【深度学习系列】用Tensorflow进行图像分类

  上个月发布了四篇文章,主要讲了深度学习中的“hello world”----mnist图像识别,以及卷积神经网络的原理详解,包括基本原理、自己手写CNN和paddlepaddle的源码解析。这篇主要跟大家讲讲如何用PaddlePaddle和Tensorflow做图像分类。所有程序都在我的github里,可以自行下载训练。

  在卷积神经网络中,有五大经典模型,分别是:LeNet-5,AlexNet,GoogleNet,Vgg和ResNet。本文首先自己设计一个小型CNN网络结构来对图像进行分类,再了解一下LeNet-5网络结构对图像做分类,并用比较流行的Tensorflow框架和百度的PaddlePaddle实现LeNet-5网络结构,并对结果对比。


 什么是图像分类

   图像分类是根据图像的语义信息将不同类别图像区分开来,是计算机视觉中重要的基本问题,也是图像检测、图像分割、物体跟踪、行为分析等其他高层视觉任务的基础。图像分类在很多领域有广泛应用,包括安防领域的人脸识别和智能视频分析等,交通领域的交通场景识别,互联网领域基于内容的图像检索和相册自动归类,医学领域的图像识别等(引用自官网)

  cifar-10数据集

  CIFAR-10分类问题是机器学习领域的一个通用基准,由60000张32*32的RGB彩色图片构成,共10个分类。50000张用于训练集,10000张用于测试集。其问题是将32X32像素的RGB图像分类成10种类别:飞机手机鹿青蛙卡车。更多信息可以参考CIFAR-10Alex Krizhevsky的演讲报告。常见的还有cifar-100,分类物体达到100类,以及ILSVRC比赛的100类。

  


自己设计CNN

  了解CNN的基本网络结构后,首先自己设计一个简单的CNN网络结构对cifar-10数据进行分类。

  网络结构

  代码实现

  1.网络结构:simple_cnn.py

 1 #coding:utf-8
 2 '''
 3 Created by huxiaoman 2017.11.27
 4 simple_cnn.py:自己设计的一个简单的cnn网络结构
 5 '''
 6 
 7 import os
 8 from PIL import Image
 9 import numpy as np
10 import paddle.fluid as fluid
11 from paddle.trainer_config_helpers import *
12 
13 with_gpu = os.getenv('WITH_GPU', '0') != '1'
14 
15 def simple_cnn(img):
16     conv_pool_1 = paddle.networks.simple_img_conv_pool(
17         input=img,
18         filter_size=5,
19         num_filters=20,
20         num_channel=3,
21         pool_size=2,
22         pool_stride=2,
23         act=paddle.activation.Relu())
24     conv_pool_2 = paddle.networks.simple_img_conv_pool(
25         input=conv_pool_1,
26         filter_size=5,
27         num_filters=50,
28         num_channel=20,
29         pool_size=2,
30         pool_stride=2,
31         act=paddle.activation.Relu())
32     fc = paddle.layer.fc(
33         input=conv_pool_2, size=512, act=paddle.activation.Softmax())

 

  2.训练程序:train_simple_cnn.py

  1 #coding:utf-8
  2 '''
  3 Created by huxiaoman 2017.11.27
  4 train_simple—_cnn.py:训练simple_cnn对cifar10数据集进行分类
  5 '''
  6 import sys, os
  7 
  8 import paddle.v2 as paddle
  9 from simple_cnn import simple_cnn
 10 
 11 with_gpu = os.getenv('WITH_GPU', '0') != '1'
 12 
 13 
 14 def main():
 15     datadim = 3 * 32 * 32
 16     classdim = 10
 17 
 18     # PaddlePaddle init
 19     paddle.init(use_gpu=with_gpu, trainer_count=7)
 20 
 21     image = paddle.layer.data(
 22         name="image", type=paddle.data_type.dense_vector(datadim))
 23 
 24     # Add neural network config
 25     # option 1. resnet
 26     # net = resnet_cifar10(image, depth=32)
 27     # option 2. vgg
 28     net = simple_cnn(image)
 29 
 30     out = paddle.layer.fc(
 31         input=net, size=classdim, act=paddle.activation.Softmax())
 32 
 33     lbl = paddle.layer.data(
 34         name="label", type=paddle.data_type.integer_value(classdim))
 35     cost = paddle.layer.classification_cost(input=out, label=lbl)
 36 
 37     # Create parameters
 38     parameters = paddle.parameters.create(cost)
 39 
 40     # Create optimizer
 41     momentum_optimizer = paddle.optimizer.Momentum(
 42         momentum=0.9,
 43         regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),
 44         learning_rate=0.1 / 128.0,
 45         learning_rate_decay_a=0.1,
 46         learning_rate_decay_b=50000 * 100,
 47         learning_rate_schedule='discexp')
 48 
 49     # End batch and end pass event handler
 50     def event_handler(event):
 51         if isinstance(event, paddle.event.EndIteration):
 52             if event.batch_id % 100 == 0:
 53                 print "\nPass %d, Batch %d, Cost %f, %s" % (
 54                     event.pass_id, event.batch_id, event.cost, event.metrics)
 55             else:
 56                 sys.stdout.write('.')
 57                 sys.stdout.flush()
 58         if isinstance(event, paddle.event.EndPass):
 59             # save parameters
 60             with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
 61                 parameters.to_tar(f)
 62 
 63             result = trainer.test(
 64                 reader=paddle.batch(
 65                     paddle.dataset.cifar.test10(), batch_size=128),
 66                 feeding={'image': 0,
 67                          'label': 1})
 68             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
 69 
 70     # Create trainer
 71     trainer = paddle.trainer.SGD(
 72         cost=cost, parameters=parameters, update_equation=momentum_optimizer)
 73 
 74     # Save the inference topology to protobuf.
 75     inference_topology = paddle.topology.Topology(layers=out)
 76     with open("inference_topology.pkl", 'wb') as f:
 77         inference_topology.serialize_for_inference(f)
 78 
 79     trainer.train(
 80         reader=paddle.batch(
 81             paddle.reader.shuffle(
 82                 paddle.dataset.cifar.train10(), buf_size=50000),
 83             batch_size=128),
 84         num_passes=200,
 85         event_handler=event_handler,
 86         feeding={'image': 0,
 87                  'label': 1})
 88 
 89     # inference
 90     from PIL import Image
 91     import numpy as np
 92     import os
 93 
 94     def load_image(file):
 95         im = Image.open(file)
 96         im = im.resize((32, 32), Image.ANTIALIAS)
 97         im = np.array(im).astype(np.float32)
 98         # The storage order of the loaded image is W(widht),
 99         # H(height), C(channel). PaddlePaddle requires
100         # the CHW order, so transpose them.
101         im = im.transpose((2, 0, 1))  # CHW
102         # In the training phase, the channel order of CIFAR
103         # image is B(Blue), G(green), R(Red). But PIL open
104         # image in RGB mode. It must swap the channel order.
105         im = im[(2, 1, 0), :, :]  # BGR
106         im = im.flatten()
107         im = im / 255.0
108         return im
109 
110     test_data = []
111     cur_dir = os.path.dirname(os.path.realpath(__file__))
112     test_data.append((load_image(cur_dir + '/image/dog.png'), ))
113 
114     # users can remove the comments and change the model name
115     # with open('params_pass_50.tar', 'r') as f:
116     #    parameters = paddle.parameters.Parameters.from_tar(f)
117 
118     probs = paddle.infer(
119         output_layer=out, parameters=parameters, input=test_data)
120     lab = np.argsort(-probs)  # probs and lab are the results of one batch data
121     print "Label of image/dog.png is: %d" % lab[0][0]
122 
123 
124 if __name__ == '__main__':
125     main()

  

  3.结果输出

 1 I1128 21:44:30.218085 14733 Util.cpp:166] commandline:  --use_gpu=True --trainer_count=7
 2 [INFO 2017-11-28 21:44:35,874 layers.py:2539] output for __conv_pool_0___conv: c = 20, h = 28, w = 28, size = 15680
 3 [INFO 2017-11-28 21:44:35,874 layers.py:2667] output for __conv_pool_0___pool: c = 20, h = 14, w = 14, size = 3920
 4 [INFO 2017-11-28 21:44:35,875 layers.py:2539] output for __conv_pool_1___conv: c = 50, h = 10, w = 10, size = 5000
 5 [INFO 2017-11-28 21:44:35,876 layers.py:2667] output for __conv_pool_1___pool: c = 50, h = 5, w = 5, size = 1250
 6 I1128 21:44:35.881502 14733 MultiGradientMachine.cpp:99] numLogicalDevices=1 numThreads=7 numDevices=8
 7 I1128 21:44:35.928449 14733 GradientMachine.cpp:85] Initing parameters..
 8 I1128 21:44:36.056259 14733 GradientMachine.cpp:92] Init parameters done.
 9 
10 Pass 0, Batch 0, Cost 2.302628, {'classification_error_evaluator': 0.9296875}
11 ................................................................................
12 ```
13 Pass 199, Batch 200, Cost 0.869726, {'classification_error_evaluator': 0.3671875}
14 ...................................................................................................
15 Pass 199, Batch 300, Cost 0.801396, {'classification_error_evaluator': 0.3046875}
16 ..........................................................................................I1128 23:21:39.443141 14733 MultiGradientMachine.cpp:99] numLogicalDevices=1 numThreads=7 numDevices=8
17 
18 Test with Pass 199, {'classification_error_evaluator': 0.5248000025749207}
19 Label of image/dog.png is: 9

  我开了7个线程,用了8个Tesla K80 GPU训练,batch_size = 128,迭代次数200次,耗时1h37min,错误分类率为0.5248,这个结果,emm,不算很高,我们可以把它作为一个baseline,后面对其进行调优。

   


LeNet-5网络结构

  Lenet-5网络结构来源于Yan LeCun提出的,原文为《Gradient-based learning applied to document recognition》,论文里使用的是mnist手写数字作为输入数据(32 * 32)进行验证。我们来看一下网络结构。

  LeNet-5一共有8层: 1个输入层+3个卷积层(C1、C3、C5)+2个下采样层(S2、S4)+1个全连接层(F6)+1个输出层,每层有多个feature map(自动提取的多组特征)。

  Input输入层

 cifar10 数据集,每一张图片尺寸:32 * 32

  C1 卷积层

  •  6个feature_map,卷积核大小 5 * 5 ,feature_map尺寸:28 * 28
  • 每个卷积神经元的参数数目:5 * 5 = 25个和一个bias参数
  • 连接数目:(5*5+1)* 6 *(28*28) = 122,304 
  • 参数共享:每个feature_map内共享参数,$\therefore$共(5*5+1)*6 = 156个参数

  S2 下采样层(池化层)

  • 6个14*14的feature_map,pooling大小 2* 2
  • 每个单元与上一层的feature_map中的一个2*2的滑动窗口连接,不重叠,因此S2每个feature_map大小是C1中feature_map大小的1/4
  • 连接数:(2*2+1)*1*14*14*6 = 5880个
  • 参数共享:每个feature_map内共享参数,有2 * 6 = 12个训练参数

  C3 卷积层

  这层略微复杂,S2神经元与C3是多对多的关系,比如最简单方式:用S2的所有feature map与C3的所有feature map做全连接(也可以对S2抽样几个feature map出来与C3某个feature map连接),这种全连接方式下:6个S2的feature map使用6个独立的5×5卷积核得到C3中1个feature map(生成每个feature map时对应一个bias),C3中共有16个feature map,所以该层需要学习的参数个数为:(5×5×6+1)×16=2416个,神经元连接数为:2416×8×8=154624个。

  S4 下采样层

  同S2,如果采用Max Pooling/Mean Pooling,则该层需要学习的参数个数为0个,神经元连接数为:(2×2+1)×16×4×4=1280个。

  C5卷积层

  类似C3,用S4的所有feature map与C5的所有feature map做全连接,这种全连接方式下:16个S4的feature map使用16个独立的1×1卷积核得到C5中1个feature map(生成每个feature map时对应一个bias),C5中共有120个feature map,所以该层需要学习的参数个数为:(1×1×16+1)×120=2040个,神经元连接数为:2040个。

  F6 全连接层

  将C5层展开得到4×4×120=1920个节点,并接一个全连接层,考虑bias,该层需要学习的参数和连接个数为:(1920+1)*84=161364个。

  输出层

  该问题是个10分类问题,所以有10个输出单元,通过softmax做概率归一化,每个分类的输出单元对应84个输入。

    


 LeNet-5的PaddlePaddle实现

  1.网络结构 lenet.py

 1 #coding:utf-8
 2 '''
 3 Created by huxiaoman 2017.11.27
 4 lenet.py:LeNet-5
 5 '''
 6 
 7 import os
 8 from PIL import Image
 9 import numpy as np
10 import paddle.v2 as paddle
11 from paddle.trainer_config_helpers import *
12 
13 with_gpu = os.getenv('WITH_GPU', '0') != '1'
14 
15 def lenet(img):
16     conv_pool_1 = paddle.networks.simple_img_conv_pool(
17         input=img,
18         filter_size=5,
19         num_filters=6,
20         num_channel=3,
21         pool_size=2,
22         pool_stride=2,
23         act=paddle.activation.Relu())
24     conv_pool_2 = paddle.networks.simple_img_conv_pool(
25         input=conv_pool_1,
26         filter_size=5,
27         num_filters=16,
28         pool_size=2,
29         pool_stride=2,
30         act=paddle.activation.Relu())
31     conv_3 = img_conv_layer(
32         input = conv_pool_2,
33         filter_size = 1,
34         num_filters = 120,
35         stride = 1)
36     fc = paddle.layer.fc(
37         input=conv_3, size=84, act=paddle.activation.Sigmoid())
38     return fc

 

  2.训练代码 train_lenet.py

  1 #coding:utf-8
  2 '''
  3 Created by huxiaoman 2017.11.27
  4 train_lenet.py:训练LeNet-5对cifar10数据集进行分类
  5 '''
  6 
  7 import sys, os
  8 
  9 import paddle.v2 as paddle
 10 from lenet import lenet
 11 
 12 with_gpu = os.getenv('WITH_GPU', '0') != '1'
 13 
 14 
 15 def main():
 16     datadim = 3 * 32 * 32
 17     classdim = 10
 18 
 19     # PaddlePaddle init
 20     paddle.init(use_gpu=with_gpu, trainer_count=7)
 21 
 22     image = paddle.layer.data(
 23         name="image", type=paddle.data_type.dense_vector(datadim))
 24 
 25     # Add neural network config
 26     # option 1. resnet
 27     # net = resnet_cifar10(image, depth=32)
 28     # option 2. vgg
 29     net = lenet(image)
 30 
 31     out = paddle.layer.fc(
 32         input=net, size=classdim, act=paddle.activation.Softmax())
 33 
 34     lbl = paddle.layer.data(
 35         name="label", type=paddle.data_type.integer_value(classdim))
 36     cost = paddle.layer.classification_cost(input=out, label=lbl)
 37 
 38     # Create parameters
 39     parameters = paddle.parameters.create(cost)
 40 
 41     # Create optimizer
 42     momentum_optimizer = paddle.optimizer.Momentum(
 43         momentum=0.9,
 44         regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),
 45         learning_rate=0.1 / 128.0,
 46         learning_rate_decay_a=0.1,
 47         learning_rate_decay_b=50000 * 100,
 48         learning_rate_schedule='discexp')
 49 
 50     # End batch and end pass event handler
 51     def event_handler(event):
 52         if isinstance(event, paddle.event.EndIteration):
 53             if event.batch_id % 100 == 0:
 54                 print "\nPass %d, Batch %d, Cost %f, %s" % (
 55                     event.pass_id, event.batch_id, event.cost, event.metrics)
 56             else:
 57                 sys.stdout.write('.')
 58                 sys.stdout.flush()
 59         if isinstance(event, paddle.event.EndPass):
 60             # save parameters
 61             with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
 62                 parameters.to_tar(f)
 63 
 64             result = trainer.test(
 65                 reader=paddle.batch(
 66                     paddle.dataset.cifar.test10(), batch_size=128),
 67                 feeding={'image': 0,
 68                          'label': 1})
 69             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
 70 
 71     # Create trainer
 72     trainer = paddle.trainer.SGD(
 73         cost=cost, parameters=parameters, update_equation=momentum_optimizer)
 74 
 75     # Save the inference topology to protobuf.
 76     inference_topology = paddle.topology.Topology(layers=out)
 77     with open("inference_topology.pkl", 'wb') as f:
 78         inference_topology.serialize_for_inference(f)
 79 
 80     trainer.train(
 81         reader=paddle.batch(
 82             paddle.reader.shuffle(
 83                 paddle.dataset.cifar.train10(), buf_size=50000),
 84             batch_size=128),
 85         num_passes=200,
 86         event_handler=event_handler,
 87         feeding={'image': 0,
 88                  'label': 1})
 89 
 90     # inference
 91     from PIL import Image
 92     import numpy as np
 93     import os
 94 
 95     def load_image(file):
 96         im = Image.open(file)
 97         im = im.resize((32, 32), Image.ANTIALIAS)
 98         im = np.array(im).astype(np.float32)
 99         # The storage order of the loaded image is W(widht),
100         # H(height), C(channel). PaddlePaddle requires
101         # the CHW order, so transpose them.
102         im = im.transpose((2, 0, 1))  # CHW
103         # In the training phase, the channel order of CIFAR
104         # image is B(Blue), G(green), R(Red). But PIL open
105         # image in RGB mode. It must swap the channel order.
106         im = im[(2, 1, 0), :, :]  # BGR
107         im = im.flatten()
108         im = im / 255.0
109         return im
110 
111     test_data = []
112     cur_dir = os.path.dirname(os.path.realpath(__file__))
113     test_data.append((load_image(cur_dir + '/image/dog.png'), ))
114 
115     # users can remove the comments and change the model name
116     # with open('params_pass_50.tar', 'r') as f:
117     #    parameters = paddle.parameters.Parameters.from_tar(f)
118 
119     probs = paddle.infer(
120         output_layer=out, parameters=parameters, input=test_data)
121     lab = np.argsort(-probs)  # probs and lab are the results of one batch data
122     print "Label of image/dog.png is: %d" % lab[0][0]
123 
124 
125 if __name__ == '__main__':
126     main()

 

  3.结果输出 

 1 I1129 14:52:44.314946 15153 Util.cpp:166] commandline:  --use_gpu=True --trainer_count=7
 2 [INFO 2017-11-29 14:52:50,490 layers.py:2539] output for __conv_pool_0___conv: c = 6, h = 28, w = 28, size = 4704
 3 [INFO 2017-11-29 14:52:50,491 layers.py:2667] output for __conv_pool_0___pool: c = 6, h = 14, w = 14, size = 1176
 4 [INFO 2017-11-29 14:52:50,491 layers.py:2539] output for __conv_pool_1___conv: c = 16, h = 10, w = 10, size = 1600
 5 [INFO 2017-11-29 14:52:50,492 layers.py:2667] output for __conv_pool_1___pool: c = 16, h = 5, w = 5, size = 400
 6 [INFO 2017-11-29 14:52:50,493 layers.py:2539] output for __conv_0__: c = 120, h = 5, w = 5, size = 3000
 7 I1129 14:52:50.498749 15153 MultiGradientMachine.cpp:99] numLogicalDevices=1 numThreads=7 numDevices=8
 8 I1129 14:52:50.545882 15153 GradientMachine.cpp:85] Initing parameters..
 9 I1129 14:52:50.651103 15153 GradientMachine.cpp:92] Init parameters done.
10 
11 Pass 0, Batch 0, Cost 2.331898, {'classification_error_evaluator': 0.9609375}
12 ```
13 ......
14 Pass 199, Batch 300, Cost 0.004373, {'classification_error_evaluator': 0.0}
15 ..........................................................................................I1129 16:17:08.678097 15153 MultiGradientMachine.cpp:99] numLogicalDevices=1 numThreads=7 numDevices=8
16 
17 Test with Pass 199, {'classification_error_evaluator': 0.39579999446868896}
18 Label of image/dog.png is: 7

   同样是7个线程,8个Tesla K80 GPU,batch_size = 128,迭代次数200次,耗时1h25min,错误分类率为0.3957,相比与simple_cnn的0.5248提高了12.91%。当然,这个结果也并不是很好,如果输出详细的日志,可以看到在训练的过程中loss先降后升,说明有一定程度的过拟合,对于如何防止过拟合,我们在后面会详细讲解。

 

  有一个可视化CNN的网站可以对mnist和cifar10分类的网络结构进行可视化,这是cifar-10 BaseCNN的网络结构:


 LeNet-5的Tensorflow实现

   tensorflow版本的LeNet-5版本的可以参照models/tutorials/image/cifar10/(https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10)的步骤来训练,不过这里面的代码包含了很多数据处理、权重衰减以及正则化的一些方法防止过拟合。按照官方写的,batch_size=128时在Tesla K40上迭代10w次需要4小时,准确率能达到86%。不过如果不对数据做处理,直接跑的话,效果应该没有这么好。不过可以仔细借鉴cifar10_inputs.py里的distorted_inouts函数对数据预处理增大数据集的思想,以及cifar10.py里对于权重和偏置的衰减设置等。目前迭代到1w次左右,cost是0.98,acc是78.4%

  对于未进行数据处理的cifar10我准备也跑一次,看看效果如何,与paddle的结果对比一下。不过得等到周末再补上了 = =

 

 


总结

  本节用常规的cifar-10数据集做图像分类,用了三种实现方式,第一种是自己设计的一个简单的cnn,第二种是LeNet-5,第三种是Tensorflow实现的LeNet-5,对比速度可以见一下表格:

 

   可以看到LeNet-5相比于原始的simple_cnn在准确率和速度方面都有一定的的提升,等tensorflow版本跑完后可以把结果加上去再对比一下。不过用Lenet-5网络结构后,结果虽然有一定的提升,但是还是不够理想,在日志里看到loss的信息基本可以推断出是过拟合,对于神经网络训练过程中出现的过拟合情况我们应该如何避免,下期我们讲着重讲解。此外在下一节将介绍AlexNet,并对分类做一个实验,对比其效果。

 

 

  

参考文献

1.LeNet-5论文:《Gradient-based learning applied to document recognition

2.可视化CNN:http://shixialiu.com/publications/cnnvis/demo/

 

 

作者:Charlotte77

出处:http://www.cnblogs.com/charlotte77/

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posted @ 2017-11-29 17:18  Charlotte77  阅读(20383)  评论(20编辑  收藏  举报