Tensorboard可視化：基于LeNet5進行面部表情分類 面部表情分類面部表情是面部肌肉的一個或多個動作或狀態的結果。這些運動表達了個體對觀察者的情緒狀態。面部表情是非語言交際的一種形式。它是表達人類之間的社會信息的主要手段，不過也發生在大多數其他哺乳動物和其他一些動物物種中。人類的面部表情至少有21種，除了常見的高興、吃驚、悲傷、憤怒、厭惡和恐懼6種，還有驚喜（高興＋吃驚）、悲憤（悲傷＋憤怒）等15種可被區分的復合表情。面部表情識別技術主要的應用領域包括人機交互、智能控制、安全、醫療、通信等領域。網絡架構LeNet-5出自論文Gradient-Based Learning Applied to Document Recognition，是一種用于手寫體字符識別的非常高效的卷積神經網絡。LeNet5的網絡架構如下：但是因為我們要做的是面部表情分類，而且CK+數據集樣本大小是48*48，因此需要對LeNet5網絡進行微調。網絡架構如下： 網絡結構如下：計算圖如下：代碼實現預處理數據集加載，并進行預處理，同時將測試集的前225張樣本拼接成15張*15張的大圖片，用于Tensorboard可視化。%matplotlib inlineimport matplotlib.pyplot as pltimport osimport cv2import numpy as npfrom tensorflow import name_scope as namespacefrom tensorflow.contrib.tensorboard.plugins import projectorNUM_PIC_SHOW=225base_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=[]#讀取圖片并將其保存至datafor 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('\\','/') #將\替換為/ img = cv2.imread(path,0) data.append(img)data=np.array(data)labels=np.array(labels)#將data打亂permutation = np.random.permutation(data.shape[0])data = data[permutation,:,:]labels = labels[permutation]#取前225個圖片拼成一張大圖片，用于tensorboard可視化img_set=data[:NUM_PIC_SHOW]#前225的數據用于顯示label_set=labels[:NUM_PIC_SHOW]big_pic=Noneindex=0for 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()#寫入大圖片cv2.imwrite(D:/Jupyter/TensorflowLearning/facial_expression_cnn_projector/data/faces.png,big_pic)#轉換數據格式和形狀data=data.reshape(-1,48*48).astype('float32')/255.0labels=labels.astype('float32')#0.3的比例測試scale=0.3test_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)#將標簽one-hottrain_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)]=1for i,label in enumerate(test_labels): test_labels_onehot[i,int(label)]=1print(train_labels_onehot.shape)print(test_labels_onehot.shape)2.定義前向網絡import tensorflow as tfIMAGE_SIZE=48 #圖片大小NUM_CHANNELS=1 #圖片通道CONV1_SIZE=5CONV1_KERNEL_NUM=32CONV2_SIZE=5CONV2_KERNEL_NUM=64FC_SIZE=512 #隱層大小OUTPUT_NODE=7 #輸出大小#參數概要，用于tensorboard實時查看訓練過程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) #標準差 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)#直方圖#獲取權重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#獲取偏置def get_bias(shape,name=None): b=tf.Variable(tf.zeros(shape),name=name) #variable_summaries(b) return b #定義前向網絡def forward(x,train,regularizer): with tf.name_scope('layer'): #把輸入reshape with namespace('reshape_input'): x_reshaped=tf.reshape(x,[-1,IMAGE_SIZE,IMAGE_SIZE,NUM_CHANNELS]) with tf.name_scope('conv1'): #定義兩個卷積層 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)) #對卷機后的輸出添加偏置，并通過relu完成非線性激活 pool2=tf.nn.max_pool(relu2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') with tf.name_scope('flatten'): #定義reshape層 pool_shape=pool2.get_shape().as_list() #獲得張量的維度并轉換為列表 nodes=pool_shape[1]*pool_shape[2]*pool_shape[3] #[0]為batch值，[1][2][3]分別為長寬和深度 #print(type(pool2)) reshaped=tf.reshape(pool2,[-1,nodes]) with tf.name_scope('fc1'): #定義兩層全連接層 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 y3.定義反向傳播 ，可視化設置，并進行訓練，BATCH_SIZE=100 #每次樣本數LEARNING_RATE_BASE=0.005 #基本學習率LEARNING_RATE_DECAY=0.99 #學習率衰減率REGULARIZER=0.0001 #正則化系數STEPS=2500 #訓練次數MOVING_AVERAGE_DECAY=0.99 #滑動平均衰減系數SAVE_PATH='.\\facial_expression_cnn_projector\\' #參數保存路徑data_len=train_data.shape[0]#將拼接為big_pic的測試樣本保存至標量，用于訓練過程可視化pic_stack=tf.stack(test_data[:NUM_PIC_SHOW]) #stack拼接圖片張量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'))#創建metadata文件，存放可視化圖片的labelif(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'))#將可視化圖片的標簽寫入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可視化圖片 with namespace('input_reshape'): image_shaped_input=tf.reshape(x,[-1,IMAGE_SIZE,IMAGE_SIZE,1]) #把輸入reshape tf.summary.image('input',image_shaped_input,7) #添加到tensorboard中顯示 y=forward(x,True,REGULARIZER) global_step=tf.Variable(0,trainable=False) with namespace('loss'): #softmax并計算交叉熵 ce=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1)) cem=tf.reduce_mean(ce) #求每個樣本的交叉熵 loss=cem+tf.add_n(tf.get_collection('losses')) tf.summary.scalar('loss',loss) #loss只有一個值，就直接輸出 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() #寫入圖結構 writer=tf.summary.FileWriter(os.path.join(SAVE_PATH,'projector'),sess.graph) saver=tf.train.Saver() #保存網絡的模型 #配置可視化 config=projector.ProjectorConfig() #tensorboard配置對象 embed=config.embeddings.add() #增加一項 embed.tensor_name=embedding.name #指定可視化的變量 embed.metadata_path='D:/Jupyter/TensorflowLearning/facial_expression_cnn_projector/projector/metadata.tsv' #路徑 embed.sprite.image_path='D:/Jupyter/TensorflowLearning/facial_expression_cnn_projector/data/faces.png' embed.sprite.single_image_dim.extend([IMAGE_SIZE,IMAGE_SIZE])#可視化圖片大小 projector.visualize_embeddings(writer,config) #斷點續訓 #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)#寫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()可視化訓練過程執行上面的代碼，打開tensorboard，可以看到訓練精度和交叉熵損失如下：由于只有六百多的訓練樣本，故得到曲線抖動很大，訓練精度大概在百分之八九十多浮動，測試精度在百分之七八十浮動，可見精度不高。下面使用Tensorboard將訓練過程可視化(圖片是用Power Point錄頻然后用迅雷應用截取gif得到的)： ————————————————版權聲明：本文為CSDN博主「陳建驅」的原創文章，遵循 CC 4.0 BY-SA 版權協議，轉載請附上原文出處鏈接及本聲明。
2021-09-20
