使用tensorflow实现AlexNet

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我是靠谱客的博主辛勤热狗，最近开发中收集的这篇文章主要介绍使用tensorflow实现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是2012年ImageNet比赛的冠军，虽然过去了很长时间，但是作为深度学习中的经典模型，AlexNet不但有助于我们理解其中所使用的很多技巧，而且非常有助于提升我们使用深度学习工具箱的熟练度。尤其是我刚入门深度学习，迫切需要一个能让自己熟悉tensorflow的小练习，于是就有了这个小玩意儿......

先放上我的代码：https://github.com/hjptriplebee/AlexNet_with_tensorflow

如果想运行代码，详细的配置要求都在上面链接的readme文件中了。本文建立在一定的tensorflow基础上，不会对太细的点进行说明。

模型结构

关于模型结构网上的文献很多，我这里不赘述，一会儿都在代码里解释。

有一点需要注意，AlexNet将网络分成了上下两个部分，在论文中两部分结构完全相同，唯一不同的是他们放在不同GPU上训练，因为每一层的feature map之间都是独立的（除了全连接层），所以这相当于是提升训练速度的一种方法。很多AlexNet的复现都将上下两部分合并了，因为他们都是在单个GPU上运行的。虽然我也是在单个GPU上运行，但是我还是很想将最原始的网络结构还原出来，所以我的代码里也是分开的。

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 LRN(x, R, alpha, beta, name = None, bias = 1.0): 
  """LRN""" 
  return tf.nn.local_response_normalization(x, depth_radius = R, alpha = alpha, 
                       beta = beta, bias = bias, name = 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", groups = 1):#group为2时等于AlexNet中分上下两部分 
  """convlutional""" 
  channel = int(x.get_shape()[-1])#获取channel 
  conv = lambda a, b: tf.nn.conv2d(a, b, strides = [1, strideY, strideX, 1], padding = padding)#定义卷积的匿名函数 
  with tf.variable_scope(name) as scope: 
    w = tf.get_variable("w", shape = [kHeight, kWidth, channel/groups, featureNum]) 
    b = tf.get_variable("b", shape = [featureNum]) 
 
    xNew = tf.split(value = x, num_or_size_splits = groups, axis = 3)#划分后的输入和权重 
    wNew = tf.split(value = w, num_or_size_splits = groups, axis = 3) 
 
    featureMap = [conv(t1, t2) for t1, t2 in zip(xNew, wNew)] #分别提取feature map 
    mergeFeatureMap = tf.concat(axis = 3, values = featureMap) #feature map整合 
    # print mergeFeatureMap.shape 
    out = tf.nn.bias_add(mergeFeatureMap, b) 
    return tf.nn.relu(tf.reshape(out, mergeFeatureMap.get_shape().as_list()), name = scope.name) #relu后的结果

定义了卷积、pooling、LRN、dropout、全连接五个模块，其中卷积模块因为将网络的上下两部分分开了，所以比较复杂。接下来定义AlexNet。

class alexNet(object): 
  """alexNet model""" 
  def __init__(self, x, keepPro, classNum, skip, modelPath = "bvlc_alexnet.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 = convLayer(self.X, 11, 11, 4, 4, 96, "conv1", "VALID") 
    pool1 = maxPoolLayer(conv1, 3, 3, 2, 2, "pool1", "VALID") 
    lrn1 = LRN(pool1, 2, 2e-05, 0.75, "norm1") 
 
    conv2 = convLayer(lrn1, 5, 5, 1, 1, 256, "conv2", groups = 2) 
    pool2 = maxPoolLayer(conv2, 3, 3, 2, 2, "pool2", "VALID") 
    lrn2 = LRN(pool2, 2, 2e-05, 0.75, "lrn2") 
 
    conv3 = convLayer(lrn2, 3, 3, 1, 1, 384, "conv3") 
 
    conv4 = convLayer(conv3, 3, 3, 1, 1, 384, "conv4", groups = 2) 
 
    conv5 = convLayer(conv4, 3, 3, 1, 1, 256, "conv5", groups = 2) 
    pool5 = maxPoolLayer(conv5, 3, 3, 2, 2, "pool5", "VALID") 
 
    fcIn = tf.reshape(pool5, [-1, 256 * 6 * 6]) 
    fc1 = fcLayer(fcIn, 256 * 6 * 6, 4096, True, "fc6") 
    dropout1 = dropout(fc1, self.KEEPPRO) 
 
    fc2 = fcLayer(dropout1, 4096, 4096, True, "fc7") 
    dropout2 = dropout(fc2, self.KEEPPRO) 
 
    self.fc3 = 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alexnet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，下面开始测试模型。

模型测试

ImageNet训练的AlexNet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渣土车图片：

然后编写测试代码：

#some params 
dropoutPro = 1 
classNum = 1000 
skip = [] 
#get testImage 
testPath = "testModel" 
testImg = [] 
for f in os.listdir(testPath): 
  testImg.append(cv2.imread(testPath + "/" + f)) 
 
imgMean = np.array([104, 117, 124], np.float) 
x = tf.placeholder("float", [1, 227, 227, 3]) 
 
model = alexnet.alexNet(x, dropoutPro, classNum, skip) 
score = model.fc3 
softmax = tf.nn.softmax(score) 
 
with tf.Session() as sess: 
  sess.run(tf.global_variables_initializer()) 
  model.loadModel(sess) #加载模型 
 
  for i, img in enumerate(testImg): 
    #img preprocess 
    test = cv2.resize(img.astype(np.float), (227, 227)) #resize成网络输入大小 
    test -= imgMean #去均值 
    test = test.reshape((1, 227, 227, 3)) #拉成tensor 
    maxx = np.argmax(sess.run(softmax, feed_dict = {x: test})) 
    res = caffe_classes.class_names[maxx] #取概率最大类的下标 
    #print(res) 
    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)#绘制类的名字 
    cv2.imshow("demo", img)  
    cv2.waitKey(5000) #显示5秒

如上代码所示，首先需要设置一些参数，然后读取指定路径下的测试图像，再对模型做一个初始化，最后是真正测试代码。测试结果如下：