代码段1：
#Autoencoder import tensorflow as tf import numpy as np import matplotlib.pyplot as plt #import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('mnist',one_hot=False) #Visualize decoder setting #parameters learning_rate = 0.01 training_epochs = 5 batch_size = 256 display_step = 1 examples_to_show = 10 #Network parameters n_input = 784 #MNIST data input (img shape :28*28) #tf.graph input (only pictures) X = tf.placeholder('float',[None,n_input]) #hidden layer settings n_hidden_1 = 256 #1st layer num features n_hidden_2 = 128 #2rd layer num features weights = { 'encoder_h1':tf.Variable(tf.random_normal([n_input,n_hidden_1])), 'encoder_h2':tf.Variable(tf.random_normal([n_hidden_1,n_hidden_2])), 'decoder_h1':tf.Variable(tf.random_normal([n_hidden_2,n_hidden_1])), 'decoder_h2':tf.Variable(tf.random_normal([n_hidden_1,n_input])) } biases = { 'encoder_b1':tf.Variable(tf.random_normal([n_hidden_1])), 'encoder_b2':tf.Variable(tf.random_normal([n_hidden_2])), 'decoder_b1':tf.Variable(tf.random_normal([n_hidden_1])), 'decoder_b2':tf.Variable(tf.random_normal([n_input])) } #building the encoder def encoder(x): layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x,weights['encoder_h1']),biases['encoder_b1'])) #encoder hidden layer with sigmoid activation #2 layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,weights['encoder_h2']),biases['encoder_b2'])) return layer_2 def decoder(x): layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x,weights['decoder_h1']),biases['decoder_b1'])) #decoder hidden layer with sigmoid activation #2 layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,weights['decoder_h2']),biases['decoder_b2'])) return layer_2 #construct model encoder_op = encoder(X) decoder_op = decoder(encoder_op) #prediction y_pred = decoder_op #targets(Labels)are the input data y_ture = X #define loss and optimizer ,minimize the squared error cost = tf.reduce_mean(tf.pow(y_ture - y_pred,2)) optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) #initializing the variable init = tf.global_variables_initializer() #launch the graph with tf.Session() as sess: sess.run(init) total_batch = int(mnist.train.num_examples/batch_size) #training cycle for epoch in range(training_epochs): #loop over all batches for i in range(total_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size)#max(x)=1,min(x)=0 #run optimizer op (backprop) and cost op(to get loss value) _,c = sess.run([optimizer,cost],feed_dict={X:batch_xs}) #display logs per epoch step if epoch % display_step == 0: print("epoch:",'d'%(epoch+1),"cost:",'{:.9f}'.format(c)) print("optimizer finished") #applying encode and decode over test set encode_decode = sess.run(y_pred,feed_dict={X:mnist.test.images[:examples_to_show]}) #compare original images with thier reconstruction %matplotlib qt5 f,a = plt.subplots(2,10,figsize = (10,2)) for i in range(examples_to_show): a[0][i].imshow(np.reshape(mnist.test.images[i],(28,28))) a[1][i].imshow(np.reshape(encode_decode[i],(28,28))) plt.show()代码段2：
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt #import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('mnist',one_hot=False) #Visualize decoder setting #parameters learning_rate = 0.001 training_epochs = 20 batch_size = 256 display_step = 1 #Network parameters n_input = 784 #MNIST data input (img shape :28*28) #tf.graph input (only pictures) X = tf.placeholder('float',[None,n_input]) #hidden layer settings n_hidden_1 = 256 #1st layer num features n_hidden_2 = 64 #2rd layer num features n_hidden_3 = 10 n_hidden_4 = 2 weights = { 'encoder_h1':tf.Variable(tf.truncated_normal([n_input,n_hidden_1])), 'encoder_h2':tf.Variable(tf.truncated_normal([n_hidden_1,n_hidden_2])), 'encoder_h3':tf.Variable(tf.truncated_normal([n_hidden_2,n_hidden_3])), 'encoder_h4':tf.Variable(tf.truncated_normal([n_hidden_3,n_hidden_4])), 'decoder_h1':tf.Variable(tf.truncated_normal([n_hidden_4,n_hidden_3])), 'decoder_h2':tf.Variable(tf.truncated_normal([n_hidden_3,n_hidden_2])), 'decoder_h3':tf.Variable(tf.truncated_normal([n_hidden_2,n_hidden_1])), 'decoder_h4':tf.Variable(tf.truncated_normal([n_hidden_1,n_input])) } biases = { 'encoder_b1':tf.Variable(tf.random_normal([n_hidden_1],)), 'encoder_b2':tf.Variable(tf.random_normal([n_hidden_2],)), 'encoder_b3':tf.Variable(tf.random_normal([n_hidden_3],)), 'encoder_b4':tf.Variable(tf.random_normal([n_hidden_4],)), 'decoder_b1':tf.Variable(tf.random_normal([n_hidden_3],)), 'decoder_b2':tf.Variable(tf.random_normal([n_hidden_2],)), 'decoder_b3':tf.Variable(tf.random_normal([n_hidden_1],)), 'decoder_b4':tf.Variable(tf.random_normal([n_input],)) } #building the encoder def encoder(x): layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x,weights['encoder_h1']),biases['encoder_b1'])) layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,weights['encoder_h2']),biases['encoder_b2'])) layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2,weights['encoder_h3']),biases['encoder_b3'])) #在第四层时，输出量不再是 [0,1] 范围内的数，而是将数据通过默认的 Linear activation function 调整为 (-∞,∞) layer_4 = tf.add(tf.matmul(layer_3,weights['encoder_h4']),biases['encoder_b4']) return layer_4 def decoder(x): layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x,weights['decoder_h1']),biases['decoder_b1'])) layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,weights['decoder_h2']),biases['decoder_b2'])) layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2,weights['decoder_h3']),biases['decoder_b3'])) layer_4 = tf.nn.sigmoid(tf.add(tf.matmul(layer_3,weights['decoder_h4']),biases['decoder_b4'])) return layer_4 #construct model encoder_op = encoder(X) decoder_op = decoder(encoder_op) #prediction y_pred = decoder_op #targets(Labels)are the input data y_ture = X #define loss and optimizer ,minimize the squared error cost = tf.reduce_mean(tf.pow(y_ture - y_pred,2)) optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) #initializing the variable init = tf.global_variables_initializer() #launch the graph with tf.Session() as sess: sess.run(init) total_batch = int(mnist.train.num_examples/batch_size) #training cycle for epoch in range(training_epochs): #loop over all batches for i in range(total_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size)#max(x)=1,min(x)=0 #run optimizer op (backprop) and cost op(to get loss value) _,c = sess.run([optimizer,cost],feed_dict={X:batch_xs}) #display logs per epoch step if epoch % display_step == 0: print("epoch:",'d'%(epoch+1),"cost:",'{:.9f}'.format(c)) print("optimizer finished") encoder_result = sess.run(encoder_op,feed_dict={X:mnist.test.images}) %matplotlib qt5 plt.scatter(encoder_result[:,0],encoder_result[:,1],c=mnist.test.labels) plt.show()区别在于是否输出为2D效果图。由于使用的是jupyter notebook，所以在展示图形画面时需使用%matplotlib qt5 代码可直接运行，故不再粘贴结果及其figure。
