-
Notifications
You must be signed in to change notification settings - Fork 9
/
architecture.py
525 lines (492 loc) · 47.1 KB
/
architecture.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
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Activation, Input, Add, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization, Concatenate, Lambda, add, GlobalAveragePooling2D, Convolution2D, LocallyConnected2D, ZeroPadding2D, concatenate, AveragePooling2D
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras import backend as K
def scaling(x, scale):
return x * scale
def InceptionResNetV2():
inputs = Input(shape=(160, 160, 3))
x = Conv2D(32, 3, strides=2, padding='valid', use_bias=False, name= 'Conv2d_1a_3x3') (inputs)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_1a_3x3_BatchNorm')(x)
x = Activation('relu', name='Conv2d_1a_3x3_Activation')(x)
x = Conv2D(32, 3, strides=1, padding='valid', use_bias=False, name= 'Conv2d_2a_3x3') (x)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_2a_3x3_BatchNorm')(x)
x = Activation('relu', name='Conv2d_2a_3x3_Activation')(x)
x = Conv2D(64, 3, strides=1, padding='same', use_bias=False, name= 'Conv2d_2b_3x3') (x)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_2b_3x3_BatchNorm')(x)
x = Activation('relu', name='Conv2d_2b_3x3_Activation')(x)
x = MaxPooling2D(3, strides=2, name='MaxPool_3a_3x3')(x)
x = Conv2D(80, 1, strides=1, padding='valid', use_bias=False, name= 'Conv2d_3b_1x1') (x)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_3b_1x1_BatchNorm')(x)
x = Activation('relu', name='Conv2d_3b_1x1_Activation')(x)
x = Conv2D(192, 3, strides=1, padding='valid', use_bias=False, name= 'Conv2d_4a_3x3') (x)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_4a_3x3_BatchNorm')(x)
x = Activation('relu', name='Conv2d_4a_3x3_Activation')(x)
x = Conv2D(256, 3, strides=2, padding='valid', use_bias=False, name= 'Conv2d_4b_3x3') (x)
x = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Conv2d_4b_3x3_BatchNorm')(x)
x = Activation('relu', name='Conv2d_4b_3x3_Activation')(x)
# 5x Block35 (Inception-ResNet-A block):
branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block35_1_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_1_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_1_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_2 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_1_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_1_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_1_Branch_2_Conv2d_0c_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_1_Branch_2_Conv2d_0c_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_1_Branch_2_Conv2d_0c_3x3_Activation')(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name='Block35_1_Concatenate')(branches)
up = Conv2D(256, 1, strides=1, padding='same', use_bias=True, name= 'Block35_1_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
x = add([x, up])
x = Activation('relu', name='Block35_1_Activation')(x)
branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block35_2_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_2_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_2_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_2 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_2_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_2_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_2_Branch_2_Conv2d_0c_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_2_Branch_2_Conv2d_0c_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_2_Branch_2_Conv2d_0c_3x3_Activation')(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name='Block35_2_Concatenate')(branches)
up = Conv2D(256, 1, strides=1, padding='same', use_bias=True, name= 'Block35_2_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
x = add([x, up])
x = Activation('relu', name='Block35_2_Activation')(x)
branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block35_3_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_3_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_3_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_2 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_3_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_3_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_3_Branch_2_Conv2d_0c_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_3_Branch_2_Conv2d_0c_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_3_Branch_2_Conv2d_0c_3x3_Activation')(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name='Block35_3_Concatenate')(branches)
up = Conv2D(256, 1, strides=1, padding='same', use_bias=True, name= 'Block35_3_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
x = add([x, up])
x = Activation('relu', name='Block35_3_Activation')(x)
branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block35_4_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_4_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_4_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_2 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_4_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_4_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_4_Branch_2_Conv2d_0c_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_4_Branch_2_Conv2d_0c_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_4_Branch_2_Conv2d_0c_3x3_Activation')(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name='Block35_4_Concatenate')(branches)
up = Conv2D(256, 1, strides=1, padding='same', use_bias=True, name= 'Block35_4_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
x = add([x, up])
x = Activation('relu', name='Block35_4_Activation')(x)
branch_0 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block35_5_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_5_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block35_5_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_2 = Conv2D(32, 1, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_5_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_5_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(32, 3, strides=1, padding='same', use_bias=False, name= 'Block35_5_Branch_2_Conv2d_0c_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block35_5_Branch_2_Conv2d_0c_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Block35_5_Branch_2_Conv2d_0c_3x3_Activation')(branch_2)
branches = [branch_0, branch_1, branch_2]
mixed = Concatenate(axis=3, name='Block35_5_Concatenate')(branches)
up = Conv2D(256, 1, strides=1, padding='same', use_bias=True, name= 'Block35_5_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.17})(up)
x = add([x, up])
x = Activation('relu', name='Block35_5_Activation')(x)
# Mixed 6a (Reduction-A block):
branch_0 = Conv2D(384, 3, strides=2, padding='valid', use_bias=False, name= 'Mixed_6a_Branch_0_Conv2d_1a_3x3') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Mixed_6a_Branch_0_Conv2d_1a_3x3_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Mixed_6a_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Mixed_6a_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, 3, strides=1, padding='same', use_bias=False, name= 'Mixed_6a_Branch_1_Conv2d_0b_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Mixed_6a_Branch_1_Conv2d_0b_3x3_Activation')(branch_1)
branch_1 = Conv2D(256, 3, strides=2, padding='valid', use_bias=False, name= 'Mixed_6a_Branch_1_Conv2d_1a_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Mixed_6a_Branch_1_Conv2d_1a_3x3_Activation')(branch_1)
branch_pool = MaxPooling2D(3, strides=2, padding='valid', name='Mixed_6a_Branch_2_MaxPool_1a_3x3')(x)
branches = [branch_0, branch_1, branch_pool]
x = Concatenate(axis=3, name='Mixed_6a')(branches)
# 10x Block17 (Inception-ResNet-B block):
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_1_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_1_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_1_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_1_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_1_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_1_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_1_Branch_1_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_1_Branch_1_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_1_Branch_1_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_1_Branch_1_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_1_Branch_1_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_1_Branch_1_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_1_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_1_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_1_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_2_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_2_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_2_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_2_Branch_2_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_2_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_2_Branch_2_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_2_Branch_2_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_2_Branch_2_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_2_Branch_2_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_2_Branch_2_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_2_Branch_2_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_2_Branch_2_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_2_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_2_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_2_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_3_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_3_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_3_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_3_Branch_3_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_3_Branch_3_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_3_Branch_3_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_3_Branch_3_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_3_Branch_3_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_3_Branch_3_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_3_Branch_3_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_3_Branch_3_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_3_Branch_3_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_3_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_3_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_3_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_4_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_4_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_4_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_4_Branch_4_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_4_Branch_4_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_4_Branch_4_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_4_Branch_4_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_4_Branch_4_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_4_Branch_4_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_4_Branch_4_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_4_Branch_4_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_4_Branch_4_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_4_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_4_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_4_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_5_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_5_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_5_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_5_Branch_5_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_5_Branch_5_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_5_Branch_5_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_5_Branch_5_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_5_Branch_5_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_5_Branch_5_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_5_Branch_5_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_5_Branch_5_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_5_Branch_5_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_5_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_5_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_5_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_6_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_6_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_6_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_6_Branch_6_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_6_Branch_6_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_6_Branch_6_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_6_Branch_6_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_6_Branch_6_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_6_Branch_6_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_6_Branch_6_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_6_Branch_6_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_6_Branch_6_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_6_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_6_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_6_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_7_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_7_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_7_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_7_Branch_7_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_7_Branch_7_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_7_Branch_7_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_7_Branch_7_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_7_Branch_7_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_7_Branch_7_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_7_Branch_7_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_7_Branch_7_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_7_Branch_7_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_7_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_7_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_7_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_8_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_8_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_8_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_8_Branch_8_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_8_Branch_8_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_8_Branch_8_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_8_Branch_8_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_8_Branch_8_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_8_Branch_8_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_8_Branch_8_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_8_Branch_8_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_8_Branch_8_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_8_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_8_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_8_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_9_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_9_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_9_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_9_Branch_9_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_9_Branch_9_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_9_Branch_9_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_9_Branch_9_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_9_Branch_9_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_9_Branch_9_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_9_Branch_9_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_9_Branch_9_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_9_Branch_9_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_9_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_9_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_9_Activation')(x)
branch_0 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_10_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_10_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block17_10_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(128, 1, strides=1, padding='same', use_bias=False, name= 'Block17_10_Branch_10_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_10_Branch_10_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_10_Branch_10_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(128, [1, 7], strides=1, padding='same', use_bias=False, name= 'Block17_10_Branch_10_Conv2d_0b_1x7') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_10_Branch_10_Conv2d_0b_1x7_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_10_Branch_10_Conv2d_0b_1x7_Activation')(branch_1)
branch_1 = Conv2D(128, [7, 1], strides=1, padding='same', use_bias=False, name= 'Block17_10_Branch_10_Conv2d_0c_7x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block17_10_Branch_10_Conv2d_0c_7x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block17_10_Branch_10_Conv2d_0c_7x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block17_10_Concatenate')(branches)
up = Conv2D(896, 1, strides=1, padding='same', use_bias=True, name= 'Block17_10_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.1})(up)
x = add([x, up])
x = Activation('relu', name='Block17_10_Activation')(x)
# Mixed 7a (Reduction-B block): 8 x 8 x 2080
branch_0 = Conv2D(256, 1, strides=1, padding='same', use_bias=False, name= 'Mixed_7a_Branch_0_Conv2d_0a_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Mixed_7a_Branch_0_Conv2d_0a_1x1_Activation')(branch_0)
branch_0 = Conv2D(384, 3, strides=2, padding='valid', use_bias=False, name= 'Mixed_7a_Branch_0_Conv2d_1a_3x3') (branch_0)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Mixed_7a_Branch_0_Conv2d_1a_3x3_Activation')(branch_0)
branch_1 = Conv2D(256, 1, strides=1, padding='same', use_bias=False, name= 'Mixed_7a_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Mixed_7a_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(256, 3, strides=2, padding='valid', use_bias=False, name= 'Mixed_7a_Branch_1_Conv2d_1a_3x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Mixed_7a_Branch_1_Conv2d_1a_3x3_Activation')(branch_1)
branch_2 = Conv2D(256, 1, strides=1, padding='same', use_bias=False, name= 'Mixed_7a_Branch_2_Conv2d_0a_1x1') (x)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Mixed_7a_Branch_2_Conv2d_0a_1x1_Activation')(branch_2)
branch_2 = Conv2D(256, 3, strides=1, padding='same', use_bias=False, name= 'Mixed_7a_Branch_2_Conv2d_0b_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Mixed_7a_Branch_2_Conv2d_0b_3x3_Activation')(branch_2)
branch_2 = Conv2D(256, 3, strides=2, padding='valid', use_bias=False, name= 'Mixed_7a_Branch_2_Conv2d_1a_3x3') (branch_2)
branch_2 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm')(branch_2)
branch_2 = Activation('relu', name='Mixed_7a_Branch_2_Conv2d_1a_3x3_Activation')(branch_2)
branch_pool = MaxPooling2D(3, strides=2, padding='valid', name='Mixed_7a_Branch_3_MaxPool_1a_3x3')(x)
branches = [branch_0, branch_1, branch_2, branch_pool]
x = Concatenate(axis=3, name='Mixed_7a')(branches)
# 5x Block8 (Inception-ResNet-C block):
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_1_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_1_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_1_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_1_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_1_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_1_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_1_Branch_1_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_1_Branch_1_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_1_Branch_1_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_1_Branch_1_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_1_Branch_1_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_1_Branch_1_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_1_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_1_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
x = add([x, up])
x = Activation('relu', name='Block8_1_Activation')(x)
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_2_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_2_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_2_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_2_Branch_2_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_2_Branch_2_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_2_Branch_2_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_2_Branch_2_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_2_Branch_2_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_2_Branch_2_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_2_Branch_2_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_2_Branch_2_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_2_Branch_2_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_2_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_2_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
x = add([x, up])
x = Activation('relu', name='Block8_2_Activation')(x)
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_3_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_3_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_3_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_3_Branch_3_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_3_Branch_3_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_3_Branch_3_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_3_Branch_3_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_3_Branch_3_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_3_Branch_3_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_3_Branch_3_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_3_Branch_3_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_3_Branch_3_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_3_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_3_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
x = add([x, up])
x = Activation('relu', name='Block8_3_Activation')(x)
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_4_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_4_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_4_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_4_Branch_4_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_4_Branch_4_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_4_Branch_4_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_4_Branch_4_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_4_Branch_4_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_4_Branch_4_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_4_Branch_4_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_4_Branch_4_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_4_Branch_4_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_4_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_4_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
x = add([x, up])
x = Activation('relu', name='Block8_4_Activation')(x)
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_5_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_5_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_5_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_5_Branch_5_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_5_Branch_5_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_5_Branch_5_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_5_Branch_5_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_5_Branch_5_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_5_Branch_5_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_5_Branch_5_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_5_Branch_5_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_5_Branch_5_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_5_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_5_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 0.2})(up)
x = add([x, up])
x = Activation('relu', name='Block8_5_Activation')(x)
branch_0 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_6_Branch_0_Conv2d_1x1') (x)
branch_0 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_6_Branch_0_Conv2d_1x1_BatchNorm')(branch_0)
branch_0 = Activation('relu', name='Block8_6_Branch_0_Conv2d_1x1_Activation')(branch_0)
branch_1 = Conv2D(192, 1, strides=1, padding='same', use_bias=False, name= 'Block8_6_Branch_1_Conv2d_0a_1x1') (x)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_6_Branch_1_Conv2d_0a_1x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_6_Branch_1_Conv2d_0a_1x1_Activation')(branch_1)
branch_1 = Conv2D(192, [1, 3], strides=1, padding='same', use_bias=False, name= 'Block8_6_Branch_1_Conv2d_0b_1x3') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_6_Branch_1_Conv2d_0b_1x3_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_6_Branch_1_Conv2d_0b_1x3_Activation')(branch_1)
branch_1 = Conv2D(192, [3, 1], strides=1, padding='same', use_bias=False, name= 'Block8_6_Branch_1_Conv2d_0c_3x1') (branch_1)
branch_1 = BatchNormalization(axis=3, momentum=0.995, epsilon=0.001, scale=False, name='Block8_6_Branch_1_Conv2d_0c_3x1_BatchNorm')(branch_1)
branch_1 = Activation('relu', name='Block8_6_Branch_1_Conv2d_0c_3x1_Activation')(branch_1)
branches = [branch_0, branch_1]
mixed = Concatenate(axis=3, name='Block8_6_Concatenate')(branches)
up = Conv2D(1792, 1, strides=1, padding='same', use_bias=True, name= 'Block8_6_Conv2d_1x1') (mixed)
up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': 1})(up)
x = add([x, up])
# Classification block
x = GlobalAveragePooling2D(name='AvgPool')(x)
x = Dropout(1.0 - 0.8, name='Dropout')(x)
# Bottleneck
x = Dense(128, use_bias=False, name='Bottleneck')(x)
x = BatchNormalization(momentum=0.995, epsilon=0.001, scale=False, name='Bottleneck_BatchNorm')(x)
# Create model
model = Model(inputs, x, name='inception_resnet_v1')
return model
# Create the FaceNet model
# face_encoder = InceptionResNetV2()
# # Load the weights of the model
# path = "facenet_keras_weights.h5"
# face_encoder.load_weights(path)