TF之AE：AE實現(xiàn)TF自帶數(shù)據(jù)集AE的encoder之后decoder之前的非監(jiān)督學習分類

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代碼設計

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代碼設計

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("/niu/mnist_data/",one_hot=False)# Parameter learning_rate = 0.001 training_epochs = 20 batch_size = 256 display_step = 1 examples_to_show = 10# Network Parameters n_input = 784 # MNIST data input (img shape: 28*28像素即784個特征值)#tf Graph input(only pictures) X=tf.placeholder("float", [None,n_input])# hidden layer settings n_hidden_1 = 128 n_hidden_2 = 64 n_hidden_3 = 10 n_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 #1layer_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#定義decoder def decoder(x): # Decoder Hidden layer with sigmoid activation #2layer_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) # 128 Features decoder_op = decoder(encoder_op) # 784 Features# Prediction y_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 graph with tf.Session() as sess:sess.run(tf.global_variables_initializer())total_batch = int(mnist.train.num_examples/batch_size)# Training cyclefor epoch in range(training_epochs):# Loop over all batchesfor 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 stepif 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()

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