Tensorflow 搭建自己的神经网络(五)

自编码Autoencoder

神经网络的非监督学习

神经网络接收图像→→给图像打马赛克→→再还原 

原有的图像被压缩，再用所储存的特征信息，经过解压获得原图。

如果神经元直接从获取的高清图像中取学习信息，会是一件很吃力的事情，所以通过特征提取，提取出能够重构出原图的主要信息，把缩减后的信息放入神经网络中进行学习，就可以更加轻松的学习。

 

输入：白色的X

输出：黑色的X

求取两者的误差，经过误差反向传递，逐步提升自编码准确性，中间的隐层就是能够提取出原数据最主要特征的神经元。

为什么说其是非监督学习：因为该过程只是用了X，而不用其标签，所以使非监督学习。

一般使用的时候只是用前半部分

因为前面已经学习了数据的精髓，我们只需要创建一个神经网络来学习这些精髓就好啦，可以达到和普通神经网络一样的效果，并且很高效。

编码器：前半部分

解码器：后半部分

自编码和PCA类似，可以提取出特征，可以给特征降维，自编码超越了PCA。

代码一：

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 11 00:02:38 2019

@author: xiexj
"""

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)

# 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 # 2nd 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):
    # Encoder Hidden layer with sigmoid activation #1
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),
                                   biases['encoder_b1']))
    # Decoder 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

# Building the decoder
def decoder(x):
    # Encoder Hidden layer with sigmoid activation #1
    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_true = X

# Define loss and optimizer, minimize the squared error
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:
    init = tf.global_variables_initializer()
    sess.run(init)
    total_batch = int(mnist.train.num_examples/batch_size)
    for epoch in range(training_epochs):
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, cost], feed_dict={X:batch_xs})
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1),"cost=", "{:.9f}".format(c))
            print("Epoch:", '%04d' % (epoch+1),"cost=", "{:.9f}".format(c))
    print("Optimization Finished!")
            
    encode_decode = sess.run(y_pred, feed_dict={X:mnist.test.images[:examples_to_show]})   
    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()

training_epochs = 5 # 训练批数

training_epochs = 10 # 训练批数

 代码二：

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 10 21:43:11 2019

@author: xiexj
"""

import tensorflow as tf
import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)

learning_rate = 0.01
trainning_epochs = 10 #20
batch_size = 256
display_step = 1
n_input = 784
X = tf.placeholder(tf.float32, [None, n_input])

n_hidden_1 = 128
n_hidden_2 = 64
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])),
        }

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']))
    # layer_4 dont use af
    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

encoder_op = encoder(X)
decoder_op = decoder(encoder_op)
y_pred = decoder_op
y_true = X

cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    total_batch = int(mnist.train.num_examples/batch_size)
    for epoch in range(trainning_epochs):
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, cost], feed_dict={X:batch_xs})
        if epoch % display_step == 0:
            print("Epoch:%04d" % (epoch+1),"cost={:.9f}".format(c))
    print("Optimization Finished!")
    
    encoder_result = sess.run(encoder_op, feed_dict={X:mnist.test.images})
    plt.scatter(encoder_result[:,0],encoder_result[:,1],c=mnist.test.labels)
    plt.show()