卷积神经网络CNN理论到实践(5)

1. 导读

前面我们用了4篇博文，详细介绍了CNN的基本理论。从这一篇开始，我们将逐渐走向实战。在本篇博文中，我们首先下来介绍一下，最经典的CNN网络，LeNet5。可以说，它是很多现代CNN的雏形。即便是现在，使用这个模型也可以处理一大批基本问题。我们会先从原理上，做一个介绍，然后使用python进行编程实现。如果有精力，再用它做个试验。

2. LeNet5

3. 一个简单CNN的实现

接下来我们来实现一个简单的CNN，CNN结构如图所示：

图3.1:本文将要实现的CNN结构
（作图很辛苦，打赏需谨慎）

#!/usr/bin/env python
# @Time    : 6/5/17 6:48 PM
# @Author  : SunXiangguo
# @version : Anaconda3.6+Ubuntu_16.04_STL_64
# @File    : cnn1.py
# @Software: PyCharm

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
sess = tf.InteractiveSession()

def weight_variable(shape):
    initial = tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(initial)

def bias_variavle(shape):
    initial = tf.constant(0.1,shape=shape)
    return tf.Variable(initial)

def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')


x = tf.placeholder(tf.float32,[None,784])
y_ = tf.placeholder(tf.float32,[None,10])
x_image = tf.reshape(x,[-1,28,28,1])

W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variavle([32])
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variavle([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7*7*64,1024])  # 'fc'means 'full connected'
b_fc1 = bias_variavle([1024])
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)

4.用CNN做mnist手写识别

#!/usr/bin/env python
# @Time    : 6/5/17 6:48 PM
# @Author  : SunXiangguo
# @version : Anaconda3.6+Ubuntu_16.04_STL_64
# @File    : cnn1.py
# @Software: PyCharm

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
sess = tf.InteractiveSession()

def weight_variable(shape):
    initial = tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(initial)

def bias_variavle(shape):
    initial = tf.constant(0.1,shape=shape)
    return tf.Variable(initial)

def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')


x = tf.placeholder(tf.float32,[None,784])
y_ = tf.placeholder(tf.float32,[None,10])
x_image = tf.reshape(x,[-1,28,28,1])

W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variavle([32])
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variavle([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7*7*64,1024])  # 'fc'means 'full connected'
b_fc1 = bias_variavle([1024])
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)

# Dropout layer
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)


W_fc2 = weight_variable([1024,10])  # 0~9,there are 10 classes
b_fc2 = bias_variavle([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)

# training target
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y_conv),reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

# precision
correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))


# start training
tf.global_variables_initializer().run()
for i in range(20000):
    batch = mnist.train.next_batch(50)
    if i%100 == 0:
        train_accuracy = accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
        print("step {0},training accuracy {1}".format(i,train_accuracy))
    train_step.run(feed_dict = {x:batch[0],y_:batch[1],keep_prob:0.5})


# start predict
print("test accuracy {0}".format(accuracy.eval(feed_dict={
    x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0})))

代码结果：

可以看到，如此简单的一个CNN，就已经可以达到非常高的性能了。

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《卷积神经网络CNN理论到实践(6)》

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welcome!

Xiangguo Sun
sunxiangguodut@qq.com
http://blog.youkuaiyun.com/github_36326955

Welcome to my blog column: Dive into ML/DL!

I devote myself to dive into typical algorithms on machine learning and deep learning, especially the application in the area of computational personality.

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In this blog column, I will introduce some typical algorithms about machine learning and deep learning used in OSNs(Online Social Networks), which means we will include NLP, networks community, information diffusion,and individual recommendation system. Apparently, our ultimate target is to dive into user portrait , especially the issues on your personality analysis.

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