AutoEncoder是包含一个压缩和解压缩的过程，属于一种无监督学习的降维技术。

神经网络接受大量信息，有时候接受的数据达到上千万，可以通过压缩

提取原图片最具有代表性的信息，压缩输入的信息量，在将缩减后的数据放入神经网络中学习，如此学习起来变得轻松了

自编码在这个时候使用，可以将自编码归为无监督学习，类似于PCA，自编码可以为属性降维

手写体识别代码AutoEncoder

from __future__ import division, print_function, absolute_import

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_data', 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 # 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

"""

# Visualize encoder setting

# Parameters

learning_rate = 0.01    # 0.01 this learning rate will be better! Tested

training_epochs = 10

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 = 128

n_hidden_2 = 64

n_hidden_3 = 10

n_hidden_4 = 2  #2D show

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 = 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_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:

    # tf.initialize_all_variables() no long valid from

    # 2017-03-02 if using tensorflow >= 0.12

    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:

        init = tf.initialize_all_variables()

    else:

        init = tf.global_variables_initializer()

    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 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!")

    # # 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 their reconstructions

    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()

    # 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.colorbar()

    # plt.show()

利用AutoEncoder进行类似于PCA的降维

代码：

from __future__ import division, print_function, absolute_import

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_data', 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 # 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

"""

# Visualize encoder setting

# Parameters

learning_rate = 0.01    # 0.01 this learning rate will be better! Tested

training_epochs = 10

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 = 128

n_hidden_2 = 64

n_hidden_3 = 10

n_hidden_4 = 2  #2D show

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 = 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_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:

    # tf.initialize_all_variables() no long valid from

    # 2017-03-02 if using tensorflow >= 0.12

    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:

        init = tf.initialize_all_variables()

    else:

        init = tf.global_variables_initializer()

    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 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!")

#     # # 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 their reconstructions

#     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()

    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.colorbar()

    plt.show()

显示如下：

2.3AutoEncoder的更多相关文章

随机推荐

  1. iOS分辨率的那些事儿(转)

    1 iOS设备的分辨率 iOS设备,目前最主要的有3种(Apple TV等不在此讨论),按分辨率分为两类 iPhone/iPod Touch 普屏分辨率    320像素 x 480像素 Retina ...

  2. PMP用语集

    AC actual cost 实际成本 ACWP actual cost of work performed 已完工作实际成本 BAC budget at completion 完工预算 BCWP b ...

  3. actor binary tree lab4

    forward 与 ! (tell) 的差异,举个例子: Main(当前actor): topNode ! Insert(requester, id=1, ele = 2) topNode: root ...

  4. WPF路由事件学习（一）

    路由事件与一般事件的区别在于:路由事件是一种用于元素树的事件,当路由事件触发后,它可以向上或向下遍历可视树和逻辑树,他用一种简单而持久的方式在每个元素上触发,而不需要任何定制的代码(如果用传统的方式实 ...

  5. ios开发之--awakeFromNib和initWithFrame分别什么时候调用

    - (void)awakeFromNib { //代码 } 这个方法只有是通过storyborad或者xib方式创建的cell时才会自动调用 - (instancetype)initWithFrame ...

  6. PHP代码审计笔记--SQL注入

    0X01 普通注入 SQL参数拼接,未做任何过滤 <?php $con = mysql_connect("localhost","root"," ...

  7. Python模块常用的几种安装方式 【转】

    转自:http://blog.chinaunix.net/uid-23500957-id-3781907.html 一. 单文件模块直接把文件拷贝到 $python_dir/Lib 二. 多文件模块, ...

  8. iOS开发--提交应用Your binary is not optimized for iPhone 5

    ERROR ITMS-: "Your binary is not optimized for iPhone 5 - New iPhone apps and app updates submi ...

  9. Ubuntu 13.10 下安装搜狗输入法

    1.卸载ibus输入法: sudo apt-get remove ibus     sudo为取得root权限的意思,Ubuntu系统默认root账户关闭,很多操作需要取得root     权限才可以 ...

  10. java如何调用另一个包里面的类

    我现在有两个包: 我想在Boss里面实现对Employee的调用, Employee.java: package payroll2; public class Employee { public vo ...