4 TensorFlow入门之dropout解决overfitting问题

2024-03-02 12:23:04

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dropout解决overfitting问题

overfitting：当机器学习学习得太好了，就会出现过拟合（overfitting)问题。所以，我们就要采取一些措施来避免过拟合的问题。此实验就来看一下dropout对于解决过拟合问题的效果。
例子实验内容：识别手写数字。此实验的步骤和上一篇的识别手写数字步骤很相似。
例子实验的数据集：sklearn中的datasets
主要运用的函数tf.nn.dropout()
主要参数keep_prob。keep_prob表示留下来的结果的百分比，比如你要drop0.4，那么keep_prob就为0.6

import tensorflow as tf

from sklearn.datasets import load_digits

from sklearn.cross_validation import train_test_split

from sklearn.preprocessing import LabelBinarizer

#加载数据

digits = load_digits()

X = digits.data

y = digits.target

y = LabelBinarizer().fit_transform(y)  #把数字变成1x10的向量

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = .3)  #把数据分成train数据和test数据

#定义添加层

def add_layer(inputs,in_size,out_size,activation_function=None):

    #定义添加层内容，返回这层的outputs

    Weights = tf.Variable(tf.random_normal([in_size,out_size]))#Weigehts是一个in_size行、out_size列的矩阵，开始时用随机数填满

    biases = tf.Variable(tf.zeros([1,out_size])+0.1) #biases是一个1行out_size列的矩阵，用0.1填满

    Wx_plus_b = tf.matmul(inputs,Weights)+biases  #预测

    #实现dropout，keep_drop为丢弃后剩下的百分比

    Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob)

    if activation_function is None:  #如果没有激励函数，那么outputs就是预测值

        outputs = Wx_plus_b

    else:  #如果有激励函数，那么outputs就是激励函数作用于预测值之后的值

        outputs = activation_function(Wx_plus_b)

    return outputs

#定义计算正确率的函数

def t_accuracy(t_xs,t_ys):

    global prediction

    y_pre = sess.run(prediction,feed_dict={xs:t_xs,keep_prob:1})#测试结果不dropout

    correct_pre = tf.equal(tf.argmax(y_pre,1),tf.argmax(t_ys,1))

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

    result = sess.run(accuracy,feed_dict={xs:t_xs,ys:t_ys,keep_prob:1})

    return result

#定义输入输出值，和keep_drop值

keep_prob = tf.placeholder(tf.float32)

xs = tf.placeholder(tf.float32, [None, 64])  # 8x8

ys = tf.placeholder(tf.float32, [None, 10])

#添加层

l1 = add_layer(xs, 64, 50,activation_function=tf.nn.tanh)

prediction = add_layer(l1, 50, 10,activation_function=tf.nn.softmax)

#误差

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1]))  # loss

#训练

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

#开始训练

sess = tf.Session()

merged = tf.summary.merge_all()

init = tf.global_variables_initializer()

sess.run(init)

for i in range(1000):

    # 设置keep_drop为1，即不进行dropout

    sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 1})

    if i % 50 == 0:

        # 输出正确率

        print (t_accuracy(X_test,y_test))

for i in range(1000):

    # 设置keep_drop为0.5

    sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5})

    if i % 50 == 0:

        # 输出正确率

        print (t_accuracy(X_test,y_test))

由上面的结果可知，当dropout为0.5时，效果明显比一点儿也不丢弃的好！

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dropout解决overfitting问题

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