Tensorboard可视化:面部表情分类

 面部表情分类

    面部表情是面部肌肉的一个或多个动作或状态的结果。这些运动表达了个体对观察者的情绪状态。面部表情是非语言交际的一种形式。它是表达人类之间的社会信息的主要手段,不过也发生在大多数其他哺乳动物和其他一些动物物种中。人类的面部表情至少有21种,除了常见的高兴、吃惊、悲伤、愤怒、厌恶和恐惧6种,还有惊喜(高兴+吃惊)、悲愤(悲伤+愤怒)等15种可被区分的复合表情。 

    面部表情识别技术主要的应用领域包括人机交互、智能控制、安全、医疗、通信等领域。


数据集

    我把我用的数据集放到网盘上了https://pan.baidu.com/s/1sRVx67zq5GfIBo-nsli4rw 提取码:b2x6 。数据集如下:

dir.jpg

    可以看见该数据集有7个文件夹,对应7个表情情感分类。



网络架构

    LeNet-5出自论文Gradient-Based Learning Applied to Document Recognition,是一种用于手写体字符识别的非常高效的卷积神经网络。LeNet5的网络架构如下:

le.jpg

    但是因为我们要做的是面部表情分类,而且CK+数据集样本大小是48*48,因此需要对LeNet5网络进行微调。网络结构如下:

layer.jpg

    计算图如下:

graph.jpg


代码实现

  1. 预处理

    数据集加载,并进行预处理,同时将测试集的前225张样本拼接成15张*15张的大图片,用于Tensorboard可视化。

%matplotlib inline
import matplotlib.pyplot as plt
import os
import cv2
import numpy as np
from tensorflow import name_scope as namespace
from tensorflow.contrib.tensorboard.plugins import projector

NUM_PIC_SHOW=225
base_filedir='D:/CV/datasets/facial_exp/CK+'

dict_str2int={'anger':0,'contempt':1,'disgust':2,'fear':3,'happy':4,'sadness':5,'surprise':6}

labels=[]
data=[]

#读取图片并将其保存至data
for expdir in os.listdir(base_filedir):
    base_expdir=os.path.join(base_filedir,expdir)
    for name in os.listdir(base_expdir):
        labels.append(dict_str2int[expdir])
        path=os.path.join(base_expdir,name)
        path=path.replace('\\','/') #将\替换为/
        img = cv2.imread(path,0)
        data.append(img)

data=np.array(data)
labels=np.array(labels)

#将data打乱
permutation = np.random.permutation(data.shape[0])
data = data[permutation,:,:]
labels = labels[permutation]

#取前225个图片拼成一张大图片,用于tensorboard可视化
img_set=data[:NUM_PIC_SHOW]#前225的数据用于显示
label_set=labels[:NUM_PIC_SHOW]
big_pic=None
index=0
for row in range(15):
    row_vector=img_set[index]
    index+=1
    for col in range(1,15):
        img=img_set[index]
        row_vector=np.hstack([row_vector,img])
        index+=1
    if(row==0):
        big_pic=row_vector
    else:
        big_pic=np.vstack([big_pic,row_vector])

plt.imshow(big_pic, cmap='gray')
plt.show()

#写入大图片
cv2.imwrite("D:/Jupyter/TensorflowLearning/facial_expression_cnn_projector/data/faces.png",big_pic)

#转换数据格式和形状
data=data.reshape(-1,48*48).astype('float32')/255.0
labels=labels.astype('float32')

#0.3的比例测试
scale=0.3
test_data=data[:int(scale*data.shape[0])]
test_labels=labels[:int(scale*data.shape[0])]
train_data=data[int(scale*data.shape[0]):]
train_labels=labels[int(scale*data.shape[0]):]

print(train_data.shape)
print(train_labels.shape)
print(test_data.shape)
print(test_labels.shape)

#将标签one-hot
train_labels_onehot=np.zeros((train_labels.shape[0],7))
test_labels_onehot=np.zeros((test_labels.shape[0],7))

for i,label in enumerate(train_labels):
    train_labels_onehot[i,int(label)]=1
for i,label in enumerate(test_labels):
    test_labels_onehot[i,int(label)]=1

print(train_labels_onehot.shape)
print(test_labels_onehot.shape)



2.定义前向网络

import tensorflow as tf

IMAGE_SIZE=48 #图片大小
NUM_CHANNELS=1 #图片通道
CONV1_SIZE=5 
CONV1_KERNEL_NUM=32
CONV2_SIZE=5
CONV2_KERNEL_NUM=64
FC_SIZE=512 #隐层大小
OUTPUT_NODE=7 #输出大小

#参数概要,用于tensorboard实时查看训练过程
def variable_summaries(var):
    with namespace('summaries'):
        mean=tf.reduce_mean(var)
        tf.summary.scalar('mean',mean) #平均值
        with namespace('stddev'):
            stddev=tf.sqrt(tf.reduce_mean(tf.square(var-mean)))
        tf.summary.scalar('stddev',stddev) #标准差
        tf.summary.scalar('max',tf.reduce_max(var))#最大值
        tf.summary.scalar('min',tf.reduce_min(var))#最小值
        tf.summary.histogram('histogram',var)#直方图

#获取权重
def get_weight(shape,regularizer,name=None):
    w=tf.Variable(tf.truncated_normal(shape,stddev=0.1),name=name)
    #variable_summaries(w)
    if(regularizer!=None):
        tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w

#获取偏置
def get_bias(shape,name=None):
    b=tf.Variable(tf.zeros(shape),name=name)
    #variable_summaries(b)
    return b
    
#定义前向网络
def forward(x,train,regularizer):
    with tf.name_scope('layer'):
        #把输入reshape
        with namespace('reshape_input'):
            x_reshaped=tf.reshape(x,[-1,IMAGE_SIZE,IMAGE_SIZE,NUM_CHANNELS])
        with tf.name_scope('conv1'):
            #定义两个卷积层
            conv1_w=get_weight([CONV1_SIZE,CONV1_SIZE,NUM_CHANNELS,CONV1_KERNEL_NUM],regularizer=regularizer,name='conv1_w')
            conv1_b=get_bias([CONV1_KERNEL_NUM],name='conv1_b')
            conv1=tf.nn.conv2d(x_reshaped,conv1_w,strides=[1,1,1,1],padding='SAME')
            relu1=tf.nn.relu(tf.nn.bias_add(conv1,conv1_b))
            pool1=tf.nn.max_pool(relu1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
            
        with tf.name_scope('conv2'):
            conv2_w=get_weight([CONV2_SIZE,CONV2_SIZE,CONV1_KERNEL_NUM,CONV2_KERNEL_NUM],regularizer=regularizer,name='conv2_w')
            conv2_b=get_bias([CONV2_KERNEL_NUM],name='conv2_b')
            conv2=tf.nn.conv2d(pool1,conv2_w,strides=[1,1,1,1],padding='SAME')
            relu2=tf.nn.relu(tf.nn.bias_add(conv2,conv2_b)) #对卷机后的输出添加偏置,并通过relu完成非线性激活
            pool2=tf.nn.max_pool(relu2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
            
        with tf.name_scope('flatten'):
            #定义reshape层
            pool_shape=pool2.get_shape().as_list() #获得张量的维度并转换为列表
            nodes=pool_shape[1]*pool_shape[2]*pool_shape[3] #[0]为batch值,[1][2][3]分别为长宽和深度
            #print(type(pool2))
            reshaped=tf.reshape(pool2,[-1,nodes])
        
        with tf.name_scope('fc1'):
            #定义两层全连接层
            fc1_w=get_weight([nodes,FC_SIZE],regularizer,name='fc1_w')
            fc1_b=get_bias([FC_SIZE],name='fc1_b')
            fc1=tf.nn.relu(tf.matmul(reshaped,fc1_w)+fc1_b)
            if(train):
                fc1=tf.nn.dropout(fc1,0.5)
    
        with tf.name_scope('fc2'):
            fc2_w=get_weight([FC_SIZE,OUTPUT_NODE],regularizer,name='fc2_w')
            fc2_b=get_bias([OUTPUT_NODE],name='fc2_b')
            y=tf.matmul(fc1,fc2_w)+fc2_b
    return y


3.定义反向传播 ,可视化设置,并进行训练,

BATCH_SIZE=100 #每次样本数
LEARNING_RATE_BASE=0.005 #基本学习率
LEARNING_RATE_DECAY=0.99 #学习率衰减率
REGULARIZER=0.0001 #正则化系数
STEPS=2500 #训练次数
MOVING_AVERAGE_DECAY=0.99 #滑动平均衰减系数

SAVE_PATH='.\\facial_expression_cnn_projector\\' #参数保存路径
data_len=train_data.shape[0]

#将拼接为big_pic的测试样本保存至标量,用于训练过程可视化
pic_stack=tf.stack(test_data[:NUM_PIC_SHOW]) #stack拼接图片张量
embedding=tf.Variable(pic_stack,trainable=False,name='embedding')

if(tf.gfile.Exists(os.path.join(SAVE_PATH,'projector'))==False):
    tf.gfile.MkDir(os.path.join(SAVE_PATH,'projector'))

#创建metadata文件,存放可视化图片的label
if(tf.gfile.Exists(os.path.join(SAVE_PATH,'projector','metadata.tsv'))==True):
    tf.gfile.DeleteRecursively(os.path.join(SAVE_PATH,'projector'))
    tf.gfile.MkDir(os.path.join(SAVE_PATH,'projector'))
#将可视化图片的标签写入
with open(os.path.join(SAVE_PATH,'projector','metadata.tsv'),'w') as f:
    for i in range(NUM_PIC_SHOW):
        f.write(str(label_set[i])+'\n')

with tf.Session() as sess:
    with tf.name_scope('input'):
        #x=tf.placeholder(tf.float32,[BATCH_SIZE,IMAGE_SIZE,IMAGE_SIZE,NUM_CHANNELS],name='x_input')
        x=tf.placeholder(tf.float32,[None,IMAGE_SIZE*IMAGE_SIZE*NUM_CHANNELS],name='x_input')
        y_=tf.placeholder(tf.float32,[None,OUTPUT_NODE],name='y_input')
    
    #reshape可视化图片
    with namespace('input_reshape'):
        image_shaped_input=tf.reshape(x,[-1,IMAGE_SIZE,IMAGE_SIZE,1]) #把输入reshape
        tf.summary.image('input',image_shaped_input,7) #添加到tensorboard中显示
    
    y=forward(x,True,REGULARIZER)
    
    global_step=tf.Variable(0,trainable=False)
    with namespace('loss'):
        #softmax并计算交叉熵
        ce=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1))
        cem=tf.reduce_mean(ce) #求每个样本的交叉熵
        loss=cem+tf.add_n(tf.get_collection('losses'))
        tf.summary.scalar('loss',loss) #loss只有一个值,就直接输出
    
    learning_rate=tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step,
        data_len/BATCH_SIZE,
        LEARNING_RATE_DECAY,
        staircase=True
    )
    with namespace('train'):
        train_step=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)
        ema=tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)
        ema_op=ema.apply(tf.trainable_variables())
    
    with namespace('accuracy'):
        correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
        accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
        tf.summary.scalar('accuracy',accuracy)
    
    with tf.control_dependencies([train_step,ema_op]):
        train_op=tf.no_op(name='train')
    
    init_op=tf.global_variables_initializer()
    sess.run(init_op)
    
    #合并所有的summary
    merged=tf.summary.merge_all()
    #写入图结构
    writer=tf.summary.FileWriter(os.path.join(SAVE_PATH,'projector'),sess.graph)
    saver=tf.train.Saver() #保存网络的模型
    #配置可视化
    config=projector.ProjectorConfig() #tensorboard配置对象
    embed=config.embeddings.add() #增加一项
    embed.tensor_name=embedding.name #指定可视化的变量
    embed.metadata_path='D:/Jupyter/TensorflowLearning/facial_expression_cnn_projector/projector/metadata.tsv' #路径
    embed.sprite.image_path='D:/Jupyter/TensorflowLearning/facial_expression_cnn_projector/data/faces.png' 
    embed.sprite.single_image_dim.extend([IMAGE_SIZE,IMAGE_SIZE])#可视化图片大小
    projector.visualize_embeddings(writer,config)
    #断点续训
    #ckpt=tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
    #if(ckpt and ckpt.model_checkpoint_path):
    #    saver.restore(sess,ckpt.model_checkpoint_path)
    for i in range(STEPS):
        run_option=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
        run_metadata=tf.RunMetadata()
        start=(i*BATCH_SIZE)%(data_len-BATCH_SIZE)
        end=start+BATCH_SIZE
        summary,_,loss_value,step=sess.run([merged,train_op,loss,global_step],
                                           feed_dict={x:train_data[start:end],y_:train_labels_onehot[start:end]},
                                           options=run_option,
                                           run_metadata=run_metadata)
        writer.add_run_metadata(run_metadata,'step%03d'%i)
        writer.add_summary(summary,i)#写summary和i到文件
        if(i%100==0):
            acc=sess.run(accuracy,feed_dict={x:test_data,y_:test_labels_onehot})
            print('%d %g'%(step,loss_value))
            print('acc:%f'%(acc))
            saver.save(sess,os.path.join(SAVE_PATH,'projector','model'),global_step=global_step)
    writer.close()


可视化训练过程

    执行上面的代码,打开tensorboard,可以看到训练精度和交叉熵损失如下:

acc_loss.jpg

    由于只有六百多的训练样本,故得到曲线抖动很大,训练精度大概在百分之八九十多浮动,测试精度在百分之七八十浮动,可见精度不高。下面使用Tensorboard将训练过程可视化(图片是用Power Point录频 然后用迅雷应用截取gif得到的):

媒体1_20191025154637.gif

    有点酷。



参考文献

[1]wuliytTaotao.经典卷积神经网络结构——LeNet-5、AlexNet、VGG-16.https://www.cnblogs.com/wuliytTaotao/p/9544625  .html. 2018-08-28

[2]WangBo_NLPR.[机器学习] 实验笔记 - 表情识别(emotion recognition).https://blog.csdn.net/walilk/article/details/58709611.  2017-04-15

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