-
Notifications
You must be signed in to change notification settings - Fork 0
/
convolucional_autoencoder.py
96 lines (71 loc) · 2.97 KB
/
convolucional_autoencoder.py
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
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
from keras.datasets import mnist
import numpy as np
np.set_printoptions(linewidth=10000, precision = 3, edgeitems= 100, suppress=True)
import matplotlib.pyplot as plt
plt.ion()
input_img = Input(shape=(28, 28, 1)) # adapt this if using `channels_first` image data format
#Conv2D first argument is the number output of filters in the convolution
x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
# at this point the representation is (4, 4, 8) i.e. 128-dimensional
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
conv_autoencoder = Model(input_img, decoded)
conv_autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
if K.image_data_format() == 'channels_last':
shape_ord = (28, 28, 1)
else:
shape_ord = (1, 28, 28)
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, ((x_train.shape[0],) + shape_ord))
x_test = np.reshape(x_test, ((x_test.shape[0],) + shape_ord))
from keras.callbacks import TensorBoard
batch_size=128
steps_per_epoch = np.int(np.floor(x_train.shape[0] / batch_size))
conv_autoencoder.fit(x_train, x_train, epochs=50, batch_size=128,
shuffle=True, validation_data=(x_test, x_test),
callbacks=[TensorBoard(log_dir='./tf_autoencoder_logs')])
decoded_imgs = conv_autoencoder.predict(x_test)
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i+1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + n + 1)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
#---------------------------------------------------------------------------------
#We could also have a look at the 128-dimensional encoded middle representation
conv_encoder = Model(input_img, encoded)
encoded_imgs = conv_encoder.predict(x_test)
n = 10
plt.figure(figsize=(20, 8))
for i in range(n):
ax = plt.subplot(1, n, i+1)
plt.imshow(encoded_imgs[i].reshape(4, 4 * 8).T)
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()