学习率

清疚 2022-03-10 02:28 321阅读 0赞

# 学习率 #

学习率（learning\_rate）:表示了每次参数更新的幅度大小。

学习率过大：会导致待优化的参数在最小值附近进行波动；

学习率过小：会导致待优化参数收敛的速度慢

# 参数更新 #

在训练过程中，参数更新向着损失函数梯度下降的方向

![W\_\{n+1\} = W\_\{n\} - learningrate\* \\bigtriangledown][W_n_1_ _ W_n_ - learningrate_ _bigtriangledown]

其中 ![\\bigtriangledown][bigtriangledown]：是梯度，就是损失函数loss的导数

# 学习率的设置 #

一般情况下，学习学习都是根据经验，设置一个较小的定值

## **指数衰减学习率** ##

指数衰减学习率：学习率随着训练轮数变化而动态更新。

在TensorFlow中，通过tf.train.exponential\_decay()实现。

exponential_decay(learning_rate, global_step, decay_steps, decay_rate,
                          staircase=False, name=None):
    
    '''
    Args:
        learning_rate: A scalar `float32` or `float64` `Tensor` or a
          Python number.  The initial learning rate.
        global_step: A scalar `int32` or `int64` `Tensor` or a Python number.
          Global step to use for the decay computation.  Must not be negative.
        decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
          Must be positive.  See the decay computation above.
        decay_rate: A scalar `float32` or `float64` `Tensor` or a
          Python number.  The decay rate.
        staircase: Boolean.  If `True` decay the learning rate at discrete intervals
        name: String.  Optional name of the operation.  Defaults to
          'ExponentialDecay'.
    
      Returns:
        A scalar `Tensor` of the same type as `learning_rate`.  The decayed
        learning rate.
    '''

其中：

learning\_rate ：学习率的初始值

global\_step ：记录训练的次数

decay\_steps ：学习率衰减速度，通常为 = 总样本数/BATCH\_SIZE

decay\_rate ：学习衰减系数

staircase ：如果为真，学习成阶梯型下降

decay\_steps 和decay\_rate 通常都是经验之谈。

#step0 准备工作
    import tensorflow as tf
    import os
    import numpy as np
    
    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"  # 忽略tensorflow警告信息
    
    learning_rate_base = 0.1
    learning_rate_decay = 0.99
    learning_rate_step = 1
    STEPS = 40
    global_step = tf.Variable(0,trainable=False)
    
    #step1 前向传播
    learning_rate = tf.train.exponential_decay(learning_rate_base,
                                               global_step,
                                               learning_rate_step,
                                               learning_rate_decay,
                                               staircase=True)
    w = tf.Variable(tf.constant(5,dtype=tf.float32)) #赋初值5
    
    #step2 反向传播
    loss = tf.square(w+1) #直接从loss开始，这里没有数据集和标签
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)
    
    #step3 循环迭代
    with tf.Session() as sess:
        init_op = tf.global_variables_initializer()
        sess.run(init_op)
        for i in range(STEPS):
            sess.run(train_step)
            v_learning_rate=sess.run(learning_rate)
            print("step:%d,learining_rate:%g,loss:%g,w:%g" % (i,v_learning_rate,sess.run(loss),sess.run(w)))
        print('final w:',sess.run(w))

运行效果

> step:0,learining\_rate:0.099,loss:23.04,w:3.8  
> step:1,learining\_rate:0.09801,loss:14.8194,w:2.8496  
> step:2,learining\_rate:0.0970299,loss:9.57903,w:2.095  
> step:3,learining\_rate:0.0960596,loss:6.22196,w:1.49439  
> step:4,learining\_rate:0.095099,loss:4.06089,w:1.01517  
> step:5,learining\_rate:0.094148,loss:2.66305,w:0.631886  
> step:6,learining\_rate:0.0932065,loss:1.75459,w:0.324608  
> step:7,learining\_rate:0.0922745,loss:1.1614,w:0.0776838  
> step:8,learining\_rate:0.0913517,loss:0.772287,w:-0.121202  
> step:9,learining\_rate:0.0904382,loss:0.515867,w:-0.281761  
> step:10,learining\_rate:0.0895338,loss:0.346128,w:-0.411674  
> step:11,learining\_rate:0.0886385,loss:0.233266,w:-0.517024  
> step:12,learining\_rate:0.0877521,loss:0.157891,w:-0.602644  
> step:13,learining\_rate:0.0868746,loss:0.107334,w:-0.672382  
> step:14,learining\_rate:0.0860058,loss:0.0732756,w:-0.729305  
> step:15,learining\_rate:0.0851458,loss:0.0502352,w:-0.775868  
> step:16,learining\_rate:0.0842943,loss:0.0345827,w:-0.814036  
> step:17,learining\_rate:0.0834514,loss:0.0239051,w:-0.845387  
> step:18,learining\_rate:0.0826169,loss:0.0165914,w:-0.871193  
> step:19,learining\_rate:0.0817907,loss:0.0115614,w:-0.892476  
> step:20,learining\_rate:0.0809728,loss:0.00808834,w:-0.910065  
> step:21,learining\_rate:0.0801631,loss:0.00568072,w:-0.924629  
> step:22,learining\_rate:0.0793614,loss:0.0040052,w:-0.936713  
> step:23,learining\_rate:0.0785678,loss:0.00283467,w:-0.946758  
> step:24,learining\_rate:0.0777822,loss:0.00201381,w:-0.955125  
> step:25,learining\_rate:0.0770043,loss:0.00143599,w:-0.962106  
> step:26,learining\_rate:0.0762343,loss:0.00102774,w:-0.967942  
> step:27,learining\_rate:0.075472,loss:0.000738235,w:-0.97283  
> step:28,learining\_rate:0.0747172,loss:0.000532191,w:-0.976931  
> step:29,learining\_rate:0.0739701,loss:0.000385019,w:-0.980378  
> step:30,learining\_rate:0.0732304,loss:0.000279526,w:-0.983281  
> step:31,learining\_rate:0.0724981,loss:0.000203643,w:-0.98573  
> step:32,learining\_rate:0.0717731,loss:0.000148869,w:-0.987799  
> step:33,learining\_rate:0.0710553,loss:0.000109198,w:-0.98955  
> step:34,learining\_rate:0.0703448,loss:8.03662e-05,w:-0.991035  
> step:35,learining\_rate:0.0696413,loss:5.93437e-05,w:-0.992297  
> step:36,learining\_rate:0.0689449,loss:4.39641e-05,w:-0.993369  
> step:37,learining\_rate:0.0682555,loss:3.26757e-05,w:-0.994284  
> step:38,learining\_rate:0.0675729,loss:2.43633e-05,w:-0.995064  
> step:39,learining\_rate:0.0668972,loss:1.82229e-05,w:-0.995731  
> final w: -0.9957312

[W_n_1_ _ W_n_ - learningrate_ _bigtriangledown]: https://private.codecogs.com/gif.latex?W_%7Bn&plus;1%7D%20%3D%20W_%7Bn%7D%20-%20learningrate*%20%5Cbigtriangledown
[bigtriangledown]: https://private.codecogs.com/gif.latex?%5Cbigtriangledown