1.代码实现
?1 # -*- coding: utf-8 -*- ?2 """ ?3 Created on Wed Nov 14 17:13:05 2018 ?4 ??5 @author: zhen ?6 """ ?7 ??8 from datetime import datetime ?9 import math 10 import time 11 import tensorflow as tf 12 ?13 batch_size = 32 14 num_batchs = 100 15 ?16 def print_activations(t): 17 ????print(t.op.name, " ", t.get_shape().as_list()) 18 ?????19 def inference(images): 20 ????parameters = [] 21 ????with tf.name_scope(‘conv1‘) as scope: 22 ????????kernel = tf.Variable(tf.truncated_normal([11,11, 3, 64],dtype=tf.float32, stddev=1e-1), name=‘weights‘) 23 ????????conv = tf.nn.conv2d(images, kernel, [1, 4, 4, 1], padding=‘SAME‘) 24 ????????biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32), trainable=True, name=‘biases‘) 25 ????????bias = tf.nn.bias_add(conv, biases) 26 ????????conv1 = tf.nn.relu(bias, name=scope) 27 ????????print_activations(conv1) 28 ????????parameters += [kernel, biases] 29 ????lrn1 = tf.nn.lrn(conv1, depth_radius=4, bias=1.0, alpha=0.001/9, beta=0.75, name=‘lrn1‘) 30 ????pool1 = tf.nn.max_pool(lrn1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding=‘VALID‘, name=‘pool1‘) 31 ????print_activations(pool1) 32 ?33 ????with tf.name_scope(‘conv2‘) as scope: 34 ????????kernel = tf.Variable(tf.truncated_normal([5, 5, 64, 128], dtype=tf.float32, stddev=1e-1, name=‘weights‘)) 35 ????????conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding=‘SAME‘) 36 ????????biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32), trainable=True, name=‘biases‘) 37 ????????bias = tf.nn.bias_add(conv, biases) 38 ????????conv2 = tf.nn.relu(bias, name=scope) 39 ????????parameters += [kernel, biases] 40 ????????print_activations(conv2) 41 ?????????42 ????lrn2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9, beta=0.75, name=‘lrn2‘) 43 ????pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding=‘VALID‘, name=‘pool2‘) 44 ????print_activations(pool2) ?????????45 ?????????46 ????with tf.name_scope(‘conv3‘) as scope: 47 ????????kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32, stddev=1e-1, name=‘weights‘)) 48 ????????conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding=‘SAME‘) 49 ????????biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), trainable=True, name=‘biases‘) 50 ????????bias = tf.nn.bias_add(conv, biases) 51 ????????conv3 = tf.nn.relu(bias, name=scope) 52 ????????parameters += [kernel, biases] 53 ????????print_activations(conv3) 54 ?????????55 ????with tf.name_scope(‘conv4‘) as scope: 56 ????????kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 128], dtype=tf.float32, stddev=1e-1, name=‘weights‘)) 57 ????????conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding=‘SAME‘) 58 ????????biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32), trainable=True, name=‘biases‘) 59 ????????bias = tf.nn.bias_add(conv, biases) 60 ????????conv4 = tf.nn.relu(bias, name=scope) 61 ????????parameters += [kernel, biases] 62 ????????print_activations(conv4) 63 ?????????64 ????with tf.name_scope(‘conv5‘) as scope: 65 ????????kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype=tf.float32, stddev=1e-1, name=‘weights‘)) 66 ????????conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding=‘SAME‘) 67 ????????biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32), trainable=True, name=‘biases‘) 68 ????????bias = tf.nn.bias_add(conv, biases) 69 ????????conv5 = tf.nn.relu(bias, name=scope) 70 ????????parameters += [kernel, biases] 71 ????????print_activations(conv5) 72 ?????????73 ????pool5 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding=‘VALID‘, name=‘pool5‘) 74 ????print_activations(pool5) 75 ?????76 ????return pool5, parameters 77 ?78 # 评估AlexNet每轮计算时间 79 def fit_date(session, target, info_string): 80 ????num_steps_burn_in = 10 # 初始计算轮数 81 ????total_duration = 0.0 82 ????total_duration_squared = 0.0 83 ?????84 ????for i in range(num_batchs + num_steps_burn_in): 85 ????????start_time = time.time() 86 ????????session.run(target) 87 ????????duration = time.time() - start_time 88 ????????if i >= num_steps_burn_in: 89 ????????????if not i % 10: 90 ????????????????print(‘%s:step %d, duration=%.3f‘%(datetime.now(), i - num_steps_burn_in, duration)) 91 ????????????total_duration += duration 92 ????????????total_duration_squared += duration * duration 93 ????mn = total_duration / num_batchs 94 ????vr = total_duration_squared / num_batchs - mn * mn 95 ????sd = math.sqrt(vr) ????????????????96 ????print(‘%s:%s across %d steps,%.3f +/- %.3f sec / batch‘%(datetime.now(), info_string, num_batchs, mn, sd)) 97 ?????98 def fit_benchmark(): 99 ????with tf.Graph().as_default():100 ????????image_size = 224101 ????????images = tf.Variable(tf.random_normal([batch_size, image_size, image_size, 3], dtype=tf.float32, stddev=1e-1))102 ????????pool5, parameters = inference(images)103 ????????init = tf.global_variables_initializer()104 ????????sess = tf.Session()105 ????????sess.run(init)106 ????????107 ????????fit_date(sess, pool5, "Forward")108 ????????objective = tf.nn.l2_loss(pool5)109 ????????grad = tf.gradients(objective, parameters)110 ????????fit_date(sess, grad, "Forward-backward")111 ????????112 fit_benchmark()
2.结果
conv1 ??[32, 56, 56, 64]pool1 ??[32, 27, 27, 64]conv2 ??[32, 27, 27, 128]pool2 ??[32, 13, 13, 128]conv3 ??[32, 13, 13, 256]conv4 ??[32, 13, 13, 128]conv5 ??[32, 13, 13, 128]pool5 ??[32, 6, 6, 128]2019-01-27 10:51:37.551617:step 0, duration=1.6252019-01-27 10:51:54.082824:step 10, duration=1.7662019-01-27 10:52:10.582787:step 20, duration=1.6412019-01-27 10:52:27.051502:step 30, duration=1.6722019-01-27 10:52:43.507558:step 40, duration=1.6252019-01-27 10:52:59.913772:step 50, duration=1.6252019-01-27 10:53:16.245750:step 60, duration=1.6722019-01-27 10:53:32.511337:step 70, duration=1.6252019-01-27 10:53:48.901938:step 80, duration=1.6092019-01-27 10:54:05.183145:step 90, duration=1.6252019-01-27 10:54:19.917492:Forward across 100 steps,1.640 +/- 0.031 sec / batch2019-01-27 10:55:47.146016:step 0, duration=7.7192019-01-27 10:57:04.602639:step 10, duration=7.7662019-01-27 10:58:26.594245:step 20, duration=9.8422019-01-27 11:00:01.957195:step 30, duration=8.3912019-01-27 11:01:35.103007:step 40, duration=10.0732019-01-27 11:03:07.656318:step 50, duration=8.9882019-01-27 11:04:31.844207:step 60, duration=8.5902019-01-27 11:06:01.173490:step 70, duration=9.4222019-01-27 11:07:28.737373:step 80, duration=10.6352019-01-27 11:09:03.830375:step 90, duration=8.6532019-01-27 11:10:19.836018:Forward-backward across 100 steps,8.804 +/- 0.817 sec / batch
3.分析
1、AlexNet是比赛分类项目的2012年冠军，top5错误率16.4%，8层神经网络。
2、AlexNet中包含了几个比较新的技术点，首次在CNN中成功应用了Relu、Dropout、 Lrn等Trick。
3、运用Relu，解决Sigmoid在网络层次较深时的梯度弥散。
4、训练Dropout，随机忽略一些神经元，避免过拟合。
5、使用重叠的最大池化，此前CNN普遍平均池化，最大池化避免平均池化的模糊化效果。
6、提出了Lrn层，局部神经元活动创建竞争机制，响应比较大的值变得更大，抑制其他反馈小的神经元，增强泛化能力。
7、数据增强，随机地从256*256的原始图像中截取224*224大小的区域，以及水平翻转的镜像，相当于增加了【（256-224）^2】*2=2048倍的数据量。
注意：没有数据增强，仅靠原始的数据量，参数众多的CNN会陷入过拟合中。
AlexNet卷积神经网络【前向反馈】
原文地址：https://www.cnblogs.com/yszd/p/10325847.html