神经网络进一步优化

最新推荐文章于 2022-05-09 23:32:52 发布

原创最新推荐文章于 2022-05-09 23:32:52 发布 · 296 阅读

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学习率设置

学习率过大会导致，参数在极优值两侧来回移动。学习率过小，会导致优化速度大大降低。常用指数衰减法设置学习率

指数衰减法

实现以下功能：

decayed_learning_rate = 
learning_rate * decay_rate^(global_step/decay_steps)

decayed_learning_rate是每一轮优化时候使用的学习率，
learning_rate是事先设定的初始学习率，
decay_rate为衰减系数，decay_steps为衰减速度。

TRAINING_STEPS = 100
global_step = tf.Variable(0)

#通过exponential_decay函数生成学习率，s
#taircase=True时，global_step/decay_steps会转化为整数，学习率为阶梯函数。
LEARNING_RATE = tf.train.exponential_decay(0.1, global_step, 1, 0.96, staircase=True)

x = tf.Variable(tf.constant(5, dtype=tf.float32), name="x")
y = tf.square(x)

#使用指数衰减的学习率，在minimize函数中传入
#global_step将自动更新global_step参数，从而使得学习率也相应更新
train_op = tf.train.GradientDescentOptimizer(
LEARNING_RATE).minimize(y, global_step=global_step)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(TRAINING_STEPS):
        sess.run(train_op)
        if i % 10 == 0:
            LEARNING_RATE_value = sess.run(LEARNING_RATE)
            x_value = sess.run(x)
            print "After %s iteration(s): x%s is %f, learning rate is %f."% (i+1, i+1, x_value, LEARNING_RATE_value)

After 1 iteration(s): x1 is 4.000000, learning rate is 0.096000.
After 11 iteration(s): x11 is 0.690561, learning rate is 0.063824.
After 21 iteration(s): x21 is 0.222583, learning rate is 0.042432.
After 31 iteration(s): x31 is 0.106405, learning rate is 0.028210.
After 41 iteration(s): x41 is 0.065548, learning rate is 0.018755.
After 51 iteration(s): x51 is 0.047625, learning rate is 0.012469.
After 61 iteration(s): x61 is 0.038558, learning rate is 0.008290.
After 71 iteration(s): x71 is 0.033523, learning rate is 0.005511.
After 81 iteration(s): x81 is 0.030553, learning rate is 0.003664.
After 91 iteration(s): x91 is 0.028727, learning rate is 0.002436.

过拟合问题

过拟合指的是当模型过复杂后，模型很好的“记忆”了每一个训练中随机噪声部分，而没有学习训练数据追中通用的趋势。

解决办法正则化

正则化：就是在损失函数中加入刻画模型复杂度的指标。
设损失函数为J（teta）,lambda是模型复杂损失在总损失中的比例，R（w）刻画模型复杂程度

J(\theta)+\lambda R(w)

R(w)可以取参数的绝对值和，也可以取参数的平方和

R(w)=||w||_1=\sum_i|w_i|

R(w)=||w||_2=\sum_i|w_i^2|

L1和L2结合的方式

R(w)=\sum_i\alpha|w_i^2| + (1-\alpha)w_i^2

tensorflow中实现

loss = tf.reduce_mean(tf.square(y_ - y))+tf.contrib.layers.l2_regularizer(lambda)(w)

滑动平均模型

在采用梯度下降算法训练神经网络时，采用滑动平均模型一定程度导航提高最终模型在测试数据上的表现。
TensorFlow中使用

tf.train.ExponentialMovingAverage(衰减率)（控制衰减率的变量）