-
Notifications
You must be signed in to change notification settings - Fork 0
/
NetworkSwitch.py
538 lines (399 loc) · 24.9 KB
/
NetworkSwitch.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
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
import os, sys
import tflearn
import tensorflow as tf
import h5py
import numpy as np
from sklearn.preprocessing import scale
from tflearn.layers.core import input_data, dropout, fully_connected, flatten
from tflearn.layers.conv import conv_2d, max_pool_2d, highway_conv_2d, avg_pool_2d
from tflearn.layers.normalization import local_response_normalization, batch_normalization
from tflearn.layers.estimator import regression
from tflearn import residual_bottleneck, activation, global_avg_pool, merge
def x3(x):
return 0.3*x**3
def whiten(X):
'''Function to ZCA whiten image matrix.'''
sigma = np.cov(X, rowvar=True) # [M x M]
# Singular Value Decomposition. X = U * np.diag(S) * V
U,S,V = np.linalg.svd(sigma)
# U: [M x M] eigenvectors of sigma.
# S: [M x 1] eigenvalues of sigma.
# V: [M x M] transpose of U
# Whitening constant: prevents division by zero
epsilon = 1e-5
# ZCA Whitening matrix: U * Lambda * U'
ZCAMatrix = np.dot(U, np.dot(np.diag(1.0/np.sqrt(S + epsilon)), U.T)) # [M x M]
return np.dot(ZCAMatrix, X)
########################################################
def DNN1(network, num_out, drop_prob=1.0):
network = tflearn.fully_connected(network, 64, activation='tanh',regularizer='L2', weight_decay=0.001)
network = tflearn.dropout(network, drop_prob)
network = tflearn.fully_connected(network, 64, activation='tanh', regularizer='L2', weight_decay=0.001)
network = tflearn.dropout(network, drop_prob)
network = tflearn.fully_connected(network, 64, activation='tanh', regularizer='L2', weight_decay=0.001)
network = tflearn.dropout(network, drop_prob)
network = tflearn.fully_connected(network, num_out, activation='softmax')
return network
########################################################
def Conv1(network, num_out, drop_prob=1.0):
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, drop_prob)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, drop_prob)
network = fully_connected(network, num_out, activation='softmax')
return network
########################################################
def Alex1(network, num_out, drop_prob=1.0):
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, drop_prob)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, drop_prob)
network = fully_connected(network, num_out, activation='softmax')
return network
########################################################
def VGG1(network, num_out, drop_prob=1.0):
network = conv_2d(network, 45, 3, activation='relu')
network = conv_2d(network, 45, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 120, 3, activation='relu')
network = conv_2d(network, 120, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 200, 3, activation='relu')
network = conv_2d(network, 200, 3, activation='relu')
network = conv_2d(network, 200, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 450, 3, activation='relu')
network = conv_2d(network, 450, 3, activation='relu')
network = conv_2d(network, 450, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 450, 3, activation='relu')
network = conv_2d(network, 450, 3, activation='relu')
network = conv_2d(network, 450, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = fully_connected(network, 3500, activation='relu')
network = dropout(network, drop_prob)
network = fully_connected(network, 3500, activation='relu')
network = dropout(network, drop_prob)
network = fully_connected(network, num_out, activation='softmax')
return network
########################################################
def Highway1(network, num_out, drop_prob=1.0):
dense1 = tflearn.fully_connected(network, 64, activation='elu', regularizer='L2', weight_decay=0.001)
highway = dense1
for i in range(10):
highway = tflearn.highway(highway, 64, activation='elu',regularizer='L2', weight_decay=0.001, transform_dropout=0.7)
network = tflearn.fully_connected(highway, num_out, activation='softmax')
return network
########################################################
def ConvHighway1(network, num_out, drop_prob=1.0):
for i in range(3):
for j in [3, 2, 1]:
network = highway_conv_2d(network, 16, j, activation='elu')
network = max_pool_2d(network, 2)
network = batch_normalization(network)
network = fully_connected(network, 128, activation='elu')
network = fully_connected(network, 256, activation='elu')
network = fully_connected(network, num_out, activation='softmax')
return network
########################################################
def Net_in_Net1(network, num_out, drop_prob=1.0):
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 160, 1, activation='relu')
network = conv_2d(network, 96, 1, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = dropout(network, drop_prob)
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = avg_pool_2d(network, 3, strides=2)
network = dropout(network, drop_prob)
network = conv_2d(network, 192, 3, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 10, 1, activation='relu')
network = avg_pool_2d(network, 8)
network = flatten(network)
network = fully_connected(network, num_out, activation='softmax')
return network
########################################################
def ResNet26(network, num_out, drop_prob=1.0):
n = 2 # number of residual blocks per layer
network = tflearn.conv_2d(network, 32, 7, regularizer='L2', strides=2, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = tflearn.residual_block(network, n, 32, activation='relu')
network = tflearn.residual_block(network, n, 32, activation='relu')
network = tflearn.residual_block(network, n, 64, downsample=True, activation='relu')
network = tflearn.residual_block(network, n, 64, activation='relu')
network = tflearn.residual_block(network, n, 128, activation='relu')
network = tflearn.residual_block(network, n, 128, activation='relu')
network = batch_normalization(network)
network = activation(network, 'relu')
network = global_avg_pool(network)
network = tflearn.fully_connected(network, num_out, activation='softmax')
return network
########################################################
def ResNeXt(network):
c = 32 # cardinality of each residual block
network = tflearn.conv_2d(network, 64, 7, regularizer='L2', strides=2, activation='linear')
network = max_pool_2d(network, 3, strides=2)
network = batch_normalization(network)
network = activation(network, 'relu')
network = resnext_block0(network, 2, 128, c, downsample=True)
network = resnext_block0(network, 2, 256, c, downsample=True)
network = resnext_block0(network, 2, 512, c, downsample=True)
network = resnext_block0(network, 2, 1024, c)
print(network)
network = global_avg_pool(network)
network = tflearn.fully_connected(network, 4, activation='softmax')
return network
########################################################
def LSTM1(network):
print(network.shape)
network = tflearn.lstm(network, 500, return_seq=True)
network = tflearn.lstm(network, 500)
network = tflearn.fully_connected(network, 4, activation='softmax')
return network
########################################################
def GoogLeNet1(network, num_out, drop_prob):
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3)
inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=3,name='inception_3b_output')
pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')
inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')
inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3,name='inception_4c_output')
inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')
inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=3, mode='concat')
pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3,mode='concat')
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' )
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat')
pool5_7_7 = global_avg_pool(inception_5b_output)
pool5_7_7 = dropout(pool5_7_7, 0.4)
network = fully_connected(pool5_7_7, num_out, activation='softmax')
return network
########################################################
def DenseNet(network):
# Growth Rate (12, 16, 32, ...)
k = 3
# Depth (40, 100, ...)
L = 28
nb_layers = int((L - 4) / 3)
# Building DenseNet Network
network = tflearn.conv_2d(network, 10, 4, regularizer='L2', weight_decay=0.0001)
network = denseblock(network, nb_layers, k, dropout=drop_prob)
network = denseblock(network, nb_layers, k, dropout=drop_prob)
network = denseblock(network, nb_layers, k, dropout=drop_prob)
network = tflearn.global_avg_pool(network)
# Regression
network = tflearn.fully_connected(network, 4, activation='softmax')
return network
########################################################
def RCNN1(network, prev_activation=None, scale=False):
if prev_activation is None:
prev_activation = tf.zeros([1, 2500])
if scale is True:
network = tf.transpose(tf.reshape(network, [-1, num_rows*num_cols*num_channels]))
mean, var = tf.nn.moments(network, [0])
network = tf.transpose((network-mean)/(tf.sqrt(var)+1e-6))
network = tf.reshape(network, [-1, num_rows, num_cols, num_channels])
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 2500, activation='tanh')
network = dropout(network, drop_prob)
feat_layer = fully_connected(network, 2500, activation='tanh')
network = merge([feat_layer, prev_activation], 'concat', axis=1)
network = lstm(network, 250, dropout=drop_prob, activation='relu')
network = tflearn.fully_connected(network, 4, activation='softmax')
return network, feat_layer
########################################################
def lstm2(network):
network = lstm(network, 10000, dropout=0.7, activation='relu')
network = tflearn.fully_connected(network, 4, activation='softmax')
return network
########################################################
def X3(y, iters, batch_sz, num_dict_features=None, D=None):
''' Dynamical systems neural network used for sparse approximation of an
input vector.
Args:
y: input signal or vector, or multiple column vectors.
num_dict_features: number of dictionary patches to learn.
iters: number of LCA iterations.
batch_sz: number of samples to send to the network at each iteration.
D: The dictionary to be used in the network.'''
assert(num_dict_features is None or D is None), 'provide D or num_dict_features, not both'
e = np.zeros([iters, 1])
D=np.random.randn(y.shape[0], num_dict_features)
for i in range(iters):
# choose random examples this iteration
batch=y[:, np.random.randint(0, y.shape[1], batch_sz)]
batch = scale(batch, 1)
batch = whiten(batch)
# scale the values in the dict to between 0 and 1
D=np.matmul(D, np.diag(1/(np.sqrt(np.sum(D**2, 0))+1e-6)))
# get similarity between each feature and each data patch
a = np.matmul(D.transpose(), batch)
# scale the alpha coefficients (cosine similarity coefficients)
a=np.matmul(a, np.diag(1/(np.sqrt(np.sum(a**2, 0))+1e-6)))
# perform cubic activation on the alphas
a=0.5*a**3
# get the SSE between reconstruction and data batch
error = batch - np.matmul(D, a)
# save the error to plot later
e[i, 0] = np.mean(error**2)
# modify the dictionary to reduce the error
D=D+np.matmul(error, a.transpose())
return D, a, e
######################################################################
def ResNet34(network):
network = tflearn.conv_2d(network, 64, 7,
strides=2, activation='linear',
regularizer='L2')
network = max_pool_2d(network, 3, strides=2)
network = tflearn.residual_block(network, 3, 64, activation='relu')
network = tflearn.residual_block(network, 1, 128, activation='relu', downsample=True)
network = tflearn.residual_block(network, 3, 128, activation='relu')
network = tflearn.residual_block(network, 1, 256, activation='relu', downsample=True)
network = tflearn.residual_block(network, 5, 256, activation='relu')
network = tflearn.residual_block(network, 1, 512, activation='relu', downsample=True)
network = tflearn.residual_block(network, 2, 512, activation='relu')
network = batch_normalization(network)
network = activation(network, 'relu')
print(network)
network = global_avg_pool(network)
network = tflearn.fully_connected(network, 4, activation='softmax')
return network
#######################################################################
def ResNeXt34(network):
c = 8 #cardinality
network = tflearn.conv_2d(network, 32, 7, strides=2, activation='linear')
network = max_pool_2d(network, 3, strides=2)
network = batch_normalization(network)
network = activation(network, 'relu')
network = resnext_block5(network, 3, 32, c)
network = resnext_block5(network, 1, 64, c, downsample=True)
network = resnext_block5(network, 3, 64, c)
network = resnext_block5(network, 1, 128, c, downsample=True)
network = resnext_block5(network, 5, 128, c)
network = resnext_block5(network, 1, 256, c, downsample=True)
network = resnext_block5(network, 2, 256, c)
network = global_avg_pool(network)
network = tflearn.fully_connected(network, 4, activation='softmax')
return network
##########################################################################
##########################################################################
##########################################################################
modelswitch = {
'FullyConnected' : DNN1,
'CNN' : Conv1,
'AlexNet' : Alex1,
'VGG' : VGG1,
'Highway' : Highway1,
'CNNHighway' : ConvHighway1,
'NetinNet' : Net_in_Net1,
'ResNet26' : ResNet26,
'ResNeXt26' : ResNeXt,
'InceptionV3' : GoogLeNet1,
'LSTM' : LSTM1,
'DenseNet' : DenseNet,
'RCNN' : RCNN1,
'LSTM2' : lstm2,
'X3' : X3,
'ResNet34' : ResNet34,
'ResNeXt34' : ResNeXt34,
}