文章作者：Tyan
博客：noahsnail.com  |  CSDN  |  简书

本文主要是介绍tensorflow中dropout的作用，dropout主要是用来防止过拟合，即提供模型的泛化能力。

#!/usr/bin/env python
# _*_ coding: utf-8 _*_

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()
# 输入数据
X = digits.data
# 输出数据
y = digits.target
# 标签变换
y = LabelBinarizer().fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)

# 创建一个神经网络层
def add_layer(input, in_size, out_size, layer_name, activation_function = None):
    """
    :param input:
        神经网络层的输入
    :param in_zize:
        输入数据的大小
    :param out_size:
        输出数据的大小
    :param layer_name
        神经网络层的名字
    :param activation_function:
        神经网络激活函数，默认没有
    """
    # 定义神经网络的初始化权重
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    # 定义神经网络的偏置
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    # 计算w*x+b
    W_mul_x_plus_b = tf.matmul(input, Weights) + biases
    # 进行dropout，可以注释和不注释来对比dropout的效果
#   W_mul_x_plus_b = tf.nn.dropout(W_mul_x_plus_b, keep_prob)
    # 根据是否有激活函数进行处理
    if activation_function is None:
        output = W_mul_x_plus_b
    else:
        output = activation_function(W_mul_x_plus_b)
    # 查看权重变化
    tf.summary.histogram(layer_name + '/output', output)
    return output


# 定义dropout的placeholder
keep_prob = tf.placeholder(tf.float32)
# 输入数据64个特征
xs = tf.placeholder(tf.float32, [None, 64])  # 8x8
ys = tf.placeholder(tf.float32, [None, 10])

# 添加隐藏层和输出层
l1 = add_layer(xs, 64, 50, 'l1', activation_function=tf.nn.tanh)
prediction = add_layer(l1, 50, 10, 'l2', activation_function=tf.nn.softmax)

# 计算loss
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))
# 存储loss
tf.summary.scalar('loss', cross_entropy)
# 神经网络训练
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# 定义Session
sess = tf.Session()
# 收集所有的数据
merged = tf.summary.merge_all()
# 将数据写入到tensorboard中
train_writer = tf.summary.FileWriter("logs/train", sess.graph)
test_writer = tf.summary.FileWriter("logs/test", sess.graph)

# 根据tensorflow版本选择初始化函数
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
    init = tf.initialize_all_variables()
else:
    init = tf.global_variables_initializer()
# 执行初始化
sess.run(init)
# 进行训练迭代
for i in range(500):
    # 执行训练，dropout为1-0.5=0.5
    sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5})
    if i % 50 == 0:
        # 记录损失
        train_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train, keep_prob: 1})
        test_result = sess.run(merged, feed_dict={xs: X_test, ys: y_test, keep_prob: 1})
        train_writer.add_summary(train_result, i)
        test_writer.add_summary(test_result, i) 

执行结果如下：

• 没有dropout

no_dropout

测试误差与训练误差的损失差的较大，说明模型更拟合训练数据。

• 有dropout

dropout

测试误差与训练误差相差不大，说明模型泛化能力较好。