Tensorflow学习笔记：CNN篇（10）——Finetuning，猫狗大战，VGGNet的重新针对训练

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前序

— 在前面的例子中，对使用已在ImageNet上训练好的VGGNet模型进行图像预测已经获得了成功，但是对于使用Tensorflow进行图片预测的人员来说，不是泛化的使用VGGNet在本身模型参数所带的1000个类别中判断所属或者近似的类别，而是对其更进一步的需求专精一项分类，这是一项非常重要的工作，需要对模型进行重新的Finetuning复用

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Kaggle 猫狗大战

猫狗大战的数据集来源于Kaggle上的一个竞赛：Dogs vs. Cats

猫狗大战的数据集下载地址http://www.kaggle.com/c/dogs-vs-cats，其中数据集有12500只猫和12500只狗

现在进入本文的重点内容，使用Finetuning对VGGNet进行调整，从而针对猫狗大战的训练集进行训练，创建工程文件，所有素材如下：

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代码示例

Step 1: 对模型的修改

首先是对模型的修改（VGG16_model.py文件），在这里原先的输出结果是对1000个不同的类别进行判定，而在此是对2个图像，也就是猫和狗的判断，因此首先第一步就是修改输出层的全连接数据。

    def fc_layers(self):

        self.fc6 = self.fc("fc1", self.pool5, 4096, trainable=False) #语句变动
        self.fc7 = self.fc("fc2", self.fc6, 4096, trainable=False) #语句变动
        self.fc8 = self.fc("fc3", self.fc7, 2)

这里是最后一层的输出通道被设置成2，而对于其他部分，定义创建卷积层和全连接层的方法则无需做出太大改动。

        def conv(self,name, input_data, out_channel):
        in_channel = input_data.get_shape()[-1]
        with tf.variable_scope(name):
            kernel = tf.get_variable("weights", [3, 3, in_channel, out_channel], dtype=tf.float32, trainable=False) #语句变动
            biases = tf.get_variable("biases", [out_channel], dtype=tf.float32, trainable=False) #语句变动
            conv_res = tf.nn.conv2d(input_data, kernel, [1, 1, 1, 1], padding="SAME")
            res = tf.nn.bias_add(conv_res, biases)
            out = tf.nn.relu(res, name=name)
        self.parameters += [kernel, biases]
        return out

    def fc(self, name, input_data, out_channel, trainable=True):
        shape = input_data.get_shape().as_list()
        if len(shape) == 4:
            size = shape[-1] * shape[-2] * shape[-3]
        else:size = shape[1]
        input_data_flat = tf.reshape(input_data,[-1,size])
        with tf.variable_scope(name):
            weights = tf.get_variable(name="weights",shape=[size,out_channel],dtype=tf.float32,trainable=trainable) #语句变动
            biases = tf.get_variable(name="biases",shape=[out_channel],dtype=tf.float32, trainable=trainable) #语句变动
            res = tf.matmul(input_data_flat,weights)
            out = tf.nn.relu(tf.nn.bias_add(res,biases))
        self.parameters += [weights, biases]
        return out

在这里读者可能已经注意到，在介绍全连接层的修改时，就有一个额外的输入参数：

trainable=False

而在卷积层和全连接层的定义中，也添加了这个参数：

def fc(self, name, input_data, out_channel, trainable=True):

直接的解释就是，在进行Finetuning对模型重新训练时，对于部分不需要训练的层可以通过设置trainable=False来确保其在训练过程中不会被修改权值。

下面还有一个非常重要的函数是VGGNet权重的载入，前文已经有所介绍，具体如下：

        def load_weights(self, weight_file, sess):
        weights = np.load(weight_file)
        keys = sorted(weights.keys())
        for i, k in enumerate(keys):
            if i not in [30,31]:
                sess.run(self.parameters[i].assign(weights[k]))
        print("-----------all done---------------")

可以看到，这里使用了一个if函数对序号进行剔除，即对于最后一层的权重不要载入。
完整代码：VGG16_model.py文件

import numpy as np
import tensorflow as tf
import global_variable


class vgg16:
    def __init__(self, imgs):
        self.parameters = []
        self.imgs = imgs
        self.convlayers()
        self.fc_layers()

        self.probs = self.fc8

    def saver(self):
        return tf.train.Saver()

    def maxpool(self,name,input_data, trainable):
        out = tf.nn.max_pool(input_data,[1,2,2,1],[1,2,2,1],padding="SAME",name=name)
        return out

    def conv(self,name, input_data, out_channel, trainable):
        in_channel = input_data.get_shape()[-1]
        with tf.variable_scope(name):
            kernel = tf.get_variable("weights", [3, 3, in_channel, out_channel], dtype=tf.float32,trainable=False)
            biases = tf.get_variable("biases", [out_channel], dtype=tf.float32,trainable=False)
            conv_res = tf.nn.conv2d(input_data, kernel, [1, 1, 1, 1], padding="SAME")
            res = tf.nn.bias_add(conv_res, biases)
            out = tf.nn.relu(res, name=name)
        self.parameters += [kernel, biases]
        return out

    def fc(self,name,input_data,out_channel,trainable = True):
        shape = input_data.get_shape().as_list()
        if len(shape) == 4:
            size = shape[-1] * shape[-2] * shape[-3]
        else:size = shape[1]
        input_data_flat = tf.reshape(input_data,[-1,size])
        with tf.variable_scope(name):
            weights = tf.get_variable(name="weights",shape=[size,out_channel],dtype=tf.float32,trainable = trainable)
            biases = tf.get_variable(name="biases",shape=[out_channel],dtype=tf.float32,trainable = trainable)
            res = tf.matmul(input_data_flat,weights)
            out = tf.nn.relu(tf.nn.bias_add(res,biases))
        self.parameters += [weights, biases]
        return out

    def convlayers(self):
        # zero-mean input
        #conv1
        self.conv1_1 = self.conv("conv1re_1",self.imgs,64,trainable=False)
        self.conv1_2 = self.conv("conv1_2",self.conv1_1,64,trainable=False)
        self.pool1 = self.maxpool("poolre1",self.conv1_2,trainable=False)

        #conv2
        self.conv2_1 = self.conv("conv2_1",self.pool1,128,trainable=False)
        self.conv2_2 = self.conv("convwe2_2",self.conv2_1,128,trainable=False)
        self.pool2 = self.maxpool("pool2",self.conv2_2,trainable=False)

        #conv3
        self.conv3_1 = self.conv("conv3_1",self.pool2,256,trainable=False)
        self.conv3_2 = self.conv("convrwe3_2",self.conv3_1,256,trainable=False)
        self.conv3_3 = self.conv("convrew3_3",self.conv3_2,256,trainable=False)
        self.pool3 = self.maxpool("poolre3",self.conv3_3,trainable=False)

        #conv4
        self.conv4_1 = self.conv("conv4_1",self.pool3,512,trainable=False)
        self.conv4_2 = self.conv("convrwe4_2",self.conv4_1,512,trainable=False)
        self.conv4_3 = self.conv("conv4rwe_3",self.conv4_2,512,trainable=False)
        self.pool4 = self.maxpool("pool4",self.conv4_3,trainable=False)


        #conv5
        self.conv5_1 = self.conv("conv5_1",self.pool4,512,trainable=False)
        self.conv5_2 = self.conv("convrwe5_2",self.conv5_1,512,trainable=False)
        self.conv5_3 = self.conv("conv5_3",self.conv5_2,512,trainable=False)
        self.pool5 = self.maxpool("poorwel5",self.conv5_3,trainable=False)

    def fc_layers(self):

        self.fc6 = self.fc("fc6", self.pool5, 4096,trainable=False)
        self.fc7 = self.fc("fc7", self.fc6, 4096,trainable=False)
        self.fc8 = self.fc("fc8", self.fc7, 2)

    def load_weights(self, weight_file, sess):
        weights = np.load(weight_file)
        keys = sorted(weights.keys())
        for i, k in enumerate(keys):
            if i not in [30,31]:
                sess.run(self.parameters[i].assign(weights[k]))
        print("-----------all done---------------")

可以看到，对于每个卷积层和全连接层中，不需要训练的权重全部被设置为trainable=False。

Step 2: 数据的输入

对于修改后的模型，需要对其进行重新训练，而首要条件就是数据输入，在这里笔者使用数据的输入流方式。代码如下：

def get_file(file_dir):
    images = []
    temp = []
    for root, sub_folders, files in os.walk(file_dir):
        for name in files:
            images.append(os.path.join(root, name))
        for name in sub_folders:
            temp.append(os.path.join(root, name))
    labels = []
    for one_folder in temp:
        n_img = len(os.listdir(one_folder))
        letter = one_folder.split('/')[-1]
        if letter == 'cat':
            labels = np.append(labels, n_img * [0])
        else:
            labels = np.append(labels, n_img * [1])
    # shuffle
    temp = np.array([images, labels])
    temp = temp.transpose()
    np.random.shuffle(temp)
    image_list = list(temp[:, 0])
    label_list = list(temp[:, 1])
    label_list = [int(float(i)) for i in label_list]

    return image_list, label_list

这里定义的get_file函数对输入文件的文件夹进行分类，通过以不同的文件夹作为分类标准将图片分为2类，使用2个列表文件分别用来存储图片地址和对应的标记地址，同时我们需要按照程序的要求，将train文件夹中的图片，分成cat和dog 文件夹，如图所示：

def get_batch(image_list, label_list, img_width, img_height, batch_size, capacity):

    image = tf.cast(image_list, tf.string)
    label = tf.cast(label_list, tf.int32)

    input_queue = tf.train.slice_input_producer([image,label])

    label = input_queue[1]
    image_contents = tf.read_file(input_queue[0])
    image = tf.image.decode_jpeg(image_contents,channels=3)

    image = tf.image.resize_image_with_crop_or_pad(image,img_width,img_height)
    image = tf.image.per_image_standardization(image) # 将图片标准化
    image_batch,label_batch = tf.train.batch([image,label],batch_size=batch_size,num_threads=64,capacity=capacity)
    label_batch = tf.reshape(label_batch,[batch_size])

    return image_batch,label_batch

get_batch函数是通过对列表地址的读取而循环载入具有参数batch_size大小而定的图片，并读取相应的图片标签作为数据标签一同进行训练，完整定义如下：
完整代码 create_and_read_TFRecord2.py文件

import tensorflow as tf
import numpy as np
import os
img_width = 224
img_height = 224


def get_file(file_dir):
    images = []
    temp = []
    for root, sub_folders, files in os.walk(file_dir):
        for name in files:
            images.append(os.path.join(root, name))
        for name in sub_folders:
            temp.append(os.path.join(root, name))
    labels = []
    for one_folder in temp:
        n_img = len(os.listdir(one_folder))
        letter = one_folder.split('/')[-1]
        if letter == 'cat':
            labels = np.append(labels, n_img * [0])
        else:
            labels = np.append(labels, n_img * [1])
    # shuffle
    temp = np.array([images, labels])
    temp = temp.transpose()
    np.random.shuffle(temp)
    image_list = list(temp[:, 0])
    label_list = list(temp[:, 1])
    label_list = [int(float(i)) for i in label_list]

    return image_list, label_list


def get_batch(image_list, label_list, img_width, img_height, batch_size, capacity):

    image = tf.cast(image_list, tf.string)
    label = tf.cast(label_list, tf.int32)

    input_queue = tf.train.slice_input_producer([image,label])

    label = input_queue[1]
    image_contents = tf.read_file(input_queue[0])
    image = tf.image.decode_jpeg(image_contents,channels=3)

    image = tf.image.resize_image_with_crop_or_pad(image,img_width,img_height)
    image = tf.image.per_image_standardization(image) # 将图片标准化
    image_batch,label_batch = tf.train.batch([image,label],batch_size=batch_size,num_threads=64,capacity=capacity)
    label_batch = tf.reshape(label_batch,[batch_size])

    return image_batch,label_batch


def onehot(labels):
    n_sample = len(labels)
    n_class = max(labels) + 1
    onehot_labels = np.zeros((n_sample, n_class))
    onehot_labels[np.arange(n_sample), labels] = 1
    return onehot_labels

Step 3: 模型的重新训练与存储

Finetuning最重要的一个步骤就是模型的重新训练与存储。
首先对于模型的值的输出，在类中已经做了定义，因此只需要将定义的模型类初始化后输出赋予一个特定的变量即可。

vgg = model.vgg16(x_imgs)
    fc3_cat_and_dog = vgg.probs
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=fc3_cat_and_dog, labels=y_imgs))
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)

这里同时定义了损失函数已经最小化方法，完整代码如下：
完整代码 model_train.py文件

import numpy as np
import tensorflow as tf
import VGG16_model as model
import create_and_read_TFRecord2 as reader2

if __name__ == '__main__':

    X_train, y_train = reader2.get_file("./train/")
    image_batch, label_batch = reader2.get_batch(X_train, y_train, 224, 224, 25, 256)

    x_imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])
    y_imgs = tf.placeholder(tf.int32, [None, 2])

    vgg = model.vgg16(x_imgs)
    fc3_cat_and_dog = vgg.probs
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=fc3_cat_and_dog, labels=y_imgs))
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)

    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    vgg.load_weights('./vgg16_weights.npz', sess)
    saver = vgg.saver()

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord, sess=sess)

    import time
    start_time = time.time()

    for i in range(200):

            image, label = sess.run([image_batch, label_batch])
            labels = reader2.onehot(label)

            sess.run(optimizer, feed_dict={x_imgs: image, y_imgs: labels})
            loss_record = sess.run(loss, feed_dict={x_imgs: image, y_imgs: labels})
            print("now the loss is %f " % loss_record)
            end_time = time.time()
            print('time: ', (end_time - start_time))
            start_time = end_time
            print("----------epoch %d is finished---------------" % i)

    saver.save(sess, "./model/")
    print("Optimization Finished!")

在训练函数中使用了Tensorflow的队列方式进行数据输入，而对于权重的重新载入也使用的是前面文章类似的方式，最终数据进行200次迭代，存储模型在model文件夹中。

Step 4: 模型的复用

对于模型的复用，代码如下：

import tensorflow as tf
from scipy.misc import imread, imresize
import VGG16_model as model

imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])
sess = tf.Session()
vgg = model.vgg16(imgs)
fc3_cat_and_dog = vgg.probs
saver = vgg.saver()
saver.restore(sess, './model/')

import os
for root, sub_folders, files in os.walk('./test/'):
    i = 0
    cat = 0
    dog = 0
    for name in files:
        i += 1
        filepath = os.path.join(root, name)

        try:
            img1 = imread(filepath, mode='RGB')
            img1 = imresize(img1, (224, 224))
        except:
            print("remove", filepath)

        prob = sess.run(fc3_cat_and_dog, feed_dict={vgg.imgs: [img1]})
        import numpy as np
        max_index = np.argmax(prob)
        if max_index == 0:
            cat += 1
        else:
            dog += 1
        if i % 50 == 0:
            acc = (cat * 1.)/(dog + cat)
            print(acc)
            print("-----------img number is %d------------" % i)