官网实例详解4.25（mnist_denoising_autoencoder.py）-keras学习笔记四

基于MNIST数据集训练去噪自编码器

 

Keras实例目录

效果展示

代码注释

'''Trains a denoising autoencoder on MNIST dataset.
基于MNIST数据集训练去噪自编码器
Denoising is one of the classic applications of autoencoders.
去噪是自编码器的一种应用
The denoising process removes unwanted noise that corrupted the
true signal.
去噪过程是清除破坏真实信号的噪音
Noise + Data ---> Denoising Autoencoder ---> Data

Given a training dataset of corrupted data as input and
true signal as output, a denoising autoencoder can recover the
hidden structure to generate clean data.
给定一个损坏数据作为输入和真实信号作为输出的训练数据集，去噪自编码器可以恢复隐藏结构以生成干净数据

This example has modular design. The encoder, decoder and autoencoder
are 3 models that share weights. For example, after training the
autoencoder, the encoder can be used to  generate latent vectors
of input data for low-dim visualization like PCA or TSNE.
这个例子具有模块化设计。编码器、解码器和自编码器是3个共享权重的模型。例如，在训练自编码器之后，编
码器可用于产生输入数据的潜向量，以用于降维可视化，如PCA或TSNE。
PCA(Principal Component Analysis，主成分分析)不仅仅是对高维数据进行降维,更重要的是经过降维去除了噪声,发现了数据中的模式
TSNE(t-distributed stochastic neighbor embedding，t-SNE)是用于降维的一种机器学习算法，是由 Laurens van der Maaten
和 Geoffrey Hinton在08年提出来。此外，t-SNE 是一种非线性降维算法，非常适用于高维数据降维到2维或者3维，进行可视化。
t-SNE是由SNE(Stochastic Neighbor Embedding, SNE; Hinton and Roweis, 2002)发展而来。我们先介绍SNE的基本原理，之后再扩
展到t-SNE。最后再看一下t-SNE的实现以及一些优化。
'''

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import keras
from keras.layers import Activation, Dense, Input
from keras.layers import Conv2D, Flatten
from keras.layers import Reshape, Conv2DTranspose
from keras.models import Model
from keras import backend as K
from keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

np.random.seed(1337)

# MNIST dataset
# MNIST数据集
(x_train, _), (x_test, _) = mnist.load_data()

image_size = x_train.shape[1]
x_train = np.reshape(x_train, [-1, image_size, image_size, 1])
x_test = np.reshape(x_test, [-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# Generate corrupted MNIST images by adding noise with normal dist
# centered at 0.5 and std=0.5
# 添加以0.5和STD＝0.5为中心的正常DIST来产生损坏的MNIST图像。
# normal dist（normal distribution),正态分布
noise = np.random.normal(loc=0.5, scale=0.5, size=x_train.shape)
x_train_noisy = x_train + noise
noise = np.random.normal(loc=0.5, scale=0.5, size=x_test.shape)
x_test_noisy = x_test + noise

x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)

# Network parameters
# 网络参数
input_shape = (image_size, image_size, 1)
batch_size = 128
kernel_size = 3
latent_dim = 16
# Encoder/Decoder number of CNN layers and filters per layer
# CNN层和每层滤波器的编码器/解码器数
layer_filters = [32, 64]

# Build the Autoencoder Model
# 建立自编码器模型
# First build the Encoder Model
# 首先建立编码器模型
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
# Stack of Conv2D blocks
# Conv2D块堆栈
# Notes:
# 1) Use Batch Normalization before ReLU on deep networks
# 2) Use MaxPooling2D as alternative to strides>1
# - faster but not as good as strides>1
#  注意:
# 1) 在深度网络，在ReLU（激活函数）前使用批次归一化
# 2) 使用MaxPooling2D（池化）作为strides>1替换（较快），但是不如strides>1


for filters in layer_filters:
    x = Conv2D(filters=filters,
               kernel_size=kernel_size,
               strides=2,
               activation='relu',
               padding='same')(x)

# Shape info needed to build Decoder Model
# 建立解码器模型所需的形状信息
shape = K.int_shape(x)

# Generate the latent vector
# 生成向量
x = Flatten()(x)
latent = Dense(latent_dim, name='latent_vector')(x)

# Instantiate Encoder Model
# 实例化编码器模型
encoder = Model(inputs, latent, name='encoder')
encoder.summary()

# Build the Decoder Model
# 建立解码器模型
latent_inputs = Input(shape=(latent_dim,), name='decoder_input')
x = Dense(shape[1] * shape[2] * shape[3])(latent_inputs)
x = Reshape((shape[1], shape[2], shape[3]))(x)

# Stack of Transposed Conv2D blocks
# 转换Conv2D块堆栈
# Notes:
# 1) Use Batch Normalization before ReLU on deep networks
# 2) Use UpSampling2D as alternative to strides>1
# - faster but not as good as strides>1
# 1) 在深度网络，在ReLU（激活函数）前使用批次归一化
# 2) 使用UpSampling2D（上采样、扩维）作为strides>1替换（较快），但是不如strides>1
for filters in layer_filters[::-1]:
    x = Conv2DTranspose(filters=filters,
                        kernel_size=kernel_size,
                        strides=2,
                        activation='relu',
                        padding='same')(x)

x = Conv2DTranspose(filters=1,
                    kernel_size=kernel_size,
                    padding='same')(x)

outputs = Activation('sigmoid', name='decoder_output')(x)

# Instantiate Decoder Model
# 初始化解码器模型
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()

# Autoencoder = Encoder + Decoder
# Instantiate Autoencoder Model
# 初始化自编码器模型
autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')
autoencoder.summary()

autoencoder.compile(loss='mse', optimizer='adam')

# Train the autoencoder
# 训练自编码器
autoencoder.fit(x_train_noisy,
                x_train,
                validation_data=(x_test_noisy, x_test),
                epochs=30,
                batch_size=batch_size)

# Predict the Autoencoder output from corrupted test images
# 从损坏的测试图像预测自动编码器输出
x_decoded = autoencoder.predict(x_test_noisy)

# Display the 1st 8 corrupted and denoised images
# 显示图片
rows, cols = 10, 30
num = rows * cols
imgs = np.concatenate([x_test[:num], x_test_noisy[:num], x_decoded[:num]])
imgs = imgs.reshape((rows * 3, cols, image_size, image_size))
imgs = np.vstack(np.split(imgs, rows, axis=1))
imgs = imgs.reshape((rows * 3, -1, image_size, image_size))
imgs = np.vstack([np.hstack(i) for i in imgs])
imgs = (imgs * 255).astype(np.uint8)
plt.figure()
plt.axis('off')
plt.title('Original images: top rows, '
          'Corrupted Input: middle rows, '
          'Denoised Input:  third rows')
plt.imshow(imgs, interpolation='none', cmap='gray')
Image.fromarray(imgs).save('corrupted_and_denoised.png')
plt.show()

代码执行

 

C:\ProgramData\Anaconda3\python.exe E:/keras-master/examples/mnist_denoising_autoencoder.py

Using TensorFlow backend.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
encoder_input (InputLayer)   (None, 28, 28, 1)         0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 14, 14, 32)        320       
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 7, 7, 64)          18496     
_________________________________________________________________
flatten_1 (Flatten)          (None, 3136)              0         
_________________________________________________________________
latent_vector (Dense)        (None, 16)                50192     
=================================================================
Total params: 69,008
Trainable params: 69,008
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
decoder_input (InputLayer)   (None, 16)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 3136)              53312     
_________________________________________________________________
reshape_1 (Reshape)          (None, 7, 7, 64)          0         
_________________________________________________________________
conv2d_transpose_1 (Conv2DTr (None, 14, 14, 64)        36928     
_________________________________________________________________
conv2d_transpose_2 (Conv2DTr (None, 28, 28, 32)        18464     
_________________________________________________________________
conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1)         289       
_________________________________________________________________
decoder_output (Activation)  (None, 28, 28, 1)         0         
=================================================================
Total params: 108,993
Trainable params: 108,993
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
encoder_input (InputLayer)   (None, 28, 28, 1)         0         
_________________________________________________________________
encoder (Model)              (None, 16)                69008     
_________________________________________________________________
decoder (Model)              (None, 28, 28, 1)         108993    
=================================================================
Total params: 178,001
Trainable params: 178,001
Non-trainable params: 0
_________________________________________________________________
Train on 60000 samples, validate on 10000 samples
Epoch 1/30

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60000/60000 [==============================] - 59s 976us/step - loss: 0.0604 - val_loss: 0.0341
Epoch 2/30

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60000/60000 [==============================] - 16s 261us/step - loss: 0.0143 - val_loss: 0.0155

Process finished with exit code 0

Keras详细介绍

英文：https://keras.io/

中文：http://keras-cn.readthedocs.io/en/latest/

实例下载

https://github.com/keras-team/keras

https://github.com/keras-team/keras/tree/master/examples

完整项目下载

方便没积分童鞋，请加企鹅452205574，共享文件夹。

包括：代码、数据集合（图片）、已生成model、安装库文件等。