-
Notifications
You must be signed in to change notification settings - Fork 3
/
associate_kanade.py
512 lines (419 loc) · 21.1 KB
/
associate_kanade.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
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
__author__ = 'joschlemper'
from models.rbm import RBM
from models import rbm_logger, rbm_config, rbm_units, DBN, associative_dbn
import kanade_loader as loader
import datastorage as store
import utils
import numpy as np
import theano
import theano.tensor as T
from models.simple_classifiers import SimpleClassifier
def train_kanade():
print "Testing RBM"
data_manager = store.StorageManager('Kanade/SimpleRBMTest')
# Load mnist hand digits
datasets = loader.load_kanade(n=500, set_name='25_25', emotions=['happy', 'sadness'], pre={'scale2unit': True})
train_x, train_y = datasets[0]
sparsity_constraint = True
# Initialise the RBM and training parameters
tr = rbm_config.TrainParam(learning_rate=0.0001,
momentum_type=rbm_config.NESTEROV,
momentum=0.9,
weight_decay=0.001,
sparsity_constraint=sparsity_constraint,
sparsity_target=0.01,
sparsity_cost=1,
sparsity_decay=0.9,
dropout=True,
epochs=100)
n_visible = train_x.get_value().shape[1]
n_hidden = 500
config = rbm_config.RBMConfig(v_n=n_visible,
v2_n=n_visible,
h_n=n_hidden,
v_unit=rbm_units.GaussianVisibleUnit,
associative=False,
cd_type=rbm_config.CLASSICAL,
cd_steps=1,
train_params=tr,
progress_logger=rbm_logger.ProgressLogger(img_shape=(25, 25)))
rbm = RBM(config)
print "... initialised RBM"
# Train RBM
rbm.train(train_x)
# Test RBM
rbm.reconstruct(train_x, k=5, plot_n=10, plot_every=1)
# Store Parameters
data_manager.persist(rbm)
def KanadeAssociativeRBM(cache=False, train_further=False):
print "Testing Associative RBM which tries to learn the ID map "
# print "Testing Associative RBM which tries to learn the following mapping: {anger, saddness, disgust} -> {sadness}, {contempt, happy, surprise} -> {happy}"
# project set-up
data_manager = store.StorageManager('Kanade/OptMFSparse0.01RBMTest', log=True)
# data_manager = store.StorageManager('Kanade/OptAssociativeRBMTest', log=True)
shape = 25
dataset_name = 'sharp_equi{}_{}'.format(shape, shape)
# Load kanade database
mapping = None # id map
# mapping = {'anger': 'sadness', 'contempt': 'happy', 'disgust': 'sadness', 'fear': 'sadness', 'happy': 'happy',
# 'sadness': 'sadness', 'surprise': 'happy'}
train, valid, test = loader.load_kanade(pre={'scale': True}, set_name=dataset_name)
train_x, train_y = train
test_x, test_y = test
# Sample associated image
train_x_mapped, train_y_mapped = loader.sample_image(train_y, mapping=mapping, pre={'scale': True},
set_name=dataset_name)
test_x_mapped, test_y_mapped = loader.sample_image(test_y, mapping=mapping, pre={'scale': True},
set_name=dataset_name)
# Concatenate images
concat1 = T.concatenate([train_x, train_x_mapped], axis=1)
# concat2 = T.concatenate([train_x_mapped, train_x], axis=1)
# concat = T.concatenate([concat1, concat2], axis=0)
# train_tX = theano.function([], concat)()
train_tX = theano.function([], concat1)()
train_X = theano.shared(train_tX)
# Train classifier to be used for classifying reconstruction associated image layer
# mapped_data = loader.load_kanade(#emotions=['sadness', 'happy'],
# pre={'scale': True},
# set_name=dataset_name) # Target Image
# clf_orig = SimpleClassifier('logistic', mapped_data[0][0], mapped_data[0][1])
clf_orig = SimpleClassifier('logistic', train_x, train_y)
# Initialise RBM
tr = rbm_config.TrainParam(learning_rate=0.0001,
momentum_type=rbm_config.NESTEROV,
momentum=0.9,
weight_decay=0.0001,
sparsity_constraint=True,
sparsity_target=0.01,
sparsity_cost=100,
sparsity_decay=0.9,
batch_size=10,
epochs=10)
n_visible = shape * shape * 2
n_hidden = 500
config = rbm_config.RBMConfig()
config.v_n = n_visible
config.h_n = n_hidden
config.v_unit = rbm_units.GaussianVisibleUnit
# config.h_unit = rbm_units.ReLUnit
config.progress_logger = rbm_logger.ProgressLogger(img_shape=(shape * 2, shape))
config.train_params = tr
rbm = RBM(config)
print "... initialised RBM"
# Load RBM (test)
loaded = data_manager.retrieve(str(rbm))
if loaded:
rbm = loaded
else:
rbm.set_initial_hidden_bias()
rbm.set_hidden_mean_activity(train_X)
# Train RBM - learn joint distribution
# rbm.pretrain_lr(train_x, train_x01)
for i in xrange(0, 10):
if not cache or train_further:
rbm.train(train_X)
data_manager.persist(rbm)
print "... reconstruction of associated images"
# Get reconstruction with train data to get 'mapped' images to train classifiers on
reconstruction = rbm.reconstruct(train_X, 1,
plot_n=100,
plot_every=1,
img_name='recon_train')
reconstruct_assoc_part = reconstruction[:, (shape ** 2):]
# Get associated images of test data
nsamples = np.random.normal(0, 1, test_x.get_value(True).shape).astype(np.float32)
initial_y = theano.shared(nsamples, name='initial_y')
utils.save_images(nsamples[0:100], 'initialisation.png', (10, 10), (25, 25))
test_x_associated = rbm.reconstruct_association_opt(test_x, initial_y,
5,
0.,
plot_n=100,
plot_every=1,
img_name='recon_test_gibbs')
mf_recon = rbm.mean_field_inference_opt(test_x, y=initial_y,
sample=False,
k=10,
img_name='recon_test_mf_raw')
# Concatenate images
test_MFX = theano.function([], T.concatenate([test_x, mf_recon], axis=1))()
test_MF = theano.shared(test_MFX)
reconstruction = rbm.reconstruct(test_MF, 1,
plot_n=100,
plot_every=1,
img_name='recon_test_mf_recon')
mf_recon = reconstruction[:, (shape ** 2):]
print "... reconstructed"
# Classify the reconstructions
# 1. Train classifier on original images
score_orig = clf_orig.get_score(test_x_associated, test_y_mapped.eval())
score_orig_mf = clf_orig.get_score(test_x_associated, test_y_mapped.eval())
# 2. Train classifier on reconstructed images
clf_recon = SimpleClassifier('logistic', reconstruct_assoc_part, train_y_mapped.eval())
score_retrain = clf_recon.get_score(test_x_associated, test_y_mapped.eval())
score_retrain_mf = clf_recon.get_score(mf_recon, test_y_mapped.eval())
out_msg = '{} (orig, retrain):{},{}'.format(rbm, score_orig, score_retrain)
out_msg2 = '{} (orig, retrain):{},{}'.format(rbm, score_orig_mf, score_retrain_mf)
print out_msg
print out_msg2
def KanadeAssociativeDBN(cache=False):
print "Testing Associative RBM which tries to learn the following mapping: " \
"ID"
# "{anger, saddness, disgust} -> {sadness}, {contempt, happy, surprise} -> {happy}"
# project set-up
data_manager = store.StorageManager('Kanade/AssociativeDBNTest', log=True)
shape = 25
dataset_name = 'sharp_equi{}_{}'.format(shape, shape)
preprocessing = {'scale': True}
# Load kanade database
mapping = None
# mapping = {'anger': 'sadness',
# 'contempt': 'happy',
# 'disgust': 'sadness',
# 'fear': 'sadness',
# 'happy': 'happy',
# 'sadness': 'sadness',
# 'surprise': 'happy'}
dataset = loader.load_kanade( n=100,
pre=preprocessing,
set_name=dataset_name)
mapped_dataset = loader.load_kanade( n=100,
# emotions=['sadness', 'happy'],
pre=preprocessing,
set_name=dataset_name) # Target Image
train, valid, test = dataset
train_x, train_y = train
test_x, test_y = test
# Sample associated image
train_x_ass, train_y_ass = loader.sample_image(train_y,
mapping=mapping,
pre=preprocessing,
set_name=dataset_name)
test_x_ass, test_y_ass = loader.sample_image(test_y,
mapping=mapping,
pre=preprocessing,
set_name=dataset_name)
# initialise AssociativeDBN
config = associative_dbn.DefaultADBNConfig()
# Gaussian Input Layer
bottom_tr = rbm_config.TrainParam(learning_rate=0.0001,
momentum_type=rbm_config.NESTEROV,
momentum=0.9,
weight_decay=0.0001,
epochs=20,
batch_size=10)
h_n = 150
bottom_logger = rbm_logger.ProgressLogger(img_shape=(shape, shape))
bottom_rbm = rbm_config.RBMConfig(v_unit=rbm_units.GaussianVisibleUnit,
v_n=shape ** 2,
h_n=h_n,
progress_logger=bottom_logger,
train_params=bottom_tr)
config.left_dbn.rbm_configs[0] = bottom_rbm
config.right_dbn.rbm_configs[0] = bottom_rbm
config.left_dbn.topology = [shape ** 2, h_n]
config.right_dbn.topology = [shape ** 2, h_n]
config.top_rbm.train_params.epochs = 20
config.top_rbm.train_params.batch_size = 10
config.n_association = 1000
config.reuse_dbn = True
adbn = associative_dbn.AssociativeDBN(config=config, data_manager=data_manager)
# Plot sample
loader.save_faces(train_x.get_value(borrow=True)[1:50], tile=(10, 10), img_name='n_orig.png', )
loader.save_faces(train_x_ass.get_value(borrow=True)[1:50], tile=(10, 10), img_name='n_ass.png')
# Train classifier to be used for classifying reconstruction associated image layer
clf_orig = SimpleClassifier('knn', mapped_dataset[0][0], mapped_dataset[0][1])
# Test DBN Performance
for i in xrange(0, 5):
# Train DBN - learn joint distribution
cache_left = [True]
cache_right = [True]
cache_top = False
cache = [cache_left, cache_right, cache_top]
adbn.train(train_x, train_x_ass, cache=cache)
print "... trained associative DBN"
# Reconstruct images
test_x_recon = adbn.recall(test_x, associate_steps=500, recall_steps=0)
print "... reconstructed images"
# Classify the reconstructions
# 1. Train classifier on original images
score_orig = clf_orig.get_score(test_x_recon, test_y_ass.eval())
# 2. Train classifier on reconstructed images - reconstruction obtained by right dbn
right_dbn = adbn.dbn_right
mapped_train_recon = right_dbn.reconstruct(mapped_dataset[0][0],
k=1,
plot_n=100,
plot_every=1,
img_name='right_dbn_reconstruction')
clf_recon = SimpleClassifier('knn', mapped_train_recon, mapped_dataset[0][1].eval())
score_retrain = clf_recon.get_score(test_x_recon, test_y_ass.eval())
out_msg = '{} (orig, retrain):{},{}'.format(adbn, score_orig, score_retrain)
print out_msg
def KanadeJointDBN(cache=False):
print "Testing JointDBN which tries to learn id map association"
# project set-up
data_manager = store.StorageManager('Kanade/JointDBN', log=True)
shape = 25
dataset_name = 'sharp_equi{}_{}'.format(shape, shape)
preprocessing = {'scale': True}
# Load kanade database
mapping = None
# mapping = {'anger': 'sadness',
# 'contempt': 'happy',
# 'disgust': 'sadness',
# 'fear': 'sadness',
# 'happy': 'happy',
# 'sadness': 'sadness',
# 'surprise': 'happy'}
dataset = loader.load_kanade( # n=3000,
pre=preprocessing,
set_name=dataset_name)
mapped_dataset = loader.load_kanade( # n=3000,
# emotions=['sadness', 'happy'],
pre=preprocessing,
set_name=dataset_name) # Target Image
train, valid, test = dataset
train_x, train_y = train
test_x, test_y = test
# Sample associated image
train_x_ass, train_y_ass = loader.sample_image(train_y,
mapping=mapping,
pre=preprocessing,
set_name=dataset_name)
test_x_ass, test_y_ass = loader.sample_image(test_y,
mapping=mapping,
pre=preprocessing,
set_name=dataset_name)
# Initialise RBM parameters
base_tr = rbm_config.TrainParam(learning_rate=0.0001,
momentum_type=rbm_config.NESTEROV,
momentum=0.9,
weight_decay=0.0001,
sparsity_constraint=False,
sparsity_target=0.00001,
sparsity_decay=0.9,
sparsity_cost=10000,
epochs=100,
batch_size=10)
rest_tr = rbm_config.TrainParam(learning_rate=0.0001,
momentum_type=rbm_config.CLASSICAL,
momentum=0.5,
weight_decay=0.01,
epochs=100,
batch_size=10)
# Layer 1
# Layer 2
# Layer 3
topology = [2 * (shape ** 2), 100, 100]
# batch_size = 10
first_progress_logger = rbm_logger.ProgressLogger(img_shape=(shape * 2, shape))
rest_progress_logger = rbm_logger.ProgressLogger()
first_rbm_config = rbm_config.RBMConfig(train_params=base_tr,
progress_logger=first_progress_logger)
first_rbm_config.v_unit = rbm_units.GaussianVisibleUnit
rest_rbm_config = rbm_config.RBMConfig(train_params=rest_tr,
progress_logger=rest_progress_logger)
rbm_configs = [first_rbm_config, rest_rbm_config, rest_rbm_config]
config = DBN.DBNConfig(topology=topology,
training_parameters=base_tr,
rbm_configs=rbm_configs,
data_manager=data_manager)
# construct the Deep Belief Network
dbn = DBN.DBN(config)
# Train DBN on concatenated images
train_tX = theano.function([], T.concatenate([train_x, train_x_ass], axis=1))()
train_X = theano.shared(train_tX)
test_tX = theano.function([], T.concatenate([test_x, test_x_ass], axis=1))()
test_X = theano.shared(test_tX)
test_tX2 = theano.function([], T.concatenate([test_x, T.zeros_like(test_x)], axis=1))()
test_X2 = theano.shared(test_tX2)
origs = []
recons = []
recons2 = []
# Train DBN
dbn.pretrain(train_X, cache=[True, True, False], train_further=[True, True, True])
recon = dbn.reconstruct(train_X, k=1, plot_n=20,
img_name='stackedRBM_train_recon_{}_{}'.format(topology, 0))
train_x_ass_recon = recon[:, shape ** 2:]
recon = dbn.reconstruct(test_X, k=1, plot_n=20,
img_name='stackedRBM_test_recon_{}_{}'.format(topology, 0))
test_x_ass_recon = recon[:, shape ** 2:]
recon = dbn.reconstruct(test_X2, k=2, plot_n=20,
img_name='stackedRBM_test_zero_recon_{}_{}'.format(topology, 0))
test_x_ass_recon2 = recon[:, shape ** 2:]
clf_recon = SimpleClassifier('logistic', train_x, train_y)
score_orig = clf_recon.get_score(test_x_ass_recon, test_y_ass.eval())
clf_recon.retrain(train_x_ass_recon, train_y_ass.eval())
score_recon = clf_recon.get_score(test_x_ass_recon, test_y_ass.eval())
score_recon2 = clf_recon.get_score(test_x_ass_recon2, test_y_ass.eval())
print 'classification rate: {}, {}, {}'.format(score_orig, score_recon, score_recon2)
origs.append(score_orig)
recons.append(score_recon)
recons2.append(score_recon2)
def associate_data2dataDBN(cache=False):
print "Testing Associative DBN which tries to learn even-oddness of numbers"
# project set-up
data_manager = store.StorageManager('Kanade/associative_dbn_test', log=True)
# Load mnist hand digits, class label is already set to binary
dataset = loader.load_kanade(n=500, emotions=['anger', 'sadness', 'happy'], pre={'scale2unit': True})
train_x, train_y = dataset
train_x01 = loader.sample_image(train_y)
dataset01 = loader.load_kanade(n=500)
# Initialise RBM parameters
# fixed base train param
base_tr = RBM.TrainParam(learning_rate=0.001,
momentum_type=RBM.CLASSICAL,
momentum=0.5,
weight_decay=0.0005,
sparsity_constraint=False,
epochs=20)
# top layer parameters
tr = RBM.TrainParam(learning_rate=0.001,
# find_learning_rate=True,
momentum_type=RBM.NESTEROV,
momentum=0.5,
weight_decay=0.001,
sparsity_constraint=False,
epochs=20)
tr_top = RBM.TrainParam(learning_rate=0.001,
# find_learning_rate=True,
momentum_type=RBM.CLASSICAL,
momentum=0.5,
weight_decay=0.001,
sparsity_constraint=False,
epochs=20)
# Layer 1
# Layer 2
# Layer 3
# topology = [784, 500, 500, 100]
config = associative_dbn.DefaultADBNConfig()
config.topology_left = [625, 500, 500, 100]
config.topology_right = [625, 500, 500, 100]
config.reuse_dbn = False
config.top_rbm_params = tr_top
config.base_rbm_params = [base_tr, tr, tr]
count = 0
for cd_type in [RBM.CLASSICAL, RBM.PERSISTENT]:
for n_ass in [100, 250, 500, 750, 1000]:
config.n_association = n_ass
config.top_cd_type = cd_type
# Construct DBN
ass_dbn = associative_dbn.AssociativeDBN(config=config, data_manager=data_manager)
# Train
for trainN in xrange(0, 5):
ass_dbn.train(train_x, train_x01, cache=cache)
for n_recall in [1, 3, 10]:
for n_think in [0, 1, 3, 5, 10]: # 1, 3, 5, 7, 10]:
# Reconstruct
sampled = ass_dbn.recall(train_x, n_recall, n_think)
# Sample from top layer to generate data
sample_n = 100
utils.save_images(sampled,
image_name='{}_reconstruced_{}_{}_{}.png'.format(count, n_ass, n_recall,
n_think),
shape=(sample_n / 10, 10), img_shape=(25, 25))
count += 1
if __name__ == '__main__':
# train_kanade()
KanadeAssociativeRBM(True, train_further=True)
KanadeAssociativeDBN()
KanadeJointDBN()