tensorflow1.1/autoencoder人脸识别

环境：tensorflow1.1，python3,matplotlib2.02

#coding:utf-8"""python3tensorflow 1.1matplotlib 2.02"""import tensorflow as tfimport pickleimport numpy as npimport matplotlib.pyplot as plt#读取数据集with open('facedataset.pickle','rb') as f:    (train_data,train_labels),(test_data,test_labels) = pickle.load(f)learning_rate = 0.01N_pictures = 3train_data = train_data.astype(np.float32)train_data = np.random.permutation(train_data)test_data = test_data.astype(np.float32)test_data = np.random.permutation(test_data)xs = tf.placeholder(tf.float32,[None,57*47])#构建autoencoder神经网络encoder0 = tf.layers.dense(xs,512,tf.nn.tanh)encoder1 = tf.layers.dense(encoder0,128,tf.nn.tanh)encoder2 = tf.layers.dense(encoder1,64,tf.nn.tanh)encoder3 = tf.layers.dense(encoder2,10)decoder0 = tf.layers.dense(encoder3,64,tf.nn.tanh)decoder1 = tf.layers.dense(decoder0,128,tf.nn.tanh)decoder2 = tf.layers.dense(decoder1,512,tf.nn.tanh)decoder3 = tf.layers.dense(decoder2,57*47,tf.nn.sigmoid)#计算lossloss = tf.losses.mean_squared_error(labels=xs,predictions=decoder3)train = tf.train.AdamOptimizer(learning_rate).minimize(loss)with tf.Session() as sess:    init = tf.global_variables_initializer()    sess.run(init)    #画图plt.subplots    fig,a = plt.subplots(2,N_pictures,figsize=(6,4))    #开始交互模式    plt.ion()    view_figures = test_data[:N_pictures]    for i in range(N_pictures):        #将测试的真实的图显示        a[0][i].imshow(np.reshape(view_figures[i],(57,47)))        #清空坐标轴        a[0][i].set_xticks(())        a[0][i].set_yticks(())    for step in range(1000):        _,encoderd,decodered,c = sess.run([train,encoder3,decoder3,loss],feed_dict={xs:train_data})        if step % 100 ==0:            print('= = = = = = > > > > > > step:',int(step/100),'train loss: %.4f'%c)        #将测试集中真实图片作为预测的图片        decoder_figures = sess.run(decoder3,feed_dict={xs:view_figures})        for i in range(N_pictures):            #清除第二行第i张图片            a[1][i].clear()            a[1][i].imshow(np.reshape(decoder_figures[i],(57,47)))            a[1][i].set_xticks(())            a[1][i].set_yticks(())        plt.show()        plt.pause(1)        #关闭交互模式    plt.ioff()

结果

由于使用的数据集太小，训练的效果一般，但也能看到脸的基本轮廓了
开始：

训练1000epochs：

训练3000epochs: