用tensorflow构建一个两层的auto-encoder【图文】

本文具体数据集与源代码可从我的GitHub地址获取
https://github.com/liuzuoping/Deep_Learning_note
用tensorflow构建一个两层的auto-encoder，把图像压缩到一个低维隐层空间并重构。

Auto-Encoder 概览

参考文献:

• Gradient-based learning applied to document recognition. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Proceedings of the IEEE, 86(11):2278-2324, November 1998.

MNIST 数据集概览

这个例子运用我们常见的手写数字识别数据集. 之前已经介绍过，这些图片的尺寸已经进行过归一化了，并且数字集中在一个固定的图片大小里（28*28像素），数值为0~1。为了简化，每张图片都被拍平并转换为一个含有784维特征的一维数组。

from __future__ import division, print_function, absolute_import

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

导入 MNIST 数据集

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

训练参数

learning_rate = 0.01
num_steps = 30000
batch_size = 256

display_step = 1000
examples_to_show = 10

网络参数

num_hidden_1 = 256 # 1st layer num features
num_hidden_2 = 128 # 2nd layer num features (the latent dim)
num_input = 784 # MNIST data input (img shape: 28*28)

设置placeholder

X = tf.placeholder("float", [None, num_input])

weights = {
    'encoder_h1': tf.Variable(tf.random_normal([num_input, num_hidden_1])),
    'encoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_hidden_2])),
    'decoder_h1': tf.Variable(tf.random_normal([num_hidden_2, num_hidden_1])),
    'decoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_input])),
}
biases = {
    'encoder_b1': tf.Variable(tf.random_normal([num_hidden_1])),
    'encoder_b2': tf.Variable(tf.random_normal([num_hidden_2])),
    'decoder_b1': tf.Variable(tf.random_normal([num_hidden_1])),
    'decoder_b2': tf.Variable(tf.random_normal([num_input])),
}

构建encoder与decoder

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']))
    # Encoder 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


# 构建decoder
def decoder(x):
    # Decoder 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

# 构建模型
encoder_op = encoder(X)
decoder_op = decoder(encoder_op)

# 预测
y_pred = decoder_op
# label即为输入值
y_true = X

# 定义loss和optimizer，最小化平方误差
loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss)

# 初始化参数
init = tf.global_variables_initializer()

开始训练，启动一个session

sess = tf.Session()

sess.run(init)

# 训练
for i in range(1, num_steps+1):
    # 准备数据
    # 取每个batch的数据
    batch_x, _ = mnist.train.next_batch(batch_size)

    # 运行optimzer的op和cost的op，获得loss值
    _, l = sess.run([optimizer, loss], feed_dict={X: batch_x})
    # 展示每一步的loss
    if i % display_step == 0 or i == 1:
        print('Step %i: Minibatch Loss: %f' % (i, l))

测试

对测试集进行编码和解码，并可视化它们的重构

n = 4
canvas_orig = np.empty((28 * n, 28 * n))
canvas_recon = np.empty((28 * n, 28 * n))
for i in range(n):
    # MNIST 测试集
    batch_x, _ = mnist.test.next_batch(n)
    # 对数字图像进encode和decode
    g = sess.run(decoder_op, feed_dict={X: batch_x})
    
    # 展示原始图像
    for j in range(n):
        # 画出生成的像素
        canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = batch_x[j].reshape([28, 28])
    # 展示重构后的图像
    for j in range(n):
        # 展示生成的像素
        canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])

print("Original Images")     
plt.figure(figsize=(n, n))
plt.imshow(canvas_orig, origin="upper", cmap="gray")
plt.show()

print("Reconstructed Images")
plt.figure(figsize=(n, n))
plt.imshow(canvas_recon, origin="upper", cmap="gray")
plt.show()

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