_entropy() label is not one_hot and dtype is int*

    # xentropy = tf.losses.sparse_softmax_cross_entropy(labels=tf.argmax(y, axis=1), logits=logits)

    # tf.nn.sparse_softmax_cross_entropy_with_logits() label is not one_hot and dtype is int*

    # xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.argmax(y, axis=1), logits=logits)

    # loss = tf.reduce_mean(xentropy)

    loss = tf.losses.softmax_cross_entropy(onehot_labels=y, logits=logits) # label is one_hot

# 4. define optimizer

learning_rate_init = 0.01

global_step = tf.Variable(0, trainable=False)

with tf.name_scope('train'):

    learning_rate = tf.train.polynomial_decay(  # 多项式衰减

        learning_rate=learning_rate_init,  # 初始学习率

        global_step=global_step,  # 当前迭代次数

        decay_steps=22000,  # 在迭代到该次数实际，学习率衰减为 learning_rate * dacay_rate

        end_learning_rate=learning_rate_init / 10,  # 最小的学习率

        power=0.9,

        cycle=False

    )

    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)  # for batch normalization

    with tf.control_dependencies(update_ops):

        optimizer_op = tf.train.GradientDescentOptimizer(

            learning_rate=learning_rate).minimize(

            loss=loss,

            var_list=tf.trainable_variables(),

            global_step=global_step # 不指定的话学习率不更新

        )

        # ================= clip gradient

        # threshold = 1.0

        # optimizer = tf.train.GradientDescentOptimizer(learning_rate)

        # grads_and_vars = optimizer.compute_gradients(loss)

        # capped_gvs = [(tf.clip_by_value(grad, -threshold, threshold), var)

        #               for grad, var in grads_and_vars]

        # optimizer_op = optimizer.apply_gradients(capped_gvs)

        # =================

with tf.name_scope('eval'):

    correct = tf.nn.in_top_k(logits, tf.argmax(y, axis=1), 1) # 目标是否在前K个预测中, label's dtype is int*

    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))

# 5. initialize

init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())

saver = tf.train.Saver()

# =================

print([v.name for v in tf.trainable_variables()])

print([v.name for v in tf.global_variables()])

# =================

# 6. train & test

n_epochs = 20

n_batches = 50

batch_size = 50

with tf.Session() as sess:

    sess.run(init_op)

    # saver.restore(sess, './my_model_final.ckpt')

    for epoch in range(n_epochs):

        for iteration in range(mnist.train.num_examples // batch_size):

            X_batch, y_batch = mnist.train.next_batch(batch_size)

            sess.run([optimizer_op, learning_rate], feed_dict={X: X_batch, y: y_batch, is_training:True})

            # ================= check gradient

            # for grad, var in grads_and_vars:

            #     grad = grad.eval(feed_dict={X: X_batch, y: y_batch, is_training:True})

            #     var = var.eval()

            # =================

        learning_rate_cur = learning_rate.eval()

        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch, is_training:False}) # 最后一个 batch 的 accuracy

        acc_test = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels, is_training:False})

        loss_test = loss.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels, is_training:False})

        print(epoch, "Current learning rate:", learning_rate_cur, "Train accuracy:", acc_train, "Test accuracy:", acc_test, "Test loss:", loss_test)

    save_path = saver.save(sess, "./my_model_final.ckpt")

with tf.Session() as sess:

    sess.run(init_op)

    saver.restore(sess, "./my_model_final.ckpt")

    acc_test = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels, is_training:False})

    loss_test = loss.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels, is_training:False})

    print("Test accuracy:", acc_test, ", Test loss:", loss_test)

Since AdaGrad, RMSProp, and Adam optimization automatically reduce the learning rate during training, it is not necessary to add an extra learning schedule. For other optimization algorithms, using exponential decay or performance scheduling can considerably speed up convergence.

TensorFlow使用记录 (三）： Learning Rate Scheduling的更多相关文章

  1. 5、Tensorflow基础（三）神经元函数及优化方法

    1.激活函数 激活函数(activation function)运行时激活神经网络中某一部分神经元,将激活信息向后传入下一层的神经网络.神经网络之所以能解决非线性问题(如语音.图像识别),本质上就是激 ...

  2. Deep Learning 32: 自己写的keras的一个callbacks函数,解决keras中不能在每个epoch实时显示学习速率learning rate的问题

    一.问题: keras中不能在每个epoch实时显示学习速率learning rate,从而方便调试,实际上也是为了调试解决这个问题:Deep Learning 31: 不同版本的keras,对同样的 ...

  3. TensorFlow使用记录 (九）： 模型保存与恢复

    模型文件 tensorflow 训练保存的模型注意包含两个部分:网络结构和参数值. .meta .meta 文件以 “protocol buffer”格式保存了整个模型的结构图,模型上定义的操作等信息 ...

  4. TensorFlow使用记录 (六）： 优化器

    0. tf.train.Optimizer tensorflow 里提供了丰富的优化器,这些优化器都继承与 Optimizer 这个类.class Optimizer 有一些方法,这里简单介绍下: 0 ...

  5. tensorflow笔记（三）之 tensorboard的使用

    tensorflow笔记(三)之 tensorboard的使用 版权声明:本文为博主原创文章,转载请指明转载地址 http://www.cnblogs.com/fydeblog/p/7429344.h ...

  6. mxnet设置动态学习率（learning rate）

    https://blog.csdn.net/xiaotao_1/article/details/78874336 如果learning rate很大,算法会在局部最优点附近来回跳动,不会收敛: 如果l ...

  7. TensorFlow使用记录 (八）： 梯度修剪 和 Max-Norm Regularization

    梯度修剪 梯度修剪主要避免训练梯度爆炸的问题,一般来说使用了 Batch Normalization 就不必要使用梯度修剪了,但还是有必要理解下实现的 In TensorFlow, the optim ...

  8. TensorFlow实战第三课（可视化、加速神经网络训练）

    matplotlib可视化 构件图形 用散点图描述真实数据之间的关系(plt.ion()用于连续显示) # plot the real data fig = plt.figure() ax = fig ...

  9. Dynamic learning rate in training - 培训中的动态学习率

    I'm using keras 2.1.* and want to change the learning rate during training. I know about the schedul ...

随机推荐

  1. .Net Core使用AutoMapper做对象关系映射

    我想很多后端开发者,纠结于如何在Dto及表实体中做属性关系映射,因为真的太繁琐了., ⒈如何使用? Mapper.Initialize(cfg => cfg.CreateMap<Users ...

  2. (转)当margin-top、padding-top的值为百分比时是如何计算的？

    本文链接:https://blog.csdn.net/qq_27437967/article/details/72625900问题:当margin-top.padding-top的值是百分比时,分别是 ...

  3. Fire Net（HDU-1045）（匈牙利最大匹配）（建图方式）

    题意 有一个 n*n 的图,. 代表空白区域,X 代表墙,现在要在空白区域放置结点,要求同一行同一列只能放一个,除非有墙阻隔,问最多能放多少个点 思路 只有在墙的阻隔情况下,才会出现一行/列出现多个点 ...

  4. java中接口知识点大总结

    接口的确很不好理解!!!!!那我来好好总结一下: 首先要理解接口是一个独立存在的,和类是不一样的东西,所以,直接用接口的定义是: 访问权限控制符 interface 接口名 [extends  接口列 ...

  5. EJS学习（三）之语法规则中

    ⚠️实例均结合node,也就是AMD规范版本 ejs中使用render()表示渲染文本 接收三个参数:模版字符串.data.options,返回一个字符串 const ejs = require('e ...

  6. php 中php-fpm工作原理

    1.CGI 是服务器与后台语言交互的协议,有了这个协议,开发者可以使用任何语言处理服务器转发过来的请求,动态地生成内容, 保证了传递过来的数据是标准格式的(规定了以什么样的格式传哪些数据(URL.查询 ...

  7. Centos7:配置防火墙

    firewalld的基本使用 启动: systemctl start firewalld 关闭:systemctl stop firewalld 查看状态: systemctl status fire ...

  8. 淘宝flexible.js的使用

    首先大家最关注的怎么使用,原理不原理是后面的事 比如设计稿量来的宽度是100px 那么我们布局的时候,就这么写{width:1.3333rem},1.3333rem是由100/75算出来的,以此类推2 ...

  9. 谈谈对this的指向问题

    普通函数中:this——window 定时器:this——window 构造函数中:this——当前实例化的对象 事件处理函数:this——事件触发对象

  10. Java操作FTP,从FTP上读取指定文件,把指定文件上传到FTP

    需要添加的依赖 <!-- https://mvnrepository.com/artifact/commons-net/commons-net --> <dependency> ...