tensorflow学习系列五：AlexNet

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我是靠谱客的博主敏感大船，最近开发中收集的这篇文章主要介绍tensorflow学习系列五：AlexNet，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

AlexNet网络结构对于今天而言并无太多特殊之处，但是它的出现却将我们卷入了深度学习的大潮，具有重要的意义。下面基于tensorflow对alexNet进行了简单的复现，主要是测试了下alexNet网络的运行时间。

# -*- coding: utf-8 -*-
"""
Created on Sun Jun 10 13:54:00 2018

@author: kuangyongjian
"""
from datetime import datetime
import math
import time
import tensorflow as tf


batch_size = 32
num_batches = 100

def print_activations(t):
    print(t.op.name,' ',t.get_shape().as_list())

#声明网络的结构    
def inference(images):
    parameters = []
    
    with tf.name_scope('conv1') as scope:
        kernel = tf.Variable(tf.truncated_normal([11,11,3,64],dtype = tf.float32,stddev = 1e-1),name = 'weights')
        conv = tf.nn.conv2d(images,kernel,[1,4,4,1],padding = 'SAME')
        biases = tf.Variable(tf.constant(0.0,shape = [64],dtype = tf.float32),trainable = True,name = 'biases')
        bias = tf.nn.bias_add(conv,biases)
        conv1 = tf.nn.relu(bias,name = scope)
        print_activations(conv1)
        parameters += [kernel,biases]
        

    lrn1 = tf.nn.lrn(conv1,4,bias = 1.0,alpha = 0.001 / 9,beta = 0.75,name = 'lrn1')
    pool1 = tf.nn.max_pool(lrn1,ksize = [1,3,3,1],strides = [1,2,2,1],padding = 'VALID',name = 'pool1')
    print_activations(pool1)
    
    with tf.name_scope('conv2') as scope:
        kernel = tf.Variable(tf.truncated_normal([5,5,64,192],dtype = tf.float32,stddev = 1e-1),name = 'weights')
        conv = tf.nn.conv2d(pool1,kernel,[1,1,1,1],padding = 'SAME')
        biases = tf.Variable(tf.constant(0.0,shape = [192],dtype = tf.float32),trainable = True,name = 'biases')
        bias = tf.nn.bias_add(conv,biases)
        conv2 = tf.nn.relu(bias,name = scope)
        print_activations(conv2)
        parameters += [kernel,biases]
        
    lrn2 = tf.nn.lrn(conv2,4,bias = 1.0,alpha = 0.001 / 9,beta = 0.75,name = 'lrn2')
    pool2 = tf.nn.max_pool(lrn2,ksize = [1,3,3,1],strides = [1,2,2,1],padding = 'VALID',name = 'pool2')
    print_activations(pool2)
    
    
    with tf.name_scope('conv3') as scope:
        kernel = tf.Variable(tf.truncated_normal([3,3,192,384],dtype = tf.float32,stddev = 1e-1),name = 'weights')
        conv = tf.nn.conv2d(pool2,kernel,[1,1,1,1],padding = 'SAME')
        biases = tf.Variable(tf.constant(0.0,shape = [384],dtype = tf.float32),trainable = True,name = 'biases')
        bias = tf.nn.bias_add(conv,biases)
        conv3 = tf.nn.relu(bias,name = scope)
        parameters += [kernel,biases]
        print_activations(conv3)
        
    
    with tf.name_scope('conv4') as scope:
        kernel = tf.Variable(tf.truncated_normal([3,3,384,256],dtype = tf.float32,stddev = 1e-1),name = 'weights')
        conv = tf.nn.conv2d(conv3,kernel,[1,1,1,1],padding = 'SAME')
        biases = tf.Variable(tf.constant(0.0,shape = [256],dtype = tf.float32),trainable = True,name = 'biases')
        bias = tf.nn.bias_add(conv,biases)
        conv4 = tf.nn.relu(bias,name = scope)
        parameters += [kernel,biases]
        print_activations(conv4)
        
        
    with tf.name_scope('conv5') as scope:
        kernel = tf.Variable(tf.truncated_normal([3,3,256,256],dtype = tf.float32,stddev = 1e-1),name = 'weights')
        conv = tf.nn.conv2d(conv4,kernel,[1,1,1,1],padding = 'SAME')
        biases = tf.Variable(tf.constant(0.0,shape = [256],dtype = tf.float32),trainable = True,name = 'biases')
        bias = tf.nn.bias_add(conv,biases)
        conv5 = tf.nn.relu(bias,name = scope)
        parameters += [kernel,biases]
        print_activations(conv5)
        

    pool5 = tf.nn.max_pool(conv5,ksize = [1,3,3,1],strides = [1,2,2,1],padding = 'VALID',name = 'pool5')
    print_activations(pool5)
    
    return pool5,parameters

#计算输入到得到pool5的运行时间
def time_tensorflow_run(session,target,info_string):
    num_steps_burn_in = 10
    total_duration = 0.0
    total_duration_squared = 0.0
    
    for i in range(num_batches + num_steps_burn_in):
        start_time = time.time()
        _ = session.run(target)
        duration = time.time() - start_time
        
        #每迭代十次计算一次平均时间
        if i >= num_steps_burn_in:
            if not i % 10:
                print('%s: step %d ,duration = %.3f'%
                      (datetime.now(),i - num_steps_burn_in,duration))
                total_duration += duration
                total_duration_squared += duration * duration
                mn = total_duration / num_batches
                vr = total_duration_squared / num_batches - mn * mn
                sd = math.sqrt(vr)
                print('%s: %s,across %d steps, %.3f +/- %.3f sec / batch'%
                      (datetime.now(),info_string,num_batches,mn,sd))


def run_benchmark():
    with tf.Graph().as_default():
        image_size = 224
        images = tf.Variable(tf.random_normal([batch_size,
                                               image_size,
                                               image_size,
                                               3],
                                                dtype = tf.float32,
                                                stddev = 1e-1))
        
        pool5,parameters = inference(images)
        
        init = tf.global_variables_initializer()
        sess = tf.Session()
        sess.run(init)
        
        time_tensorflow_run(sess,pool5,'Forward')
        


run_benchmark()