基于卷积神经网络算法的人脸识别项目源码+项目说明.zip

#coding=utf-8 #import tensorflow as tf import tensorflow.compat.v1 as tf tf.disable_v2_behavior() import CNN_train_data as train_data import cv2 import random import matplotlib.pyplot as plt import numpy as np from sklearn.metrics import confusion_matrix, classification_report train_epochs=3000 batch_size = 9 drop_prob = 0.4 learning_rate=0.00001 def weight_init(shape): weight = tf.truncated_normal(shape,stddev=0.1,dtype=tf.float32) return tf.Variable(weight) def bias_init(shape): bias = tf.random_normal(shape,dtype=tf.float32) return tf.Variable(bias) images_input = tf.placeholder(tf.float32,[None,112*92*3],name='input_images') labels_input = tf.placeholder(tf.float32,[None,2],name='input_labels') def fch_init(layer1,layer2,const=1): min = -const * (6.0 / (layer1 + layer2)); max = -min; weight = tf.random_uniform([layer1, layer2], minval=min, maxval=max, dtype=tf.float32) return tf.Variable(weight) def conv2d(images,weight): return tf.nn.conv2d(images,weight,strides=[1,1,1,1],padding='SAME') def max_pool2x2(images,tname): return tf.nn.max_pool(images,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name=tname) x_input = tf.reshape(images_input,[-1,112,92,3]) # 卷积核3*3*3 16个 第一层卷积 w1 = weight_init([3,3,3,16]) b1 = bias_init([16]) # 结果 NHWC N H W C conv_1 = conv2d(x_input,w1)+b1 relu_1 = tf.nn.relu(conv_1,name='relu_1') max_pool_1 = max_pool2x2(relu_1,'max_pool_1') # 卷积核3*3*16 32个 第二层卷积 w2 = weight_init([3,3,16,32]) b2 = bias_init([32]) conv_2 = conv2d(max_pool_1,w2) + b2 relu_2 = tf.nn.relu(conv_2,name='relu_2') max_pool_2 = max_pool2x2(relu_2,'max_pool_2') # 卷积核3*3*32 64个 第三层卷积 w3 = weight_init([3,3,32,64]) b3 = bias_init([64]) conv_3 = conv2d(max_pool_2,w3)+b3 relu_3 = tf.nn.relu(conv_3,name='relu_3') max_pool_3 = max_pool2x2(relu_3,'max_pool_3') f_input = tf.reshape(max_pool_3,[-1,14*12*64]) #全连接第一层 31*31*32,512 f_w1= fch_init(14*12*64,512) f_b1 = bias_init([512]) f_r1 = tf.matmul(f_input,f_w1) + f_b1 f_relu_r1 = tf.nn.relu(f_r1) f_dropout_r1 = tf.nn.dropout(f_relu_r1,drop_prob) f_w2 = fch_init(512,128) f_b2 = bias_init([128]) f_r2 = tf.matmul(f_dropout_r1,f_w2) + f_b2 f_relu_r2 = tf.nn.relu(f_r2) f_dropout_r2 = tf.nn.dropout(f_relu_r2,drop_prob) #全连接第二层 512,2 f_w3 = fch_init(128,2) f_b3 = bias_init([2]) f_r3 = tf.matmul(f_dropout_r2,f_w3) + f_b3 f_softmax = tf.nn.softmax(f_r3,name='f_softmax') #定义交叉熵 cross_entry = tf.reduce_mean(tf.reduce_sum(-labels_input*tf.log(f_softmax))) optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cross_entry) #计算准确率 arg1 = tf.argmax(labels_input,1) arg2 = tf.argmax(f_softmax,1) cos = tf.equal(arg1,arg2) acc = tf.reduce_mean(tf.cast(cos,dtype=tf.float32)) init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) Cost = [] Accuracy=[] for i in range(train_epochs): idx=random.randint(0,len(train_data.images)-20) batch= random.randint(6,18) train_input = train_data.images[idx:(idx+batch)] train_labels = train_data.labels[idx:(idx+batch)] result,acc1,cross_entry_r,cos1,f_softmax1,relu_1_r= sess.run([optimizer,acc,cross_entry,cos,f_softmax,relu_1],feed_dict={images_input:train_input,labels_input:train_labels}) print (acc1) Cost.append(cross_entry_r) Accuracy.append(acc1) # 代价函数曲线 fig1,ax1 = plt.subplots(figsize=(10,7)) plt.plot(Cost) ax1.set_xlabel('Epochs') ax1.set_ylabel('Cost') plt.title('Cross Loss') plt.grid() plt.show() # 准确率曲线 fig7,ax7 = plt.subplots(figsize=(10,7)) plt.plot(Accuracy) ax7.set_xlabel('Epochs') ax7.set_ylabel('Accuracy Rate') plt.title('Train Accuracy Rate') plt.grid() plt.show() #测试 arg2_r = sess.run(arg2,feed_dict={images_input:train_data.test_images,labels_input:train_data.test_labels}) arg1_r = sess.run(arg1,feed_dict={images_input:train_data.test_images,labels_input:train_data.test_labels}) print (classification_report(arg1_r, arg2_r)) #保存模型 saver = tf.train.Saver() saver.save(sess, './model/my-gender-v1.0')