【DL笔记】LeNet5神经网络简介及TensorFlow实现

忘是亡心i 2022-06-13 04:16 249阅读 0赞

## 1.前言 ##

这篇文章是我通过学习黄文坚、唐源所著的《TensorFlow实战》之后的简单总结，通过这本书我对深度学习更加了解，现整理出一部分分享给大家，错误之处可以在评论区指出，以便我加以改正，谢谢！

## 2.模型特点 ##

LeNet5诞生于1994年，由Yann LeCun提出，充分考虑图像的相关性。当时结构的特点如下：

1）每个卷积层包含三个部分：卷积（Conv）、池化（ave-pooling）、非线性激活函数（sigmoid）

2）MLP作为最终的分类器

3）层与层之间稀疏连接减少计算复杂度

## 3.结构模型 ##

![Center][]

## 4.网络层介绍 ##

Input Layer：1\*32\*32图像

Conv1 Layer：包含6个卷积核，kernal size：5\*5，parameters:（5\*5+1）\*6=156个

Subsampling Layer：average pooling，size：2\*2

Activation Function：sigmoid

Conv3 Layer：包含16个卷积核，kernal size：5\*5  ->16个Feature Map

Subsampling Layer：average pooling，size：2\*2

Conv5 Layer：包含120个卷积核，kernal size：5\*5

Fully Connected Layer：Activation Function：sigmoid

Output Layer：Gaussian connection

## 5.代码实现 ##

说明一下，原本应该放LeNet5的TensorFlow实现代码，发现LeNet5的模型在现在使用过程中好多地方进行了更改，比如激励函数换做ReLU，采用max pooling等等，因此，我只是简单地进行了一个CNN的TensorFlow代码实现，用的MNIST数据集，代码如下：

import tensorflow as tf 
    from tensorflow.examples.tutorials.mnist import input_data
    
    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_variable(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])
    
    # Conv1 Layer
    W_conv1 = weight_variable([5,5,1,32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    
    # Conv2 Layer
    W_conv2 = weight_variable([5,5,32,64])
    b_conv2 = bias_variable([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])
    b_fc1 = bias_variable([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)
    
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)
    
    W_fc2 = weight_variable([1024,10])
    b_fc2 = bias_variable([10])
    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2) + b_fc2)
    
    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)
    
    correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    
    tf.global_variables_initializer().run()
    for i in range(20000):
    	batch = mnist.train.next_batch(50)
    	if i % 1000 == 0:
    		train_accuracy = accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
    		print("step %d, training accuracy %g"%(i,train_accuracy))
    	train_step.run(feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})
    
    print("test accuracy %g"%accuracy.eval(feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))

在GPU上面训练，大概不到两分钟，准确率99.11%

![Center 1][]

注：系统环境：ubuntu、Python3.5.2、TensorFlow1.2.0

[Center]: /images/20220613/a5d293c2a3c5421d90001eb27a1b584a.png
[Center 1]: /images/20220613/b55b6bc17388486393a935cc59b2996b.png