Tensorflow加载Vgg预训练模型操作

1、Tensorflow加载Vgg预训练模型操作很多深度神经网络模型需要加载预训练过的Vgg参数，比如说：风格迁移、目标检测、图像标注等计算机视觉中常见的任务。那么到底如何加载Vgg模型呢？Vgg文件的参数到底有何意义呢？加载后的模型该如何使用呢？本文将以Vgg为例子，详细说明Tensorflov如何加载Vgg预训练模型。实验环境TnowTensorflow展示Vgg构造orensorflowsfornsnorsooelg指下载下来的Vgg预训练模型的文件地址读取Vgg文件solo打印Vgg的数据类型及其组成rnee代码输出结果如下：elsseseyseaderersionglolslerseco

2、nreluconrelupoolconreluconrelupoolconreluprint("data.keys:",data.keys()#得到对应卷积核的矩阵wegs定义Vgg的组成lersa'layers'on'relu1'conv1_2','relu1_2','pool'conv2_1','relu2'conv2_2','relu2_2','pool'conv3_1','relu3'conv3_2'

3、,'relu3_2','con'relu3_3','conv3'relu3_4','pool3','conv4_1','relu4'conv4_2','relu4_2','con'relu4_3','conv4'relu4_4','pool4','conv5_1','relu5_3',''relu5onv5'conv5_2''

4、relu5_4','relu5_2','con打印Vgg不同卷积层所对应的维度fornameinenumerate(layers):nd=nameifind=conv':rint("%s:%s"%(name,weightsi00200.shapeelifkind'relu':rint(name)elifkind'pool':rint(name)conv3_2:(3,3,256,256)relu3_2conv3_3:(3,3,256,256)relu3_3conv3_4:(3,3,256,256)relu

5、3_4pool3conv4_1:(3,3,256,512)relu4_1conv4_2:(3,3,512,512)relu4_2conv4_3:(3,3,512,512)relu4_3conv4_4:(3,3,512,512)relu4_4pool4conv5_1:(3,3,512,512)relu5_1conv5_2:(3,3,512,512)relu5_2conv5_3:(3,3,512,512)relu5_3conv5_4:(3,3,512,512)relu5_4Table2:Numbero(fparameter(innallijna).AA-LRNE那么Vgg19真实的网络结构是怎么样

6、子的呢，如下图所示:Table1:OonvNetconfigurations(此ownincoEumns).Thedepthof止已configurationsfromtheleft(A)loth&right(E),asmorekyersareadded(lheaddedJayenaieshowninbold).Theconvolutionalhjerparametersaredenoted雷"conv(receptivefieldsize)-；jiumberofchtn船l(Q二HeReLUac曲亂ionfuixtimi§not曲o训nfarbr列ity,Conv

7、NetAA-LRNBCDE1weigh:layers11weightlayers13weightlayers】6wcighllayers16weightlayers19wcighllayersicipu:(224x224RGBimagf：）conv3-64COUV3-&4LICNcoav3-64ct>iiv3-Mconv3-54coiwS*&4conv3-64cxmv3-64conv3-64ccmvS-M:naxpoolcmvJ12Scon3-12Scanv3428ronvJ-128corav3-128corav3-12Bcony3428convJ-liiicanv3-1

8、28canv3-128rnajpooiconv3-256conv3-25bcan3-25<5convj-256conv3-256convl-256oom3-z5S003256convi-256conv3'25tconv3-23<cx)m3-256cfjnv3-25&conv3-256cosi3-256cm>3-2£6jiarpoolconv3-5I2conv35I2conv3-512canv3-512Mm3-512conv3512cam3-212ooriv3d12a>nvl-5i2conv3-512conv3-52ccnv3-512canv3-

9、512canv3-512conv3'512cchi*3-S11laxpoolcoitv3-5l2canvJ-512canv3-312amv3-512sonv3-512oom3412ooai3-512convl-512conv3-5：2conv3-5L2ccnvSllcoiiv3-512conv3-512canv3-512CMV3-512xpc»lFC4096FC096FV1000scfl-nux5cvori£一十s_在本文，主要讨论卷积模块，華家通过对比可1以发现，我们打印出来的Vgg19结构一致。网g押结梅區构建Vgg19模型def_conv_layer(inpu

10、t,weights,bias):conv=tf.nn.conv2d(input,tf.constant(weights),strides=(1,1,1,1),padding='SAME')returntf.nn.bias_add(conv,bias)def_pool_layer(input):returntf.nn.max_pool(input,ksize=(1,2,2,1),strides=(1,2,2,1),padding='SAME')classVGG19:layers=('conv1_1','relu1_1','c

11、onv1_2','relu1_2','pool1','conv2_1','relu2_1','conv2_2','relu2_2','pool2','conv3_1','relu3_1','conv3_2','relu3_2','conv3_3','relu3_3','conv3_4','relu3_4','pool3','c

12、onv4_1','relu4_1','conv4_2','relu4_2','conv4_3','relu4_3','conv4_4','relu4_4','pool4','conv5_1','relu5_1','conv5_2','relu5_2','conv5_3','relu5_3','conv5_4','relu5_4')de

13、f_init_(self,data_path):data=scipy.io.loadmat(data_path)self.weights=data'layers'0deffeed_forward(self,input_image,scope=None):#定义net用来保存模型每一步输出的特征图net=current=input_imagewithtf.variable_scope(scope):fori,nameinenumerate(self.layers):kind=name:4ifkind='conv':kernels=self.weightsi0020

14、0bias=self.weightsi00201kernels=np.transpose(kernels,(1,0,2,3)bias=bias.reshape(-1)current=_conv_layer(current,kernels,bias)elifkind='relu':current=tf.nn.relu(current)elifkind='pool':current=_pool_layer(current)#在每一步都保存当前输出的特征图netname=currentreturnnet在上面的代码中，我们定义了一个Vgg19的类别专门用来加载Vgg1

15、9模型，并且将每一层卷积得到的特征图保存到net中，最后返回这个net，用于代码后续的处理。测试Vgg19模型在给出Vgg19的构造模型后，我们下一步就是如何用它，我们的思路如下：加载本地图片定义Vgg19模型，传入本地图片得到返回每一层的特征图image_path="data/test.jpg"#本地的测试图片image_raw=tf.gfile.GFile(image_path,'rb').read()#一定要tf.float()，否则会报错image_decoded=tf.to_float(tf.image.decode_jpeg(image_raw)

16、#扩展图片的维度，从三维变成四维，符合Vgg19的输入接口image_expand_dim=tf.expand_dims(image_decoded,0)#定义Vgg19模型vgg19=VGG19(data_path)net=vgg19.feed_forward(image_expand_dim,'vgg19')print(net)代码结果如下所示：'conv1_1':<tf.Tensor'vgg19_1/BiasAdd:0'shape=(1,?,?,64)dtype=float32>,'relu1_1':<tf

17、.Tensor'vgg19_1/Relu:0'shape=(1,?,?,64)dtype=float32>,'conv1_2':<tf.Tensor'vgg19_1/BiasAdd_1:0'shape=(1,?,?,64)dtype=float32>,'relu1_2':<tf.Tensor'vgg19_1/Relu_1:0'shape=(1,?,?,64)dtype=float32>,'pool1':<tf.Tensor'vgg19_1/MaxPool:

18、0'shape=(1,?,?,64)dtype=float32>,'conv2_1':<tf.Tensor'vgg19_1/BiasAdd_2:0'shape=(1,?,?,128)dtype=float32>,'relu2_1':<tf.Tensor'vgg19_1/Relu_2:0'shape=(1,?,?,128)dtype=float32>,'conv2_2':<tf.Tensor'vgg19_1/BiasAdd_3:0'shape=(1,?,?,

19、128)dtype=float32>,'relu2_2':<tf.Tensor'vgg19_1/Relu_3:0'shape=(1,?,?,128)dtype=float32>,'pool2':<tf.Tensor'vgg19_1/MaxPool_1:0'shape=(1,?,?,128)dtype=float32>,'conv3_1':<tf.Tensor'vgg19_1/BiasAdd_4:0'shape=(1,?,?,256)dtype=float32>

20、;,'relu3_1':<tf.Tensor'vgg19_1/Relu_4:0'shape=(1,?,?,256)dtype=float32>,'conv3_2':<tf.Tensor'vgg19_1/BiasAdd_5:0'shape=(1,?,?,256)dtype=float32>,'relu3_2':<tf.Tensor'vgg19_1/Relu_5:0'shape=(1,?,?,256)dtype=float32>,'conv3_3':&

21、lt;tf.Tensor'vgg19_1/BiasAdd_6:0'shape=(1,?,?,256)dtype=float32>,'relu3_3':<tf.Tensor'vgg19_1/Relu_6:0'shape=(1,?,?,256)dtype=float32>,'conv3_4':<tf.Tensor'vgg19_1/BiasAdd_7:0'shape=(1,?,?,256)dtype=float32>,'relu3_4':<tf.Tensor'v

22、gg19_1/Relu_7:0'shape=(1,?,?,256)dtype=float32>,'pool3':<tf.Tensor'vgg19_1/MaxPool_2:0'shape=(1,?,?,256)dtype=float32>,'conv4_1':<tf.Tensor'vgg19_1/BiasAdd_8:0'shape=(1,?,?,512)dtype=float32>,'relu4_1':<tf.Tensor'vgg19_1/Relu_8:0'

23、shape=(1,?,?,512)dtype=float32>,'conv4_2':<tf.Tensor'vgg19_1/BiasAdd_9:0'shape=(1,?,?,512)dtype=float32>,'relu4_2':<tf.Tensor'vgg19_1/Relu_9:0'shape=(1,?,?,512)dtype=float32>,'conv4_3':<tf.Tensor'vgg19_1/BiasAdd_10:0'shape=(1,?,?,512)

24、dtype=float32>,'relu4_3':<tf.Tensor'vgg19_1/Relu_10:0'shape=(1,?,?,512)dtype=float32>,'conv4_4':<tf.Tensor'vgg19_1/BiasAdd_11:0'shape=(1,?,?,512)dtype=float32>,'relu4_4':<tf.Tensor'vgg19_1/Relu_11:0'shape=(1,?,?,512)dtype=float32>,'pool4':<tf.Tensor'vgg19_1/MaxPool_3:0'shape=(1,?,?,512)dtype=float32>,'conv5_1':<tf.Tensor'vgg19_1/BiasAdd_12:0'shape=(1,?,?,512)dtype=float32>,'relu5_1':&