1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
|
Trains a denoising autoencoder on MNIST dataset.
Denoising is one of the classic applications of autoencoders.
The denoising process removes unwanted noise that corrupted the
true signal.
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.
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.
```python
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
(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
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
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
layer_filters = [32, 64]
# Build the Autoencoder Model
# First build the Encoder Model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
# Stack of Conv2D blocks
# 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
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
shape = K.int_shape(x)
# Generate the latent vector
x = Flatten()(x)
latent = Dense(latent_dim, name='latent_vector')(x)
# Instantiate Encoder Model
encoder = Model(inputs, latent, name='encoder')
encoder.summary()
# Build the Decoder Model
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
# 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
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
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
# Autoencoder = Encoder + Decoder
# Instantiate Autoencoder Model
autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')
autoencoder.summary()
autoencoder.compile(loss='mse', optimizer='adam')
# Train the autoencoder
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
x_decoded = autoencoder.predict(x_test_noisy)
# Display the 1st 8 corrupted and denoised images
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()
```
|
