TF之AE：AE实现TF自带数据集AE的encoder之后decoder之前的非监督学习分类

import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt#Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_datamnist=input_data.read_data_sets("/niu/mnist_data/",one_hot=False)# Parameterlearning_rate = 0.001 training_epochs = 20 ??batch_size = 256display_step = 1examples_to_show = 10# Network Parametersn_input = 784 ?# MNIST data input (img shape: 28*28像素即784个特征值)#tf Graph input(only pictures)X=tf.placeholder("float", [None,n_input])# hidden layer settingsn_hidden_1 = 128 n_hidden_2 = 64 ?n_hidden_3 = 10n_hidden_4 = 2 ??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])), ???‘encoder_h3‘: tf.Variable(tf.random_normal([n_hidden_2,n_hidden_3])), ???‘encoder_h4‘: tf.Variable(tf.random_normal([n_hidden_3,n_hidden_4])), ???????????‘decoder_h1‘: tf.Variable(tf.random_normal([n_hidden_4,n_hidden_3])), ???‘decoder_h2‘: tf.Variable(tf.random_normal([n_hidden_3,n_hidden_2])), ???‘decoder_h3‘: tf.Variable(tf.random_normal([n_hidden_2,n_hidden_1])), ???‘decoder_h4‘: 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])), ???‘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])), ???}def encoder(x): ????# Encoder Hidden layer with sigmoid activation #1 ???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‘])) ???layer_4 = tf.add(tf.matmul(layer_3, weights[‘encoder_h4‘]), ???????????????????????????????????biases[‘encoder_b4‘]) ???return layer_4 ???#定义decoderdef decoder(x): ????# Decoder Hidden layer with sigmoid activation #2 ???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 modelencoder_op = encoder(X) ????????????# 128 Featuresdecoder_op = decoder(encoder_op) ???# 784 Features# Predictiony_pred = decoder_op ???#After# Targets (Labels) are the input data.y_true = X ????????????#Beforecost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)# Launch the graphwith tf.Session() as sess: ???sess.run(tf.global_variables_initializer()) ???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 optimization 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:", ‘%04d‘ % (epoch+1), ?????????????????"cost=", "{:.9f}".format(c)) ???print("Optimization Finished!") ???encode_result = sess.run(encoder_op,feed_dict={X:mnist.test.images}) ???plt.scatter(encode_result[:,0],encode_result[:,1],c=mnist.test.labels) ???plt.title(‘Matplotlib,AE,classification--Jason Niu‘) ???plt.show()