tensorflow1.1/variational_autoencoder

环境tensorflow1.1,matplotlib2.02,python3

近年，非监督学习成为了研究热点。VAE（Variational Auto-Encoder，变分自编码器）和 GAN（Generative Adversarial Networks） 等模型，受到越来越多的关注

VAE:模型结构：

其中：loss = mse+KLDivergence

#coding:utf-8"""tensorflow 1.1matplotlib 2.02"""import tensorflow as tfimport numpy as npimport input_dataimport matplotlib.pyplot as pltinput_dim = 784hidden_encoder_dim = 1200hidden_decoder_dim = 1200latent_dim = 200epochs = 3000batch_size = 100N_pictures=3mnist = input_data.read_data_sets('mnist/')def weight_variable(shape):    #tf.truncated_normal()截断的标准正态分布    return tf.Variable(tf.truncated_normal(shape,stddev=0.001))def bias_variable(shape):    return tf.Variable(tf.truncated_normal(shape))x = tf.placeholder('float32',[None,input_dim])#在全连接层加入l2_regularizationl2_loss = tf.constant(0.0)#encoder网络w_encoder1 =weight_variable([input_dim,hidden_encoder_dim])b_encoder1 = bias_variable([hidden_encoder_dim])encoder1 = tf.nn.relu(tf.matmul(x,w_encoder1)+b_encoder1)#第一层的l2_lossl2_loss += tf.nn.l2_loss(w_encoder1)#定义一个mu网络mu_w_encoder2 = weight_variable([hidden_encoder_dim,latent_dim])mu_b_encoder2 = bias_variable([latent_dim])mu_encoder2 = tf.matmul(encoder1,mu_w_encoder2)+mu_b_encoder2#mu网络的l2_lossl2_loss += tf.nn.l2_loss(mu_w_encoder2)#定义一个var网络var_w_encoder2 = weight_variable([hidden_encoder_dim,latent_dim])var_b_encoder2 = bias_variable([latent_dim])var_encoder2 = tf.matmul(encoder1,var_w_encoder2)+var_b_encoder2#var网络的l2_lossl2_loss += tf.nn.l2_loss(var_w_encoder2)#抽样#生成标准正态分布epsilon = tf.random_normal(tf.shape(var_encoder2))new_var_encoder2 = tf.sqrt(tf.exp(var_encoder2))#z的维度是latent_dimz = mu_encoder2+tf.multiply(new_var_encoder2,epsilon)#定义decoder网络w_decoder1 = weight_variable([latent_dim,hidden_decoder_dim])b_decoder1 = bias_variable([hidden_decoder_dim])decoder1 = tf.nn.relu(tf.matmul(z,w_decoder1)+b_decoder1)l2_loss += tf.nn.l2_loss(w_decoder1)w_decoder2 = weight_variable([hidden_decoder_dim,input_dim])b_decoder2 = bias_variable([input_dim])#输出层没有使用激活函数(加入激活函数后面用log_px_given_z,不加入激活函数用cost1)decoder2 = tf.nn.sigmoid(tf.matmul(decoder1,w_decoder2)+b_decoder2)l2_loss += tf.nn.l2_loss(w_decoder2)#计算costlog_px_given_z = -tf.reduce_sum(x*tf.log(decoder2+1e-10)+(1-x)*tf.log(1-decoder2+1e-10),1)#cost1 = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=decoder2,labels=x),reduction_indices=1)#计算KL DivergenceKLD = -0.5*tf.reduce_sum(1+var_encoder2-tf.pow(mu_encoder2,2)-tf.exp(var_encoder2),reduction_indices=1)cost = tf.reduce_mean(log_px_given_z+KLD)#加上regularization regularized_cost = cost + l2_losstrain = tf.train.AdamOptimizer(0.01).minimize(cost)with tf.Session() as sess:    init = tf.global_variables_initializer()    sess.run(init)    #画图,2行5列返回图和子图    figure_,a = plt.subplots(2,N_pictures,figsize=(6,4))    #开始交互模式    plt.ion()    #测试的图    view_figures = mnist.test.images[:N_pictures]    for i in range(N_pictures):        #将图片reshape为28行28列显示        a[0][i].imshow(np.reshape(view_figures[i],(28,28)))        #清空x轴,y轴坐标        a[0][i].set_xticks(())        a[0][i].set_yticks(())    for step in range(10000):        batch_x,batch_y = mnist.train.next_batch(batch_size)        #encoder3和decoder3需要进行run        _,encoded,decoded,c = sess.run([train,z,decoder2,cost],feed_dict={x:batch_x})        if step % 1000 ==0:            print('= = = = = = > > > > > >','train loss:% .4f' % c)            #将真实的图片和autoencoder后的图片对比            decoder_figures = sess.run(decoder2,feed_dict={x:view_figures})            for i in range(N_pictures):                #清除第一行图片                a[1][i].clear()                a[1][i].imshow(np.reshape(decoder_figures[i],(28,28)))                a[1][i].set_xticks(())                a[1][i].set_yticks(())            plt.draw()            plt.pause(1)    plt.ioff() #关闭交互模式"""with tf.Session() as sess:    init = tf.global_variables_initializer()    sess.run(init)    for epoch in range(epochs):        batch_x,batch_y = mnist.train.next_batch(batch_size)        _,c = sess.run([train,cost],feed_dict={x:batch_x})        if epoch % 100 == 0:            print('- - - - - - > > > > > > epoch: ',int(epoch/100),'cost: %.4f' %c)    #输出结果可视化    encoder_result = sess.run(z,feed_dict={x:mnist.test.images})    plt.scatter(encoder_result[:,0],encoder_result[:,1],c = mnist.test.labels,label='mnist distributions')    plt.legend(loc='best')    plt.title('different mnist digits shows in figure')    plt.colorbar()    plt.show()"""

结果

聚类效果：