/
ALICE_A.py
368 lines (284 loc) · 14 KB
/
ALICE_A.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
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import pdb
GPUID = 1
os.environ["CUDA_VISIBLE_DEVICES"] = str(GPUID)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import tensorflow as tf
from utils.data_gmm import GMM_distribution, sample_GMM, plot_GMM
from utils.data_utils import shuffle, iter_data
from tqdm import tqdm
slim = tf.contrib.slim
ds = tf.contrib.distributions
st = tf.contrib.bayesflow.stochastic_tensor
graph_replace = tf.contrib.graph_editor.graph_replace
""" parameters """
n_epoch = 100
batch_size = 128
dataset_size_x = 512*4
dataset_size_z = 512*4
dataset_size_x_test = 512*2
dataset_size_z_test = 512*2
input_dim = 2
latent_dim = 2
eps_dim = 2
n_layer_disc = 2
n_hidden_disc = 256
n_layer_gen = 2
n_hidden_gen= 256
n_layer_inf = 2
n_hidden_inf= 256
""" Create directory for results """
result_dir = 'results/ALICE_A/'
directory = result_dir
if not os.path.exists(directory):
os.makedirs(directory)
""" Create dataset """
# create X dataset
# means = map(lambda x: np.array(x), [[0, 0]])
means_x = map(lambda x: np.array(x), [[0, 0],
[2, 2],
[-2, -2],
[2, -2],
[-2, 2]])
means_x = list(means_x)
std_x = 0.04
variances_x = [np.eye(2) * std_x for _ in means_x]
priors_x = [1.0/len(means_x) for _ in means_x]
gaussian_mixture = GMM_distribution(means=means_x,
variances=variances_x,
priors=priors_x)
dataset_x = sample_GMM(dataset_size_x, means_x, variances_x, priors_x, sources=('features', ))
save_path_x = result_dir + 'X_gmm_data_train.pdf'
# plot_GMM(dataset, save_path)
## reconstruced x
X_dataset = dataset_x.data['samples']
X_targets = dataset_x.data['label']
fig_mx, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4.5))
ax.scatter(X_dataset[:, 0], X_dataset[:, 1], c=cm.Set1(X_targets.astype(float)/input_dim/2.0),
edgecolor='none', alpha=0.5)
ax.set_xlim(-3, 3); ax.set_ylim(-3.5, 3.5)
ax.set_xlabel('$x_1$'); ax.set_ylabel('$x_2$')
ax.axis('on')
plt.savefig(save_path_x, transparent=True, bbox_inches='tight')
# create Z dataset
means_z = map(lambda x: np.array(x), [[0, 0]])
# means = map(lambda x: np.array(x), [[-1, -1],[1, 1]])
means_z = list(means_z)
std_z = 1.0
variances_z = [np.eye(2) * std_z for _ in means_z]
priors_z = [1.0/len(means_z) for _ in means_z]
dataset_z = sample_GMM(dataset_size_z, means_z, variances_z, priors_z, sources=('features', ))
save_path_z = result_dir + 'Z_gmm_data_train.pdf'
# plot_GMM(dataset, save_path)
## reconstruced x
Z_dataset = dataset_z.data['samples']
Z_labels = dataset_z.data['label']
fig_mx, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4.5))
ax.scatter(Z_dataset[:, 0], Z_dataset[:, 1], c=cm.Set1(Z_labels.astype(float)/input_dim/2.0),
edgecolor='none', alpha=0.5)
ax.set_xlim(-3, 3); ax.set_ylim(-3.5, 3.5)
ax.set_xlabel('$z_1$'); ax.set_ylabel('$z_2$')
ax.axis('on')
plt.savefig(save_path_z, transparent=True, bbox_inches='tight')
""" Networks """
def standard_normal(shape, **kwargs):
"""Create a standard Normal StochasticTensor."""
return tf.cast(st.StochasticTensor(
ds.MultivariateNormalDiag(mu=tf.zeros(shape), diag_stdev=tf.ones(shape), **kwargs)), tf.float32)
def generative_network(z, input_dim, n_layer, n_hidden, eps_dim):
with tf.variable_scope("generative"):
eps = standard_normal([z.get_shape().as_list()[0], eps_dim], name="eps") * 1.0
h = tf.concat([z, eps], 1)
h = slim.repeat(h, n_layer, slim.fully_connected, n_hidden, activation_fn=tf.nn.relu)
x = slim.fully_connected(h, input_dim, activation_fn=None, scope="p_x")
return x
def inference_network(x, latent_dim, n_layer, n_hidden, eps_dim):
with tf.variable_scope("inference"):
eps = standard_normal([x.get_shape().as_list()[0], eps_dim], name="eps") * 1.0
h = tf.concat([x, eps], 1)
h = slim.repeat(h, n_layer, slim.fully_connected, n_hidden, activation_fn=tf.nn.relu)
z = slim.fully_connected(h, latent_dim, activation_fn=None, scope="q_z")
return z
def data_network(x,z, n_layers=2, n_hidden=256, activation_fn=None):
"""Approximate x log data density."""
h = tf.concat([x,z], 1)
with tf.variable_scope('discriminator'):
h = slim.repeat(h, n_layers, slim.fully_connected, n_hidden, activation_fn=tf.nn.relu)
log_d = slim.fully_connected(h, 1, activation_fn=activation_fn)
return tf.squeeze(log_d, squeeze_dims=[1])
def data_network_xx(x,x1, n_layers=2, n_hidden=256, activation_fn=None):
"""Approximate x log data density."""
# pdb.set_trace()
h = tf.concat([x,x1], 1)
with tf.variable_scope('discriminator_xx'):
h = slim.repeat(h, n_layers, slim.fully_connected, n_hidden, activation_fn=tf.nn.relu)
log_d = slim.fully_connected(h, 1, activation_fn=activation_fn)
return tf.squeeze(log_d, squeeze_dims=[1])
def data_network_zz(z,z1, n_layers=2, n_hidden=256, activation_fn=None):
"""Approximate x log data density."""
# pdb.set_trace()
h = tf.concat([z,z1], 1)
with tf.variable_scope('discriminator_zz'):
h = slim.repeat(h, n_layers, slim.fully_connected, n_hidden, activation_fn=tf.nn.relu)
log_d = slim.fully_connected(h, 1, activation_fn=activation_fn)
return tf.squeeze(log_d, squeeze_dims=[1])
""" Construct model and training ops """
tf.reset_default_graph()
x = tf.placeholder(tf.float32, shape=(batch_size, input_dim))
z = tf.placeholder(tf.float32, shape=(batch_size, latent_dim))
# decoder and encoder
p_x = generative_network(z, input_dim , n_layer_gen, n_hidden_gen, eps_dim)
q_z = inference_network(x, latent_dim, n_layer_inf, n_hidden_inf, eps_dim)
decoder_logit = data_network(p_x, z, n_layers=n_layer_disc, n_hidden=n_hidden_disc)
encoder_logit = graph_replace(decoder_logit, {p_x: x, z:q_z})
decoder_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.zeros_like(decoder_logit), logits=decoder_logit)
encoder_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.ones_like(encoder_logit), logits=encoder_logit)
dis_loss_xz = tf.reduce_mean( encoder_loss ) + tf.reduce_mean( decoder_loss)
rec_z = inference_network(p_x, latent_dim, n_layer_inf, n_hidden_inf, eps_dim )
rec_x = generative_network(q_z, input_dim , n_layer_gen, n_hidden_gen, eps_dim )
x_logit_real = data_network_xx(x, x)
x_logit_fake = data_network_xx(x, rec_x)
z_logit_real = data_network_zz(z, z)
z_logit_fake = data_network_zz(z, rec_z)
x_sigmoid_real = tf.nn.sigmoid_cross_entropy_with_logits(logits=x_logit_real, labels=tf.ones_like(x_logit_real))
x_sigmoid_fake = tf.nn.sigmoid_cross_entropy_with_logits(logits=x_logit_fake, labels=tf.zeros_like(x_logit_fake))
z_sigmoid_real = tf.nn.sigmoid_cross_entropy_with_logits(logits=z_logit_real, labels=tf.ones_like(z_logit_real))
z_sigmoid_fake = tf.nn.sigmoid_cross_entropy_with_logits(logits=z_logit_fake, labels=tf.zeros_like(z_logit_fake))
x_sigmoid_real2 = tf.nn.sigmoid_cross_entropy_with_logits(logits=x_logit_real, labels=tf.zeros_like(x_logit_real))
x_sigmoid_fake2 = tf.nn.sigmoid_cross_entropy_with_logits(logits=x_logit_fake, labels=tf.ones_like(x_logit_fake))
z_sigmoid_real2 = tf.nn.sigmoid_cross_entropy_with_logits(logits=z_logit_real, labels=tf.zeros_like(z_logit_real))
z_sigmoid_fake2 = tf.nn.sigmoid_cross_entropy_with_logits(logits=z_logit_fake, labels=tf.ones_like(z_logit_fake))
dis_loss_x = tf.reduce_mean(x_sigmoid_real + x_sigmoid_fake)
dis_loss_z = tf.reduce_mean(z_sigmoid_real + z_sigmoid_fake)
disc_loss = dis_loss_xz + dis_loss_x + dis_loss_z
cost_x = tf.reduce_mean(1.0 * x_sigmoid_real2 + 1.0 * x_sigmoid_fake2) # + tf.reduce_mean(tf.pow(x_feature_real - x_feature_fake, 2))
cost_z = tf.reduce_mean(1.0 * z_sigmoid_real2 + 1.0 * z_sigmoid_fake2) # + tf.reduce_mean(tf.pow(z_feature_real - z_feature_fake, 2))
decoder_loss2 = tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.ones_like(decoder_logit), logits=decoder_logit)
encoder_loss2 = tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.zeros_like(encoder_logit), logits=encoder_logit)
gen_loss_xz = tf.reduce_mean( decoder_loss2 ) + tf.reduce_mean( encoder_loss2 )
# gen_loss = 1.*gen_loss_xz + .01*cost_x + .01*cost_z
gen_loss = 1.*gen_loss_xz + 0.1*cost_x + 0.1*cost_z
qvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "inference")
pvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "generative")
dvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "discriminator")
dvars_xx = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "discriminator_xx")
dvars_zz = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "discriminator_zz")
opt = tf.train.AdamOptimizer(1e-3, beta1=0.5)
train_gen_op = opt.minimize(gen_loss, var_list=qvars + pvars)
train_disc_op = opt.minimize(disc_loss, var_list=dvars + dvars_xx + dvars_zz)
""" training """
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.1
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
FG = []
FD = []
for epoch in tqdm( range(n_epoch), total=n_epoch):
X_dataset= shuffle(X_dataset)
Z_dataset= shuffle(Z_dataset)
i = 0
for xmb, zmb in iter_data(X_dataset, Z_dataset, size=batch_size):
i = i + 1
for _ in range(1):
f_d, _ = sess.run([disc_loss, train_disc_op], feed_dict={x: xmb, z:zmb})
for _ in range(5):
f_g, _ = sess.run([[gen_loss, gen_loss_xz, cost_x, cost_z], train_gen_op], feed_dict={x: xmb, z:zmb})
FG.append(f_g)
FD.append(f_d)
print("epoch %d iter %d: discloss %f genloss %f adv_x %f recons_x %f recons_z %f" % (epoch, i, f_d, f_g[0], f_g[1], f_g[2], f_g[3]))
# tmpx, tmpz = sess.run([dvars_x, dvars_z])
# pdb.set_trace()
""" plot the results """
# test dataset
# create X dataset
datasetX_test = sample_GMM(dataset_size_x_test, means_x, variances_x, priors_x, sources=('features', ))
save_path = result_dir + 'X_gmm_data_test.pdf'
# plot_GMM(dataset, save_path)
## reconstruced x
X_np_data_test = datasetX_test.data['samples']
X_targets_test = datasetX_test.data['label']
fig_mx, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4.5))
ax.scatter(X_np_data_test[:, 0], X_np_data_test[:, 1], c=cm.Set1(X_targets_test.astype(float)/input_dim/2.0),
edgecolor='none', alpha=0.5)
ax.set_xlim(-3, 3); ax.set_ylim(-3.5, 3.5)
ax.set_xlabel('$x_1$'); ax.set_ylabel('$x_2$')
ax.axis('on')
plt.savefig(save_path, transparent=True, bbox_inches='tight')
# create Z dataset
datasetZ_test = sample_GMM(dataset_size_z_test, means_z, variances_z, priors_z, sources=('features', ))
save_path = result_dir + 'Z_gmm_data_test.pdf'
# plot_GMM(dataset, save_path)
## reconstruced x
Z_np_data_test = datasetZ_test.data['samples']
Z_targets_test = datasetZ_test.data['label']
fig_mx, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4.5))
ax.scatter(Z_np_data_test[:, 0], Z_np_data_test[:, 1], c=cm.Set1(Z_targets_test.astype(float)/input_dim/2.0),
edgecolor='none', alpha=0.5)
ax.set_xlim(-3, 3); ax.set_ylim(-3.5, 3.5)
ax.set_xlabel('$z_1$'); ax.set_ylabel('$z_2$')
ax.axis('on')
plt.savefig(save_path, transparent=True, bbox_inches='tight')
n_viz = 1
imz = np.array([]); rmz = np.array([]); imx = np.array([]); rmx = np.array([]);
for _ in range(n_viz):
for xmb, zmb in iter_data(X_np_data_test, Z_np_data_test, size=batch_size):
temp_imz = sess.run(q_z, feed_dict={x: xmb, z:zmb})
imz = np.vstack([imz, temp_imz]) if imz.size else temp_imz
temp_rmz = sess.run(rec_z, feed_dict={x: xmb, z:zmb})
rmz = np.vstack([rmz, temp_rmz]) if rmz.size else temp_rmz
temp_imx = sess.run(p_x, feed_dict={x: xmb, z:zmb})
imx = np.vstack([imx, temp_imx]) if imx.size else temp_imx
temp_rmx = sess.run(rec_x, feed_dict={x: xmb, z:zmb})
rmx = np.vstack([rmx, temp_rmx]) if rmx.size else temp_rmx
## inferred marginal z
fig_mz, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4.5))
ll = np.tile(X_targets_test, (n_viz))
ax.scatter(imz[:, 0], imz[:, 1], c=cm.Set1(ll.astype(float)/input_dim/2.0),
edgecolor='none', alpha=0.5)
ax.set_xlim(-3, 3); ax.set_ylim(-3.5, 3.5)
ax.set_xlabel('$z_1$'); ax.set_ylabel('$z_2$')
ax.axis('on')
plt.savefig(result_dir + 'inferred_mz.pdf', transparent=True, bbox_inches='tight')
## reconstruced z
fig_pz, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4.5))
ll = np.tile(Z_targets_test, (n_viz))
ax.scatter(rmz[:, 0], rmz[:, 1], c=cm.Set1(ll.astype(float)/input_dim/2.0),
edgecolor='none', alpha=0.5)
ax.set_xlim(-3, 3); ax.set_ylim(-3.5, 3.5)
ax.set_xlabel('$z_1$'); ax.set_ylabel('$z_2$')
ax.axis('on')
plt.savefig(result_dir + 'reconstruct_mz.pdf', transparent=True, bbox_inches='tight')
## inferred marginal x
fig_pz, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4.5))
ll = np.tile(Z_targets_test, (n_viz))
ax.scatter(imx[:, 0], imx[:, 1], c=cm.Set1(ll.astype(float)/input_dim/2.0),
edgecolor='none', alpha=0.5)
ax.set_xlim(-3, 3); ax.set_ylim(-3.5, 3.5)
ax.set_xlabel('$x_1$'); ax.set_ylabel('$x_2$')
ax.axis('on')
plt.savefig(result_dir + 'inferred_mx.pdf', transparent=True, bbox_inches='tight')
## reconstruced x
fig_mx, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4.5))
ll = np.tile(X_targets_test, (n_viz))
ax.scatter(rmx[:, 0], rmx[:, 1], c=cm.Set1(ll.astype(float)/input_dim/2.0),
edgecolor='none', alpha=0.5)
ax.set_xlim(-3, 3); ax.set_ylim(-3.5, 3.5)
ax.set_xlabel('$x_1$'); ax.set_ylabel('$x_2$')
ax.axis('on')
plt.savefig(result_dir + 'reconstruct_mx.pdf', transparent=True, bbox_inches='tight')
## learning curves
fig_curve, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4.5))
ax.plot(FD, label="Discriminator")
ax.plot(np.array(FG)[:,0], label="Generator")
ax.plot(np.array(FG)[:,1], label="Reconstruction x")
ax.plot(np.array(FG)[:,2], label="Reconstruction Z")
plt.xlabel('Iteration')
plt.xlabel('Loss')
ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
ax.axis('on')
plt.savefig(result_dir + 'learning_curves.pdf', bbox_inches='tight')