gtx1080
i7

训练集规模：8000 .jpg（ 爬取自 京东 百度）
测试集规模：3000. jpg（爬取自 ）
STEPS=20000
Learning_rate=0.005
accuracy=0.84

request&正则表达式
略，懒得写

filter 卷积核
pooling 池化
fc 全连接
bias 偏置
backpropagation 反向传播
loss 损失
optimizer 优化器
Momentum动量随机梯度下降法
Learningrate 学习率
relu 激活函数

一张图了解tensorflow搭建神经网络模型结构:

CNN的实现：（conv-pool）*n ->dropout-> flatten->(fullconnected)*n->outputs

NN算法的前向传播过程（forward）我理解为 矩阵乘，

//
import tensorflow as tf

slim = tf.contrib.slim

class Model(object):
    def __init__(self,
                 is_training,
                 num_classes):
        self._num_classes = num_classes
        self._is_training = is_training
    @property
    def num_classes(self):
        return self._num_classes
  	def preprocess(self, inputs):
        preprocessed_inputs = tf.to_float(inputs)
        preprocessed_inputs = tf.subtract(preprocessed_inputs, 128.0)
        preprocessed_inputs = tf.div(preprocessed_inputs, 128.0)
        #使图片灰度值归一化到-1~1之间，小数据集的preprocessing中使用rotate，crop&border_expend是有用的，这次时间紧懒得搞了
        return preprocessed_inputs   
    def predict(self, preprocessed_inputs):
        with slim.arg_scope([slim.conv2d, slim.fully_connected],
                            activation_fn=tf.nn.relu):
            net = preprocessed_inputs
            net = slim.repeat(net, 2, slim.conv2d, 32, [3, 3], scope='conv1')
            net = slim.max_pool2d(net, [2, 2], scope='pool1')
            
            net = slim.repeat(net, 2, slim.conv2d, 64, [3, 3], scope='conv2')
            net = slim.max_pool2d(net, [2, 2], scope='pool2')
            
            net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv3')
            net = slim.flatten(net, scope='flatten')
            net = slim.dropout(net, keep_prob=0.5, 
                               is_training=self._is_training)
            net = slim.fully_connected(net, 512, scope='fc1')
            
            net = slim.fully_connected(net, self.num_classes, 
                                       activation_fn=None, scope='fc2')           
            #net=slim.resnet_v1.predict()
            #调用slim搭建网络，3个conv，2个fc，logits是形如[x,y].后面训练SVM会用到        
        prediction_dict = {'logits': net}
        return prediction_dict    
    def postprocess_logits(self, prediction_dict):
        logits = prediction_dict['logits']
        logits = tf.cast(logits, dtype=tf.float32)
        logits_dict = {'logits': logits}
        return logits_dict#有用，但是懒得解释  
    def postprocess_classes(self, prediction_dict):
        logits = prediction_dict['logits']
        logits = tf.nn.softmax(logits)
        classes = tf.cast(tf.argmax(logits, axis=1), dtype=tf.int32)
        postprecessed_dict = {'classes': classes}
        return postprecessed_dict#softmax使logits=[x,y]->logits[x/(x+y),y/(x+y)]    
    def loss(self, prediction_dict, groundtruth_lists):
        logits = prediction_dict['logits']
        loss = tf.reduce_mean(
            tf.nn.sparse_softmax_cross_entropy_with_logits(#计算交叉熵
                logits=logits, labels=groundtruth_lists))#这个说起来太麻烦，见(https://blog.csdn.net/qq_22194315/article/details/77991283)
        loss_dict = {'loss': loss}
        return loss_dict

使用：anaconda navigater -> environment -> python3.5 -> open terminal ->(进去你的path) -> tensorboard --logdir=‘yourpath’-> ctrl+c “http”-> open it in your chrome

拟合，观察loss曲线，调参

main中先调用get_train_data导入图片

//  
def get_train_data(images_path):
	if not os.path.exists(images_path):
        raise ValueError('images_path is not exist.')		 
	images = []
    	labels = []
	images_path = os.path.join(images_path, '*.jpg')
    	count = 0
    	for image_file in glob.glob(images_path):
        count += 1
        if count % 100 == 0:
            print('Load {} images.'.format(count))
        image = cv2.imread(image_file)
        image = cv2.resize(image,(220,220),interpolation=cv2.INTER_CUBIC)#resize
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)#灰度图
        # Assume the name of each image is imagexxx@label$.jpg
        label = int(image_file.split('@')[-1].split('$')[0])#用一个小trick读取标签
        images.append(image)
        labels.append(label)
    images = np.array(images)
    labels = np.array(labels)
    return images, labels
    #np.ndarray [all,length,wideth,channel]  labels: np.ndarray one dimension tensor 

由于训练时的

//  
def next_batch_set(images, labels, batch_size=60):
	indices = np.random.choice(len(images), batch_size)
	batch_images = images[indices]
	batch_labels = labels[indices]
	return batch_images, batch_label

是随机取的，所以为了保证每一个都被取到的概率，STEPS应远大于len（train_images）

// 
def main(_):
    inputs = tf.placeholder(tf.float32, shape=[None, 220, 220, 3], name='inputs')
    labels = tf.placeholder(tf.int32, shape=[None], name='labels')   
    cls_model = model2.Model(is_training=True, num_classes=2)   
    preprocessed_inputs = cls_model.preprocess(inputs)#inputs预处理   
    prediction_dict = cls_model.predict(preprocessed_inputs)#predict  （forward）  
    postprocessed_logits=cls_model.postprocess_logits(prediction_dict)#后处理   
    logits=postprocessed_logits['logits']#    
    logits_ = tf.identity(logits,name='logits')#用于测试（necessary）   
    loss_dict = cls_model.loss(prediction_dict, labels)   
    loss = loss_dict['loss']           
    postprocessed_dict = cls_model.postprocess_classes(prediction_dict)    
    classes = postprocessed_dict['classes']    
    classes_ = tf.identity(classes, name='classes')#用于测试（necessary）    
    acc = tf.reduce_mean(tf.cast(tf.equal(classes, labels), 'float'))    
    global_step = tf.Variable(0, trainable=False)   
    learning_rate = tf.train.exponential_decay(Learning_RATE, global_step, 150, 0.99)#指数衰减学习率   
    optimizer = tf.train.MomentumOptimizer(learning_rate, 0.99)#优化器   
    train_step = optimizer.minimize(loss, global_step)   
    saver = tf.train.Saver() #保存   
    images, targets = get_train_data(images_path)#获取训练images labels （np.ndarray）    
    init = tf.global_variables_initializer()#
    loss_graph=[]    
    x=[]    
    with tf.Session() as sess:
        sess.run(init)
        writer=tf.summary.FileWriter("E:\\zxtdeeplearning\\tensorboard\\3",sess.graph)#tensorboard绘图
        #打开tensorboard：anaconda navigator->open terminal ->cd logdir -> tensorboard --logdir=....-> ...                
        ckpt=tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess,ckpt.model_checkpoint_path) #断点存续                     
        for i in range(STEPS):
            batch_images, batch_labels = next_batch_set(images, targets)
            train_dict = {inputs: batch_images, labels: batch_labels}            
            sess.run(train_step, feed_dict=train_dict)                                  
            loss_, acc_ = sess.run([loss, acc], feed_dict=train_dict)           
            train_text = 'step: {}, loss: {}, acc: {}'.format(
                i+1, loss_, acc_)
            loss_graph.append(loss_)
            x.append(i)
         print(train_text)            
        writer=tf.summary.FileWriter("E:\\zxtdeeplearning\\tensorboard\\1",sess.graph)    
        saver.save(sess,os.path.join(MODEL_SAVE_PATH,MODEL_NAME),global_step=global_step)
    writer.close()
    plt.figure()
    plt.plot(x,loss_graph)
    plt.show()#画loss曲线，懒得用tensorboard  

 

根据loss曲线判断收敛情况调整learning_rate,(凹凸)

于是加大力度

考虑到网络结构较浅，训练集较少，还可以

from sklearn.preprocessing import StandardScaler
import os
import numpy as np
import tensorflow as tf
import train2
from sklearn.externals import joblib
import datawriter
model_path="E:\\zxtdeeplearning\\outputs-2.3.ckpt\\6-20000"
#img_path='E:\\zxtdeeplearning\\pic_source\\test_pic_1'
#img_path=input('input the path and the img name')
#img_path='E:\\zxtdeeplearning\\test_pic(dirt)'
#img_path='E:\\zxtdeeplearning\\pic_source\\dirty'
img_path='E:\\zxtdeeplearning\\test_pic'
#img_path='E:\\zxtdeeplearning\\test_pic_misty'
#img_path="E:\\zxtdeeplearning\\test_pic_formal"
logits_outputs_path='E:\\zxtdeeplearning\\svm_train_data\\dataset_test.txt'
#我意外发现你的test_pic的string不能包含中文字符，不然会报-215Asseration
#注意，测试集不能太多，否则gtx1080也会炸，batch几十都ok（2353：->oom）
foot=10
--------------------------------------------#随手写的零件，可以忽略
def accuracy(predicted_label,labels):
    a=predicted_label-labels
    t=0
    for i in range(len(a)):
        if a[i] !=0:
            t+=1
    return 1-t/len(a)                    
def ave(mum):
    total=0
    for i in range(len(mum)):
        total=total+mum[i]
    return total/len(mum)
------------------------------

def main(_):    
    images,labels=train2.get_train_data(img_path)    
    with tf.Session() as sess:
        ckpt_path = model_path
        saver = tf.train.import_meta_graph(ckpt_path + '.meta')#取出.meta文件
        saver.restore(sess, ckpt_path)#恢复参数
        inputs = tf.get_default_graph().get_tensor_by_name('inputs:0')#找到图中变量，建议查这两个api用法       
        classes = tf.get_default_graph().get_tensor_by_name('classes:0')        
        logits = tf.get_default_graph().get_tensor_by_name('logits:0')#logits   
        predicted_label_list=[]        
        predicted_logits_list=[]        
        a,b=0,9        
        for i in range(int(len(images)/foot)):            
            image_batch=datawriter.get_batch(images,a,b)                                 
            a,b=(a+foot),(b+foot)            
            predicted_logits=sess.run(logits, feed_dict={inputs: image_batch})
            predicted_label = sess.run(classes, feed_dict={inputs: image_batch})
            for j in range(len(predicted_label)):
                predicted_label_list.append(predicted_label[j])                
                predicted_logits_list.append(predicted_logits[j])            
        print(predicted_logits,logits_outputs_path)        
        for i in range(len(predicted_logits)):
            predicted_logits[i][0],predicted_logits[i][1]=predicted_logits[i][0],predicted_logits[i][1]                        
        for i in range(len(labels)):           
            print("fact:",labels[i],' ',"predict:",predicted_label_list[i],' ','logits:',predicted_logits_list[i])            
        print(accuracy(predicted_label_list,labels))

	结果：84%

对于pic_test_1,在测试过程中发现准确率很低（66%）->发现是爬取得图片有问题，包含一些脏数据（由于易于爬取得（如京东百度）已经被爬完了），抽样调查脏数据约占到1/3，出于相信模型的可靠度（由于另一测试集pic_test_0准确率在83%，尝试设计方法还原原准确率）

使用数学手段证明了在logits输出层使用聚类算法（无监督学习二分类），聚类算法accura>0.5，则必然比原来更接近原准确率，即能降低脏数据的影响。（再数学证明从略，这是显然的）其实使用训练的svm在logits分类也是可以还原原准确率的（显然），但是性能和聚类算法孰优孰劣？或是重新制作好的测试集进行测试即可得到真实值。

懒得写了，就是用2000张misty和1000羽绒服1000短袖训练了个SVM，发现效果不好（70%），本来感觉没用不想用了，又发现可以用来搞上面那个问题，略了

// An highlighted block
var foo = 'bar';

1.我需要更多的数据！
2.cool idea？服装识别？识别出一个人身上的所有装饰？that is easy I think,甚至堆叠起来就可以！之后做打分？穿搭？或者尝试expand it ，读取一张图片估计它的季节？温度？
3.tensorboard需要熟悉
4.fine-tune在专业领域会更好！
5.下次我应该尝试学习resnet？或许gan是个不错的主意
6.图像识别的算法是很重要的一大块，聚类，追踪，分割都是很有趣的
7.titantitantitan！
8.努力达到比赛级别！

附件：爬虫的代码：

// An highlighted block
import requests
import os

def getPages(keyword, pages):
    params = []
    for i in range(30, 30 * pages + 30, 30):
        params.append({
            'tn': 'resultjson_com',
            'ipn': 'rj',
            'ct': 201326592,
            'is': '',
            'fp': 'result',
            'queryWord': keyword,
            'cl': 2,
            'lm': -1,
            'ie': 'utf-8',
            'oe': 'utf-8',
            'adpicid': '',
            'st': -1,
            'z': '',
            'ic': 0,
            'word': keyword,
            's': '',
            'se': '',
            'tab': '',
            'width': '',
            'height': '',
            'face': 0,
            'istype': 2,
            'qc': '',
            'nc': 1,
            'fr': '',
            'pn': i,
            'rn': 30,
            'gsm': '1e',
            '1509347955280': ''
        })
    url = 'http://image.so.com/'
    urls = []
    for i in params:
        urls.append(requests.get(url, params=i).json().get('data'))
    return urls


def getImg(dataList, localPath):
    if not os.path.exists(localPath):
        os.mkdir(localPath)

    try:
        x = 0
        for list in dataList:
            for i in list:
                if i.get('thumbURL') != None:
                    print('正在下载：%s' % i.get('thumbURL'))
                    ir = requests.get(i.get('thumbURL'))
                    open(localPath + '%d.jpg' % x, 'wb').write(ir.content)
                    x += 1
        print('图片下载完成')
    except Exception:
        print("图片下载失败")


if __name__ == '__main__':    
    dataList = getPages('羽绒服',10)
    print(dataList)
    getImg(dataList, 'D:/大学/大二/python课/项目/pictures/测试集/pic_f_yurong')」
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