【深度学习】使用tensorflow实现VGG19网络

谁借莪１个温暖的怀抱￠ 2024-04-20 11:35 93阅读 0赞

## 【深度学习】使用tensorflow实现VGG19网络 ##

本文章向大家介绍【深度学习】使用tensorflow实现VGG19网络，主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项，具有一定的参考价值，需要的朋友可以参考一下。

VGG网络与AlexNet类似，也是一种CNN，VGG在2014年的 ILSVRC localization and classification 两个问题上分别取得了第一名和第二名。VGG网络非常深，通常有16－19层，卷积核大小为 3 x 3，16和19层的区别主要在于后面三个卷积部分卷积层的数量。第二个用tensorflow独立完成的小玩意儿......

**模型结构**

可以看到VGG的前几层为卷积和maxpool的交替，每个卷积包含多个卷积层，后面紧跟三个全连接层。激活函数采用Relu，训练采用了dropout，但并没有像AlexNet一样采用LRN（论文给出的理由是加LRN实验效果不好）。

**模型定义**

def maxPoolLayer(x, kHeight, kWidth, strideX, strideY, name, padding = "SAME"):
        """max-pooling"""
        return tf.nn.max_pool(x, ksize = [1, kHeight, kWidth, 1],
                              strides = [1, strideX, strideY, 1], padding = padding, name = name)
    
    def dropout(x, keepPro, name = None):
        """dropout"""
        return tf.nn.dropout(x, keepPro, name)
    
    def fcLayer(x, inputD, outputD, reluFlag, name):
        """fully-connect"""
        with tf.variable_scope(name) as scope:
            w = tf.get_variable("w", shape = [inputD, outputD], dtype = "float")
            b = tf.get_variable("b", [outputD], dtype = "float")
            out = tf.nn.xw_plus_b(x, w, b, name = scope.name)
            if reluFlag:
                return tf.nn.relu(out)
            else:
                return out
    
    def convLayer(x, kHeight, kWidth, strideX, strideY,
                  featureNum, name, padding = "SAME"):
        """convlutional"""
        channel = int(x.get_shape()[-1]) #获取channel数
        with tf.variable_scope(name) as scope:
            w = tf.get_variable("w", shape = [kHeight, kWidth, channel, featureNum])
            b = tf.get_variable("b", shape = [featureNum])
            featureMap = tf.nn.conv2d(x, w, strides = [1, strideY, strideX, 1], padding = padding)
            out = tf.nn.bias_add(featureMap, b)
            return tf.nn.relu(tf.reshape(out, featureMap.get_shape().as_list()), name = scope.name)

定义了卷积、pooling、dropout、全连接五个模块，使用了上一篇AlexNet中的代码，其中卷积模块去除了group参数，因为网络没有像AlexNet一样分成两部分。接下来定义VGG19。

class VGG19(object):
        """VGG model"""
        def __init__(self, x, keepPro, classNum, skip, modelPath = "vgg19.npy"):
            self.X = x
            self.KEEPPRO = keepPro
            self.CLASSNUM = classNum
            self.SKIP = skip
            self.MODELPATH = modelPath
            #build CNN
            self.buildCNN()
    
        def buildCNN(self):
            """build model"""
            conv1_1 = convLayer(self.X, 3, 3, 1, 1, 64, "conv1_1" )
            conv1_2 = convLayer(conv1_1, 3, 3, 1, 1, 64, "conv1_2")
            pool1 = maxPoolLayer(conv1_2, 2, 2, 2, 2, "pool1")
    
            conv2_1 = convLayer(pool1, 3, 3, 1, 1, 128, "conv2_1")
            conv2_2 = convLayer(conv2_1, 3, 3, 1, 1, 128, "conv2_2")
            pool2 = maxPoolLayer(conv2_2, 2, 2, 2, 2, "pool2")
    
            conv3_1 = convLayer(pool2, 3, 3, 1, 1, 256, "conv3_1")
            conv3_2 = convLayer(conv3_1, 3, 3, 1, 1, 256, "conv3_2")
            conv3_3 = convLayer(conv3_2, 3, 3, 1, 1, 256, "conv3_3")
            conv3_4 = convLayer(conv3_3, 3, 3, 1, 1, 256, "conv3_4")
            pool3 = maxPoolLayer(conv3_4, 2, 2, 2, 2, "pool3")
    
            conv4_1 = convLayer(pool3, 3, 3, 1, 1, 512, "conv4_1")
            conv4_2 = convLayer(conv4_1, 3, 3, 1, 1, 512, "conv4_2")
            conv4_3 = convLayer(conv4_2, 3, 3, 1, 1, 512, "conv4_3")
            conv4_4 = convLayer(conv4_3, 3, 3, 1, 1, 512, "conv4_4")
            pool4 = maxPoolLayer(conv4_4, 2, 2, 2, 2, "pool4")
    
            conv5_1 = convLayer(pool4, 3, 3, 1, 1, 512, "conv5_1")
            conv5_2 = convLayer(conv5_1, 3, 3, 1, 1, 512, "conv5_2")
            conv5_3 = convLayer(conv5_2, 3, 3, 1, 1, 512, "conv5_3")
            conv5_4 = convLayer(conv5_3, 3, 3, 1, 1, 512, "conv5_4")
            pool5 = maxPoolLayer(conv5_4, 2, 2, 2, 2, "pool5")
    
            fcIn = tf.reshape(pool5, [-1, 7*7*512])
            fc6 = fcLayer(fcIn, 7*7*512, 4096, True, "fc6")
            dropout1 = dropout(fc6, self.KEEPPRO)
    
            fc7 = fcLayer(dropout1, 4096, 4096, True, "fc7")
            dropout2 = dropout(fc7, self.KEEPPRO)
    
            self.fc8 = fcLayer(dropout2, 4096, self.CLASSNUM, True, "fc8")
    
        def loadModel(self, sess):
            """load model"""
            wDict = np.load(self.MODELPATH, encoding = "bytes").item()
            #for layers in model
            for name in wDict:
                if name not in self.SKIP:
                    with tf.variable_scope(name, reuse = True):
                        for p in wDict[name]:
                            if len(p.shape) == 1:
                                #bias 只有一维
                                sess.run(tf.get_variable('b', trainable = False).assign(p))
                            else:
                                #weights
                                sess.run(tf.get_variable('w', trainable = False).assign(p))

buildCNN函数完全按照VGG的结构搭建网络。

loadModel函数从模型文件中读取参数，采用的模型文件见github上的readme说明。 至此，我们定义了完整的模型，下面开始[测试][Link 1]模型。

**模型测试**

ImageNet训练的VGG有很多类，几乎包含所有常见的物体，因此我们随便从网上找几张图片测试。比如我直接用了之前做项目的图片，为了避免审美疲劳，我们不只用渣土车，还要用挖掘机、采沙船：

然后编写测试代码：

parser = argparse.ArgumentParser(description='Classify some images.')
    parser.add_argument('mode', choices=['folder', 'url'], default='folder')
    parser.add_argument('path', help='Specify a path [e.g. testModel]')
    args = parser.parse_args(sys.argv[1:])
    
    if args.mode == 'folder': #测试方式为本地文件夹
        #get testImage
        withPath = lambda f: '{}/{}'.format(args.path,f)
        testImg = dict((f,cv2.imread(withPath(f))) for f in os.listdir(args.path) if os.path.isfile(withPath(f)))
    elif args.mode == 'url': #测试方式为URL
        def url2img(url): #获取URL图像
            '''url to image'''
            resp = urllib.request.urlopen(url)
            image = np.asarray(bytearray(resp.read()), dtype="uint8")
            image = cv2.imdecode(image, cv2.IMREAD_COLOR)
            return image
        testImg = {args.path:url2img(args.path)}
    
    if testImg.values():
        #some params
        dropoutPro = 1
        classNum = 1000
        skip = []
    
        imgMean = np.array([104, 117, 124], np.float)
        x = tf.placeholder("float", [1, 224, 224, 3])
    
        model = vgg19.VGG19(x, dropoutPro, classNum, skip)
        score = model.fc8
        softmax = tf.nn.softmax(score)
    
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            model.loadModel(sess) #加载模型
    
            for key,img in testImg.items():
                #img preprocess
                resized = cv2.resize(img.astype(np.float), (224, 224)) - imgMean #去均值
                maxx = np.argmax(sess.run(softmax, feed_dict = {x: resized.reshape((1, 224, 224, 3))})) #网络输入为224*224
                res = caffe_classes.class_names[maxx]
    
                font = cv2.FONT_HERSHEY_SIMPLEX
                cv2.putText(img, res, (int(img.shape[0]/3), int(img.shape[1]/3)), font, 1, (0, 255, 0), 2) #在图像上绘制结果
                print("{}: {}n----".format(key,res)) #输出测试结果
                cv2.imshow("demo", img)
                cv2.waitKey(0)

如果你看完了我AlexNet的博客，那么一定会发现我这里的测试代码做了一些小的修改，增加了URL测试的功能，可以测试网上的图像 ，测试结果如下：

[Link 1]: http://lib.csdn.net/base/softwaretest