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

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

目录

输出结果　　

代码设计

输出结果　　

代码设计

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个特征值)
 
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 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decoder
def 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 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             Before
 
 
cost = 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 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()

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