-
Notifications
You must be signed in to change notification settings - Fork 0
/
vaes_orig_mnist.py
208 lines (169 loc) · 7.8 KB
/
vaes_orig_mnist.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
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
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
'''This script demonstrates how to build a variational autoencoder with Keras.
Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
'''
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda, Layer
from keras.models import Model
from keras import backend as K
from keras import metrics
from keras.datasets import mnist
import tensorflow as tf
import fbm_data
from fbm_data import generate_2d_fbms
# train the VAE on MNIST digits
original_dim = 784
(x_train0, y_train0), (x_test0, y_test0) = mnist.load_data()
n=32
print 'USING fBms'
x_train0, x_test0, y_train0, y_test0 = \
generate_2d_fbms(N=50000,n=n,reCalc=False,resize=original_dim)
############################################################
batch_size = 100
latent_dim = 2
latent_dim_w = 30
intermediate_dim = 256
epochs = 15
epsilon_std = 1.0
x = Input(batch_shape=(batch_size, original_dim))
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
z_log_var = Dense(latent_dim)(h)
w_mean = Dense(latent_dim_w)(h)
w_log_var = Dense(latent_dim_w)(h)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(batch_size, latent_dim), mean=0.,
stddev=epsilon_std)
return z_mean + K.exp(z_log_var / 2) * epsilon
def sampling_w(args):
w_mean, w_log_var = args
epsilon = K.random_normal(shape=(batch_size, latent_dim_w), mean=0.,
stddev=epsilon_std)
return w_mean + K.exp(w_log_var / 2) * epsilon
# note that "output_shape" isn't necessary with the TensorFlow backend
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
w = Lambda(sampling_w, output_shape=(latent_dim_w,))([w_mean, w_log_var])
# we instantiate these layers separately so as to reuse them later
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='tanh')
w_decoder = Dense(intermediate_dim, activation='relu')
w_decoder_mean = Dense(original_dim, activation='tanh')
def decode(z,w):
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)
w_decoded = w_decoder(w)
w_decoded_mean = w_decoder_mean(w_decoded)
x_decoded_mean = Lambda(lambda x: 0.5*(x+w_decoded_mean))(x_decoded_mean)
return x_decoded_mean
x_decoded_mean = decode(z,w)
# Custom loss layer
class CustomVariationalLayer(Layer):
def __init__(self, **kwargs):
self.is_placeholder = True
super(CustomVariationalLayer, self).__init__(**kwargs)
def vae_loss(self, x, x_decoded_mean):
xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
kl_loss_w = - 0.5 * K.sum(1 + w_log_var - K.square(w_mean) - K.exp(w_log_var), axis=-1)
#kl_loss_w = - 0.5 * K.sum(1 + w_log_var - K.square(w_mean) - K.exp(w_log_var), axis=-1)
#kl_loss = tf.Print(kl_loss,
# [K.get_variable_shape(xent_loss)],
# 'xent ssim ')
return K.mean(xent_loss + kl_loss + kl_loss_w)
def vae_frac_loss(self, x,x_decoded_mean):
x = K.reshape(x,[-1,int(np.sqrt(original_dim)),int(np.sqrt(original_dim))])
x_decoded_mean = K.reshape(x_decoded_mean,[-1,int(np.sqrt(original_dim)),int(np.sqrt(original_dim))])
lvi_loss = original_dim * fbm_data.loss_logvarinc(x,x_decoded_mean,int(np.sqrt(original_dim)))
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
#kl_loss_w = - 0.5 * K.sum(1 + w_log_var - K.square(w_mean) - K.exp(w_log_var), axis=-1)
#lvi_loss = tf.Print(lvi_loss,[lvi_loss],'lvi')
return K.mean(lvi_loss + kl_loss )#+ kl_loss_w)
def vae_ssim_loss(self, x,x_decoded_mean):
#xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
dim1=int(np.sqrt(original_dim))
x = K.reshape(x,[-1,dim1,dim1])
x_decoded_mean = K.reshape(x_decoded_mean,[-1,dim1,dim1])
ssim_loss = original_dim * fbm_data.loss_DSSIS_tf11(x,x_decoded_mean, False)#,int(np.sqrt(original_dim)))
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
kl_loss_w = - 0.5 * K.sum(1 + w_log_var - K.square(w_mean) - K.exp(w_log_var), axis=-1)
#ssim_loss = tf.Print(ssim_loss,[K.get_variable_shape(xent_loss+kl_loss), K.get_variable_shape(kl_loss)],'xent ssim ')
#kl_loss_w = - 0.5 * K.sum(1 + w_log_var - K.square(w_mean) - K.exp(w_log_var), axis=-1)
#print xent_loss, ssim_loss, kl_loss
return K.mean(ssim_loss + kl_loss + kl_loss_w)
def call(self, inputs):
x = inputs[0]
x_decoded_mean = inputs[1]
loss = self.vae_loss(x, x_decoded_mean)
#loss = self.vae_frac_loss(x, x_decoded_mean)
#loss = self.vae_ssim_loss(x, x_decoded_mean)
self.add_loss(loss, inputs=inputs)
# We won't actually use the output.
return x
y = CustomVariationalLayer()([x, x_decoded_mean])
vae = Model(x, y)
vae.compile(optimizer='rmsprop', loss=None)
x_train = x_train0.astype('float32') / 255.
x_test = x_test0.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
y_train = y_train0
y_test = y_test0
vae.fit(x_train,
shuffle=True,
epochs=epochs,
verbose=2,
batch_size=batch_size,
validation_data=(x_test, x_test))
# build a model to project inputs on the latent space
encoder = Model(x, z_mean)
# display a 2D plot of the digit classes in the latent space
x_test_encoded = encoder.predict(x_test, batch_size=batch_size)
plt.figure(figsize=(6, 6))
plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test)
plt.colorbar()
plt.show()
# build a digit generator that can sample from the learned distribution
decoder_input = Input(shape=(latent_dim,))
decoder_input_w = Input(shape=(latent_dim_w,))
#_h_decoded = decoder_h(decoder_input)
#_x_decoded_mean = decoder_mean(_h_decoded)
_x_decoded_mean = decode(decoder_input,decoder_input_w)
generator = Model([decoder_input, decoder_input_w], _x_decoded_mean)
# display a 2D manifold of the digits
n = 15 # figure with 15x15 digits
digit_size = int(np.sqrt(original_dim))
figure = np.zeros((digit_size * n, digit_size * n))
h_figure = np.zeros((n,n,2))
# linearly spaced coordinates on the unit square were transformed through the inverse CDF (ppf) of the Gaussian
# to produce values of the latent variables z, since the prior of the latent space is Gaussian
grid_x = norm.ppf(np.linspace(0.05, 0.95, n))
grid_y = norm.ppf(np.linspace(0.05, 0.95, n))
##
for i, yi in enumerate(grid_x):
for j, xi in enumerate(grid_y):
z_sample = np.array([[xi, yi]])
w_sample = np.random.randn(1,latent_dim_w)*1
# randomize w instead of z as the manifold parameters
# here we should see a disoriented behaviour
#z_sample = np.random.randn(1,latent_dim)*1
#w_sample = np.concatenate([np.array([[xi, yi]]), [np.random.randn(latent_dim_w-2)*1]],axis=1)
x_decoded = generator.predict([z_sample,w_sample])
digit = x_decoded[0].reshape(digit_size, digit_size)
h_figure[i,j,:] = fbm_data.hurst2d(digit,max_tau=5)
figure[i * digit_size: (i + 1) * digit_size,
j * digit_size: (j + 1) * digit_size] = digit
#plt.figure(figsize=(10, 10))
figure=figure-np.min(figure)
figure=figure/np.max(figure)
_,pp=plt.subplots(2,2)
pp[0,0].imshow(figure, cmap='gray',interpolation='none')
#.show()
res=pp[0,1].imshow(h_figure[:,:,0],cmap='gray',interpolation='none')
plt.colorbar(res,ax=pp[0,1])
pp[0,1].set_title('H')
res=pp[1,0].imshow(h_figure[:,:,1],cmap='gray',interpolation='none')
plt.colorbar(res,ax=pp[1,0])
pp[1,0].set_title('R^2')
#plt.show()