本文所使用的开源数据集（kaggle猫狗大战）：
www.kaggle.com/c/dogs-vs-c…
国内百度网盘下载地址：
pan.baidu.com/s/12ab32UNY…
利用本文代码训练并生成的模型（对应项目中的model文件夹）：
pan.baidu.com/s/1tBkVQKoH…
简单介绍：
（需要预先安装pip install opencv-python, pip install flask, pip install tensorflow/pip install tensorflow-gpu） 本文使用Python3，TensorFlow实现适合新手的VGG16模型（不了解VGG16的同学可以自行百度一下，本文没有使用slim或者keras实现，对VGG16逐层实现，便于新手理解，有经验的同学可以用高级库重写这部分）可应用于单标签分类（一张图片要么是猫，要么是狗）任务。
预告：之后会写一篇多标签分类任务，与单标签分类有些区别 juejin.im/post/5c073b…
整体训练逻辑：
0，使用pipeline方式异步读取训练集图片，节省内存消耗，提高效率
1，将图像传入到CNN（VGG16）中提取特征
2，将特征图拉伸输入到FC layer中得出分类预测向量
3，通过softmax交叉熵函数对预测向量和标签向量进行训练，得出最终模型
整体预测逻辑：
1，将图像传入到CNN（VGG16）中提取特征
2，将特征图拉伸输入到FC layer中得出分类预测向量
3，将预测向量做softmax操作，取向量中的最大值，并映射到对应类别中
制作成web服务：
利用flask框架将整个项目启动成web服务，使得项目支持http方式调用
启动服务后调用以下地址测试：
http://127.0.0.1:5050/dogOrCat?img_path=./data/test1/1.jpg
http://127.0.0.1:5050/dogOrCat?img_path=./data/test1/5.jpg
后续优化逻辑：
可以采用迁移学习，模型融合等方案进一步提高acc
可以左右翻转图片，将训练集翻倍
运行命令：
对数据集进行训练：python DogVsCat.py train
对新的图片进行测试：python DogVsCat.py test
启动成http服务：python DogVsCat.py start
项目整体目录结构：
训练过程：
整体代码如下：
# coding:utf-8import tensorflow as tf
import os, sys, random
import numpy as np
import cv2from flask import request
from flask import Flask
import json
app = Flask(__name__)class DogVsCat:def __init__(self):# 可调参数self.save_epoch = 1 # 每相隔多少个epoch保存一次模型self.train_max_num = 25000 # 训练时读取的最大图片数目 0~25000之间，内存不足的可以调小self.epoch_max = 13 # 最大迭代epoch次数self.batch_size = 16 # 训练时每个批次参与训练的图像数目，显存不足的可以调小self.class_num = 2 # 分类数目，猫狗共两类self.val_num = 20 * self.batch_size # 不能大于self.train_max_num 做验证集用self.lr = 1e-4 # 初始学习率# 无需修改参数self.x_val = []self.y_val = []self.x = None # 每批次的图像数据self.y = None # 每批次的one-hot标签self.learning_rate = None # 学习率self.sess = None # 持久化的tf.sessionself.pred = None # cnn网络结构的预测self.keep_drop = tf.placeholder(tf.float32) # dropout比例def dogOrCat(self, img_path):"""猫狗分类:param img_path::return:"""im = cv2.imread(img_path)im = cv2.resize(im, (224, 224))im = [im]im = np.array(im, dtype=np.float32)im -= 147output = self.sess.run(self.output, feed_dict={self.x: im, self.keep_drop: 1.})ret = output.tolist()[0]ret = 'It is a cat' if ret[0] <= ret[1] else 'It is a dog'return retdef test(self, img_path):"""测试接口:param img_path::return:"""self.x = tf.placeholder(tf.float32, [None, 224, 224, 3]) # 输入数据self.pred = self.CNN()self.output = tf.nn.softmax(self.pred)saver = tf.train.Saver()# tfconfig = tf.ConfigProto(allow_soft_placement=True)# tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.3 # 占用显存的比例# self.ses = tf.Session(config=tfconfig)self.sess = tf.Session()self.sess.run(tf.global_variables_initializer()) # 全局tf变量初始化# 加载w,b参数saver.restore(self.sess, './model/DogVsCat-13')im = cv2.imread(img_path)im = cv2.resize(im, (224, 224))im = [im]im = np.array(im, dtype=np.float32)im -= 147output = self.sess.run(self.output, feed_dict={self.x: im, self.keep_drop: 1.})ret = output.tolist()[0]ret = 'It is a cat' if ret[0] <= ret[1] else 'It is a dog'print(ret)def train(self):"""开始训练:return: """self.x = tf.placeholder(tf.float32, [None, 224, 224, 3]) # 输入数据self.y = tf.placeholder(tf.float32, [None, self.class_num]) # 标签数据self.learning_rate = tf.placeholder(tf.float32) # 学习率# 生成训练用数据集x_train_list, y_train_list, x_val_list, y_val_list = self.getTrainDataset()print('开始转换tensor队列')x_train_list_tensor = tf.convert_to_tensor(x_train_list, dtype=tf.string)y_train_list_tensor = tf.convert_to_tensor(y_train_list, dtype=tf.float32)x_val_list_tensor = tf.convert_to_tensor(x_val_list, dtype=tf.string)y_val_list_tensor = tf.convert_to_tensor(y_val_list, dtype=tf.float32)x_train_queue = tf.train.slice_input_producer(tensor_list=[x_train_list_tensor], shuffle=False)y_train_queue = tf.train.slice_input_producer(tensor_list=[y_train_list_tensor], shuffle=False)x_val_queue = tf.train.slice_input_producer(tensor_list=[x_val_list_tensor], shuffle=False)y_val_queue = tf.train.slice_input_producer(tensor_list=[y_val_list_tensor], shuffle=False)train_im, train_label = self.dataset_opt(x_train_queue, y_train_queue)train_batch = tf.train.batch(tensors=[train_im, train_label], batch_size=self.batch_size, num_threads=2)val_im, val_label = self.dataset_opt(x_val_queue, y_val_queue)val_batch = tf.train.batch(tensors=[val_im, val_label], batch_size=self.batch_size, num_threads=2)# VGG16网络print('开始加载网络')self.pred = self.CNN()# 损失函数self.loss = tf.nn.softmax_cross_entropy_with_logits(logits=self.pred, labels=self.y)# 优化器self.opt = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.loss)# accself.acc_tf = tf.equal(tf.argmax(self.pred, 1), tf.argmax(self.y, 1))self.acc = tf.reduce_mean(tf.cast(self.acc_tf, tf.float32))with tf.Session() as self.sess:# 全局tf变量初始化self.sess.run(tf.global_variables_initializer())coordinator = tf.train.Coordinator()threads = tf.train.start_queue_runners(sess=self.sess, coord=coordinator)# 模型保存saver = tf.train.Saver()batch_max = len(x_train_list) // self.batch_sizetotal_step = 1for epoch_num in range(self.epoch_max):lr = self.lr * (1 - (epoch_num/self.epoch_max) ** 2) # 动态学习率for batch_num in range(batch_max):x_train_tmp, y_train_tmp = self.sess.run(train_batch)self.sess.run(self.opt, feed_dict={self.x: x_train_tmp, self.y: y_train_tmp, self.learning_rate: lr, self.keep_drop: 0.5})# 输出评价标准if total_step % 20 == 0 or total_step == 1:print()print('epoch:%d/%d batch:%d/%d step:%d lr:%.10f' % ((epoch_num + 1), self.epoch_max, (batch_num + 1), batch_max, total_step, lr))# 输出训练集评价train_loss, train_acc = self.sess.run([self.loss, self.acc], feed_dict={self.x: x_train_tmp, self.y: y_train_tmp, self.keep_drop: 1.})print('train_loss:%.10f train_acc:%.10f' % (np.mean(train_loss), train_acc))# 输出验证集评价val_loss_list, val_acc_list = [], []for i in range(int(self.val_num/self.batch_size)):x_val_tmp, y_val_tmp = self.sess.run(val_batch)val_loss, val_acc = self.sess.run([self.loss, self.acc], feed_dict={self.x: x_val_tmp, self.y: y_val_tmp, self.keep_drop: 1.})val_loss_list.append(np.mean(val_loss))val_acc_list.append(np.mean(val_acc))print(' val_loss:%.10f val_acc:%.10f' % (np.mean(val_loss), np.mean(val_acc)))total_step += 1# 保存模型if (epoch_num + 1) % self.save_epoch == 0:print('正在保存模型：')saver.save(self.sess, './model/DogVsCat', global_step=(epoch_num + 1))coordinator.request_stop()coordinator.join(threads)def CNN(self):"""VGG16 + FC:return: """# 权重weight = {# 输入 batch_size*224*224*3# 第一层'wc1_1': tf.get_variable('wc1_1', [3, 3, 3, 64]), # 卷积 输出：batch_size*224*224*64'wc1_2': tf.get_variable('wc1_2', [3, 3, 64, 64]), # 卷积 输出：batch_size*224*224*64# 池化 输出：112*112*64# 第二层'wc2_1': tf.get_variable('wc2_1', [3, 3, 64, 128]), # 卷积 输出：batch_size*112*112*128'wc2_2': tf.get_variable('wc2_2', [3, 3, 128, 128]), # 卷积 输出：batch_size*112*112*128# 池化 输出：56*56*128# 第三层'wc3_1': tf.get_variable('wc3_1', [3, 3, 128, 256]), # 卷积 输出：batch_size*56*56*256'wc3_2': tf.get_variable('wc3_2', [3, 3, 256, 256]), # 卷积 输出：batch_size*56*56*256'wc3_3': tf.get_variable('wc3_3', [3, 3, 256, 256]), # 卷积 输出：batch_size*56*56*256# 池化 输出：28*28*256# 第四层'wc4_1': tf.get_variable('wc4_1', [3, 3, 256, 512]), # 卷积 输出：batch_size*28*28*512'wc4_2': tf.get_variable('wc4_2', [3, 3, 512, 512]), # 卷积 输出：batch_size*28*28*512'wc4_3': tf.get_variable('wc4_3', [3, 3, 512, 512]), # 卷积 输出：batch_size*28*28*512# 池化 输出：14*14*512# 第五层'wc5_1': tf.get_variable('wc5_1', [3, 3, 512, 512]), # 卷积 输出：batch_size*14*14*512'wc5_2': tf.get_variable('wc5_2', [3, 3, 512, 512]), # 卷积 输出：batch_size*14*14*512'wc5_3': tf.get_variable('wc5_3', [3, 3, 512, 512]), # 卷积 输出：batch_size*14*14*512# 池化 输出：7*7*512# 全链接第一层'wfc_1': tf.get_variable('wfc_1', [7*7*512, 4096]),# 全链接第二层'wfc_2': tf.get_variable('wfc_2', [4096, 4096]),# 全链接第三层'wfc_3': tf.get_variable('wfc_3', [4096, self.class_num]),}# 偏移量biase = {# 第一层'bc1_1': tf.get_variable('bc1_1', [64]),'bc1_2': tf.get_variable('bc1_2', [64]),# 第二层'bc2_1': tf.get_variable('bc2_1', [128]),'bc2_2': tf.get_variable('bc2_2', [128]),# 第三层'bc3_1': tf.get_variable('bc3_1', [256]),'bc3_2': tf.get_variable('bc3_2', [256]),'bc3_3': tf.get_variable('bc3_3', [256]),# 第四层'bc4_1': tf.get_variable('bc4_1', [512]),'bc4_2': tf.get_variable('bc4_2', [512]),'bc4_3': tf.get_variable('bc4_3', [512]),# 第五层'bc5_1': tf.get_variable('bc5_1', [512]),'bc5_2': tf.get_variable('bc5_2', [512]),'bc5_3': tf.get_variable('bc5_3', [512]),# 全链接第一层'bfc_1': tf.get_variable('bfc_1', [4096]),# 全链接第二层'bfc_2': tf.get_variable('bfc_2', [4096]),# 全链接第三层'bfc_3': tf.get_variable('bfc_3', [self.class_num]),}# 第一层net = tf.nn.conv2d(input=self.x, filter=weight['wc1_1'], strides=[1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc1_1'])) # 加b 然后 激活net = tf.nn.conv2d(net, filter=weight['wc1_2'], strides=[1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc1_2'])) # 加b 然后 激活net = tf.nn.max_pool(value=net, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') # 池化# 第二层net = tf.nn.conv2d(net, weight['wc2_1'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc2_1'])) # 加b 然后 激活net = tf.nn.conv2d(net, weight['wc2_2'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc2_2'])) # 加b 然后 激活net = tf.nn.max_pool(net, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID') # 池化# 第三层net = tf.nn.conv2d(net, weight['wc3_1'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc3_1'])) # 加b 然后 激活net = tf.nn.conv2d(net, weight['wc3_2'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc3_2'])) # 加b 然后 激活net = tf.nn.conv2d(net, weight['wc3_3'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc3_3'])) # 加b 然后 激活net = tf.nn.max_pool(net, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID') # 池化# 第四层net = tf.nn.conv2d(net, weight['wc4_1'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc4_1'])) # 加b 然后 激活net = tf.nn.conv2d(net, weight['wc4_2'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc4_2'])) # 加b 然后 激活net = tf.nn.conv2d(net, weight['wc4_3'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc4_3'])) # 加b 然后 激活net = tf.nn.max_pool(net, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID') # 池化# 第五层net = tf.nn.conv2d(net, weight['wc5_1'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc5_1'])) # 加b 然后 激活net = tf.nn.conv2d(net, weight['wc5_2'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc5_2'])) # 加b 然后 激活net = tf.nn.conv2d(net, weight['wc5_3'], [1, 1, 1, 1], padding='SAME') # 卷积net = tf.nn.leaky_relu(tf.nn.bias_add(net, biase['bc5_3'])) # 加b 然后 激活net = tf.nn.max_pool(net, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID') # 池化print('last-net', net)# 拉伸flatten，把多个图片同时分别拉伸成一条向量net = tf.reshape(net, shape=[-1, weight['wfc_1'].get_shape()[0]])print(weight['wfc_1'].get_shape()[0])print('拉伸flatten', net)# 全链接层# fc第一层net = tf.matmul(net, weight['wfc_1']) + biase['bfc_1']net = tf.nn.dropout(net, self.keep_drop)net = tf.nn.relu(net)print('fc第一层', net)# fc第二层net = tf.matmul(net, weight['wfc_2']) + biase['bfc_2']net = tf.nn.dropout(net, self.keep_drop)net = tf.nn.relu(net)print('fc第二层', net)# fc第三层net = tf.matmul(net, weight['wfc_3']) + biase['bfc_3']print('fc第三层', net)return netdef getTrainDataset(self):"""整理数据集，把图像resize为224*224*3，训练集做成25000*224*224*3，把label做成one-hot形式:return: """train_data_list = os.listdir('./data/train_data/')print('共有%d张训练图片， 读取%d张：' % (len(train_data_list), self.train_max_num))random.shuffle(train_data_list) # 打乱顺序x_val_list = train_data_list[:self.val_num]y_val_list = [[0, 1] if file_name.find('cat') > -1 else [1, 0] for file_name in x_val_list]x_train_list = train_data_list[self.val_num:self.train_max_num]y_train_list = [[0, 1] if file_name.find('cat') > -1 else [1, 0] for file_name in x_train_list]return x_train_list, y_train_list, x_val_list, y_val_listdef dataset_opt(self, x_train_queue, y_train_queue):"""处理图片和标签:param queue: :return: """queue = x_train_queue[0]contents = tf.read_file('./data/train_data/' + queue)im = tf.image.decode_jpeg(contents)im = tf.image.resize_images(images=im, size=[224, 224])im = tf.reshape(im, tf.stack([224, 224, 3]))im -= 147 # 去均值化# im /= 255 # 将像素处理在0~1之间，加速收敛# im -= 0.5 # 将像素处理在-0.5~0.5之间return im, y_train_queue[0]if __name__ == '__main__':opt_type = sys.argv[1:][0]instance = DogVsCat()if opt_type == 'train':instance.train()elif opt_type == 'test':instance.test('./data/test1/1.jpg')elif opt_type == 'start':# 将session持久化到内存中instance.test('./data/test1/1.jpg')# 启动web服务# http://127.0.0.1:5050/dogOrCat?img_path=./data/test1/1.jpg@app.route('/dogOrCat', methods=['GET', 'POST'])def dogOrCat():img_path = ''if request.method == 'POST':img_path = request.form.to_dict().get('img_path')elif request.method == 'GET':# img_path = request.args.get('img_path')img_path = request.args.to_dict().get('img_path')print(img_path)ret = instance.dogOrCat(img_path)print(ret)return json.dumps({'type': ret})app.run(host='0.0.0.0', port=5050, debug=False)
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