-
Notifications
You must be signed in to change notification settings - Fork 676
/
VAE Stacked DAE
157 lines (135 loc) · 4.78 KB
/
VAE Stacked DAE
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
152
153
154
155
156
157
import keras
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda
from keras.models import Model
from keras import backend as K
from keras import objectives
from keras.datasets import mnist
from keras.layers.core import Reshape
from __future__ import print_function
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Merge
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, UpSampling2D
from keras.utils import np_utils
from keras.layers.normalization import BatchNormalization
from keras.callbacks import ModelCheckpoint,LearningRateScheduler
from keras.optimizers import SGD
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
zero=np.where(y_train==0)
x_train=x_train[zero][0:20]
batch_size = 30
nb_classes = 10
img_rows, img_cols = shape, shape
nb_filters = 32
pool_size = (2, 2)
kernel_size = (3, 3)
input_shape=(shape,shape,1)
original_dim = 784
latent_dim = 2
intermediate_dim = 256
epsilon_std = 1.0
learning_rate = 0.028
decay_rate = 5e-5
momentum = 0.9
sgd = SGD(lr=learning_rate,momentum=momentum, decay=decay_rate, nesterov=False)
def norm(x):
return (x-np.min(x))/(np.max(x)-np.min(x))
part=8
thre=1
# START VAE
recog=Sequential()
recog.add(Dense(64,activation='relu',input_shape=(784,),init='glorot_uniform'))
get_0_layer_output=K.function([recog.layers[0].input,
K.learning_phase()],[recog.layers[0].output])
c=get_0_layer_output([x_train[0].reshape((1,784)), 0])[0][0]
recog_left=recog
recog_right.add(Lambda(lambda x: x + np.mean(c), output_shape=(64,)))
recog_right=recog
recog_right.add(Lambda(lambda x: x + K.exp(x / 2) * K.random_normal(shape=(1, 64), mean=0.,
std=epsilon_std), output_shape=(64,)))
recog1=Sequential()
recog1.add(Merge([recog_left,recog_right],mode = 'ave'))
recog1.add(Dense(64, activation='relu',init='glorot_uniform'))
recog1.add(Dense(784, activation='relu',init='glorot_uniform'))
### END FIRST MODEL VAE
### START DAE
recog1.add(Reshape((28,28,1)))
recog1.add(Convolution2D(20, 3,3,
border_mode='valid',
input_shape=input_shape))
recog1.add(BatchNormalization(mode=2))
recog1.add(Activation('relu'))
recog1.add(UpSampling2D(size=(2, 2)))
recog1.add(Convolution2D(20, 3, 3,
init='glorot_uniform'))
recog1.add(BatchNormalization(mode=2))
recog1.add(Activation('relu'))
recog1.add(Convolution2D(20, 3, 3,init='glorot_uniform'))
recog1.add(BatchNormalization(mode=2))
recog1.add(Activation('relu'))
recog1.add(MaxPooling2D(pool_size=(3,3)))
recog1.add(Convolution2D(4, 3, 3,init='glorot_uniform'))
recog1.add(BatchNormalization(mode=2))
recog1.add(Activation('relu'))
recog1.add(Reshape((28,28,1)))
recog1.add(Reshape((784,)))
recog1.add(Dense(784, activation='sigmoid',init='glorot_uniform'))
recog1.compile(loss='mean_squared_error', optimizer=sgd,metrics = ['mae'])
### VANISHING GRADIENT w/ SIGMOID TANH
recog1.fit(x_train[0].reshape((1,784)), x_train[0].reshape((1,784)),
nb_epoch=150,
batch_size=30,verbose=1)
n = 4
plt.figure(figsize=(10, 2))
for i in range(0,n):
ax = plt.subplot(1, n, i+1)
plt.imshow(x_train[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
pred0=7
a=recog1.predict(x_train[pred0].reshape((1,784)))
from matplotlib.colors import LinearSegmentedColormap
n_classes=2
colors = [(0, 0, 0), (0, 1, 0), (0, 0, 1),(1,1,0)]
cm = LinearSegmentedColormap.from_list(
a, colors, N=2)
colors2 = [(0, 0, 0), (0, 1, 0), (0, 0, 1),(1,0,0)]
cm2 = LinearSegmentedColormap.from_list(
a, colors2, N=2)
plt.figure(figsize=(10,10))
ax = plt.subplot(1, 3, 1)
plt.imshow(x_train[pred0].reshape((28,28)))
plt.gray()
plt.title('Original')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(1, 3, 2)
plt.imshow(a.reshape((shape,shape)),cmap=cm2)
plt.title('Prediction')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(1, 3, 3)
plt.imshow(x_train[pred0].reshape((28,28)))
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(1, 3, 3)
plt.imshow(a.reshape((shape,shape)),cmap=cm,alpha=0.35,interpolation='nearest')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.title('MASK')
plt.show()
import pydot
import graphviz
import pydot_ng as pydot
from IPython.display import SVG
from keras.utils.visualize_util import model_to_dot
SVG(model_to_dot(recog_right).create(prog='dot', format='svg'))