C247-/cnn.py at main · Zilai-WANG/C247- · GitHub

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
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Flatten,Dropout\n",
    "from keras.layers import Conv2D,BatchNormalization,MaxPooling2D,Reshape\n",
    "from keras.utils import to_categorical\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2115, 22, 1000)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f15c6be31c0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Loading and visualizing the data\n",
    "\n",
    "## Loading the dataset\n",
    "\n",
    "path = \"./project/\"\n",
    "\n",
    "X_test = np.load(path + \"X_test.npy\")\n",
    "y_test = np.load(path + \"y_test.npy\")\n",
    "person_train_valid = np.load(path + \"person_train_valid.npy\")\n",
    "X_train_valid = np.load(path + \"X_train_valid.npy\")\n",
    "print(X_train_valid.shape)\n",
    "y_train_valid = np.load(path + \"y_train_valid.npy\")\n",
    "person_test = np.load(path + \"person_test.npy\")\n",
    "\n",
    "## Adjusting the labels so that \n",
    "\n",
    "# Cue onset left - 0\n",
    "# Cue onset right - 1\n",
    "# Cue onset foot - 2\n",
    "# Cue onset tongue - 3\n",
    "\n",
    "y_train_valid -= 769\n",
    "y_test -= 769\n",
    "\n",
    "## Visualizing the data\n",
    "\n",
    "ch_data = X_train_valid[:,8,:]\n",
    "\n",
    "\n",
    "class_0_ind = np.where(y_train_valid == 0)\n",
    "ch_data_class_0 = ch_data[class_0_ind]\n",
    "avg_ch_data_class_0 = np.mean(ch_data_class_0,axis=0)\n",
    "\n",
    "\n",
    "class_1_ind = np.where(y_train_valid == 1)\n",
    "ch_data_class_1 = ch_data[class_1_ind]\n",
    "avg_ch_data_class_1 = np.mean(ch_data_class_1,axis=0)\n",
    "\n",
    "class_2_ind = np.where(y_train_valid == 2)\n",
    "ch_data_class_2 = ch_data[class_2_ind]\n",
    "avg_ch_data_class_2 = np.mean(ch_data_class_2,axis=0)\n",
    "\n",
    "class_3_ind = np.where(y_train_valid == 3)\n",
    "ch_data_class_3 = ch_data[class_3_ind]\n",
    "avg_ch_data_class_3 = np.mean(ch_data_class_3,axis=0)\n",
    "\n",
    "\n",
    "plt.plot(np.arange(1000),avg_ch_data_class_0)\n",
    "plt.plot(np.arange(1000),avg_ch_data_class_1)\n",
    "plt.plot(np.arange(1000),avg_ch_data_class_2)\n",
    "plt.plot(np.arange(1000),avg_ch_data_class_3)\n",
    "plt.axvline(x=500, label='line at t=500',c='cyan')\n",
    "\n",
    "plt.legend([\"Cue Onset left\", \"Cue Onset right\", \"Cue onset foot\", \"Cue onset tongue\"])\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2115, 22, 500)\n",
      "(2115,)\n",
      "(443, 22, 500)\n",
      "(443,)\n",
      "Shape of training set: (1615, 22, 500)\n",
      "Shape of validation set: (500, 22, 500)\n",
      "Shape of training labels: (1615,)\n",
      "Shape of validation labels: (500,)\n",
      "Shape of training labels after categorical conversion: (1615, 4)\n",
      "Shape of validation labels after categorical conversion: (500, 4)\n",
      "Shape of test labels after categorical conversion: (443, 4)\n",
      "Shape of training set after adding width info: (1615, 22, 500, 1)\n",
      "Shape of validation set after adding width info: (500, 22, 500, 1)\n",
      "Shape of test set after adding width info: (443, 22, 500, 1)\n",
      "Shape of training set after dimension reshaping: (1615, 500, 1, 22)\n",
      "Shape of validation set after dimension reshaping: (500, 500, 1, 22)\n",
      "Shape of test set after dimension reshaping: (443, 500, 1, 22)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "## Preprocessing the dataset\n",
    "\n",
    "X_train_valid_prep = X_train_valid[:,:,0:500]\n",
    "X_test_prep = X_test[:,:,0:500]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(X_train_valid_prep.shape)\n",
    "print(y_train_valid.shape)\n",
    "print(X_test_prep.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "\n",
    "\n",
    "## Random splitting and reshaping the data\n",
    "\n",
    "# First generating the training and validation indices using random splitting\n",
    "ind_valid = np.random.choice(2115, 500, replace=False)\n",
    "ind_train = np.array(list(set(range(2115)).difference(set(ind_valid))))\n",
    "\n",
    "# Creating the training and validation sets using the generated indices\n",
    "(x_train, x_valid) = X_train_valid_prep[ind_train], X_train_valid_prep[ind_valid] \n",
    "(y_train, y_valid) = y_train_valid[ind_train], y_train_valid[ind_valid]\n",
    "print('Shape of training set:',x_train.shape)\n",
    "print('Shape of validation set:',x_valid.shape)\n",
    "print('Shape of training labels:',y_train.shape)\n",
    "print('Shape of validation labels:',y_valid.shape)\n",
    "\n",
    "\n",
    "# Converting the labels to categorical variables for multiclass classification\n",
    "y_train = to_categorical(y_train, 4)\n",
    "y_valid = to_categorical(y_valid, 4)\n",
    "y_test = to_categorical(y_test, 4)\n",
    "print('Shape of training labels after categorical conversion:',y_train.shape)\n",
    "print('Shape of validation labels after categorical conversion:',y_valid.shape)\n",
    "print('Shape of test labels after categorical conversion:',y_test.shape)\n",
    "\n",
    "# Adding width of the segment to be 1\n",
    "x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)\n",
    "x_valid = x_valid.reshape(x_valid.shape[0], x_valid.shape[1], x_train.shape[2], 1)\n",
    "x_test = X_test_prep.reshape(X_test_prep.shape[0], X_test_prep.shape[1], X_test_prep.shape[2], 1)\n",
    "print('Shape of training set after adding width info:',x_train.shape)\n",
    "print('Shape of validation set after adding width info:',x_valid.shape)\n",
    "print('Shape of test set after adding width info:',x_test.shape)\n",
    "\n",
    "\n",
    "# Reshaping the training and validation dataset\n",
    "x_train = np.swapaxes(x_train, 1,3)\n",
    "x_train = np.swapaxes(x_train, 1,2)\n",
    "x_valid = np.swapaxes(x_valid, 1,3)\n",
    "x_valid = np.swapaxes(x_valid, 1,2)\n",
    "x_test = np.swapaxes(x_test, 1,3)\n",
    "x_test = np.swapaxes(x_test, 1,2)\n",
    "print('Shape of training set after dimension reshaping:',x_train.shape)\n",
    "print('Shape of validation set after dimension reshaping:',x_valid.shape)\n",
    "print('Shape of test set after dimension reshaping:',x_test.shape)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 500, 1, 25)        5525      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 167, 1, 25)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 167, 1, 25)       100       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 167, 1, 25)        0         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 167, 1, 50)        12550     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 56, 1, 50)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 56, 1, 50)        200       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 56, 1, 50)         0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 56, 1, 100)        50100     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 19, 1, 100)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 19, 1, 100)       400       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 19, 1, 100)        0         \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 19, 1, 200)        200200    \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 7, 1, 200)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 7, 1, 200)        800       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 7, 1, 200)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1400)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 4)                 5604      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 275,479\n",
      "Trainable params: 274,729\n",
      "Non-trainable params: 750\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-11 19:05:26.954598: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "# Building the CNN model using sequential class\n",
    "basic_cnn_model = Sequential()\n",
    "\n",
    "# Conv. block 1\n",
    "basic_cnn_model.add(Conv2D(filters=25, kernel_size=(10,1), padding='same', activation='elu', input_shape=(500,1,22)))\n",
    "basic_cnn_model.add(MaxPooling2D(pool_size=(3,1), padding='same')) # Read the keras documentation\n",
    "basic_cnn_model.add(BatchNormalization())\n",
    "basic_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Conv. block 2\n",
    "basic_cnn_model.add(Conv2D(filters=50, kernel_size=(10,1), padding='same', activation='elu'))\n",
    "basic_cnn_model.add(MaxPooling2D(pool_size=(3,1), padding='same'))\n",
    "basic_cnn_model.add(BatchNormalization())\n",
    "basic_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Conv. block 3\n",
    "basic_cnn_model.add(Conv2D(filters=100, kernel_size=(10,1), padding='same', activation='elu'))\n",
    "basic_cnn_model.add(MaxPooling2D(pool_size=(3,1), padding='same'))\n",
    "basic_cnn_model.add(BatchNormalization())\n",
    "basic_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Conv. block 4\n",
    "basic_cnn_model.add(Conv2D(filters=200, kernel_size=(10,1), padding='same', activation='elu'))\n",
    "basic_cnn_model.add(MaxPooling2D(pool_size=(3,1), padding='same'))\n",
    "basic_cnn_model.add(BatchNormalization())\n",
    "basic_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Output layer with Softmax activation\n",
    "basic_cnn_model.add(Flatten()) # Flattens the input\n",
    "basic_cnn_model.add(Dense(4, activation='softmax')) # Output FC layer with softmax activation\n",
    "\n",
    "\n",
    "# Printing the model summary\n",
    "basic_cnn_model.summary()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/u/garrick/miniconda3/envs/d2l/lib/python3.9/site-packages/keras/optimizers/legacy/adam.py:117: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Model parameters\n",
    "learning_rate = 1e-3\n",
    "epochs = 50\n",
    "cnn_optimizer = keras.optimizers.Adam(lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "26/26 [==============================] - 5s 171ms/step - loss: 2.1929 - accuracy: 0.2941 - val_loss: 1.8615 - val_accuracy: 0.3500\n",
      "Epoch 2/50\n",
      "26/26 [==============================] - 4s 143ms/step - loss: 1.9784 - accuracy: 0.3554 - val_loss: 1.3242 - val_accuracy: 0.4460\n",
      "Epoch 3/50\n",
      "26/26 [==============================] - 4s 148ms/step - loss: 1.8424 - accuracy: 0.3554 - val_loss: 1.6583 - val_accuracy: 0.3200\n",
      "Epoch 4/50\n",
      "26/26 [==============================] - 4s 158ms/step - loss: 1.7127 - accuracy: 0.3765 - val_loss: 1.5836 - val_accuracy: 0.3660\n",
      "Epoch 5/50\n",
      "26/26 [==============================] - 4s 158ms/step - loss: 1.5837 - accuracy: 0.3802 - val_loss: 1.3236 - val_accuracy: 0.4260\n",
      "Epoch 6/50\n",
      "26/26 [==============================] - 4s 149ms/step - loss: 1.5359 - accuracy: 0.4161 - val_loss: 1.3096 - val_accuracy: 0.4640\n",
      "Epoch 7/50\n",
      "26/26 [==============================] - 4s 167ms/step - loss: 1.4691 - accuracy: 0.4111 - val_loss: 1.2922 - val_accuracy: 0.4460\n",
      "Epoch 8/50\n",
      "26/26 [==============================] - 4s 146ms/step - loss: 1.4017 - accuracy: 0.4415 - val_loss: 1.2472 - val_accuracy: 0.4460\n",
      "Epoch 9/50\n",
      "26/26 [==============================] - 3s 129ms/step - loss: 1.3578 - accuracy: 0.4508 - val_loss: 1.2764 - val_accuracy: 0.3940\n",
      "Epoch 10/50\n",
      "26/26 [==============================] - 3s 134ms/step - loss: 1.3121 - accuracy: 0.4904 - val_loss: 1.2114 - val_accuracy: 0.4760\n",
      "Epoch 11/50\n",
      "26/26 [==============================] - 4s 150ms/step - loss: 1.2451 - accuracy: 0.4681 - val_loss: 1.1590 - val_accuracy: 0.4880\n",
      "Epoch 12/50\n",
      "26/26 [==============================] - 4s 153ms/step - loss: 1.1986 - accuracy: 0.4991 - val_loss: 1.3153 - val_accuracy: 0.3780\n",
      "Epoch 13/50\n",
      "26/26 [==============================] - 4s 149ms/step - loss: 1.2022 - accuracy: 0.5015 - val_loss: 1.2205 - val_accuracy: 0.4740\n",
      "Epoch 14/50\n",
      "26/26 [==============================] - 4s 146ms/step - loss: 1.1574 - accuracy: 0.5176 - val_loss: 1.2431 - val_accuracy: 0.4800\n",
      "Epoch 15/50\n",
      "26/26 [==============================] - 4s 144ms/step - loss: 1.1453 - accuracy: 0.5362 - val_loss: 1.1997 - val_accuracy: 0.4900\n",
      "Epoch 16/50\n",
      "26/26 [==============================] - 4s 165ms/step - loss: 1.1035 - accuracy: 0.5443 - val_loss: 1.0793 - val_accuracy: 0.5680\n",
      "Epoch 17/50\n",
      "26/26 [==============================] - 4s 150ms/step - loss: 1.1247 - accuracy: 0.5344 - val_loss: 1.0478 - val_accuracy: 0.5780\n",
      "Epoch 18/50\n",
      "26/26 [==============================] - 4s 139ms/step - loss: 1.1133 - accuracy: 0.5591 - val_loss: 1.2462 - val_accuracy: 0.5060\n",
      "Epoch 19/50\n",
      "26/26 [==============================] - 4s 158ms/step - loss: 1.0784 - accuracy: 0.5616 - val_loss: 1.1542 - val_accuracy: 0.4960\n",
      "Epoch 20/50\n",
      "26/26 [==============================] - 4s 158ms/step - loss: 1.0442 - accuracy: 0.5728 - val_loss: 1.1240 - val_accuracy: 0.5280\n",
      "Epoch 21/50\n",
      "26/26 [==============================] - 4s 152ms/step - loss: 1.0219 - accuracy: 0.5895 - val_loss: 1.1707 - val_accuracy: 0.4920\n",
      "Epoch 22/50\n",
      "26/26 [==============================] - 4s 153ms/step - loss: 0.9943 - accuracy: 0.5913 - val_loss: 1.1005 - val_accuracy: 0.5200\n",
      "Epoch 23/50\n",
      "26/26 [==============================] - 4s 153ms/step - loss: 0.9625 - accuracy: 0.6118 - val_loss: 1.0259 - val_accuracy: 0.5520\n",
      "Epoch 24/50\n",
      "26/26 [==============================] - 4s 150ms/step - loss: 0.9839 - accuracy: 0.5950 - val_loss: 1.0104 - val_accuracy: 0.5860\n",
      "Epoch 25/50\n",
      "26/26 [==============================] - 4s 150ms/step - loss: 0.9639 - accuracy: 0.6025 - val_loss: 0.9759 - val_accuracy: 0.5980\n",
      "Epoch 26/50\n",
      "26/26 [==============================] - 4s 145ms/step - loss: 0.9544 - accuracy: 0.6235 - val_loss: 0.8931 - val_accuracy: 0.6580\n",
      "Epoch 27/50\n",
      "26/26 [==============================] - 4s 152ms/step - loss: 0.9903 - accuracy: 0.5938 - val_loss: 1.1057 - val_accuracy: 0.5340\n",
      "Epoch 28/50\n",
      "26/26 [==============================] - 4s 145ms/step - loss: 0.9364 - accuracy: 0.6000 - val_loss: 0.9531 - val_accuracy: 0.6100\n",
      "Epoch 29/50\n",
      "26/26 [==============================] - 4s 137ms/step - loss: 0.9059 - accuracy: 0.6279 - val_loss: 0.9859 - val_accuracy: 0.6160\n",
      "Epoch 30/50\n",
      "26/26 [==============================] - 4s 141ms/step - loss: 0.9022 - accuracy: 0.6390 - val_loss: 0.9997 - val_accuracy: 0.5840\n",
      "Epoch 31/50\n",
      "26/26 [==============================] - 3s 132ms/step - loss: 0.8893 - accuracy: 0.6557 - val_loss: 0.9628 - val_accuracy: 0.6120\n",
      "Epoch 32/50\n",
      "26/26 [==============================] - 4s 157ms/step - loss: 0.8961 - accuracy: 0.6477 - val_loss: 0.9052 - val_accuracy: 0.6380\n",
      "Epoch 33/50\n",
      "26/26 [==============================] - 4s 146ms/step - loss: 0.8577 - accuracy: 0.6625 - val_loss: 0.9787 - val_accuracy: 0.5940\n",
      "Epoch 34/50\n",
      "26/26 [==============================] - 4s 150ms/step - loss: 0.8408 - accuracy: 0.6681 - val_loss: 0.9761 - val_accuracy: 0.5920\n",
      "Epoch 35/50\n",
      "26/26 [==============================] - 4s 143ms/step - loss: 0.7983 - accuracy: 0.6687 - val_loss: 1.0005 - val_accuracy: 0.6120\n",
      "Epoch 36/50\n",
      "26/26 [==============================] - 3s 128ms/step - loss: 0.8570 - accuracy: 0.6539 - val_loss: 1.0144 - val_accuracy: 0.5880\n",
      "Epoch 37/50\n",
      "26/26 [==============================] - 4s 137ms/step - loss: 0.8282 - accuracy: 0.6551 - val_loss: 0.9279 - val_accuracy: 0.6280\n",
      "Epoch 38/50\n",
      "26/26 [==============================] - 4s 153ms/step - loss: 0.8036 - accuracy: 0.6780 - val_loss: 0.9167 - val_accuracy: 0.6120\n",
      "Epoch 39/50\n",
      "26/26 [==============================] - 4s 157ms/step - loss: 0.8260 - accuracy: 0.6588 - val_loss: 0.8966 - val_accuracy: 0.6500\n",
      "Epoch 40/50\n",
      "26/26 [==============================] - 4s 152ms/step - loss: 0.7781 - accuracy: 0.6848 - val_loss: 0.9872 - val_accuracy: 0.6200\n",
      "Epoch 41/50\n",
      "26/26 [==============================] - 4s 150ms/step - loss: 0.7812 - accuracy: 0.6910 - val_loss: 0.9305 - val_accuracy: 0.6360\n",
      "Epoch 42/50\n",
      "26/26 [==============================] - 4s 155ms/step - loss: 0.7498 - accuracy: 0.7071 - val_loss: 0.9484 - val_accuracy: 0.6160\n",
      "Epoch 43/50\n",
      "26/26 [==============================] - 4s 157ms/step - loss: 0.7611 - accuracy: 0.7034 - val_loss: 1.0119 - val_accuracy: 0.5920\n",
      "Epoch 44/50\n",
      "26/26 [==============================] - 4s 151ms/step - loss: 0.7528 - accuracy: 0.7040 - val_loss: 0.8704 - val_accuracy: 0.6620\n",
      "Epoch 45/50\n",
      "26/26 [==============================] - 4s 143ms/step - loss: 0.7156 - accuracy: 0.7214 - val_loss: 0.8767 - val_accuracy: 0.6440\n",
      "Epoch 46/50\n",
      "26/26 [==============================] - 4s 151ms/step - loss: 0.7318 - accuracy: 0.7146 - val_loss: 0.8860 - val_accuracy: 0.6520\n",
      "Epoch 47/50\n",
      "26/26 [==============================] - 4s 160ms/step - loss: 0.7259 - accuracy: 0.7195 - val_loss: 0.8606 - val_accuracy: 0.6460\n",
      "Epoch 48/50\n",
      "26/26 [==============================] - 4s 152ms/step - loss: 0.6996 - accuracy: 0.7288 - val_loss: 0.9742 - val_accuracy: 0.6180\n",
      "Epoch 49/50\n",
      "26/26 [==============================] - 4s 137ms/step - loss: 0.7406 - accuracy: 0.7034 - val_loss: 0.8470 - val_accuracy: 0.6660\n",
      "Epoch 50/50\n",
      "26/26 [==============================] - 4s 159ms/step - loss: 0.7041 - accuracy: 0.7195 - val_loss: 0.9098 - val_accuracy: 0.6460\n"
     ]
    }
   ],
   "source": [
    "# Compiling the model\n",
    "basic_cnn_model.compile(loss='categorical_crossentropy',\n",
    "                 optimizer=cnn_optimizer,\n",
    "                 metrics=['accuracy'])\n",
    "\n",
    "# Training and validating the model\n",
    "basic_cnn_model_results = basic_cnn_model.fit(x_train,\n",
    "             y_train,\n",
    "             batch_size=64,\n",
    "             epochs=epochs,\n",
    "             validation_data=(x_valid, y_valid), verbose=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting accuracy trajectory\n",
    "plt.plot(basic_cnn_model_results.history['accuracy'])\n",
    "plt.plot(basic_cnn_model_results.history['val_accuracy'])\n",
    "plt.title('Basic CNN model accuracy trajectory')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plotting loss trajectory\n",
    "plt.plot(basic_cnn_model_results.history['loss'],'o')\n",
    "plt.plot(basic_cnn_model_results.history['val_loss'],'o')\n",
    "plt.title('Basic CNN model loss trajectory')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy of the basic CNN model: 0.6320541501045227\n"
     ]
    }
   ],
   "source": [
    "## Testing the basic CNN model\n",
    "\n",
    "cnn_score = basic_cnn_model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test accuracy of the basic CNN model:',cnn_score[1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN + More complex architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_6 (Conv2D)           (None, 500, 1, 25)        5525      \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPooling  (None, 167, 1, 25)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_5 (Batc  (None, 167, 1, 25)       100       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 167, 1, 25)        0         \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 167, 1, 50)        12550     \n",
      "                                                                 \n",
      " max_pooling2d_6 (MaxPooling  (None, 56, 1, 50)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_6 (Batc  (None, 56, 1, 50)        200       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_6 (Dropout)         (None, 56, 1, 50)         0         \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 56, 1, 100)        50100     \n",
      "                                                                 \n",
      " max_pooling2d_7 (MaxPooling  (None, 19, 1, 100)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_7 (Batc  (None, 19, 1, 100)       400       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_7 (Dropout)         (None, 19, 1, 100)        0         \n",
      "                                                                 \n",
      " conv2d_9 (Conv2D)           (None, 19, 1, 200)        200200    \n",
      "                                                                 \n",
      " max_pooling2d_8 (MaxPooling  (None, 7, 1, 200)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_8 (Batc  (None, 7, 1, 200)        800       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_8 (Dropout)         (None, 7, 1, 200)         0         \n",
      "                                                                 \n",
      " conv2d_10 (Conv2D)          (None, 7, 1, 400)         800400    \n",
      "                                                                 \n",
      " max_pooling2d_9 (MaxPooling  (None, 3, 1, 400)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_9 (Batc  (None, 3, 1, 400)        1600      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_9 (Dropout)         (None, 3, 1, 400)         0         \n",
      "                                                                 \n",
      " conv2d_11 (Conv2D)          (None, 3, 1, 800)         3200800   \n",
      "                                                                 \n",
      " max_pooling2d_10 (MaxPoolin  (None, 1, 1, 800)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_10 (Bat  (None, 1, 1, 800)        3200      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_10 (Dropout)        (None, 1, 1, 800)         0         \n",
      "                                                                 \n",
      " conv2d_12 (Conv2D)          (None, 1, 1, 1600)        12801600  \n",
      "                                                                 \n",
      " max_pooling2d_11 (MaxPoolin  (None, 1, 1, 1600)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_11 (Bat  (None, 1, 1, 1600)       6400      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_11 (Dropout)        (None, 1, 1, 1600)        0         \n",
      "                                                                 \n",
      " conv2d_13 (Conv2D)          (None, 1, 1, 3200)        51203200  \n",
      "                                                                 \n",
      " max_pooling2d_12 (MaxPoolin  (None, 1, 1, 3200)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_12 (Bat  (None, 1, 1, 3200)       12800     \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_12 (Dropout)        (None, 1, 1, 3200)        0         \n",
      "                                                                 \n",
      " conv2d_14 (Conv2D)          (None, 1, 1, 6400)        204806400 \n",
      "                                                                 \n",
      " max_pooling2d_13 (MaxPoolin  (None, 1, 1, 6400)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_13 (Bat  (None, 1, 1, 6400)       25600     \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_13 (Dropout)        (None, 1, 1, 6400)        0         \n",
      "                                                                 \n",
      " conv2d_15 (Conv2D)          (None, 1, 1, 12800)       819212800 \n",
      "                                                                 \n",
      " max_pooling2d_14 (MaxPoolin  (None, 1, 1, 12800)      0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_14 (Bat  (None, 1, 1, 12800)      51200     \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_14 (Dropout)        (None, 1, 1, 12800)       0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 12800)             0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4)                 51204     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,092,447,079\n",
      "Trainable params: 1,092,395,929\n",
      "Non-trainable params: 51,150\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Dropout, Flatten, Dense\n",
    "\n",
    "# Building the CNN model using sequential class\n",
    "complex_cnn_model = Sequential()\n",
    "\n",
    "# Input shape\n",
    "input_shape = (500, 1, 22)\n",
    "\n",
    "\n",
    "    \n",
    "# Conv. block 1\n",
    "complex_cnn_model.add(Conv2D(filters=25, kernel_size=(10, 1), padding='same', activation='elu', input_shape=(500,1,22)))\n",
    "complex_cnn_model.add(MaxPooling2D(pool_size=(3, 1), padding='same'))\n",
    "complex_cnn_model.add(BatchNormalization())\n",
    "complex_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Conv. block 2\n",
    "complex_cnn_model.add(Conv2D(filters=50, kernel_size=(10, 1), padding='same', activation='elu'))\n",
    "complex_cnn_model.add(MaxPooling2D(pool_size=(3, 1), padding='same'))\n",
    "complex_cnn_model.add(BatchNormalization())\n",
    "complex_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Conv. block 3\n",
    "complex_cnn_model.add(Conv2D(filters=100, kernel_size=(10, 1), padding='same', activation='elu'))\n",
    "complex_cnn_model.add(MaxPooling2D(pool_size=(3, 1), padding='same'))\n",
    "complex_cnn_model.add(BatchNormalization())\n",
    "complex_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Conv. block 4\n",
    "complex_cnn_model.add(Conv2D(filters=200, kernel_size=(10, 1), padding='same', activation='elu'))\n",
    "complex_cnn_model.add(MaxPooling2D(pool_size=(3, 1), padding='same'))\n",
    "complex_cnn_model.add(BatchNormalization())\n",
    "complex_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Conv. block 5\n",
    "complex_cnn_model.add(Conv2D(filters=400, kernel_size=(10, 1), padding='same', activation='elu'))\n",
    "complex_cnn_model.add(MaxPooling2D(pool_size=(3, 1), padding='same'))\n",
    "complex_cnn_model.add(BatchNormalization())\n",
    "complex_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Conv. block 6\n",
    "complex_cnn_model.add(Conv2D(filters=800, kernel_size=(10, 1), padding='same', activation='elu'))\n",
    "complex_cnn_model.add(MaxPooling2D(pool_size=(3, 1), padding='same'))\n",
    "complex_cnn_model.add(BatchNormalization())\n",
    "complex_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Conv. block 7\n",
    "complex_cnn_model.add(Conv2D(filters=1600, kernel_size=(10, 1), padding='same', activation='elu'))\n",
    "complex_cnn_model.add(MaxPooling2D(pool_size=(3, 1), padding='same'))\n",
    "complex_cnn_model.add(BatchNormalization())\n",
    "complex_cnn_model.add(Dropout(0.5))\n",
    "\n",
    "# Output layer with Softmax activation\n",
    "complex_cnn_model.add(Flatten()) # Flattens the input\n",
    "complex_cnn_model.add(Dense(4, activation='softmax')) # Output FC layer with softmax activation\n",
    "\n",
    "# Printing the model summary\n",
    "complex_cnn_model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda:1\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# Set device to GPU 1\n",
    "device = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Check which device is being used\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# # To ensure your tensors are created on GPU 1\n",
    "# tensor_on_gpu1 = torch.tensor([1.0, 2.0], device=device)\n",
    "\n",
    "# # To move an existing tensor to GPU 1\n",
    "# tensor = torch.tensor([1.0, 2.0])\n",
    "# tensor_to_gpu1 = tensor.to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/u/garrick/miniconda3/envs/d2l/lib/python3.9/site-packages/keras/optimizers/legacy/adam.py:117: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "26/26 [==============================] - 434s 15s/step - loss: 3.2229 - accuracy: 0.2644 - val_loss: 43.5561 - val_accuracy: 0.2080\n",
      "Epoch 2/50\n",
      "26/26 [==============================] - 388s 15s/step - loss: 2.1320 - accuracy: 0.2762 - val_loss: 9.2604 - val_accuracy: 0.2660\n",
      "Epoch 3/50\n",
      "26/26 [==============================] - 387s 15s/step - loss: 1.9149 - accuracy: 0.2836 - val_loss: 5.0904 - val_accuracy: 0.3040\n",
      "Epoch 4/50\n",
      "26/26 [==============================] - 398s 15s/step - loss: 1.9937 - accuracy: 0.2879 - val_loss: 4.7879 - val_accuracy: 0.3280\n",
      "Epoch 5/50\n",
      "26/26 [==============================] - 397s 15s/step - loss: 2.0539 - accuracy: 0.3121 - val_loss: 4.8783 - val_accuracy: 0.3020\n",
      "Epoch 6/50\n",
      "26/26 [==============================] - 392s 15s/step - loss: 2.3080 - accuracy: 0.2985 - val_loss: 2.4492 - val_accuracy: 0.3520\n",
      "Epoch 7/50\n",
      "26/26 [==============================] - 397s 15s/step - loss: 1.9439 - accuracy: 0.2904 - val_loss: 1.7042 - val_accuracy: 0.3180\n",
      "Epoch 8/50\n",
      "26/26 [==============================] - 359s 14s/step - loss: 1.8992 - accuracy: 0.3084 - val_loss: 1.8769 - val_accuracy: 0.2640\n",
      "Epoch 9/50\n",
      "26/26 [==============================] - 269s 10s/step - loss: 1.9659 - accuracy: 0.3090 - val_loss: 1.5738 - val_accuracy: 0.3380\n",
      "Epoch 10/50\n",
      "26/26 [==============================] - 267s 10s/step - loss: 1.8475 - accuracy: 0.3418 - val_loss: 1.4998 - val_accuracy: 0.3360\n",
      "Epoch 11/50\n",
      "26/26 [==============================] - 270s 10s/step - loss: 1.8082 - accuracy: 0.3672 - val_loss: 1.8868 - val_accuracy: 0.3180\n",
      "Epoch 12/50\n",
      "26/26 [==============================] - 269s 10s/step - loss: 1.8340 - accuracy: 0.3653 - val_loss: 3.1223 - val_accuracy: 0.2800\n",
      "Epoch 13/50\n",
      "26/26 [==============================] - 270s 10s/step - loss: 1.9514 - accuracy: 0.3474 - val_loss: 2.5005 - val_accuracy: 0.2580\n",
      "Epoch 14/50\n",
      "26/26 [==============================] - 269s 10s/step - loss: 1.9195 - accuracy: 0.3257 - val_loss: 2.0648 - val_accuracy: 0.3000\n",
      "Epoch 15/50\n",
      "26/26 [==============================] - 268s 10s/step - loss: 2.0145 - accuracy: 0.3467 - val_loss: 2.1053 - val_accuracy: 0.2900\n",
      "Epoch 16/50\n",
      "26/26 [==============================] - 268s 10s/step - loss: 1.9752 - accuracy: 0.3511 - val_loss: 2.9267 - val_accuracy: 0.2960\n",
      "Epoch 17/50\n",
      "26/26 [==============================] - 270s 10s/step - loss: 2.2488 - accuracy: 0.3127 - val_loss: 2.4391 - val_accuracy: 0.3140\n",
      "Epoch 18/50\n",
      "26/26 [==============================] - 266s 10s/step - loss: 2.0403 - accuracy: 0.3226 - val_loss: 1.8011 - val_accuracy: 0.4080\n",
      "Epoch 19/50\n",
      "26/26 [==============================] - 269s 10s/step - loss: 1.8294 - accuracy: 0.3579 - val_loss: 1.4532 - val_accuracy: 0.3720\n",
      "Epoch 20/50\n",
      "26/26 [==============================] - 267s 10s/step - loss: 1.9674 - accuracy: 0.3622 - val_loss: 1.4569 - val_accuracy: 0.3860\n",
      "Epoch 21/50\n",
      "26/26 [==============================] - 267s 10s/step - loss: 2.4639 - accuracy: 0.3245 - val_loss: 1.8891 - val_accuracy: 0.3600\n",
      "Epoch 22/50\n",
      "26/26 [==============================] - 268s 10s/step - loss: 2.7077 - accuracy: 0.3331 - val_loss: 2.3671 - val_accuracy: 0.2980\n",
      "Epoch 23/50\n",
      "26/26 [==============================] - 278s 11s/step - loss: 1.9209 - accuracy: 0.3406 - val_loss: 6.6767 - val_accuracy: 0.2880\n",
      "Epoch 24/50\n",
      "26/26 [==============================] - 269s 10s/step - loss: 2.1173 - accuracy: 0.3232 - val_loss: 1.7481 - val_accuracy: 0.2720\n",
      "Epoch 25/50\n",
      "26/26 [==============================] - 270s 10s/step - loss: 1.9607 - accuracy: 0.3350 - val_loss: 1.5260 - val_accuracy: 0.3180\n",
      "Epoch 26/50\n",
      "26/26 [==============================] - 270s 10s/step - loss: 1.9782 - accuracy: 0.3437 - val_loss: 1.4431 - val_accuracy: 0.3900\n",
      "Epoch 27/50\n",
      "26/26 [==============================] - 268s 10s/step - loss: 2.2260 - accuracy: 0.3393 - val_loss: 4.1256 - val_accuracy: 0.3380\n",
      "Epoch 28/50\n",
      "26/26 [==============================] - 268s 10s/step - loss: 2.0120 - accuracy: 0.3418 - val_loss: 1.9940 - val_accuracy: 0.3360\n",
      "Epoch 29/50\n",
      "26/26 [==============================] - 270s 10s/step - loss: 2.2383 - accuracy: 0.3288 - val_loss: 2.6056 - val_accuracy: 0.3400\n",
      "Epoch 30/50\n",
      "26/26 [==============================] - 268s 10s/step - loss: 1.9443 - accuracy: 0.3610 - val_loss: 1.5100 - val_accuracy: 0.3800\n",
      "Epoch 31/50\n",
      "26/26 [==============================] - 270s 10s/step - loss: 1.9174 - accuracy: 0.3455 - val_loss: 1.4739 - val_accuracy: 0.4300\n",
      "Epoch 32/50\n",
      "26/26 [==============================] - 270s 10s/step - loss: 1.7800 - accuracy: 0.3585 - val_loss: 1.6376 - val_accuracy: 0.3740\n",
      "Epoch 33/50\n",
      "26/26 [==============================] - 268s 10s/step - loss: 1.9617 - accuracy: 0.3381 - val_loss: 1.2890 - val_accuracy: 0.4120\n",
      "Epoch 34/50\n",
      "26/26 [==============================] - 267s 10s/step - loss: 1.9723 - accuracy: 0.3498 - val_loss: 1.3985 - val_accuracy: 0.4060\n",
      "Epoch 35/50\n",
      "26/26 [==============================] - 268s 10s/step - loss: 1.7597 - accuracy: 0.3554 - val_loss: 1.4269 - val_accuracy: 0.3560\n",
      "Epoch 36/50\n",
      "26/26 [==============================] - 270s 10s/step - loss: 2.4302 - accuracy: 0.3474 - val_loss: 1.7134 - val_accuracy: 0.3080\n",
      "Epoch 37/50\n",
      "26/26 [==============================] - 278s 11s/step - loss: 2.3078 - accuracy: 0.3102 - val_loss: 1.9448 - val_accuracy: 0.2480\n",
      "Epoch 38/50\n",
      "26/26 [==============================] - 267s 10s/step - loss: 1.9804 - accuracy: 0.3375 - val_loss: 1.3760 - val_accuracy: 0.3520\n",
      "Epoch 39/50\n",
      "26/26 [==============================] - 269s 10s/step - loss: 1.9859 - accuracy: 0.3331 - val_loss: 1.4064 - val_accuracy: 0.3780\n",
      "Epoch 40/50\n",
      "26/26 [==============================] - 268s 10s/step - loss: 2.5804 - accuracy: 0.3393 - val_loss: 15.0901 - val_accuracy: 0.3220\n",
      "Epoch 41/50\n",
      "26/26 [==============================] - 278s 11s/step - loss: 2.2567 - accuracy: 0.3474 - val_loss: 3.9362 - val_accuracy: 0.4100\n",
      "Epoch 42/50\n",
      "26/26 [==============================] - 269s 10s/step - loss: 2.1768 - accuracy: 0.3598 - val_loss: 1.5640 - val_accuracy: 0.3780\n",
      "Epoch 43/50\n",
      "26/26 [==============================] - 269s 10s/step - loss: 2.0228 - accuracy: 0.3381 - val_loss: 1.5064 - val_accuracy: 0.2860\n",
      "Epoch 44/50\n",
      "26/26 [==============================] - 340s 13s/step - loss: 2.3746 - accuracy: 0.3313 - val_loss: 1.7039 - val_accuracy: 0.2640\n",
      "Epoch 45/50\n",
      "26/26 [==============================] - 331s 13s/step - loss: 2.1554 - accuracy: 0.3467 - val_loss: 1.4891 - val_accuracy: 0.3160\n",
      "Epoch 46/50\n",
      "26/26 [==============================] - 323s 12s/step - loss: 1.7742 - accuracy: 0.3498 - val_loss: 1.3598 - val_accuracy: 0.2840\n",
      "Epoch 47/50\n",
      "26/26 [==============================] - 323s 12s/step - loss: 1.8672 - accuracy: 0.3498 - val_loss: 1.3407 - val_accuracy: 0.3640\n",
      "Epoch 48/50\n",
      "26/26 [==============================] - 319s 12s/step - loss: 1.9158 - accuracy: 0.3635 - val_loss: 1.3001 - val_accuracy: 0.4020\n",
      "Epoch 49/50\n",
      "26/26 [==============================] - 322s 12s/step - loss: 2.1353 - accuracy: 0.3399 - val_loss: 1.3780 - val_accuracy: 0.3200\n",
      "Epoch 50/50\n",
      "26/26 [==============================] - 320s 12s/step - loss: 2.4837 - accuracy: 0.3399 - val_loss: 2.0706 - val_accuracy: 0.2760\n"
     ]
    }
   ],
   "source": [
    "# Model parameters\n",
    "learning_rate = 1e-3\n",
    "epochs = 50\n",
    "cnn_optimizer = keras.optimizers.Adam(lr=learning_rate)\n",
    "\n",
    "# Compiling the model\n",
    "complex_cnn_model.compile(loss='categorical_crossentropy',\n",
    "                 optimizer=cnn_optimizer,\n",
    "                 metrics=['accuracy'])\n",
    "\n",
    "# Training and validating the model\n",
    "complex_cnn_model_results = complex_cnn_model.fit(x_train,\n",
    "             y_train,\n",
    "             batch_size=64,\n",
    "             epochs=epochs,\n",
    "             validation_data=(x_valid, y_valid), verbose=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAAHHCAYAAABQhTneAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAADCfElEQVR4nOydd3xT9frHPydJkzbdi27a0rJXZYNMQUEQwYHiAvSK14GiOH7ugfMqKq4rjuueV3GgXEVZArJH2bO0ZXTv3TTJ9/fHN9+T0YyT1STt9/168UpIz0m+TZNznvN5Ps/zCIQQAg6Hw+FwOJwugszXC+BwOBwOh8PpSHjww+FwOBwOp0vBgx8Oh8PhcDhdCh78cDgcDofD6VLw4IfD4XA4HE6Xggc/HA6Hw+FwuhQ8+OFwOBwOh9Ol4MEPh8PhcDicLgUPfjgcDofD4XQpePDD4dhAEAQ8/fTTvl4GxwobN26EIAjYuHGj0/t+8sknEAQBBQUFHl8Xp+PIyMjAggULfL0MToDCgx+OV8nLy8M///lP9OjRA8HBwYiIiMCFF16IN954A83Nzb5ent9SWlqKBx54AH369IFarUZoaCiGDh2K5557DjU1NeJ2EydOhCAImDlzZrvnKCgogCAIWLZsmfgYCxoEQcCePXva7bNgwQKEhYV55XfidC22bt2Kp59+2uzzGki88MIL+Omnn3y9DI6XUPh6AZzOy+rVqzFnzhyoVCrMmzcPAwYMgEajwZYtW/Dggw/i8OHDeP/99329TL9j165dmD59OhoaGnDjjTdi6NChAIDdu3fjpZdewqZNm/DHH3+Y7fPrr79iz5494rZSePrpp/HLL794dO0cDmPr1q145plnsGDBAkRFRXn8+Y8fPw6ZzHvX7y+88AKuvvpqzJ4922uvwfEdPPjheIX8/HzMnTsX6enpWL9+PZKSksSf3XXXXTh16hRWr17twxX6JzU1Nbjiiisgl8uxb98+9OnTx+znzz//PD744AOzx7p37476+no888wzWLVqlaTXycnJwa+//oq9e/diyJAhHls/x3c0NTVBrVb7ehkuodfrodFoEBwcLHkflUrlxRV5h8bGRoSGhvp6GRzwtBfHS7z88stoaGjAf/7zH7PAh5GdnY3FixeL/9dqtXj22WeRlZUFlUqFjIwMPProo2htbTXbLyMjA5dddhk2btyIYcOGISQkBAMHDhS9Hz/88AMGDhyI4OBgDB06FPv27TPbn6V1Tp8+jalTpyI0NBTJyclYunQpCCEOf6/z58/jlltuQUJCAlQqFfr374+PPvpI/HlzczP69OmDPn36mKX1qqqqkJSUhDFjxkCn09l8/vfeew/nz5/Ha6+91i7wAYCEhAQ8/vjjZo+Fh4fjvvvuwy+//IK9e/c6/B0A4O6770Z0dLTLnib2Pp45cwaXXXYZwsLCkJKSgnfeeQcAcPDgQVx00UUIDQ1Feno6vvrqq3bPcfr0acyZMwcxMTFQq9UYNWqU1YD43LlzmD17NkJDQ9GtWzfcd9997T4XjB07dmDatGmIjIyEWq3GhAkT8Pfff7v0Ox44cAALFiwQU7aJiYm45ZZbUFlZ2W7b8+fP4x//+AeSk5OhUqmQmZmJO+64AxqNRtympqYG9913HzIyMqBSqZCamop58+ahoqICgG0vkjV/08SJEzFgwADs2bMH48ePh1qtxqOPPgoA+PnnnzFjxgxxLVlZWXj22Wetfu527NiB6dOnIzo6GqGhoRg0aBDeeOMNAMDHH38MQRDafYcAqorI5XKcP3/e6nv39NNP48EHHwQAZGZmiqlW9rsJgoBFixbhyy+/RP/+/aFSqfD7778DAJYtW4YxY8YgNjYWISEhGDp0KL7//vt2r2HN81NTU4N7770XaWlpUKlUyM7Oxr/+9S/o9Xqz7fR6Pd544w3xWBEfH49p06Zh9+7d4voaGxvx6aefims3fa19+/bh0ksvRUREBMLCwjB58mRs377d7DXY3/Ovv/7CnXfeiW7duiE1NRUbNmyAIAj48ccf2/1OX331FQRBwLZt26y+rxwPQjgcL5CSkkJ69Oghefv58+cTAOTqq68m77zzDpk3bx4BQGbPnm22XXp6OunduzdJSkoiTz/9NHn99ddJSkoKCQsLI1988QXp3r07eemll8hLL71EIiMjSXZ2NtHpdGavExwcTHr27Eluuukm8vbbb5PLLruMACBPPPGE2WsBIE899ZT4/5KSEpKamkrS0tLI0qVLybvvvksuv/xyAoC8/vrr4nbbt28ncrmc3HfffeJjc+fOJSEhIeT48eN234cxY8aQkJAQ0traKul9mzBhAunfvz+pra0l0dHRZObMmeLP8vPzCQDyyiuviI9t2LCBACDfffcdWbp0KQFA9uzZY/b+hIaGOnxd9j7269eP3H777eSdd94hY8aMIQDIxx9/TJKTk8mDDz5I3nrrLdK/f38il8vJ6dOnxf1LSkpIQkICCQ8PJ4899hh57bXXyODBg4lMJiM//PCDuF1TUxPp1asXCQ4OJg899BBZvnw5GTp0KBk0aBABQDZs2CBuu27dOqJUKsno0aPJq6++Sl5//XUyaNAgolQqyY4dO8TtPv74YwKA5Ofn2/0dly1bRsaNG0eWLl1K3n//fbJ48WISEhJCRowYQfR6vbjd+fPnSXJyMlGr1eTee+8lK1asIE888QTp27cvqa6uJoQQUl9fTwYMGEDkcjlZuHAheffdd8mzzz5Lhg8fTvbt22d3XexvZvq7TpgwgSQmJpL4+Hhy9913k/fee4/89NNPhBBCZs+eTa655hryyiuvkHfffZfMmTOHACAPPPCA2fP+8ccfRKlUkvT0dPLUU0+Rd999l9xzzz1kypQphBBC6urqSEhICLn//vvbvTf9+vUjF110kc33bv/+/eS6664Tvxuff/45+fzzz0lDQwMhhH63+vbtS+Lj48kzzzxD3nnnHfF9SE1NJXfeeSd5++23yWuvvUZGjBhBAJBff/3V7DXS09PJ/Pnzxf83NjaSQYMGkdjYWPLoo4+SFStWkHnz5hFBEMjixYvN9l2wYAEBQC699FKyfPlysmzZMjJr1izy1ltvEUII+fzzz4lKpSLjxo0T175161ZCCCGHDh0ioaGhJCkpiTz77LPkpZdeIpmZmUSlUpHt27eLr8H+nv369SMTJkwgb731FnnppZeIXq8naWlp5Kqrrmr3vk2fPp1kZWXZfF85noMHPxyPU1tbSwCQWbNmSdo+NzeXACC33nqr2eMPPPAAAUDWr18vPpaenk4AiAciQghZs2YNAUBCQkJIYWGh+Ph7773X7qTBgqy7775bfEyv15MZM2YQpVJJysvLxcctg59//OMfJCkpiVRUVJitc+7cuSQyMpI0NTWJjz3yyCNEJpORTZs2ke+++44AIMuXL3f4XkRHR5PBgwc73I7Bgh9CCHnmmWfMghlHwU9NTQ2Jjo4ml19+ufhzZ4IfAOSFF14QH6uuriYhISFEEATyzTffiI8fO3as3Xt57733EgBk8+bN4mP19fUkMzOTZGRkiAHr8uXLCQDy3//+V9yusbGRZGdnm/1t9Xo96dmzJ5k6dapZYNLU1EQyMzPJxRdfLD4mNfgx/Xsyvv76awKAbNq0SXxs3rx5RCaTkV27drXbnq3lySefJADMAjvLbZwNfgCQFStWSFr3P//5T6JWq0lLSwshhBCtVksyMzNJenq6GKBZrocQQq677jqSnJxsdgGxd+9eMci1xyuvvGLzfQZAZDIZOXz4sMP1azQaMmDAgHbBlmXw8+yzz5LQ0FBy4sQJs+0efvhhIpfLyZkzZwghhKxfv54AIPfcc0+71zb93UNDQ82enzF79myiVCpJXl6e+FhRUREJDw8n48ePFx9jf8+xY8cSrVZr9hyPPPIIUalUpKamRnysrKyMKBQKs+8Jx3vwtBfH49TV1QGg6Rgp/O9//wMALFmyxOzx+++/HwDapUL69euH0aNHi/8fOXIkAOCiiy5C9+7d2z1++vTpdq+5aNEi8T6T4DUaDdauXWt1jYQQrFy5EjNnzgQhBBUVFeK/qVOnora21izl9PTTT6N///6YP38+7rzzTkyYMAH33HOPw/eirq5O8vtmyeLFixEdHY1nnnlG0vaRkZG49957sWrVKqupDSnceuut4v2oqCj07t0boaGhuOaaa8THe/fujaioKLO/w//+9z+MGDECY8eOFR8LCwvDbbfdhoKCAhw5ckTcLikpCVdffbW4nVqtxm233Wa2jtzcXJw8eRLXX389Kisrxb9NY2MjJk+ejE2bNrVLfTgiJCREvN/S0oKKigqMGjUKAMS/tV6vx08//YSZM2di2LBh7Z5DEAQAwMqVKzF48GBcccUVNrdxFpVKhZtvvtnuuuvr61FRUYFx48ahqakJx44dA0DTNvn5+bj33nvbmZFN1zNv3jwUFRVhw4YN4mNffvklQkJCcNVVV7m0bsaECRPQr18/u+uvrq5GbW0txo0b5zCl+91332HcuHGIjo42+35OmTIFOp0OmzZtAkD/FoIg4Kmnnmr3HI7+FjqdDn/88Qdmz56NHj16iI8nJSXh+uuvx5YtW8TjH2PhwoWQy+Vmj82bNw+tra1m6bxvv/0WWq0WN954o901cDwDD344HiciIgIAPfBKobCwEDKZDNnZ2WaPJyYmIioqCoWFhWaPmwY4AD2JA0BaWprVx6urq80el8lkZgcuAOjVqxcA2Oz9Ul5ejpqaGrz//vuIj483+8dOQGVlZeL2SqUSH330EfLz81FfXy/6JxwREREh+X2zxJVgZvHixYiKinLJ+8O8EpZrSE1Nbfe7RkZGmv0dCgsL0bt373bP2bdvX/Hn7DY7O7vd81nue/LkSQDA/Pnz2/19PvzwQ7S2tqK2ttap36+qqgqLFy9GQkICQkJCEB8fj8zMTAAQn6u8vBx1dXUYMGCA3efKy8tzuI2zpKSkQKlUtnv88OHDuOKKKxAZGYmIiAjEx8eLJ1S27ry8PABwuKaLL74YSUlJ+PLLLwHQYO/rr7/GrFmzXA7SGey9tOTXX3/FqFGjEBwcjJiYGMTHx+Pdd991+Pc7efIkfv/993Z//ylTpgAwfj/z8vKQnJyMmJgYp9dcXl6OpqYmm59dvV6Ps2fPOvw9+/Tpg+HDh4vvK0CDylGjRrU7DnK8A6/24niciIgIJCcn49ChQ07tJ/UK2PIqytHjRIKR2RFMNbjxxhsxf/58q9sMGjTI7P9r1qwBQFWDkydP2jzYm9KnTx/k5uZCo9FYPbE5YvHixXj99dfxzDPPYPny5Q63ZwHT008/7bT644u/gy3Y3+eVV15BTk6O1W2c7V90zTXXYOvWrXjwwQeRk5ODsLAw6PV6TJs2zWkVSQq2Pv+2DPKmCgmjpqYGEyZMQEREBJYuXYqsrCwEBwdj7969+L//+z+n1y2Xy3H99dfjgw8+wL///W/8/fffKCoq8og6YW39mzdvxuWXX47x48fj3//+N5KSkhAUFISPP/7YqmneFL1ej4svvhgPPfSQ1Z+zC5yOxtrvCVD1Z/HixTh37hxaW1uxfft2vP322x28uq4LD344XuGyyy7D+++/j23btpmlqKyRnp4OvV6PkydPilf+AG30V1NTg/T0dI+uTa/X4/Tp02YHwxMnTgCgFSTWiI+PR3h4OHQ6nXglaY8DBw5g6dKluPnmm5Gbm4tbb70VBw8eFNUoW8ycORPbtm3DypUrcd1110n/pQyYBjO2gjRL7r33XixfvhzPPPOMV/qxWCM9PR3Hjx9v9zhLy7C/eXp6Og4dOgRCiFlwYLlvVlYWABp4S/n7OKK6uhrr1q3DM888gyeffFJ8nClMjPj4eERERDgM9LOyshxuEx0dDQDtmgJaKp/22LhxIyorK/HDDz9g/Pjx4uP5+fnt1gMAhw4dcvh+zZs3D6+++ip++eUX/Pbbb4iPj8fUqVMdrsWVdN7KlSsRHByMNWvWmJWyf/zxxw73zcrKQkNDg8PfJysrC2vWrEFVVZVd9cfa+uPj46FWq21+dmUyWTsF2hZz587FkiVL8PXXX6O5uRlBQUG49tprJe3LcR+e9uJ4hYceegihoaG49dZbUVpa2u7neXl5Yknt9OnTAaCdUvHaa68BAGbMmOHx9ZleYRFC8PbbbyMoKAiTJ0+2ur1cLsdVV12FlStXWj2JlZeXi/fb2tqwYMECJCcn44033sAnn3yC0tJS3HfffQ7XdfvttyMpKQn333+/GJCZUlZWhueee87uczAfx9KlSx2+HmAMmH7++Wfk5uZK2sddpk+fjp07d5qV9DY2NuL9999HRkaG6AWZPn06ioqKzLwRTU1N7ZpjDh06FFlZWVi2bBkaGhravZ7p30cKTL2yVKssP6MymQyzZ8/GL7/8IpZJm8L2v+qqq7B//36r5c1sGxaQMG8KQFUfZxqBWlu3RqPBv//9b7PthgwZgszMTCxfvrxdsGX5Ow8aNAiDBg3Chx9+iJUrV2Lu3LlQKBxfN7N+Ns50eJbL5RAEwUztKigokNRp+ZprrsG2bdtExdWUmpoaaLVaAPRvQQix6o0z/d1DQ0PbrV0ul+OSSy7Bzz//bJYiLy0txVdffYWxY8eKaX9HxMXF4dJLL8UXX3yBL7/8EtOmTUNcXJykfTnuw5UfjlfIysrCV199hWuvvRZ9+/Y16/C8detWfPfdd2LfjMGDB2P+/Pl4//33Rdl+586d+PTTTzF79mxMmjTJo2sLDg7G77//jvnz52PkyJH47bffsHr1ajz66KPtPCymvPTSS9iwYQNGjhyJhQsXol+/fqiqqsLevXuxdu1aVFVVAQCee+455ObmYt26dQgPD8egQYPw5JNP4vHHH8fVV18tBnvWiI6Oxo8//ojp06cjJyfHrMPz3r178fXXXztU0iIjI7F48WLJxmfAmC7bv39/hzRhe/jhh/H111/j0ksvxT333IOYmBh8+umnyM/Px8qVK8XOvQsXLsTbb7+NefPmYc+ePUhKSsLnn3/erpmfTCbDhx9+iEsvvRT9+/fHzTffjJSUFJw/fx4bNmxARESEU92sIyIiMH78eLz88stoa2tDSkoK/vjjj3YKCkB73vzxxx+YMGECbrvtNvTt2xfFxcX47rvvsGXLFkRFReHBBx/E999/jzlz5uCWW27B0KFDUVVVhVWrVmHFihUYPHgw+vfvj1GjRuGRRx4RVYlvvvlGPGlLYcyYMYiOjsb8+fNxzz33QBAEfP755+0CGplMhnfffRczZ85ETk4Obr75ZiQlJeHYsWM4fPhwuwBi3rx5eOCBBwBAcsqLfW4fe+wxzJ07F0FBQZg5c6bdz9eMGTPw2muvYdq0abj++utRVlaGd955B9nZ2Thw4IDd13vwwQexatUqXHbZZViwYAGGDh2KxsZGHDx4EN9//z0KCgoQFxeHSZMm4aabbsKbb76JkydPimnMzZs3Y9KkSWIxxNChQ7F27Vq89tprSE5ORmZmJkaOHInnnnsOf/75J8aOHYs777wTCoUC7733HlpbW/Hyyy9Lem8Y8+bNE838zz77rFP7ctzEFyVmnK7DiRMnyMKFC0lGRgZRKpUkPDycXHjhheStt94Sy24JIaStrY0888wzJDMzkwQFBZG0tDTyyCOPmG1DCC1vnTFjRrvXAUDuuusus8eslXqzUu68vDxyySWXELVaTRISEshTTz1lVs7LntOy7LS0tJTcddddJC0tjQQFBZHExEQyefJk8v777xNCCNmzZw9RKBRmpfSE0NLi4cOHk+Tk5HalxdYoKioi9913n9jjRq1Wk6FDh5Lnn3+e1NbWituZlrqbUl1dTSIjI+2Wulvy1FNPEQCSS92tbWdrPdb+bnl5eeTqq68mUVFRJDg4mIwYMaJdLxdCCCksLCSXX345UavVJC4ujixevJj8/vvv7cq/CSFk37595MorrySxsbFEpVKR9PR0cs0115B169aJ20gtdT937hy54oorSFRUFImMjCRz5swhRUVFVj8XhYWFZN68eSQ+Pp6oVCrSo0cPctddd5n1a6qsrCSLFi0iKSkpRKlUktTUVDJ//nyz1gl5eXlkypQpRKVSkYSEBPLoo4+SP//802qpu7X3mRBC/v77bzJq1CgSEhJCkpOTyUMPPSS2g7B8v7Zs2UIuvvhiEh4eTkJDQ8mgQYPEXjemFBcXE7lcTnr16mX3PbPk2WefJSkpKUQmk5m959a+r4z//Oc/pGfPnkSlUpE+ffqQjz/+WPxsmmJZ6k4IbZfwyCOPkOzsbKJUKklcXBwZM2YMWbZsGdFoNOJ2Wq2WvPLKK6RPnz5EqVSS+Ph4cumll5r1vDp27BgZP348CQkJIQDMXmvv3r1k6tSpJCwsjKjVajJp0iSz9huEGD9n1logMFpbW0l0dDSJjIwkzc3N9t5KjocRCPGiC5HD8TMWLFiA77//3mpqhMPhWKeiogJJSUl48skn8cQTT/h6OQBodefUqVPx4Ycf+nopLqPVapGcnIyZM2fiP//5j6+X06Xgnh8Oh8Ph2OWTTz6BTqfDTTfd5OulAKC+usrKyoD3yPz0008oLy/HvHnzfL2ULgf3/HA4HA7HKuvXr8eRI0fw/PPPY/bs2TarITuSNWvW4JtvvkFzc7PNAgV/Z8eOHThw4ACeffZZXHDBBZgwYYKvl9Tl4MEPh8PhcKyydOlSbN26FRdeeCHeeustXy8HAC08OHXqFJ5//nlcfPHFvl6OS7z77rv44osvkJOTg08++cTXy+mScM8Ph8PhcDicLgX3/HA4HA6Hw+lS8OCHw+FwOBxOl4J7fqyg1+tRVFSE8PBwlycuczgcDofD6VgIIaivr0dycrLYLNUaPPixQlFRkeT5LBwOh8PhcPyLs2fPIjU11ebPefBjhfDwcAD0zZM6p4XD4XA4HI5vqaurQ1pamngetwUPfqzAUl0RERE8+OFwOBwOJ8BwZFnhhmcOh8PhcDhdCh78cDgcDofD6VLw4IfD4XA4HE6Xgnt+3ECn06Gtrc3XywhIlEql3TJEDofD4XC8BQ9+XIAQgpKSEtTU1Ph6KQGLTCZDZmYmlEqlr5fC4XA4nC4GD35cgAU+3bp1g1qt5o0QnYQ1kSwuLkb37t35+8fhcDicDoUHP06i0+nEwCc2NtbXywlY4uPjUVRUBK1Wi6CgIF8vh8PhcDhdCG66cBLm8VGr1T5eSWDD0l06nc7HK+FwOBxOV4MHPy7CUzXuwd8/DofD4fgKHvxwOBwOh8PpUvDgh+MSGRkZWL58ua+XweFwOByO03DDcxdi4sSJyMnJ8UjQsmvXLoSGhrq/KA6Hw+FwOhge/HBECCHQ6XRQKBx/LOLj4ztgRRwOxy6aJkDJiy84HGfhaa8uwoIFC/DXX3/hjTfegCAIEAQBn3zyCQRBwG+//YahQ4dCpVJhy5YtyMvLw6xZs5CQkICwsDAMHz4ca9euNXs+y7SXIAj48MMPccUVV0CtVqNnz55YtWpVB/+WHE4X4uD3wIspQO5Xvl4JhxNw8ODHTQghaNJoffKPECJ5nW+88QZGjx6NhQsXori4GMXFxUhLSwMAPPzww3jppZdw9OhRDBo0CA0NDZg+fTrWrVuHffv2Ydq0aZg5cybOnDlj9zWeeeYZXHPNNThw4ACmT5+OG264AVVVVW69vxwOxwanNwJED+Rv9vVKOJyAg6e93KS5TYd+T67xyWsfWToVaqW0P2FkZCSUSiXUajUSExMBAMeOHQMALF26FBdffLG4bUxMDAYPHiz+/9lnn8WPP/6IVatWYdGiRTZfY8GCBbjuuusAAC+88ALefPNN7Ny5E9OmTXP6d+NwOA6oPUtv6877dh0cTgDClR8Ohg0bZvb/hoYGPPDAA+jbty+ioqIQFhaGo0ePOlR+Bg0aJN4PDQ1FREQEysrKvLJmDqfLU2MIfuqLfbsODicA4cqPm4QEyXFk6VSfvbYnsKzaeuCBB/Dnn39i2bJlyM7ORkhICK6++mpoNBq7z2M5pkIQBOj1eo+skcPhmKDXA7Xn6P3a8wAhAG8cyuFIhgc/biIIguTUk69RKpWSxkn8/fffWLBgAa644goAVAkqKCjw8uo4HI5kGssBXSu939YItNYBwZG+XROHE0DwtFcXIiMjAzt27EBBQQEqKipsqjI9e/bEDz/8gNzcXOzfvx/XX389V3A4HH+C+X0YdUW+WQeHE6Dw4KcL8cADD0Aul6Nfv36Ij4+36eF57bXXEB0djTFjxmDmzJmYOnUqhgwZ0sGr5XA4NqkpNP8/Nz1zOE4RGPkajkfo1asXtm3bZvbYggUL2m2XkZGB9evXmz121113mf3fMg1mrey+pqbGpXVyOBwH1FgqP9z0zOE4A1d+OBwOJ9DgaS8Oxy148MPhcDiBBlN+wpPoLU97cThOwYMfDofDCTSY8pM2kt5y5YfDcQoe/HA4HE4gQYhR+ek+it7y4IfDcQoe/HA4HE4g0VIDaOrp/bQR9LaeBz8cjjPw4IfD4XACCab6qOOA2Gx6v7ka0DT5bk0cToDBgx8Oh8MJJGoM/bmi0gBVBBBkGE/DZ3xxOJLhwQ+Hw+EEEszsHJlG53lFJNP/84ovDkcyPPjhcDicQIKlvaK601sx+OHKD4cjFR78cCSTkZGB5cuX+3oZHE7XppalvVjwk0JvufLD4UiGBz8cDocTSNSYpL0AE+WHV3xxOFLhwQ+Hw+EEEszzE8WCH9blmQc/HI5UePDTRXj//feRnJwMvV5v9visWbNwyy23IC8vD7NmzUJCQgLCwsIwfPhwrF271ker5XA4VtE0Ak2V9L6o/PC0F4fjLDz4cRdC6AHJF/+sTFK3xZw5c1BZWYkNGzaIj1VVVeH333/HDTfcgIaGBkyfPh3r1q3Dvn37MG3aNMycORNnzpzxxrvG4XBcofYcvVVFACFR9D5Le/FSdw5HMgpfLyDgaWsCXkj2zWs/WgQoQyVtGh0djUsvvRRfffUVJk+eDAD4/vvvERcXh0mTJkEmk2Hw4MHi9s8++yx+/PFHrFq1CosWLfLK8jkcjpOwHj9M9QGMyk9DGaDVAAplx6+LwwkwuPLThbjhhhuwcuVKtLa2AgC+/PJLzJ07FzKZDA0NDXjggQfQt29fREVFISwsDEePHuXKD4fjT5g2OGSoYwG5EgABGkp8siwOJ9Dgyo+7BKmpAuOr13aCmTNnghCC1atXY/jw4di8eTNef/11AMADDzyAP//8E8uWLUN2djZCQkJw9dVXQ6PReGPlHA7HFWotevwAtNFheBJQU0hNz6Y/43A4VuHBj7sIguTUk68JDg7GlVdeiS+//BKnTp1C7969MWTIEADA33//jQULFuCKK64AADQ0NKCgoMCHq+VwOO2wLHNnRKQYgh9ueuZwpMCDny7GDTfcgMsuuwyHDx/GjTfeKD7es2dP/PDDD5g5cyYEQcATTzzRrjKMw+H4GMsyd4Y7XZ71OuC7+UBwFDDjVUChcmuJHE4gwD0/XYyLLroIMTExOH78OK6//nrx8ddeew3R0dEYM2YMZs6cialTp4qqEIfD8RNE5cciteVOo8PSQ8DRX4B9nwPf3AC0tbi3Rg4nAODKTxdDJpOhqKj9ATIjIwPr1683e+yuu+4y+z9Pg3E4PkSrMZazt1N+3Oj1U37CeP/Un8A31wFzvwKCQlxbJ4cTAHDlh8PhcAKBuvMACKAIBkLjzX/mTpfniuP0NmUoEBQK5K0Hvp4LaJrcWm6no/gA8NGlQOFWX6+E4wF48MPhcDiBgNjjJ5UWWpgiKj8uBD/lhuBnwFXAjd8DyjDg9Ebgq2toM1UO5fAPwJmtwM4PfL0SjgfgwQ+Hw+EEAtbK3BnM89NQQg3MzlBhSHvF9QbSxwA3rgSU4UDBZuDLa4DWBtfX3Jlorqa3JQd8uw6OR+DBD4fTWdHrgJW3Aptf9fVKOJ7AVpk7AIQlAIIc0GuBxnLpz6nTApV59H58L3rbfRRw0490hEbhFuDLq4HWevfW3hlgwU9lHg8IOwE8+HER4sRcLU57+PvXAZQfAw5+B2x4EdC1+Xo1HHexVeYOADI5EJ5I7ztjeq7OB/RttGFqRKrx8bThwE0/AapI4Mw24IurgJY6l5feKWiuMdwhQOlhX66E4wF48OMkQUFBAICmJm4GdAfWOVoul/t4JZ0YdrDWtwFVp326FI4HED0/Njo4h7tgemZ+n7iegMzidJA6FJj3ExAcCZzdAXxxJdBS69SSOxVM+QF46qsTwEvdnUQulyMqKgplZWUAALVaDcHSfMixi16vR3l5OdRqNRQK/hH0Gi01xvtlR4H43j5bCscD2FN+AOr7OQ/nGh2ySq84G5+NlCHAvFXAZ7OAc7uoAnTLGqo0dTVE5Qc8+OkE8DOPCyQmUnmZBUAc55HJZOjevTsPHL2J6VV6+THfrYPjPno9UGtIZ1nz/ACu9fphPX6Y38cayTnA/F+Aj6bRAKjkAJB8gfTX6CyYXkwU8+An0OHBjwsIgoCkpCR069YNbW3cS+EKSqUSMkuZneNZTK9Uy476bBkcD9BQQtOXgtyY3rLElS7PjpQfRtIgIKEfDX5qznS94EfXBrSaeJ7KjtDH5EG+WxPHLXjw4wZyuZx7Vjj+i6nyw4OfwEb0+6QAchuHbWeDH0KAipP0vpSUaGSaIfg5K+35OxOm3yVlOKCppy0CEvr7bk0ct+CX3hxOZ8VUpq/Ko+MROIGJrZlepojBj8S0V915QNMAyBRATA/H2zOvUW0XDH6Y2VkVCSQOpPd56iug4cEPh9NZMb1a1WuBylO+WwvHPWoNyo8tszNgDH7qi6mq4whW6RXTQ1r6hnmNuqLyw1LIIVHG4KfkoK9Ww/EAPg9+3nnnHWRkZCA4OBgjR47Ezp07Je33zTffQBAEzJ492+xxQgiefPJJJCUlISQkBFOmTMHJkye9sHIOx88x9fwAQDlPfQUs9hocMpgXSNtiXpZtC7Gzsx2zsymsszQLxLoS7P0Miab+J6BrVnytexbY+pavV+ERfBr8fPvtt1iyZAmeeuop7N27F4MHD8bUqVMdVlEVFBTggQcewLhx49r97OWXX8abb76JFStWYMeOHQgNDcXUqVPR0tLirV+Dw/FPWNorOIrect9P4OKozB0AFCrjwFMpqS+m/EhtgSAqP105+IkCEk2Cn67UrLUqH9i8DPjzyU7RNNWnwc9rr72GhQsX4uabb0a/fv2wYsUKqNVqfPTRRzb30el0uOGGG/DMM8+gRw/zPDUhBMuXL8fjjz+OWbNmYdCgQfjss89QVFSEn376ycu/DYfjZ7C0V/dR9JYHP4GLFOUHcK7RoelMLymwwKultut1e2YXEiHRQHwfQBZE34euFAiyYJno26vKAYjPgh+NRoM9e/ZgypQpxsXIZJgyZQq2bdtmc7+lS5eiW7du+Mc//tHuZ/n5+SgpKTF7zsjISIwcOdLuc7a2tqKurs7sH4cT8LADFAt+eK+fwIQQ+0NNTXGm149pd2cpqMLpyR/oeqZn07SXQgl060P/35VSX6wtAgA0V/luHR7CZ8FPRUUFdDodEhISzB5PSEhASUmJ1X22bNmC//znP/jggw+s/pzt58xzAsCLL76IyMhI8V9amoOrKw4nEGDKT5oh+Kk6DbTx9G/A0VQFtBnG6USm2t9WrPhy0OW5qQpoqqD3pXp+gK5rembBD0shJw6mt12p4os1xATo5yfA8bnhWSr19fW46aab8MEHHyAuLs6jz/3II4+gtrZW/Hf2bBf7YnM6H7o2oK2R3o/vTQ/aRG9MdXACh5pCehuWSH099pDa64epPhGpgCpM+lpE03MXO0aK1V4G5asrVnx1MuXHZ00O4+LiIJfLUVpaavZ4aWmpOD7ClLy8PBQUFGDmzJniY3q9HgCgUChw/Phxcb/S0lIkJRm7oJaWliInJ8fmWlQqFVQqBwcVDieQMC1zD44EuvWl07nLjxmrVTiBgRSzM0Nq2oudyOyNtbBGVzU9m6a9gK5X8UUIV348hVKpxNChQ7Fu3TrxMb1ej3Xr1mH06NHttu/Tpw8OHjyI3Nxc8d/ll1+OSZMmITc3F2lpacjMzERiYqLZc9bV1WHHjh1Wn5PD6bSwK1VVBB1CGW/wKHDTc+Ah1ewMABESDc/lTpqdGV210aFptRcAJAygt3XngcZKnyypQ2koBVpNLqi48uMeS5Yswfz58zFs2DCMGDECy5cvR2NjI26++WYAwLx585CSkoIXX3wRwcHBGDBggNn+UVFRAGD2+L333ovnnnsOPXv2RGZmJp544gkkJye36wfE4XRqLMvcu/Wlt9z0HHi4pPw4CH7cVn66WPBjWu0FAMERQHQmUJ1P1Z+sST5bWodQftz8/51A+fFp8HPttdeivLwcTz75JEpKSpCTk4Pff/9dNCyfOXPG6eGXDz30EBobG3HbbbehpqYGY8eOxe+//47g4GBv/Aocjn8iBj+R9JYFP1z5CTycUX5YqbumnpajB0dY385d5aerp70AmvrqKsGPpVeQKz/us2jRIixatMjqzzZu3Gh3308++aTdY4IgYOnSpVi6dKkHVsfhBCim7fgBIN4Q/FQXAJomQKn2waI4LiGOtnBQ5g5Q83JwJPV81RdbD340jcbnlNrgkBGVTm8by2jlYFAXuKgkpH21F0BNz0d+7hqmZ6b8qCJp+qsTKD8BU+3F4XCcgBmemfITFg+oYwEQ86oNjv/jjPIDODY9s0nuITFAqJOVsyHRQFAovV97zrl9AxVNA52NB5grP12p3J0dM9JG0Fsp41P8HB78cDidEUvPD2BUf8q47ydgaKkz/i2leH4Ax12eWfDjrOoDAIJgYnruIqkvpqLKVUBQiPFxVvFVeZKqqZ0ZliZlDVO58sPhcPwSpvywtBdgYnrmvp+AgZmdQ6Jph2UpOGp0yK7inWluaEpXMz2b+n0Ewfh4WAKdpUb0QNkR36ytI2ipBRoMTYJZ8NMJPD88+OFwOiPsapWlvQBjS35ueg4cnE15AY7TXs4ONLWkq5W7W5a5MwTBOOS0eH+HLqlDYUphWKLR89VUFfBDXXnww+F0RkTPT5TxMZ72CjykzvQyxVGXZ2cHmlrS1ZQfyzJ3U8Rmh53Y9Fxu0hZBHUPv69uoFyqA4cEPh9MZsSx1B4xpr9ozQGtgH7i6DKyk3CXlx0rwo9MClXn0vrM9fhgsEOsq5e7WytwZ4piLTmx6FtOkvYEgNfU+AQHv++HBD4fTGbEsdQfoVVuYYeivZdMyjn/iTINDhtjl2UraqzqfXrUHqelcL1eI7KJpL1MVlcEqvkoP08CyM8LMzvG9aaqPqT8B7vvhwQ+H0xmxLHVniGMuOrFB01nqS4G/XgHqS3y9kva45PkxpL2aq2gvHlNY0BvXE3CygawIU37qijrvCd8Uy6GmpsT0oKX/2hag8lSHLqvDsDTIhxiCH678cDgcv8NaqTvAx1xYY+d7wIbngO3/9vVK2uOK5yc4iio7AFBvkfoyTWG4SlgCIFcCRNf++Tsj9tJeMhmQaBiv1BlTX20ttDEqYDTIi8pPYPf64cEPh9PZIMR6qTvAB5xao9aQHqrK9+06LGlroQMlAeeCH0GwbXoWUxgu+n0AesJnvqKuYHq2Ve3F6MwVX1V5tJRfFWlMmbMgkCs/HA7Hr2itpwcsoH3aq1s/esuVHyON5fTW3zoWs/UEhVpXHexhK/jxhPIDdK1yd3vVXoCJ6bkTVnyZVnqxHkfc88PhcPwSpvpYdqQFjNJ13Xmjl6Gr01RBb/0u+GEzvdLMm+tJIZwFPyamZ0Lc6+5sSiSr+OoCwY8j5Ucsdz8Q8L1v2mGtLQL3/HA4HL/EWpk7IyTKeGLkFV+URkPww4Z1+guumJ0Z1ro8152nvVlkCmrUdYeuNOJCbBhqQ/mJ70vf0+Zq240lAxVT5YfBlR8Oh+OXWCtzN4V1enZmzEVlHrD7Y0Cvd2dl/gchxuAH8K+Tlytl7owIK8oPO5HF9ADkQe6tTWx02IWCH1vfp6BgozLS2YaccuWHw+EEDLbK3BnM9yO10zMhwDfXA7/eCxz5yd3V+Ret9YCu1fh/f0p9uaX8WGl0KJ7I3DA7M6K6SJdnXRugqaf37fmuTFNfnQW9ziRNypUfDofj79gqc2fEO6n85K03GqQLNruzMv+DmZ0Z/hT8uFLmzrBmeHZ3ppcpbE215zqfGmiKqS/O1sUEYKz46kym55pCemEgVxlnegFc+eFwOH6KrTJ3Buv1I7Xcffu7xvtndri8LL+kqdL8//4U/NR4IPhpKKXqBeD+TC+z508BBBk9OVoGkJ0JsbtzJCCT296OVXx1prQXa4sQ19P8d+d9fjgcjl9ibaK7KezKv6HU8dVbxUng1J8ADNVGZUcC/qBnRjvlx0/SODqt0a/jStpLHQfIggAQY68ga+ZVV5EHAeGGMRr+8p55A0dl7gwW/NSeCXhFRMSyszODKT+tdcbAOgDhwQ+H09mwNtHdFFW4sVTZUb+fHe/R217TgJgsAAQ4u8sTq/QP/DXtVV9EOyjLlcbmcs4gk5nM+CqiJ2RW0h/b0zNr7AqmZ3tzvUwJiTKmhkoPeXNFHYfpTC9TQqIgXgwF8IUQD344nM6GvVJ3RjcJnZ6ba4Dcr+j9UbcD3UfT+2e2ubtC/4FVeonzqvyk2oulvCJSXJ/BJZqezxtVn4hUQBXm/vqArtHo0N5oC0s6W+rLlvIjkxuPLQGscvHgh8PpbDjy/ADSxlzs+wJoa6TVYZkTgO6j6ONntntkmX4BC36Scuht7Tn/aFTnTpk7w9T0XOHBlBejSyg/NfRWSvCTZJjw3hlMz4TYVn6ATlHxxYMfDqez4cjzAzgecKrX0YGfADDydtphmCk/5/cA2lbr+wUaLBXETlxtTf4h5Ytl7i6YnRnhJmmvcg+anRldodzdFeWnM5S7N5QCrbXU1B6b3f7nnaDiiwc/HE5nw1GpO+C44uv4/+gVfUgMMOga+lhsFjXS6lqBolwPLdbHMM9PZCoQ2o3e94c0jjjawo3gx7TXj1eUH1bu7gfvl7dwNNrCFFbuXn4caGv22pI6BJYmjc4AFKr2P+fKD4fD8TscNTkEDAqAQJWPBiulyttX0NuhC4zzwQTBmPo620lSXyztFRpHAyDAP0zPLJXkqbSXV5Qfk/le/pAq9AZSq70A+n6rY6lRXWobCX/FUVsErvxwOBy/w1E7fgBQqoFoQ3WKZbPD4gNA4RZAkAPDbzX/WWfz/bDgR+1nwU/FKXobk+X6czDlp/KUUUnyRINDBnu/NPXGIKGz4UzaSxA6T+rLUVsErvxwOBy/QtsKaA2Su6Py3HiW+rLw/bDy9n6zgMgU85+JFV/bA7+zLyFGz09ovNHA6+s0jqYJqDMEYHFulKUz5Yf9jiExVOHyFEo1DRoB//b9lJ8ATv/l2r5SS90ZLPUV6BVfYqUXV344HE4gwFJeEABVhP1tRdOzifLTUA4c/C+9P+qO9vskDgIUIfSKr/Kk28v1KS01gF5L7/tT2qsqj96GRBuvsF0hLIEaVhmeVH0Y/l7uTgjwxZXAZ5cD1YXO7+9MtRdgDH7Kjjj/Wv6EvUovAFAb3g9/KA5wER78cDidCdHvE+G4P0w3K8rPno8BnQZIGQqkDm+/j0IJpA6j9wO93w9LeakiqKnTX4IfNkzS3WaEcoV5g0RPDDS1xN/L3atOGwOzylPO7+9M2gsAwhPpreXYlECipRZoKKH3bSmPXPnhcDh+hZQyd4bY6+cIvULWaoBdH9LHRt5BPQzW6Cy+H1bpxVJB/hL8sJO0OykvBkt9AV5SfkxMz/6IaYBuOuRVCoQ4V+1lup3pQNRAg6k+4Um2jyPc88PhcPwKKWXujLheNC3SUkP7ehz5id6GJVK/jy3E4KeTKD/Mt8JUjPoSGgj6ClH5sdJfxVlMgx9PVnoxRJ+Unyo/7gQ/rfW0cguQrvywYKGlJnAr4Gx1djaFKT887cXhcPwCKWXujKBgIKYHvV921Di9ffitNL1li9QRNGiqLqCBQqAiKj/xhts4QK4CQOhsLV/BvFQeUX5MDOue7PHD8PdGh6bqpLN/U3YhoQg2tntwBLvo0GkCt9ePWOllJ1hWm6S9AjTI48EPh9OZcFamZ6mvvZ8BRXvpyX/Yzfb3CY4AuvWn9wM59WXa4wegaT5fp74IMZa5e2IAKevyHKSmc708TZQfNzpsKDf3+Tir/Dhb6QXQocGCnN4P1PJ/scePBOVH3wZoGry/Ji/Agx8OpzPhjPIDGE3Ph3+gt4PmSCuH7gy+nyaL4AfwffDTUEr75ggyICbT/edjv09cL9cHpNp9foPy01QJaBo9//zucHaH+f9dDX6kprwAGkCz716g+n6kKD9KNVXEgIA1PfPgh8PpTDjj+QGMyg9jpJXydmt0Bt+PZdoL8H2vH+b3iUq3PlbAWXpNAy64CZj8hPvPZY2QKGNLBV8bxS1hn03Wm6ruvHP7O1vmzmCqq9h2IoBoawFqDC0BHHnEQgLb9MyDHw6nMyFlorspTPkBgIxxQOIAafuxE0rJAWoMDUQsDc+AifLj5InSU3jS7wMAqjBg1ttA9hTPPJ81Iv3U98NUyf5X0tvmaud8OM6mkBnswiMQ016VpwCip+pVWDf726oDu9ydBz8cTmdCLHWPkrZ9bE9AFkTvj7xd+utEptDBlkQPnNvtzAr9B0vPD2DsaO0rFcOTfp+OQjQ9u9BE0FtomoDiXHq/1yVAUCi970zqy5W0FxDY5e6mlV62Wl0wQgK70SEPfjiczoTo+YmStr1CCUx/GRh7H9D7UudeK9B9P1bTXj72/Ig9fjxQ5t5R+DpVaI3ze2j37vAkmkI0HfIqFWeGmpoSyMoPS7tKaYsQ4MqPwtcL4HA4HkT0/Eg0PAPAsFtce63uo+gojED0/eh1Rq+CmfJjciInxPHVr6ep9GCPn47CH8vdWUDefRT9G0Yk0/fWJeUnyrnXDmTlx9FAU1O454fD4fgNUia6ewqm/JzbDei03n89T9JcTVN2AKCONT7O+uJoGjresKrVGOdPBVLayx+Vn7Ms+DF409jf1RnTsyul7qbbB6Tyw8rcO7/yw4MfDqcz4WypuzvE9wVUkUBbI1B60Puv50mY3yckGpAHGR9Xqo3BUEenvqrzaUdhZZhxRlQg4G8jLvQ64OxOep8F6BGGfkdOKT819LareH70OmPaS5Lywzw/PPjhcDi+RK933vPjDjIZ0H0kvR9ovh/m91Fb6WnkK9+P6ViLjk63uQMLfuqLfTsWhFF2BGitA5ThxmaczPNTXyz9eVwNfgJV+akpBHSttNFpVLrj7QN8uCkPfjiczoKmHoCh1XxHKD9A4Pb7sWZ2ZvgqjePpMveOIjTe0PCOON9LxxuwQDxtOJ1sD7iX9nK61D1AmxyygaZxPQGZ3PH2AT7clAc/nE7LL/uL0OeJ3zD7nb/xzoZTOFFaDxKgc2gkwQ62imA6t6sjYJ6KM9sDa8ZPUyW9DY1t/zOfKT8BWOYOmI8FqfGDAaeWzQ2Bjq32Epsc1ji3n6+RMtDUlABXfni1F6fT8t/dZ9HSpkfu2Rrknq3BK2uOo3uMGlP6JuDifgkYnhENhbwTxf8dmfJiJA8B5Eo6lqE63zgo1d+xq/z4KPgRlZ8AqvRiRKbRMn1fm54JAQpZ8DPK+DhTfhrKaGrO3uBegG7DZla5mvYKVOXH3lgLU0TlJzD7/PDgh9Mp0er02FtIv5T3XJSNQ0V12HKqAmeqmvDR3/n46O98RIYEYVLveEzpR4MhlUKC1OvPuFLm7i5BwUBSDnBuJ1V/ePDjOqLnJ8CUH8B/yt1rz9Lp7TIFkDLU+Lg6lgbpOg3QUGL0KdlCVG0Eaup3hq6m/LTWAbo288KBAIAHP5xOyZHiOjRqdIgIVmDxlF6QywQ0tmqx+WQF1h4txfpjZahq1OCn3CL8lFuEKy5IwevX5vh62e7RkWXupnQfZQh+tgE513fsa7uKtdEWDNHz04HBT1OV0TsRm9Vxr+spIv1kuvsZwzDTxEGAMtT4uCDQhoc1hUBdsePgRyxzj3R+ICxTfrQtdFZWR6Wg3YEQ55WfkCgAAgBC3y9H4zD8jE6k+XM4Rnbm0xPJsIwYyGW0ciZUpcC0AYlYNmcwdj02Bd/dPhoLxmQAAH47VIzG1gDrVWNJR5a5m2Lq+wkUrI22YDDlp76o4/oXsc7OEanmJ+1AQVR+fOz5seb3YThjena10gswDHo1VOsFynDThlKgtRYQZNIbbMrkxmNNAPp+ePDD6ZSw4Gd4RozVn8tlAoZnxOCpmf2QHqtGS5sea4+WduQSPY+zE909RZqh3L3iBNBY2bGv7SpNLPixkvYK7UbnnRG9c6XR7iCmvAJQ9QH8p9GhaWdnS5wxPbta6QVQpYgFBYGS+mLNDaMzAIXK5ma5Z2twtLjO+EAAV3zx4IfT6SCEYLfB7zMi03rwwxAEAZcNog3QVh/ooBOdt/CV8hMaa+wIe3ZHx762q4ieHyvKj0zW8QNOA7XMncHSSLXnab8pX9BcTXv8AB4MflxQfoDAa3RYb7jwY+qYFU6XN+Dqd7fi2ve2oaVNRx8M4IovHvxwOh155Q2oatQgOEiGgSmOA4EZA+lBceOJctS3tHl7ed7Di56ffWeq8eHm02jT2TixBVK/H12b8eRmTfkBaPoJ6Li+NYFsdgaon0aQA/o2aij2BWd3ASBATJZ1/4kY/Ej4m7pa5s4ItEaHTLlRW2n9YOCjv/Oh1RPUtWix94zh+8OVHw7Hf9hhSHnlpEVBqXD8Ee+bFI4e8aHQaPX480gAp768UOreptPj1T+O46p3t+K51Udtvz+B5PsRr1IF2yc3seKrg9I4gTjN3RS5wqga+Mr3Y8/vAzjX5dnVuV6MQFN+2HdCbV0pr27U4Ps9RhV0W54hvc2VHw7Hf9hlCH5GZNq+ijGFpr7ogTGgU18eLnUvrGzEnBXb8Nb6U9Ab+hceM833m8LGXBTtA9qaPfL6XkMcbRFru5NtR5a763VA1Wl636D87DtTjRf/dxRNmgAy4fu63J2lXK2lvAATw3MHpL0CTflhTT9tKD9f7TyDlja9WDyylQU/XPnhcPyHXQUGv48Ns7M1mO9n08ly1DYFaOqLKT9upr0IIfhu91lMf2Mzcs/WICJYgYv60DTCqfIG6ztFZwJhCTTtcX6vW6/vdeyZnRkdGfzUFNL+M4pg0Tj8/OqjeG/TaXy0Jd/7r+8pRNOzD5QfbStwfg+9L0X50evsP5871V5A4Ck/LHgJaX/MbNXq8MnWAgDAvZNpcL7/bA0aWrVc+eFw/IVz1U04X9MMuUzABd2jJO/XKyEcvRLC0KYjWHPER54Fd2EHWjeUn9qmNiz6eh8e/P4AGjU6jMiMwW/3jsdNo+mgw5OlNoIfQTBecZ/189SXvTJ3Rkf2+mFjLWKyAJkMOj3B4SKqsH27+yz0THbzd3yp/BTvp3111HG2K+bCEgy+JK1R/bOFi9Ve1Y0a3PXVXpysM7TQCzjlp33w88v+YpTXtyIhQoV/TshC9xg1tHpCFXY1m+weeF2eefDD6VTsKqBXIAOSIxCqcq6HZ8Cnvtwsdd9+uhKXvrEJqw8UQyET8ODU3vh64SikRIUgOz4MAFBQ2QitLdNzt370trrQpdfvMOxVejE60vNjMdaioLIRzYZqmrNVzcYUg7/jy3L3MyYjLQTB+jYyOQ2AAMemZxfTXu9sOIXVB4rxx+kWw/PUOLW/z2iybngmhODDzTQlO39MBpQKGcZk0W225lVw5YfD8Rd25ksrcbfGDEPq6+9TFahu1Hh0XR2Ci6XubTo9XllzDNd9sB1FtS3IiFVj5R1jcNekbDHHnxIVgpAgOdp0BIVVTdafiJ1YGvzcNN4oJe1l8Ie01AItNnxOnsKi0utIkfnrfb3L9TTS2aomzHp7Cz41pC28ii+VH3v9fUwRK74cXOC4UO1V0dCKL3fQv9X5FpX58/g7TdbTXlvzKnGspB4hQXJcP4K2MxgtBj+V3PPD4fgLO/PpVbKt5ob2yIoPQ9+kCGj1BL8fDrDUV1sLlf0Bp6X6u77ci3c25IEQ4JphqVh9zzgMTjN/DplMQFY32nn4VJmN1Fd4Ir3tqMaAriIanu0oP6pwo4Lm7XJ3sdKLBj8s5cXStn8cLkGVi8H4W+tPYv+5Wry1/qT302dRNDWK2rN0XEJHodebBD82/D4Mqb1+XFB+PtycLyp2tcTQpTtQlJ9m69VeTPWZMywVUWo6DHZMFv3eHCmuQx3C6YZc+eFwfEdlQyvyyhsBuBb8AAjchodiG30BUIZL3q2+pQ1/GMrX37l+CF6+erDNdCFLfdkMfpjyUx8oyo+d4AfoON8PC36Y8mOoqLt6aCoGpkSiTUfww17n11Bc24wf99HAraJBg/3najyyXJuwaqq2JqOHpCOoPElP3ooQOtPLHlJGXOj1Tpe6Vzdq8Nm2AgDAuJ5xqIUh+AkE5aetmf7NALPg51RZPTYcL4cgALdcmCk+Hh+uQq+EMBAC7K0wpBibqzo24PUAPPjhdBpYlVevhDBEhypdeg4W/GzNq0BFQ6vH1uZ1TMvcnRjEeNIQyCREqMS0ny2yuzkIfpjy01jmuJrGlzRJDX46wPfTWm9UygxGXZb26p8cibkjaAD29c4zIE6eXP6zOR9tOuM+64+VeWDBdggKNgbAHdnrh6k+qcMAhYPvfYThM25P+dHU09EmgGQV9aO/89Gk0aF/cgSemtlfVH70gaD8MNVGpjDMJaP8x1BpeHHfBGTEmc+bY+rP5vOG90mvpZ/lAIIHP5xOAzM7u6r6AEB6bCgGpkRCT4DfDgVQ6svFMvcTJfSA1SvBsVrkMPgJ7QZAoCcOpq74I6Lh2Y7nB+iYcnem+oTGAyFRKKtrQUVDK2QC0DshHJcPTkZIkBx55Y3YUyi9oqamSYOvd9IA5PLBNNWz7qiXgx8AxBemZ6l+H0Barx8WsChCgKAQh09Z29SGT/4uAADcfVFPZMWHIiSCHoMCI/gxaVhoMItXNrRi5V6qjt06rke7XZjv56/8RtqiAQg43w8Pfjidhp1ic0PXgx/ANPUloRmav+BimfvxUhr89HYi+Mkrb7DuH5ErjGqKr0YcSIENX/WH4KfCPOV12JDy6hEfhhClHOHBQeLn8eud0gOKz7cVolGjQ9+kCDw1sx8EgabTimu914CyvL4Vm8tpsLDvwAGvvU47TCu9HCH2+rEX/DhX5v7x1nzUt2rRJzEcl/RLgCAIGJSdAQBQaJvoOBV/xspoiy+2n4FGq8eg1EgMz2jvexqVGQtBoBdCumDDzwPM98ODH06noKFVi8NFVP1wR/kBjFVfO/KrUFbX4vbaOgQXR1ucMAQ/vRIdBz/psaFQyAQ0aXQotvW+hDHTs5/6frStQKvhvbIzxwhAByk/5mXuxpSXMf0w11Bls/pgEeokzJ5r1ujwsaG66/YJPRAbpsKQ7vQE5a3U19mqJsxZsRWHm6IAAIeOHMSh87X2d/IE9SVAdT4AAUgd7nh7U8OzrTSiE2bn+pY2sRHloouyITNUR47om2HyfDWO1+VLLEZbtLTp8Pn2AgBU9RGstA6IVAdhQDK90GqQGT6rXPnhcDqevYXV0BMgNToEyVGOpWp7pEarkZMWBUKA/x0MEOOzi6MtjpfQFJYU5SdILhNz/w59P/6q/LB0nEzhOFDsiBSOjTL3fknG4GdI9yj07BaGljY9fs51rEb+d/dZVDVqkBYTghkDaSDPOnSv90Lq61hJHa56dysKKpvQEELTSkmkDIu+2uv9QcEs5ZUwQNpnP9zg+dG22G7M50SZ+2fbClHXokV2tzBcOsDomRvTMwF1RA0AOFfs5wqyRYPDn3PPo6JBg+TIYFw6INHmbmOy6cVDuU5teJ7AanTIgx9Op0BMebmp+jDE1FegBD8uTHSvbGgVTd09E8Ik7cMqvk6W2jA3hvt5xRczO6vjHBvDmfJTV+Q9A7eo/JhXevUzUX4EQRDVn28d9Pxp0+nx/iZannzbuB5QyOnvOLkvDX62nKpAs8Zzv8uugipcs2Ibyupb0TshHLfOnAgA6KGoQEFlEx5eedBpo7ZTOOP3AQCFytjiwFbFl8RKr4ZWLT4wlILffZGxJxYAhKkUaFHQC4oDp/y86aeodMUYmhpSJWvBhRkIktv+jjDT89lm7vnhdGZ0/j1gcWeBZ/w+DJb62lVQ7VWfhMdwQflhfp/uMWqoldK6YZv6fqwS5u/Kj4TuzozwROM4hAYvpIv0eqAyj96P7YmGVi0KKmmrBlPlBwCuuCAFSrkMh87X2U0nrT5QjPM1zYgNVWLOsDTx8d4J4UiJCkGrVk8783qA9cdKceOHO1DXosWw9Gj895+jEZ1Mg7h0eSWC5PTi4bNtXjz5n3Uy+AEc9/qRmPb6YnshaprakBkXKnaHN0UwXIicKPDRoFepmCg/m05W4GRZA0KVclw7vLvd3YZnREMhE1CkYcoPD36c4p133kFGRgaCg4MxcuRI7Ny50+a2P/zwA4YNG4aoqCiEhoYiJycHn3/+udk2CxYsgCAIZv+mTZvm7V+jc7P/G+DFFOD4775eiVVatTrknq0BAAz3UPCTFBkiGv0CouePC6MtWKVXbwl+H4bkcvd6fw1+mNlZQvAjkxtPlN7w/dQX0f4qMgUQnY5jxXUgBEiMCEZsmMps05hQJaYaUhDf2FB/CCFY8RcNpm4Zm4ngIOPEekEQRPVnnQd8Pyv3nMPCz/agVavHRX264fN/jESkOkhUy+TaJjw9ma73udVHcMAbPYY0jUCxwVjtqLmhKY4qviSoqM0aHT4wKGymndBNCYmgaaHzxcVo1fpz6wej4Zk1Nbx2eHdEhgTZ3U2tVOCC7lGohkE15sqPdL799lssWbIETz31FPbu3YvBgwdj6tSpKCuz/uWMiYnBY489hm3btuHAgQO4+eabcfPNN2PNmjVm202bNg3FxcXiv6+//rojfp3Oy4k1NEd+7Fdfr8QqB87VQqPVIy5MiR4W/SjcgfklOjr1taewCo/+eBBnbY2RsIYLoy2Ol0r3+zAcBj9io0N/DX4kdHc2xZu9fpjfJzoTkAdZTXmZct1wquT8vK8ITZr2SuzG4+U4VlKPMJUCN45Kb/dzU9+PO6moDzefxv3f7YdOT3DlkBS8d9NQhCgNgVZQsKj+Xd8LmNo/AW06gju/3IvaJg/7f+qKAaKjTT3ZOBIpSFZ+omw+xZc7ClHZqEH3GDVm5bRXfQAgNJJ+xoJ19dhT4Md+GIPyU6wJweaTFZAJwM0XZkjadXRWHKqJIfjhyo90XnvtNSxcuBA333wz+vXrhxUrVkCtVuOjjz6yuv3EiRNxxRVXoG/fvsjKysLixYsxaNAgbNmyxWw7lUqFxMRE8V90tHPD6TgWsAN/6WHfrsMGzO8zLD3GamWCq0wfmARBAPadqcG5aicCERfR6vR47c8TmLNiG77acQafODOPSbxalf5Zd6bSi5EVHwZBAKqb2lBprQmkaHj2U8+P1B4/DG9WfFmMtbBW6WXKqB6x6B6jRn2r1qoa+e5GqvpcP9L6VfuoHrEICZKjpK5FDLScgRCCf/1+DM+tPgoAuHVsJpZdPbi9LySaBl5CTSFevnow0mJCcK66GQ98v9+z/p9W5wN+AG6nvVradHjPoPrcOTHLpi9GMKwrEo3464SDKfK+xKDY/HaaBqfTBiQiLUYtadcxWbGoIfT4QbjyIw2NRoM9e/ZgypQpxsXIZJgyZQq2bdvmcH9CCNatW4fjx49j/PjxZj/buHEjunXrht69e+OOO+5AZaX9Vuutra2oq6sz+8cxgQ0qLDvql517d3nY78PoFhGMkYbn9Hbqq7CyEXPe24Y3150Ea6FjU12xhpOl7oQQY9rLCeUnRClHiqGa7qS19ZkON/XHdvdSuzszOiL4MXR2Pmyl0ssUmUzAtQb159td5krU7oIq7CyoglIuwz/GZlrbHcFBcoztSX9vV6q+nvnliBhg/d+0PnhsRl+xtNuMKINXpOYMIkOC8O/rh0Ipl+HPI6Vi12CPwAbOBlt/v2wiBj82DM9iw1Drwc83O8+gvL4VKVEhuHJIqu3XMShHkYLvgh+NVo8vdxTiqne3Ysabm63+KymhQeBvefRi5h9j2zc1tMUF3aPQKKfHj9Y6P25sagWfBT8VFRXQ6XRISEgwezwhIQElJbYl89raWoSFhUGpVGLGjBl46623cPHFF4s/nzZtGj777DOsW7cO//rXv/DXX3/h0ksvhU5n+6T94osvIjIyUvyXlpZmc9suR1uL0byqbQaqC3y6HEt0eiJKyp4OfgBghsHI6K3UFyEEK/ecw/Q3NmPfmRqEBytwq+Hk5VzwU0NvJV4FF9e2oL5VC4VMQKaTqUK7qS8W/Og0tkuJfYnUuV4MbwY/JmXubTq9aEC3lfYCgDlDUyGXCdhdWG1Wcce8PlcOSUFCRLDN/ScbUl9rnfT9HDxXi0+2FkAQgH9dNRB3TMyyrbKyAaeGERcDUyPx+GV9AQAv/XYMe8946HPRagh+VK4GPw6UHysXEq1aHVb8RVWfOyZmQamwcwo17B8pNOJYST1KajuuZ1ibTo//7jqLi17diMd+PIQ9hdU4XFRn9V+ojr6P5fowjMiMwdB06eqxSiFHUhJNObbVB1bwI63Ew48IDw9Hbm4uGhoasG7dOixZsgQ9evTAxIkTAQBz584Vtx04cCAGDRqErKwsbNy4EZMnT7b6nI888giWLFki/r+uro4HQAzLq6PSQ+KVqj9wtLgO9a1ahKkU6GvjitkdLh2QiKd+PoQD52pRWNmI9FjPeYpqm9vw2I8H8atBVRqREYPX5+YgWCHDh1vycb6mGU0arbRKrGbnxluwE22P+FD7B3Ar9OwWho3Hy60HP0HB9KDfUkN9P2rPB6Ru4XTay4u9fkzK3E+XN0Kj1SNcpUBatO2UQ7eIYFzUpxv+PFKKb3edxeOX9cOJ0nqsPVoGQQBuG2//qp35fvafrUF5fSviw1V2t2e89udxAMDsnBSHVUBG5cdY5XXTqHTsyK/C6gPFuPurffj17rEuz98TYcqPSrpyCcBoeK63cUFjJ+313e5zKKlrQWJEMOYMs6P6AOJ3MS2kDWgDNp0sxzXDvHte0ekJfs49jzfWnURhJU3Vx4er8M/xPdDTisIr6DQI/5ZWs754wwT0N3SmdobsjO5AGaBorXFn6R2Oz5SfuLg4yOVylJaaewNKS0uRmGi7sZJMJkN2djZycnJw//334+qrr8aLL75oc/sePXogLi4Op06dsrmNSqVCRESE2T+OAcsBhX7m+2Epr6Hp0VYrLtwlLkwl9rP41YOprx2nKzH9jc349UAx5DIBD07tja9vG4WUqBDEhqkQraaejdOGKfV20euNV8ESlR9nZnpZ4rDcnTWS88dyd1H58bHnp63ZmE6O7Sl2J++bFGE9lWTCXEPqa+XecwYlgqo+0/onoke8/X5N3SKCMSiVfkY2HJem/uwprMKG4+WQywQsntzT8Q7R5soPQKvNXrpyIDLjQnG+phn3f7ff+ogUZ2h1Me3FPp+tdcYAyhQb/jmNVi+m/W6f0AMqhRx2MSg/qcE0neTN1JdeT7BqfxEufv0vLPnvfhRWNiE2VInHZ/TF5ocm4dZxPTChV3y7f+NTDb+DIMPoflmICLZf4WWNQT1pwB1CmqBrC5xh0D4LfpRKJYYOHYp169aJj+n1eqxbtw6jR0svW9Tr9Whttf2Gnzt3DpWVlUhKsj+xmmMDyytePw1+vJHyYswQZ325H/y06fR4Zc0xzP1gO87XNCM9Vo2Vd4xpVy7rMMAwpbUWgOFEIjH4cWamlyWOy939uNEhC34cjbZgsOCnuYqWVnuKqtMACP17hcYZOzvbSXkxJvSKR2JEMKqb2vDp1gKsMnR9vn2CNEXW2W7Py9acAEBTbpbTva1i4vkx9X2FBwfh7esvgFIhw/pjZfh2t5tqWouLaS9VmPF7Yqn+aFuBNsPf2UJF/XJHIc7XNCM+XCU2nbSLYf9YBVVgtpysgFand26tDtDrCX47WIxpb2zCPV/vw+nyRkSpg/B/0/pgkyHoMW150A5WoRUS7bjppw36ZqRCT+ix62SB/Sac/oRPq72WLFmCDz74AJ9++imOHj2KO+64A42Njbj55psBAPPmzcMjjzwibv/iiy/izz//xOnTp3H06FG8+uqr+Pzzz3HjjTcCABoaGvDggw9i+/btKCgowLp16zBr1ixkZ2dj6tSpPvkdAx7x6pTOHvKn4IcQIlZ6uTvPyx5T+yeKgyHdzds/+fMhvLMhD4TQk8nqe8YhJy2q3XZZhiv4PCm+H2bQVITQDrYSOF7ifKUXIzue7lNc22J9fIG/NjrUNBlPbFKVn+BI48m11oZB1hVMx1oIgsMyd1MUchmuMaRcXvztGLR6gguzYzHYyufIGpP70OB088lyh/1ntp6qwLbTlVDKZbhbiuoDABGpgCCj7TEsmkP2T47EHYYgbctJNz0irio/ABBuw/QszuESAJXxQuLQ+Vq8+L9jAGg3Z7sBBcMw8DNEV4/IkCDUNrdh/znPzjv715pjuOPLvThR2oCIYAXuv7gXNj80CXdMzEKoSkK6XGxwKPFiwAqKoCA0yenx6sBJDxravYxPg59rr70Wy5Ytw5NPPomcnBzk5ubi999/F03QZ86cQXGxMTJvbGzEnXfeif79++PCCy/EypUr8cUXX+DWW28FAMjlchw4cACXX345evXqhX/84x8YOnQoNm/eDJVK2kmBYwFTfnoZGkVW5wOtThhxvUh+RSMqGjRQKmSilO8NYkKVGJQaBYDm7V2lVasTZzO9cvUgvDJnMMJsHKBEdUWK8uPkaAudnoiVWq4oP5HqIMQZmvDlWUvL+avywyq95ErnfCLe6PVj4vchhDis9LJkzrA0CIJRWJGq+gC0lL5buAqNGp148WANQgiW/UG9PteP7C5W+TlEoTQGFya+H0ZO9ygAwMkyGyNSpOKq4RmwbXo27fFjUELqWtpw55d7odHpMaVvN9xkpYeSVQzfR6G5FmOzaerck6mvhlYtPjd0z759QhY2/99FuHtyT4Q7k7pi5ekh7l08ssnup7jyI51FixahsLAQra2t2LFjB0aOHCn+bOPGjfjkk0/E/z/33HM4efIkmpubUVVVha1bt+Laa68Vfx4SEoI1a9agrKwMGo0GBQUFeP/999tVlHGcgCk/STnGK/qyoz5bjiks5ZWTGiXtSswNJvSiSoE7B69d+dVo0ugQH67CVfZKZGGq/EhItTjZ4LCwkpprg4Nkkvt5WNLTbsWXnyo/pmZnZ/pBecP3U8HK3LNRVNuC2uY2KGSC5BlraTFq8YQ6ICVCvC8FmUwQU1/r7KS+Nhwvw94zNQgOkuHOiU4WOZimvixgn538ika0uZMGanHO52aGGPxYpL0shpoSQvB/3x/AmaompESF4NU5OdJ7ibFqMU09JmbT59vkweBnVW4RmjQ69IgPxf9N6+2wI7NVPKD8AIAynH7+ikuKoNF6NrXnLXwe/HD8nFrDwSsqDUjoT++XHvLdekzYwVJemd5vYsmCH3fy9sxgOrFXvENTa7bJCcLh6zk52kJsbpgQ7rJJ3K7vx1+VH2dGW5jijeDHRPlhfp+eCeGOTbQm/N+0PhiRGYOlswY43dxzcl/6N1p3rNRq40G9nuDVP6jXZ/7oDHSzUz5vFdH03F75SY4MgVopR5uOiBVJLuGW8sNGXFimvczL3D/dWoDfDpUgSC7gnRuG0BEeUjEJysan08q2/edqUN2ocX69VmBDbucOT3O9uas42sK9Y2hwBD0+qnV13hll4gV48MOxjU5r9DlEmgQ/ZUd8tyYTjGZn965apDA4NdLtvP1GQ/AzyXDVbY/kqBCoFDJodHqcq3YwWFVsyhYlaR3HS2jA4kqlF8N+rx8238vPZqLZGG3R0qbDrweKUNNk46Tk6eCHEBPlx1jpJTXlxRiQEon//nM0hnR3/sR1YXYslAoZzlY1W/0brjlcgsNFdQhTKfBPJ1JqIkz5qW4f/Mhkgsnnx43Ul6tNDgEJaa9o5J6twfP/oyr3I5f2terNs4tcQUdvAEgIakGfxHAQAmw+5X4/nCNFddh/rhZBcsF+o0VHNHkm7SUYWlpEox5b8+w3FfYXePDDsU29YXaOTEHHFojKj+9NzyW1LThb1QyZAAwxeAi8iUIucytvf6ayCXnljZDLBLHLrj3kMkEsW3bY7JB5fqSWubtR6cWwe/IyHXHhT12exe7ORrMzIQSLvtqHRV/tw7Tlm7HV2onJ071+GssNFXoCENPDqUovT6FWKjAmi140WA461ekJXvuTqj63jM1EjCv9eKLal7ubwj4/J0vd8A96xfNTAwDQKCNx15d70aYjmNY/UfKsq3awC5LmGmPq/Lj7qS+m+lzcL0H037kE8/y4mfZiwVOU0ICteYHR7JAHPxzbsIN9ZCqdcG2a9vLxSW2nQfXplxzhnMHPDdjBy5W8/cYT9AQzLD1aci+NrHhaVuyw3N3JtNdxF2Z6WcJ8G2eqmtDSZlExxLo8tzUBrW6aWj2J6PkxBp/f7DqLtUdpeq6krgU3/GcHXvztqLlvgaVIPKX8sLEWUWlAULBY6WVrppe3mGyj5H3V/vM4WdaAyJAgm6MyHGKl0aEpPbvRz57VESlS8YjyYz3tteWcFudrmtE9Ro2X5wxyPa3ELkhaqo3Hj5Plbs04a2nT4cd9dN1zHTWcdITo+XGzWtbgkYpGA/YW1rQ/JvghPPjh2IaZndmVb1wvqgK11Nqei9NB7MynX1pvlrhbMt5w8HIlb7/BcHU9sbfjlBfDYT8dhhOG51atDvkV1ETtjvITH65CeLACegIUVFqYslVhotzvVwNOLUZb5Fc0YukvNIW75OJeuG5EdxACvPfXaVz57t/GoJOlveqKaENJGxRUNOKNtSfx+yEH6T6TMvfapjYxremNDuX2YOnX3YVVYsqvTafH8rV0fbeN7+GaiRYw8fyctfqeSf5s28MTyk9zFR3hwzAEP4eqZVDKZfj3DUNcavwnwi5ImmswNCMaIUFylNe34mix6xcFvx0qRl2LFilRIU4Z3a3S5CHlx+AZSlQ2QaPTY7c/T7E3wIMfjm1Es7Ph6kKhogEQ4PPU174zNQA6NvhJjAx2KW/f0qYT8+CT+kjsLwMnGh06Uep+urwROj1BRLACCRGuy+WCIEg0PftRxZdJd+c2nR73fpuL5jYdRveIxaJJ2XjxyoFYceNQRKmDcOh8HS57cwu+3nkGJDwJgADoWo2pMwNtOj1+P1SMm/6zAxOXbcTra09g0Vf7cL7Gjk/L1OxsUH1So0NcDzRcJDVajT6J4dATYKMhFbNyzzkUVjYhLkzpeqoHoKXughzQt1n1fvU0+WzrXOn0rNXQPkKAa8pPcBQQZKh0rDemvior6UVKLQnDEzP7YUCKmy002HeypQYqhVxMNbpTNfr1TnpReu3wNIeFEw5hyo+bnh+2f2ow/ZsEQuqLBz8c21gqP4Bf+H40Wr3oFRjo7sHJSVzJ2287XYlWrR5JkcFOqS1ZJp4fuzK5E8qP6PdJDHddyjeQbc+TFGbi+3GSVq0OT/18CJ9uLXBjdVYwMTy/vf4U9p+tQUSwAq9eM1g8iUwbkIg1947HhdmxaG7T4ZEfDuKfXx2Anv0+hlRwUU0zXvvzBMb+az1u/2IvNp+sgCAAUeogaPUEH24+bXsdJmXuvkp5MSb3NZS8HytDq1aHN9fRwOyOidnSZsrZQq4AIg3pQiu+n7QYNZQKGVq1epyrdqHii6k+gGvKjyC08/1UNWpwIp+uNSM1BTeOdDOlBBiVH8N3dEJv1jLDucGyjNPlDdiZXwWZAMezxaTgKc+PIW0WL6MqcCCYngNusCmnA2GenyiT4KdbP3rrw+Anr7wBGh0dApkaLbHxmoeY0Cse7206LebtpQQQG01SXs4EHJlxoRAEoK5Fi4oGje0hlE54fo65MdPLEtG06kHlhxCCx348hO/3nINcJmD6wCTJwzcdYlB+jjWo8PYGGoA8d8VAJFs070uICMbnt4zEf7bk4+U1x/DHkVIcConAIBTj0NHDeGMdwbqjpWCCRVyYEtcMS8N1I7ojv6IR8z7aiW92nsU9F/VsP7yz9jxQnGvYsScO72aVXh0bxDMu6pOAdzbk4a/jZfh8WyGKaunQzhs8ceKPSqeBT80ZIN18ZJFcJiArPgxHi+twsrTB+YHBLOBXhlE/oiuEJ1H/VV0R9HqCJf/Nxb26OkAGXDXW+fYBVjExPAPGi6fdBdVoMAxkdoZvd9Fj8sTe3ZAU6eaxT6c1vo9ue37o/qF6GpQeOFeD6W9sdrjb3Rdl49KBvhk9xZUfjm2sKj8D6K0Pg5+jhqvlvkkRnjlAOYGzeXtCCDYYVKJJvaWnvAAgOEguTvi2641wRvkpMSo/7sIa8lkdweFio8OP/i7A93uosVinJ/jfQQ+VyxMipqyeWlsKnZ5gVk4yLh+cbHVzmUzAwvE98OOdFyIrPhSFWupp+HHDDvx5hAY+o3vE4u3rL8DWhyfjoWl9kBajxrieceifHIHmNh0+3VZg/vp7PgH+PYqmgUKigaTBPqn0MiUnLQoxoUrUtWjx8u+0m/MiqeMbHCFWfNkyPdsJnh3hjt+HYdLr5411J7HxeDkiBapcqCOc+67aRFR+agAA6bGhyIhVQ6sn1isL7aDR6rFyL/1uXDvcA9PhWVk/BMnFEjYxBE/ylmoMSKap1CPFdQ7/VTdZGY/TQXDlh2MdQqwrPyztVXGCDgGUOEvKk7ATRt8k90/gzsLy9uuOleGvE+UOT1qnKxpxpqoJQXIBF7pgTszuFoYzVU3IK2/A6Cwb0rQTnp/jpR5Ufgwzvk5XUB+RWcNEFxodbjpRjudXUwPy4LQo7D9bg59zz2P+mAy31wpNg+gROVAdhOTIYCydNcDhbgNSIvHr3eOw+8NvgbLtyAiqwi2jMnH9yO6i8mWKIAi4Y2IWFn21D59sLcBt43tA3XgeWHU3kP8X3Sh1ODDrHbQGRYhBra/SXnKZgIm94/HD3vPQ6PRIiwnBNcM8cGIF7DY6BEyDHxfMv+5UejEMaa+DR4/hjTzqZUxWtgBtkNwzyyEWyg9ACycKthXirxPluKR/ouSnWne0VFSAL5LQK8whLOUVHEnTlO7APEN6Lb64qT8OVuglFQRL7WjuDbjyw7FOY7nhZCHQQYWMiGR6lUB0NADyBmufAT6eDmxZDlS1H5R3tMS3V8vO5O1ZldfIzFhpgwYtYOXuNpUfQiSnvRpatWJlkTuVXoyUaEMjRq0eZ6ssfBtOKj/5FY1Y9NVe6Alw9dBUfHDTUAgCsPdMTfvndgVDyquJqNAiBOPVa3IkG4xDlHKMG3oBAOCGmBN4MuMIsiNtH9kvHZCE9Fg1aptasX/lK8C/R9PARxECTH0BuGUNEN8bJ0sboNUTRKmDkBTpZAdlD8IGnQLA4sm9oFR46LRgp9EhYDzxuVTxZVB+iCrC9bJxQ/Bz/kweAGDJlGyotIagKsRDXeMtlB/AmPpad7QMDa1ayU/1jSHlNWdoKoLkHvgbeWi0BQBAqQYU9DMchXqM6xmP8b0c/3M7decGLr2DGzZs8PQ6OP4GS3mFJ9FBhQxB8K7pWacFtrwOFP4NrH0KeDMHWDEO2PwqUJkHQoiYburo0mCGZd7eHqyKZqKTKS+Gw4ovbQugM5TdO0h7nTSoPt3CVe29KC5gtxGjE56fupY23PrpLtS1aDGkexSev2IAukUEi5Uxq/YXOXgGx1SV09YMlSQCt43rYVtFs0XyBQAEyKpOASv/AbycBXx9PXDgv8a0owG5TMCSoQp8o3wOo4+/RCfJp18I3PE3MPou0aPCFMz+yR2fvjVlQu94dI9RY2h6NGbnWE8DuoTDRoc0AD9V1gC9sxVfBuVnZ7EWF7++yaWRCuuLaPCbKFTinsk9cc+FCQAxlOW7mwZiWFF+xmTFIT5chZK6FtzxxR5Js7DOVTeJQ5U9kvICTMrcPVQxy9Qf9rx+jkvBz7Rp05CVlYXnnnsOZ896cNIxx38wnelliTdnfDVVAiAABKDHRFouW3IAWLcUeGsItO+MwQ0t36CnrMgjqRtXkJq3b2zVYkc+K3F3TaY2Dji1Efywg6ogczip3LTSy1PYND2HG0yMDtJeOj3Bvd/kIq+8EYkRwVhx01BxvtWswdSTsSrXveCHEILP1+4GADQFRWHJJb2cf5K04cDtW4CxS4CYHrTs/fhq4IeFwCvZwFfXArlf0wP/1rdx+dY5GCk7hkaiwr6BjwPzfwVizcdEsEovZ8daeJowlQJ/PTgR3/1zNBSeUBQYTPmpPUcvaixIj1UjSC6gSaNDUa2DES6WGJSfMo0Sp8oacOW/t+LfG09JLpv/YnshXttBvw/ZqjrcN6WnUZ1RhABBHlLirCg/IUo5Ppw3DCFBcmw+WYGHVx5wqF79d/c5EAKMyYp13hxuC0+VuTNYENXciYOf8+fPY9GiRfj+++/Ro0cPTJ06Ff/973+h0XhmYBvHD7BmdmZ4U/lhfVTUMcC8n4EHTgIz3wSyJgMyBYIqjuD+oO/xp/IBBH86zWdXGabdWm3x96kKtOkIuseo0SPOtQMWCy6KalvQaE1lMjU7O1APPDHTyxKb091Zl+fWWqDN9ontlTXHsf5YGVQKGd6fNxTdwo0nnakDEqGUy3C8tB7HSupsPocjvthxBueLqFE0JaW7U8NDzUgcAEx5Crh7Lw2Exj8IxPakytuJ34GfbgdezgT+eAyCthlno0diquZlPFQ4Anq0/9uIM718lL41RRAE93vGWBKeCMiCaIq8vn0AGySXIdPwvXDa9GxQfuqJGqFKObR6gpd/P44bPtyOIns9lgB8s/MMHv/pEEoIVf9C2yoh6LVmc708hqj8mKuDg9Oi8O8bh0AuE/DDvvN4ec1xm0+h0xN8t9vY28djeKrMncHetyb/b3AIuBj8xMXF4b777kNubi527NiBXr164c4770RycjLuuece7N+/39Pr5HQ01szODG9WfLFGdGz4ZGgsMHQ+cNMPwAMnsa73U1ivy4EWCuDcTlpB4wNYt+eNx223qjet8nI1rRGlViIujKaoTpc3tt/AiTJ3T8z0skRsdGiZlguOFD0AtlJfP+eex4q/qN/i5asHYVBqlNnPI0OCxHShq+pPXnkDnl99BHGgJ8uwGOkGU5sIApA4ELjocWDRLuDO7cDER4D4vvTnqghg5puIvO1X1CqTcLKsAest5mfp9cb0bf9k35S5ex2Z3Hj8cDDjy6ayaQuD8lMHNZZc0hsvXz0IaqUc209X4dI3NtusEvxu91k88uNBAMDlYwaByIIggNB+VN4Iftj3srUW0JuPfJjUuxteunIgAODdjXk2+1ptOlGO4toWRKmDMNUJg7RDPDXagtEVlB9ThgwZgkceeQSLFi1CQ0MDPvroIwwdOhTjxo3D4cO+H4DJcRF7yk98H3rbUGoMVjyFOHzSSmWUOgY/kIm4pe0hbOn9CH3s8A+efX2JjOoRC6VchnPVzeK4CFMIIeIU94luVmaIvppyK1UxTpS5e2KmlyWmJy+zIFAQjOqPlUaHB87V4KHvDwAAbp+QhVk5KVafnz3+c26R08ZWQgge+eEgWtr0GBhtKKm19rlyB0EAuvUFJj4M3LUduPcQsOQIMHQ+IkKUuGEU9b38e+Mps/WfrW5CQ6sWSoXMZVUwIHBgema+H2cHnBLD576eqDGkexSuGZaG1feMw+DUSNQ2t+HOL/fiwe/2m6mlP+47h4dWHgAhwPzR6XhiZn8IEYb0bF2RU1WTkjH9Xlp4wwBgzrA0PGBIwz79y2GrQds3hiGmV1yQ4pkWBAym0HDPj3O0tbXh+++/x/Tp05Geno41a9bg7bffRmlpKU6dOoX09HTMmTPHk2vldCSi8mOl2ZkqDIg2DDz0tPrTaL8C4ajBJKoccDmdM1Zy0NgxtwMJVSkwPJNeIVprVX+8tB7FtS1QKWQY3cM9WdkYYFhRfiQesKsaNSivbwVgTFV5gozYUMhlAhpatSitazX/IZvubqH8lNW14LbP9qBVq8dFfbrhwam9bT7/5L7dEKqU43xNM/aecU5O33yyAjvzq6BUyDAh1aC8hXqof4stotLMvFe3XJgBpUKGvWdqsMtk3tFhw+e4T2K4Z302/oYD07Or5e6NdfQE2yxTi8pZZlwovr9jDO6alAVBAL7bcw4z3tyM3LM1WLW/CPf/dz8IAW4Y2R1PX96fqrEmvX68ovwolMYxGia+H1PumpSNG0fRuXL3fpuLHaeN3ZHL6luwzjB41u0hppZwz4/z3H333UhKSsI///lP9OrVC/v27cO2bdtw6623IjQ0FBkZGVi2bBmOHTvm6fVyOgp2sLKm/ADe8/3YUX6aNFrkG4Zo9sxIp4ZoADj8o2fXIBFx1IWV4IdVeY3JinX7ai3L3hgJUfmJsvscxw3NDdNiQlwqubeFUiFDeiw9uLc7gVlRflq1Ovzziz0oqWtBVnwols/NMe8PZEFwkBxTB9Ag6mcnUl+EELz6B/VR3DgyHWoN8zd4WPlxQLeIYFw1hLaKeHejMUg3rfTq1Dia7p5gNMw7o+w11tJAJSo6zqw0P0guw4NT++DrhaOQHBmMgsomXP3uVtz3bS70BJg7PA3PzjLp3hxuqvyw4CdK+u8nBZPhptYQBAHPXD4Al/RLgEarx8LPdosp6pV7zkOrJ7ige5RHCxXoejzt+ekCys+RI0fw1ltvoaioCMuXL8eAAe2bhcXFxfGS+EClucbYQdWa5wfwnu/H0vNjwrGSehACxIWp6MiD/lfQH/gs+KHprO2nK9HSZp7PZ/19XK3yMsVuubvo+bGf9vKG34dhc8aXFeXn5d+PY98ZOlPrw/nDJU3MZl2YVx8ohlbnuCwYAP48Uor952oREiTHHROzTIJqLys/Vvjn+B6QCdQDxrqT+0ull9eJzqC3NpSfzLhQyASgvkWLsvpWq9tYQ2NI2SQlWP9+jeoRi98Wj8eMQUnQ6gl0eoKrh6bihSsGmhu7Ted7se+SJ5UfwGS4afu0F0MuE/DmdRdgaHo06lq0mP/RThTVNONbQ8rrOk+rPoDnS927gvKzbt06XHfddVCpbHf3VSgUmDBhgssL4/gQlvJSxwJKG34EpvyUdZzyw04cYnVMnxm0mqTsMFBuu1rCW/RKCENiRDBa2vTYVWD8wte1tGF3IT04T+zlfvDDGh0WVDa2P/lLTHt5srOzJTanu1soP3+fqsB/ttCmla9dkyNW+jjiwuw4xIYqUdmowd8SBibq9QSv/UkbcN58YQYNlBvteMm8TEZcqDi/6D2DwdufKr28igPPj0ohR4ahdNsp34+h2qt7sm0DcKQ6CG9fdwHevWEInp7ZD/+6alD7ijYx7WWi/Hiqxw/DSrm71c2C5PjP/GHIig9FcW0LZr3zNwoqmxCmUmDGIC/Mv/Jkk0Ogayg/L774Ij766KN2j3/00Uf417/+5faiOD7GntmZIQY/R9tVMbiFHc+PcaaX4QQeEg1kXUTv+0D9EQQB43vRk6nplPctJyug0xNkxYeiuyEl5A7JkSEICZKjTUdwxrLbsUTDsydnelliM/gxUX5qmjS4/7+0CvT6kd0xpV8CpBIkl4kH/59zzzvcfvXBYhwrqUe4SoHbxvegXbB9GPwAwB0TaI+fXw4UI/dsDUrrWiEIQJ/Ezh78GDw/9UWA1norlGwnfT/1LW1Q6Qzp7zTrRnmGIAi4dGASFlyYaT29aqr8iBcSXlJ+bKS9TIlSK/HpLSPQLVwlevRmDk72aKoaAD1mi0oXV34k895776FPnz7tHu/fvz9WrFjh9qI4PsZemTsjOoMa+bQtQNVpz722HeVHHAJpmioYcCW9PfQDJA2T8TAs9WXq+9lgMsXdE8hkAnrYGnMhodSdECIqP94Mftql5QzBD2kowWM/HUJJXQsy40Lx+Iy+Tr/GLEPn4TWHStqlGE3R6vR43aD63DquB6LUShog6g3VXh3s+WEMSInEuJ5x0OkJ/s9Q5ZYZG+r5k5q/EdaNtjwgemoqtoKp70cK+8/WIhz0IiAmxk3VoiOCH4nKDyM1Wo1Pbh6BcJUCgkAN2h6npdbYzdrj1V6duM9PSUkJkpLay3Dx8fEoLvbQFGaO7xDNzna+dDI5LfEFPNvp2YbnR68nOGZQL8yCn96XAnIlUHGcqlAdzNjsOMgEeuAuqmmGXk+w8QTr7+OZ4AcwDTAsKr4kKD8ldS2ob9FCIRPQI87zgwSZIbuiQYPqRpOre8N8L011EVYfKIZcJuD1a3OgVjp/wh/SPRqp0SFo1OjE6hdr/LjvPE5XNCJaHYRbxmbQB9lnShnuuc69LsDUHxaI9u3sKS+AtgJgCrLNAaeGMRcS0165BeUIEaSNdHEIC37qi0yqn6Lce05LnFB+GP2SI/DL3WPx7W2jMSDFC32gWGpKFQHIpc24cwgLojT1NlU+f8Kl4CctLQ1///13u8f//vtvJCd7cDYMxzdIUX4Az1d86fVGydRC+SmsakKTRgeVQmbuFQmOBLIvpvd9kPqKVAfhgu70SnHTiXIcKa5DeX0r1Eq5WArvCWyaiiV4flilV2ZcqOeGVpoQqlIgJYoOKDRrdmhQflSaagRBi8WTeyInzfY67SEIAmYajM+r9ltXEDRaPd5YdxIA7R0UzszU9npHdSCjs2IxONV4Iuv0lV6MaPvl7s6mvY4VnjP+x8FIF4eEJdDRMHqtUcH2sfLDyIgLxYhMD6kyloiBnicbOkYCrJN5s/+rPy4dCRcuXIh7770XH3/8MQoLC1FYWIiPPvoI9913HxYuXOjpNXI6GimeHwDoxoKfI5553eZqEynWXM5mfp/e1vqiiFVfvkp9GUveWcrrwuw418coWCHLVmpJQqn7CS80N7Qky4rvRxccDS3oezAxheDOiVlW95UKS31tOFaO2ua2dj//dvdZnKtuRny4CvNGZxh/0GhISfqg0ssUQRBw+wTje9DpK70YDkzPWfFhEASguqkNlQ32K770eoL8czS7oFeEuK9ayIOMxnyd4bV96PnpMDxd5g7QbID4u/q/78el4OfBBx/EP/7xD9x5553o0aMHevTogbvvvhv33HMPHnnkEU+vkeMqO94HNv7L+YCAXaFJVn48lPZiV+jBke0Oaszv09eaQbT3NOorqDzlnWGrDmCjLracrMDao7SyyZMpL8BOJ2UJnh8208sbZe4Ma8rUB1sKUEao0rH0oji3m/n1SYxA74RwaHR6rDlk3jixpU2Ht9dT1WfRpGyEKE0CTzH48a3yAwCX9E/EkO5RiAtTiophp8dBo8MQpRyp0VQ5dOT7OV3RKLbhENxNeTEiLLIVPqr26lA8PdqCEUAVXy4djQRBwL/+9S+Ul5dj+/bt2L9/P6qqqvDkk096en0cV2msBH57ENj4gnNeGE2TMQix1t3ZFBb81BSKpaduYafHT7syd1NU4UBPQ+rrUMePuxiYEolodRDqW7XYf44qMWwmladIj1XTfiitWrEKBHqdsR+TnbTXCS+WuTMsTauHztfi1T+Oo4zQdSXJbfc4cYbLDerPzxapry+2F6K0rhXJkcGYO8IiaGcVhH4Q/MhlAr65bTS2PjwZkSEe8lr4O2KjQ+vBD2D0/TgKfvaeqUaEQM3OQrCHlLNwE/+qIKM+GE/ij8pPkxeUHyCgKr7cuhQLCwvD8OHDMWDAALs9fzg+oHCL8f6ZbdL3qzXk05Xhjq+A1DFAuOGqyRNmYwk9fvraShWYNjzs4NSXXCZgXE9jsNMnMRzJBg+Mp1Ap5EiPtaj4Mm2aZuMqWKcnxgaHXkx7mSpTLW063PdtLtp0RDQ92xpu6iys4eG2vEqU1bUAABpbtXh3I+2fc8/knu3TjX6S9mIoFTKveK/8FtHzYz3tBRjHXJwqte/72XemWqz08liQEmFSLh8cBcg8/LfxZ+XHU2XujM6u/ADA7t278dBDD2Hu3Lm48sorzf5x/ID8zcb7Z3dI36/WJOUlZRJ5Qj9664lmhzaUn5omDYpq6YmuT5KNE3ivaYAiBKjOB4pz3V+LkzDfD+C5EndLWLND0ffDDqZBoTa9D2eqmtCq1UOlkKF7jPs9h2zB0l7na5rx1M+HcbKsAfHhKvTp2ZNuYGW4qSukxdBBlnoC/HqAej8+2VqAykYNMmLVuGpoavudmmwripwOQOz1UwxorXt6jKZn+8rPnsJqhKGZ/sdTyo9p2svTlV6A8cLEn5Qfb3h+gM6v/HzzzTcYM2YMjh49ih9//BFtbW04fPgw1q9fj8hIL5TlcZynwCT4cUb5kWp2Zniy4otdjYSafyHZKIC0mBDb4xCUoUCvqfS+D6q+xvUynlgneTjlxWhnKpZQ5s4qvXomhNmdoeUu0aFKxIYqAVDjMQC8cvUgBEezUmLPKD+AyaT3/UWobW4TuybfO6UXgqz5ivxM+elyqGONwz3Z8cWCngmO0151LW04WdaAcMEQ/HhD+fG02RkwH2+hlzaexeuIaS9Pm7s7ufLzwgsv4PXXX8cvv/wCpVKJN954A8eOHcM111yD7t290JCJ4xwNZUC5YaisIKO59lrHnXEBSC9zZ3hyxpcN5cdqc0NrsIaHPkh9dQsPxn1TeuH6kd0xLMM75anigFOm/EgoczfO9PJ+ZVGWybT4+aPTqQLGujx7SPkBgOkDkyCXCdh/tgZP/nwIdS1a9OwWJpbCt0Ps7uzhq1yONATBxPRsPfXFlJ/y+lbUNFnvEZN7pgaEAKlqQ6WfV5QfLwQ/on2AGD16vsZrnh/D+9dZlZ+8vDzMmDEDAKBUKtHY2AhBEHDffffh/fff9+gCOS7AVJ+EgUDiQHr/7HZp+7qs/BxxP+Cw4fk5WmxoCuco+Mm+mKaAas4A5/e6txYXWDylJ164YqDXFBajr8bQ6FCK8iP6fTzf3NCSXgbTc1Z8KB6+1NAA08OeHwCID1fhwmz6GWGT3u+/pJft910Mfrjy4zMcTHcPUymQHEkbULbrZWVg7xnaOyYr3NDh22PKj4nh2dOVXgBtrKkwNNe0M9y0Q2HBidc8P520z090dDTq6+lBNSUlBYcO0fLimpoaNDU12duV0xEwv0/mOKD7aHr/jNTgR2KZOyO2Jx0u2lprNEu7ii3lR+oEbKWadnwGaM+fTgZTfmjH5jZJZe5sppc3K70Yt47tgetGpOH9ecOMpebhhh4qHgx+AGCWicrTPzkCU/vbGHCp1/t0ojvHgINGhwCQ7SD1tfdMDQAgVa2lD3iq1D3cy8oP4H+mZ08PNWV0ds/P+PHj8eeffwIA5syZg8WLF2PhwoW47rrrMHnyZI8ukOMCTPnJGAt0H0XvS/X9sLSXvdEWpiiUQFwvet/d1JcVz49Gq8epMonKD2BS9fWT/+TXPURkSBCdUA7gdHmjQ+WnVatDfgVVibxZ6cXIiAvFi1cOEoM0AEblp7HMowNwL+mfgOAgevh64JLeEGyZ8+00zuR0IBLK3W12MQdtbrjPoPwkKA2maU8pP0HBxs+Gt4Iffyp3J8Qk7dV1q71cmqr39ttvo6WFVt889thjCAoKwtatW3HVVVfh8ccf9+gCOU5SV0yb/UEA0scAbfTvhNLDtBePvTy5ro1WZACOe/yYktCfVnuVHqINB13FivKTV96ANh1BeLBCbIRml+wptEy/7hxwbhfQfaTr6/FDsuPDUF7filNlDRjswPPz28ESaPUEUeogJEb4aKZVaDwAgQYgjRVGJchNwoOD8P5Nw1BS12K/p5LYODPKczOMOM7DPD82ujwD9gec5pU3oL5Fi5AgudHw7CnPD0B9P02V3qn2AvxL+WmpBYjhQsTTaa/OrPxotVr8+uuvkMuprC2TyfDwww9j1apVePXVVxEd3UW6lvorBYb+PkmD6FVMRBKdwE70NBiwR915up1c5VyKwBMVX4RY9fyInZ2TImxf3ZsSFAz0mU7v+6Dqy9tkdTMpd7eT9qptbsNzq+nYkYXjekh777yBXGH8LDV4NvU1vlc8rhmWZv9345Ve/oGkRoe2e/0wv8+g1EjImGnYk80I43ob1pnuuec0xZ+UHxaYBIV6ftAvC6aaq30yasgZnA5+FAoFbr/9dlH54fgZBZvobcY442NpLPXlwPcjmp1TnWv05YmKr5YaOlwQMFN+jkr1+5jS31D1deSnTpf6MksN2El7LVtzHBUNGmTFh2LhuB4ducT2iL4fz1V8SabRuome08Gw4KexjHaRtwIz9BfVGjxtJuwppMHPkPRoYzd5Tyo/014Erv2C9gvzBv6k/Hgr5WX6nHqt/1S22cAlz8+IESOQm5vr4aVwPIJodh5vfEyq78fZMncGa3RYecqYZnMWNoJAGWZ2NSLZ7GxK1iRAFUlTeFKr3AIEswGnNtJeuWdr8MUOml54dvYA33cTZr4fDys/kvCjuV5dmpBoo1JTa73XT5RaKXra8sobzX7GzM5DukcbT6qeVH7CugF9Z1Kl0hv4k/LjzeAnKIQ2mzV9HT/FpaPinXfeiSVLluDtt9/Gtm3bcODAAbN/HB9Re452OBbkxiovwHj/3G7q67GFs2XujPAkenAjOqDiuHP7MsQuvEZTKiHE8VgLayhUQN/L6H0fzPryJuzquLCyCXoryo9OT/D4TwdBCHDFBSkYk+UHJ33W68eXyg/v7uxbBMHhdHfAmPo6aZL6qm1qE03QF3SP8o7y4238Svnx0mgLBrvQYBcefopLwc/cuXORn5+Pe+65BxdeeCFycnJwwQUXiLccH8FUn+Qc8wNDXC8anGibgWI7wak42sLJRpWC4H7qy0ovlpK6FlQ3tUEuE0QzpGRY1deRnz1aZeRrEiOCEaqUQ6sn0DYarqxMPD+fbyvAofN1iAhW4NHpfX2zSEvCfaj88DJ3/8FBo0PAxPdjYnred5amvDJi1YgLkQNtBlVIFUDTBPxJ+fHWaAuGaG4v8M7zewiXNL78/HxPr4PjCUxL3E2Ryajv58RvNPWVOtT6/syM6KzyA1DTc8Fm14MfK2ZnpvpkxYciOEhubS/b9JhIg4LGMqDwb/M0YAAjCAKyuoXhwLlakGZz5aesrgWv/nECAPDQtD5iCsHnhHmn148kuOHZf5BS7m6l14/VlBfAlR9X8WbaCwBiMuhg7arT3nl+D+FS8JOe7iVHPMc9xODHyom+u0nwM2aR9f1rXPT8AEA3g8rAxmo4i5X0hGmll9PIg4A+lwG5XwCn1naa4AegpucD52qg0BiCH8NV5bOrj6K+VYvBaVG4foQfjZnxwogLyTSU0Vvu+fE9Tkx3P1lmTHux/j4XpJsEP4qQwGpd4E/DTb3V4JARYyiwqPJvkcSl4Oezzz6z+/N58+a5tBiOG1QX0isqmcJocDalu0nFFyHtJ7br9bTUHXA+7QUY1SKpM8QssdLgkI21cMrsbEpyDg1+yk+4tr+fktUtDCFohZwYO91uPlmOX/YXQSYAz88eAJkXh5g6jRdGXEiG+Uui+QWbz3Gi3P1cdTOaNTqoFDLkispPFNBi+HsGkuoDmAw3rfHlKijeGm3BiM6kt9WdMPhZvHix2f/b2trQ1NQEpVIJtVrNgx9fwFSf5CGAyoo/JvkC2r+nqYLKkbFZ5j9vKAV0GmqWDrcxINIekan0ts7F4MeK8uOS2dkU1nnaVRO2n5IVH4YIGMqFBTlahBA8+fMeAMC80RkYkOJnXghW6t5Qaj3w9hZtLUA9nf2FqIyOeU2ObSQ0OowNUyFaHYTqpjbklTcgSC5DfasWaqUcvRPCgbNeqPTqCMS0l4dne5UfB46uAkbdRcf7SMHraS9D8OPnaS+XDM/V1dVm/xoaGnD8+HGMHTsWX3/9tafXyJGC6TwvayhUQMoQet9ayTsrP41Idq3cMyKF3rbWGasxnMHC89Ok0SK/khobXQ5+4g2Ny6oLXC/B90Oyu4UiUqDvDQmJwopNp5Ff0Yhu4Srcf0kvH6/OCszzo9PQ5mcdBVMYlOHeO9BzpMPS6c1VQGv7RoaMnt2o7+dUWYPY32dwahQUcllgVnoB5oZnTzb/W/sMsP454MA30vfxdvDDlJ/Gcrt/Z1/jsQYgPXv2xEsvvdROFeJ0AISY+H1sBD+A/X4/7pidAao2sbx2XZHz+1soP8dK6kEIneDtsnE3LIFWhBA9UJXn2nP4IemxoYiWUeWnRR6Gf2+kv9sTl/VDeLAf+iAUKuPMpI5MfbFqk+iMjlObOLYJjjQqIHZNz0bfD+vsPCTdsJ83evx0BOz3JjpAY31wq0uw1FLRPun7eNvzExJlTKn5se/Ho93PFAoFiopcOPFx3KPqNE03yYKANDuzrOxNeHe1waEpESz15cJ0dwvPDzM7u+z3AegJL96ghJR3ntRXkFyG7Ajq9zndEASNVo9xPeNw2aAkH6/MDr5odCgGP9zv4zdImO5u7PXTIAY/Q9MNwXOgKj9BIYBcSe970vRcazjWlhyUtj0h3vf8AEbTsx/7flzy/Kxatcrs/4QQFBcX4+2338aFF17okYVxnICpPqnD7Od900bQ28pTQEM5EGZS/utqg0NTIlPogFNnTc+EtFN+3Pb7MOJ605lmFZ3L9DxQXQ00A+XaUCgVMiydNcB387ukEJ4IlB/t2EaHpsoPxz+I6g4U73cQ/NC0194zNahooBPcL0gzBD+tBs9MoCk/gmBsvdFSA8CN4yyjpdaohJUeoQ1sHVXAaRpo+hnwbio4JhM4v9uvlR+Xgp/Zs2eb/V8QBMTHx+Oiiy7Cq6++6ol1cZwhX0LKC6Cph279gLIjdOxD35nGn7GDkVvKj8Eo7azpWdMA6OhBjnl+xLEWyW4e5Dqh8gMAF2r+BgBs1g/EnZOykBkX6uMVOcAXjQ5ZSTUPfvwHJ6a7s8CnR1wookMNqomo/PiZqV8KIVE0+PGU8lNrorDrWukFHhsybQvm91EEA0ESDdKuEO3/pmeXgh99JxsWGdAQYpzkbsvsbEraSBr8nKHBz+6CKvx9qhJ315yhOVB3lB+W9nJW+WGqjyIEUIZCryc4XsLK3MNdXw9gnNbcmZSfuiKk1e8HAByImIDPJ2Q52MEPCPPBcFOu/PgfEro8dwtXITxYgfoWmtq9oHu08Yei58fN44Iv8HSjw1oLe0HJQQnBj8loC28qxQGQ9vLxxEOO21SeolfTchWQOsLx9ha+n/9beQCvrz0OXTVTftzwR0QaKr6c9fyIfh+q+hRWNaHJ0OMjI9ZNRYMpPxUnO8+Yi8M/AQDKoy/A67dd5nz3a1/Q0coPITz48UckNDoUBEH0/QAmZmfAqPwEWtoL8PyIC8sBsfZGFzG8PdqCIZa7F3j3ddzApeDnqquuwr/+9a92j7/88suYM2eO24viOEH+JnqbNsJsGrpNWMVXcS5KK6uQV96IKDQgSNdMH2f9elyBlbs7W+0l+n3Mzc59EsNpeas7RKXTwFDXaveAG1Ac/hEAED9yLlKjvShde5KOVn6aKg1VNYJ7aibHs0hodAgYfT+AYawFozVADc+A95QflgIskRD8NBlaTaij7W/nLiztVXsW0LZ697VcxKUzy6ZNmzB9+vR2j1966aXYtGmT24viOIGUEndTorrTJoZ6LU7u/QsAkCLQ4KNFFSctgLJFpEnay5leFhY9fjxmdgYAmRyI60nvd4ZOzzVngXM7AQhAv1m+Xo10Olr5YapPRLJ7n2mOZ2GBaEutXQUk26D8hKkU6JVgkuLiyo8RFvz0vITelhxwfNz1dpk7I6wbEBQKgDgMdH2FS8FPQ0MDlEplu8eDgoJQV+dCgzuOazjr9wFonteg/jSdovtmq+jVwBmdm+5/Znhua3Tu6sai0stjZmdGZ+r0fOQneps+Bojw49J2S0yHm3qyyZsteMrLP1GFGbu42zkpjs6KhUwAJvftBrnpqJaAVn4MCo2nlZ/sKXSsUUut40CjI8rcAXqe8fNOzy4FPwMHDsS3337b7vFvvvkG/fr1c3tRHImUH6NdNBXBQIqNSe3WMPh+oiroSIQF/ejH4ERrtNhR1SWCQoxfKhumZ72eoKKhFYeLarHhWBm+2XkGe46eAgCsKdDisrc2Y8tJGgx5RPkBjJ2eO4Pyc+gHetv/Ct+uw1mY8tPW1DFdX3nw479ISH0NSInEpocm4aUrB5n/IJCVH5b28rTyE5MFxBsGSzvq99NRyg9gEvz4p+nZpWqvJ554AldeeSXy8vJw0UUXAQDWrVuHr7/+Gt99951HF8ixAytxTxtJu+hKxaD89NUeg0LQo5+a9s44T+KwevNpDE13IpCyJDKFXl3UnQcSB5j9qLapDZe9vRlnq5rNHl8WVIihciC3UoFDOnpw6xauQn+u/JhTXQAU7QUEWWClvABAGUrHTGjq6Ywvb1+5s+DHHQM/xztEp9PPsQMPnlU/m6j8BGipO+CZ+V46rdFbGZkKJA0CSg/S1Fffy2zv5+3RFqb4+YBTl4KfmTNn4qeffsILL7yA77//HiEhIRg0aBDWrl2LCRMmeHqNHFswv4/UlBcjoT/aFKEI1zZiRkIVVA1UpTlP4rDmcAnOVDahe6yLRtqIVHr1YVmGCWD1wWKcrWqGIACxoSokRKiQEBGMAZUaoB6YOKQvhvcfhm7hwegRHwq10qWPZ3tMlZ+OHKzpaQxVXsgYS3PqgUZ4IlBZT1NfzIflLbjy479IND23Q28yGiKQlR9PpL0aSuioDFkQTSknDqSPO6r48ony459pL5fPLjNmzMCMGTM8uRaOM+j1Rr9Pxnjn9pXJkR/cD70admF65BnxIBSVlAX9OeDjrfl4aqaDfhG2iLRd8fXrAfrYQ1P74I6JJr1p3qfBz8j+vYA+Ca69rj1is6la0lpLVQeWggk0DgdoyosRnghUnqR/A2/Dmujx4Mf/YFWh9cXO7ddq4icNRM+PJw3P7OIyIhmQyYBEQ3rQUdqrozw/gLHXj5+mvVzy/OzatQs7duxo9/iOHTuwe/dutxfFkUDZEfpBDgo1Tmt3gs2t2QCAHHJU7BcxfgRNd/1311nUNre5ti6x3N3c81Ne34rtp+lVR7sZVBbVXh5HoTKeBAO103NlHh0LIMiBvpf7ejWuYWp69ia6NmOvKR78+B+ufg6YV0yuci7N7y94UvlhwQ+rnmMWg7pzxtSWNcS0l5dL3QFj2qum0C97rLkU/Nx11104e/Zsu8fPnz+Pu+66y+1FcSTAUl7dRzme52JBUU0z1jbSqDy+fDvQbBgeOGggeiWEoVGjwzc7XSxPZMGPRdrr90PF0BNgcGok0mIsUmqNHSDFBnqnZ0NvH2SO916Q6G06qty99ixA9LRjeCCmBzs74YaLH2eVn0AdasowVX7crXgUgx9De5HgSGOgX7zf9n5i8NMBaa/IVJqW02mcH3nUAbgU/Bw5cgRDhrRXGy644AIcOXLE7UVxJJDvot8HwPbTlcjVZ0ELOWTNhsAjOBJCcCRuHUuDok+2FqBN58IYk0jrys8vB+iB7rJByebbtzXT0njAuyf1QJ/xxfw+A6706TLcoqMaHZr6fQLV39WZYUFwfalzQUBrAFd6AUblR99Gqx7dwTL4ARynvjRNgNZQbNIRaS+Z3NjR2w9TXy4FPyqVCqWl7Q9gxcXFUCg8ZFLl2EavAwqZ38e14KcZwSgL7W18MJKaEGddkIy4MBWKa1vwv4NOXpkB5l2eDQe20roW7CqgVxzTLVNerMePLMi7BzWx4isAlZ+Kk7SSQ6YA+tip5PB3Okr5EYMfXunll7DPga5VVJ0lEejKjzKUfocB930/1oKfJBb82DA9M7+PLKjjZqP58YBTl4KfSy65BI888ghqa40lezU1NXj00Udx8cUXe2xxHBuUH6PlksowICnH6d23Gbw3utSRxgcN09xVCjnmjaYnjQ8354M4K8+yRofaFrGy4H8Hi0EIMKR7FFKiQsy3N/X7ePMqPZDTXizl1WNix5SoegtfKD8c/0OhMioPzvh+Al35EQTP+X4sPT+AY+XHtMy9oxRRPx5w6lLws2zZMpw9exbp6emYNGkSJk2ahMzMTJSUlODVV1/19Bo5lpQdpbcJ/QG5c0rbueomnK1qhlwmIK6/SVsCky/RDSO7Q6WQ4eD5WuzMt2Oes4ZCBYQafBaG1NevtlJegInfx8s+Fpb2qi/2TJ+NjkRsbBjAKS/AJN3RUcpPhndfh+M6rvh+2Pc2UJUfwHMVX2yoKbMZAMbgp+IETXFZ0pFl7gw/bnToUvCTkpKCAwcO4OWXX0a/fv0wdOhQvPHGGzh48CDS0pwbIvjOO+8gIyMDwcHBGDlyJHbu3Glz2x9++AHDhg1DVFQUQkNDkZOTg88//9xsG0IInnzySSQlJSEkJARTpkzByZMnXfk1/RfmW2GpHCfYfpoGMwNTIhHS40LjD6KMf7fYMBWuGkrl1A+3uPChZV/I2vMoqmnGnsJqCAIwfaCVcQyi8uPlL2RwJBBmOPlWBNDnoewoUH6UStV92s/TCyhY8NNaS71e3oIHP/6PK4GwqPwEYINDhieUn9Z64/4RJsFPeCIQGk/N/mVWvLcdWebOiO5kwQ8AhIaGYuzYsZg5cybGjx+PqKgo/Pbbb1i1apXk5/j222+xZMkSPPXUU9i7dy8GDx6MqVOnoqyszOr2MTExeOyxx7Bt2zYcOHAAN998M26++WasWbNG3Obll1/Gm2++iRUrVmDHjh0IDQ3F1KlT0dLS4uqv6n+w1E18b/vbWYGVm4/OiqWVMLG05F38kBq45UL6/7VHS5Ff0ejci5iUuzPf0PD0GCRGWhkwaTHXy6sEoumZpbyyJwMhHVCe6k1UEbQCC/Cu+sN7/Pg/Lik/Ae75ATyj/LDRQcGR5u+FIBibHVrz/XRkd2eGadqrI2b6OYFL7uTTp0/jiiuuwMGDByEIAgghEExyiDqdtJr+1157DQsXLsTNN98MAFixYgVWr16Njz76CA8//HC77SdOnGj2/8WLF+PTTz/Fli1bMHXqVBBCsHz5cjz++OOYNYu2///ss8+QkJCAn376CXPnznXl1/U/WPAT53zwsy2PBj+jehiUlsuWA6fWAr2mmW2X3S0MF/XphvXHyvDRlnw8O3sAJGNS7v7LCUPKa7CNIZze7vFjSlxvIH9T4Iy5IMQY/ARqY0NTBAEIT6DKTEOpURL3JM3VxqtiPtrCf3FL+Qng4McTw02t+X0YiYOAvPXWOz37IviJTgcg0M7cjRVAWHzHvbYDXFJ+Fi9ejMzMTJSVlUGtVuPQoUP466+/MGzYMGzcuFHSc2g0GuzZswdTpkwxLkYmw5QpU7Bt2zaH+xNCsG7dOhw/fhzjx9MOx/n5+SgpKTF7zsjISIwcOVLScwYEOi1QSQeBikqGRM5WNeF8TTMUMgHD0g0qQuY44OJnrHqHbh1LT07f7TmLmiaN9BcypL0aKwqx/2wNZAIwbYCNrsodqvx4ecBpdQFQethzz1d6mAa6chXQO8BTXowwL/t+mOoT2g1QujiiheN9xOCniyk/nhhuKvp9Utv/zK7y4wPPj0JlXKefVXy5FPxs27YNS5cuRVxcHGQyGeRyOcaOHYsXX3wR99xzj6TnqKiogE6nQ0KC+TiDhIQElJTYPjDW1tYiLCwMSqUSM2bMwFtvvSVWmLH9nH3O1tZW1NXVmf3zW2oKadMoRYhYni4VVuU1KDUSoSrHot/orFj0S4pAS5seX+5woumhQfmpLSkAAIzMjEW3cCspL8D4hfS25wfw7oBTQoCPZwArxgHn93rmOcWU15TAPuCbEm74bnprxAX3+wQGYtqriyk/nhhuaq3MnZE0mN6WHqYXyqb4wvMDGL+Lflbx5VLwo9PpEB5O+wTExcWhqIjObEpPT8fx495NKYSHhyM3Nxe7du3C888/jyVLlkhWm2zx4osvIjIyUvznrGm7QxHNztl0posTML+PmPJygCAIuHUcVX8+3VoAjVZi00PDl1JWTz8XNlNegG+Un+oCoM3DHrCGUtpanuiA1fe7387dNOUVyI0NLfG68lNAb3nw49+4Evx0JuXHI2kvK8FPTA868kjbYswQMHyh/AB+O+DUpeBnwIAB2L+fttAeOXIkXn75Zfz9999YunQpevToIek54uLiIJfL2zVLLC0tRWKi7cGTMpkM2dnZyMnJwf3334+rr74aL774IgCI+zn7nKxnEftnbXSH38BUCyf9PoQQbM8zMTtL5LJByUiIUKGsvhXf7pb4vhiUnxhdBRQygmn97QwSFT0/HZALDkuglSJED1Tlefa5K02er2gvsPdT956v5ABdoyIY6DXVvefyJ7jywwHMG17qJV5UdSblx620lx3Pj0xOW6AA7VNfvvD8AH474NSl4Ofxxx+H3vCBXbp0KfLz8zFu3Dj873//w5tvvinpOZRKJYYOHYp169aJj+n1eqxbtw6jR4+WvBa9Xo/W1lYAQGZmJhITE82es66uDjt27LD7nCqVChEREWb//JZy1yq9zlY1o6i2BUFyAUPTpVcNKRUy3DaeTmB/fvURHC+pd7xTeCIIBCgFHaZlyBEbZmcIYWMHGp4FwXsVXyyYkht+17XPAA3lrj8fU316XtJx3Vg7gnBDr6fi/dJPes5Qwyu9AoKwbgAEQK81KhKO4MoPxZ7nB7Dd6bnJV2kvg/LTGdJeU6dOxZVXUik+Ozsbx44dQ0VFBcrKynDRRRdJfp4lS5bggw8+wKeffoqjR4/ijjvuQGNjo1j9NW/ePDzyyCPi9i+++CL+/PNPnD59GkePHsWrr76Kzz//HDfeeCMAmqa599578dxzz2HVqlU4ePAg5s2bh+TkZMyePduVX9X/qHCtxw9LeQ1OjYJa6VyR381jMjCuZxxa2vS488s9aGzV2t9BHoRKgQZYszLtlDdqW41Xcx0lxXqr0zNTfi64kZoOW2qAtU+59lyEmDQ27ARVXqZkTwaU4UDpIWDfZ55/fj7aIjCQBxnVXqmm587Q58dd5Uevo6ODANvBD2t2aFnx1exr5acTpL2sERMTY1buLoVrr70Wy5Ytw5NPPomcnBzk5ubi999/Fw3LZ86cQXGx8YvR2NiIO++8E/3798eFF16IlStX4osvvsCtt94qbvPQQw/h7rvvxm233Ybhw4ejoaEBv//+O4KDbRhuAwlCXFZ+tjnp9zFFJhOw/NocJESokFfeiMd/OmR37MWpsgac1dEv2Jh4O94adsUnyI1XRN7G28pPXC9gxmv0fu6XQKELVYZF+6iCEaTuXCkvgF7xT3qU3l/7tLHDtyfQ64AagzGfKz/+jzPl7no9be4HdG3lp6GMDkYV5Eb/nCVixddBY28dbSstNwd8EPwYlJ+mSqN65wd4LPhxlUWLFqGwsBCtra3YsWMHRo40zpvauHEjPvnkE/H/zz33HE6ePInm5mZUVVVh69atuPbaa82eTxAELF26FCUlJWhpacHatWvRq5fznZD9kvpiQFNPP/gxWZJ3I4Q4bXa2JDZMhbeuGwK5TMCP+87j2122/T+/HihCEaFfsNAWO94O0ewc67R522W8pvwYrmpis4C0EcCQefT/q+9vX3VhD00T8OeT9H6vqXQYYmdjxG1AwgDak8dVdcwadedpGkWuNBpqOf6LM+XumnoAhhN5V/b8ML9PRLLt0Ubd+tFzRHOVOGJITHkJ8o5XzlThxoIWP0p9+Tz44TgBUytiMgGFUvJuhZVNKHbB72PJiMwYPHAJDR6eWnUYR4qsR/GrDxSjmBiCLPbls0ZHNjhkMOWn4qT7FVkMvd4o6TKJd8ozNLdedhjY+Z6059E0Al9dAxRsphUbF97rmfX5G3KFUR3b9zlwZodnnpelvKK6U+Mnx79xRvlhioFcCQQFsIrPlB9dq2sjXhz5fQD6/sT3ofdZ6kssc4/uuAtNU/ww9cWDn0DCxc7OTPXJSYtCiNK9k8I/x/fApN7xaNXqcddXe1Hf0mb28+Ml9ThZ1oAywRDQ1NoJfhp9UHoZlU5NybpWoznWXeqLAW0zIFMYuwqrY4ApT9P7G14w5ult0doAfDmHBj7KcOCmH4DkHM+szx/pPpL6owBg9RLn1DFb8EqvwIKpcw0Sgh/R7xPg5n9VOFVfANfUH3YxaTrTyxqmqS/Ad2XuDD8ccMqDn0CCKT9OdnZmfp/RLqa8TJHJBLx2TQ6SI4ORX9GIR344aOb/+fUAPclHJxk+7P6m/MjkQFxPet9TnZ6Z3ycq3VyKvuAmIHU4zbWvecz2/q31wJdXA4V/U0n/ph+B7qM8szZ/ZspSeiVaegjY9YH7z8dnegUWrig/gZzyAmjFqTsjLuz1+DHFsuLLV2XuDD+s+OLBTyDhgvLjCb+PJdGhSrx9wxAoZAJ+PVCMLwzdnwkhWH2A5u/79e5LN7ar/HRgg0NTPN3pmVV6xVr4sGQymt4RZMDhH+jMHUta6oAvrgLObKO5+Jt+AtKGe2Zd/k5orFEdW/88UOfEqANriGkvXukVEDgz3JQpP4Fsdma44/uRGvww5YelvXyu/Phfrx8e/AQSLig/+RWNKK1rhVIuwxA3/D6WDOkejYcvpXnlZ385gkPna3GkuA6nKxqhUsgwbLBhEGp9sW1vjS+UH8DzM75YJ1VrJvSkQdTgCwCrH6BVF4yWWuCLK4GzO+jV4LyfgNShnllToHDBPCBlGDW0/mFHHZMCT3sFFl1R+QHcVH6Y58fBFAIW/NSeoYUFpp4fX8DTXhyXaa4GGsvofSd6/Gw/TT/0Od2jEBzkWRPoP8Zm4uJ+CdDo9Ljzy734eidVgCb17obQmBTqgSE62wc302qvjsTTyk+VSaWXNSY9SrtLV+UBWw1NQJtrgM9mA+d2URPkvFVAyhDPrCeQkMmAGa9SdezQSuD0Rtefiwc/gYXo+Sl1XHzQapiFFRzAPX4YYrm7C/O9pCo/IdHU+A9Q34+Y9vKx8lN33vOjhVyEBz+BAlMpIlKcMv150u9jiSAIWHb1YKRGh+BMVRO+2E6DnxmDkqi3hh3cbJl9xaGmPlR+7PQrkgxLe7EvuCXBkcDUF+j9TcuAolzgs1l0DEZIDDD/l85tbnZEcg4wfCG9b6mOSaW1wagk8gaHgUFoPA16iR5odNANvTMpP66mvTRNxmOmo+AHMG926GvPjzqWFnKAeK7QxE148BMouNDZ2Rt+H0si1UH49w1DoJTTj1JIkByT+3ajP2QVCXXnrO/sK89PbDY96LbW0qZh7qDXGU18tpQfABhwFZAxjg4c/OAioDiXHhDm/2I0J3ZlLnoMCO0GVJ4Etr7l/P7sgBoS0znUga6ATE4VUcCx76czeX5cbXTIikeU4dI+4yz4KTloDJo6erQFQxCAmAx6309SXzz4CRREv490s/PpikaU17dCqZDhgu5R3lkXgEGpUXjiMmpwnjk4yTg+I8Iwx8mW6dlXnh+FypgacTf1VXsO0Glo/xF7eXhBoOkdWRBNBYbGA/N/BRIHuPf6nYXgSGDq8/T+pmXGyi2p8JRXYCLV98OVH/MeP1KmKZhWfDX7OO0F+F2vHx78BApipZd05WebYYr7EC/4fSy5aXQGNj4wEc/ONjmZRzLlx0rwo9NSHxPQ8coPYKyYc3fMBStzj85w3Fgvvjcw8w0gazINfBL6uffanY2BcwzqWDPw+8PO7ctnegUmUiu+uPIj3e/DYKbn8uPGSkpfpb0Avyt358FPoFDufNrL2ykvSzLiQqFSmAQAEYYvaa2VtBe7EoHgmy8k6/Xj7pgLscw9W9r2F9xAGxh26+Pe63ZGBAGYvowa5Y//Dzj2P+n7cuUnMOHKj3ScDX4iUmiai+iAeoPv0qfKj39VfPHgJxBoazYObJSY9tpVUIW/TlAToTfMzpKwp/wwv09ItG9GEcR7SvmxGGvBcY9ufYDRi+j9NY/S0SFS4MFPYMKVH+k4G/wIQns/oa88PwBPe3FcoOIkAEK/NKHxdjdt0+nx6h/Hce1721DfokWfxHBc0N1HvR1Ew7OVai9f+X0YnhpwaqvBIcd1JjxEGz5W5wMFm6Ttw4OfwIQrP9KR2uPHFJb6AgAIxtf2BSztVXPGM+Ns3IQHP4EAO0HH97ZrdCusbMScFdvw1vpT0BPgqiGp+O720VAqfPRnZsFPfQmgM58B5rNKLwZrFFlf7Fq/DQbz/FhrcMhxDWUoMPAqen/fl4631+uNyigPfgILycpPPb3tDJV8HaX8AEDiYOP9kCjfDvyNSKFzFfVttiuAOxAe/AQCDvw+hBB8v+ccpr+xGblnaxAerMBb112AV68ZjPDgoA5cqAWh8bS6CaT9wU3s8eOjlFxwJBBmuOqsOOnac+i0RsWBKz+eJccw9PToKsfBaUMpbSEgyI0+M05gIJa6O1B+WJPDTqX8VEtP6+r1xqrZSAdDTU0xTXv50u8D0IamrCDBD3w/PPgJBCpsl7nXNrVh0df78MB3+9Go0WFERgx+v3c8Zg5O7uBFWkEms13u7mvlBzCqP676fmrPAHotoAgGwv3g/e5MpAwB4vvQoObQSvvbsgA0MtV8sCzH/2HKT2N5e3WYQYiJ8tMJgp+IFEAZRj/bbPCoI5oqAF0rAMG5Y01sNqAIofd96fdh+FHFFw9+AoFy6wNNt5+uxKVvbMLqA8WQywQ8OLU3vr5tFFKiQnywSBswidbS9Oxrzw9g4vtxMfipNDE7y/hXyaMIAnCBQf1xlPrifp/ARR1Lq/sA2w1HNQ20CzTQOZQfeRCQOZ7ez1snbR/m9wlPBBRK6a8lkwMJ/el9Xys/gF+ZnvkR29/RaY2+EoNSQQjBq38cx3UfbEdRbQvSY9VYeccY3DUpG3KZhOZXHQnz/ViWu/uF8uPmgNMqB2MtOO4x6Fqayjq/Gyg7Zns7HvwELjKZMf1sK/XFzM4yBRDkRxd27pB1Eb09tV7a9q74fRjM9OzLHj8MPyp358GPv1NTSDsIK0KASDqo7u31p/DW+lMgBLhmWCpW3zMOOWlRvl2nLSJtVHz5aq6XKe4OOGXT3LnfxzuEdQN6TaX3c7+wvR0PfgIbseLLhum51aTSS0pn40AgezK9PbvDmNKzh+j3cSH4GXwdENsT6Dfb+X09DbtQZN9ZH8KDH39HNDtnAzIZ/jhcglf/pErFs7P64+WrByNM5cc+hwgbvX58NdHdFKb8VBe4Nmm4kld6eR2W+tr/rW1PCJvrxYOfwMRR8MOUn87g92HE9KCfV30bULDF8fbuKD/dRwJ37wZ6XeL8vp4m2kT58cRQaTfgwY+/Iw407Y3jJfW479tcAMD80em4aXSGz5YlGVtpL3/w/IQl0H4yRG9MYTlDFe/x43V6XkKrBhvLgFNrrW/DR1sENmK5u420V2sn6vFjSpZB/TklwffjSo8ffySqOx0q3dbo/lBpN+HBj79j8KM0R2bj1s92oVGjw5isWDx+WYDMhbLW5VmvB5rYoD0fBj+C4HrFl1Zj7C3DlR/vIQ+i3h8A2Gcl9dXWbFQM2FUlJ7Bw1OiQtTroDD1+TGGpLymmZ3eUH39CoTT+Dj42PfPgx98xKD8rjihwtqoZ3WPUeOf6IQiSB8ifjvVdaSwHtK30fksNnTcD+L4CwdVOzzWFVDEKCjUevDneIecGenvid6Ch3PxnLABVRdBRKZzAw1Gjw86q/GSMoybuqtOODcCdJfgB/KbcPUDOoF0UQkTl538lEQhVyvHBvGGIDnWi1NHXqGNoHxzAqP4wv48q0rmyTW8gKj92qomsUWlS6dVZTJj+SkI/IHkI7al08L/mPzNNefG/Q2DiUPnphJ4fgP4+qSPo/Tw7VV9tLTTtCwR+2gvwm4ovHvz4M/XFgKYeWiJDAUnE69fmoHdiuK9X5RyC0H7Gl+j38YO+E0k59LZwq3MGPNHvw8vcO4QLDOrPvi/N/0680ivw6arKDwBkG0re7QU/7KIxSN051E0/6fXDgx8/5tjB3QCAQpKAey7uh0v6B2h6xbLLM1N+HAxp7RC6j6IHlYZSoPSw9P14pVfHMuAqOheo7DBQtM/4eDWv9Ap4mPLTXGVMjZvSWZUfwGh6Pv2X7WpG05RXZ1A3edqLY49z1U1YtXYDAKAhvAcWXZTt4xW5gdjl2fAlbvKDBocMhQrIGEvvS+22CvBKr44mJBroO5PezzXp+MyUnyhe6RWwhETTwBawnvrqzMpPUg4dO6GpB87ttr5NZ/L7ACbKDw9+OBY0abS47bM9SGqjZs5+g4ZBCOSIXyx3Z8qPj4eaWuJMySlDHG3Bg58Og6W+Dn5n7Mskpr14pVfAIgj2fT+dWfmRyYCsSfS+rYsvFvxEODHQ1J9hKm1zFdBc47Nl8ODHzyCE4MHvDuBIcR36KmgOPCihr49X5SaW5e7+pPwAxlbzZ7YBmkbH27e1GPtucOWn48icQKsHW2qBY79S7w/3/HQO7Pl+OrPyAzi++OosPX4YqjAgtBu978PUFw9+/Izvdp/D6oPFCJILGPz/7d19dFTV+S/w7+RlJoHJK4HJCwkJAkGoCYWQNEXK1YwEr9cl1i7ASy8RbuuqYBcSqCAuiJqulUDFn0UoeKUtZf2UoHZhl6VlAZGkV254C/DjpRQBEVLJCyAkIe/M7PvHyZzMQECEnLPPzPl+1jprTmZODnu2MXnm2c/eO6xeedIzI8lfeaa731LzY5DgJ2648ovF1akUPn+bq18BEIA1whh1S2YRFAyMeVY5P/K+8nPU1QLAAkQHyB8Gs7qbzE/ABj/dH74uHu7JinsLtGEvwBBDXwx+DKTjhgtv71Kmtr/ySAJC27uDhDg/D35uzvy0dK/VYpTMj8XitdHgXQx9edf7+PNwpD8a8z+Vx7O7gfPd2wJEJim1W+S/7rTFRYdnkcMADX4iE4BBowEI4FzFra8HZPDTPUx9uxl+OmDwYyBl+2twsbEdjkgbZj7QpjwZmQTY/Gx6+808s73avgE6W702NTVIzQ/w3VZbvcJiZ2lihwJDHgYggMqVynMc8vJ/Zs78AD11Pzfv8i5EYAY/k38NLL0I5M6T1gQGPwbR1unCmt3KLuG/fHQ4bFe7dwz396wPAIRFKyshA8paPy0Gq/kBlHoSS5Cy0vO1mjtf+w2nuUvlKXxu+KfyyD29/J+n5uf6TcGPED27ngdq5gfw/fDlvY5V6zfADa8PwoGifxxg7S+1CQx+DGJT1Ve41NyBwTHhmJaV3LPXlGfncX9msfQMfTXWeGV+DBT8hEcDSVnK+Z0WHAOY+ZFt1FOA1d7zNTM//u92mZ/Olp6tcAI585PyQyAkXBkG8l5t3lPs3H8QEBomp20BisGPATS3d2F9pfIHdX7ecFhDgnr2mgqEzA/Q86nl0r8Ad/diXkbK/AB3P/TFBQ7lsvYHRj/d8zWDH/93u9lenplelmDpmQJNhYYBqROUc++6w0Ac8jIIBj8G8IfPv8LV1i4MHdgfT3/fEyQEUOYH6Mn81P6X8mi1G++TjLraagXgutH7NZ2tQHP3Nh3M/Mjz/Z/2nDP48X+ezE97o/L/mIda7xMR+JMLPJMuvD98eSaJMPjpcwx+JLvW2okN/1dZMG+BcwRCgoOArrae3arjAiT4ibwp+JG9m3tvksYCYVHKL+CLh3q/xrMfTVi0smkryZGcA6T9CIhOARyjZbeG7pctUtlmBvCt++kI4AUOb+b58HX+/yl/A4DAW+PHQBj8SPZ//vElmjtuYGR8BJ54qDv1e/k0AKEs+26kupj7EXlTRsuI7ysoGBj635Tz29X9cFsLY7BYgP/1CTD/aGAPh5jF7VZ5VjM/Ufq3SW8D05Xfkzfae9Yb47CXZhj8SHSpuQN/3PMVAGDh5HQEBXWnddV6n/TASfV6hr08xYtGq/fx+LbVVlnvYxxBwYHz/wf1XvcT6Gv8ePNeb8zz4YvBj2YY/Ei0ruIs2rpcyEyOhvPBQT0vqPU+AVLsDPSs8uxhxMwP0FP0/PVBoO3qra8z80OkjTtmfkwQ/AC3LrbK4EczDH4kqW1sw3/uOw8AWDR5hO/GpZe7g59AmekF9GR+PIxY8wMov2Ti0gHhBr6svPV1bmhKpI1eMz8mqvkBlGF3SxBw6aSyjY4nEGTw0+cY/Ejyzmdn0HnDjey0WDw87KYsyCWvYa9AYYvwHbc3auYHuDX17E3N/AzVrz1EZsDMjzKJInGscn74fQACCLYZt0zAjzH4keDClVZ8eECp4l80Od036+O6AVzpXt05kIa9gJ5tLgBj/8+srvfzme9qqx3NwPXuzWaZ+SHqW2rmx6SzvTw8v38O/6fyGJUEBPFPdV9jj0rwdvkXuOEW+NGIgchOu2m69NWvlEUAQ8KBqBQp7dOM99CXkTM/QyYon7Yaa7pn3nXzTHPvN0BZEZqI+k5vm5t6trYwS+YH6Mk8e9YT45CXJhj86OxMQzM+OawsXLXwsV4yO+pMr2GBF+17701j5MyPtR8wJFc5915wjDO9iLTTW+an3YSZn6Qs3xIBrvGjiQD762p8/7HrNNwCeGyUA5nJ0bdecPUr5TEQ/8B6f4Ix0o7uvemt7oczvYi0Y3coj53XezI+HSar+QGA4BBg6I96vmbmRxMMfnR04mIjth2thcUCLJx8m3oeT6ozkHbw9fCXzA/Qs97PV58DNzqUc870ItKOzQ5YI5RzT/an3bPOjwkWOfTm+f0DMPjRCIMfHf3HTmVI639kJGJk/G0+yTR1j3dHJujUKh15an5Cwoy/Kq9jNGCPB7pagQtVynOc6UWkrZtnfJkx8wP0ZJ4BBj8aYfCjo//98FDkpMXiJefw21/kKfaLCMDgx/E9pZA7YYzxV+b1Xm3Vs+CYZxYeMz9E2rg5+DFjzQ8AxAwBhjys1P7EZ8huTUAKkd0AM8l9YAByH8i980VNnmGvxDtf54/6xwELjis7uvuDBx4F/usD4OxuoO0a0HpFeZ41P0Ta8F7oUAjzZn4AYOZHgKuTM0s1wuDHSITo+cTj+QQUaIw8xf1mDzwCwALUH+sZ+rI7lAUbiajveWd+utoA9w3la7NlfgBl1in6yW5FwOKwl5G0XwNutCnngTjs5W/6xwEJmcr5gQ3KI4e8iLTjnfnxZH0sQf6TLSa/weDHSDzFzuExQGi43LaQwrPa6pldyiOLnYm04535Ube2iDB+jSD5HQY/RuKZ5h4RgPU+/sp71gXAzA+RlnrL/NhMNs2ddMHgx0gCeZq7vxqc7ZtyZ7EzkXZ8Mj+eNX5MWO9DmmPwYySBPM3dX4VYgTSv1VaZ+SHSjif4udGm7K0HmHOmF2mOwY+RMPgxJu+hr1jW/BBpJjQcCItWzi9173PI2ZWkAQY/RsJhL2MaMQUI7Q8kfr97+ikRacbz4e/yKeWRw16kAa7zYyQseDam6GRg3l5OtyXSQ0Q8cOmkV+aHwQ/1PQY/RsLMj3FFp8huAZE5eDI/jReUR2Z+SAMc9jIKVxfQckk5Z+aHiMzq5tXtmfkhDTD4MYrmOgACCAoF+g2Q3RoiIjluDn6Y+SENMPgxCu89vYL4n4WITOqWzA8XOaS+x7+yRqEWO7Peh4hM7Obfgcz8kAYY/BgFi52JiFjzQ7qQHvysXbsWqampCAsLQ05ODvbv33/ba9977z1MnDgRMTExiImJgdPpvOX65557DhaLxeeYMmWK1m/j/nGaOxERYHf4fs3MD2lAavCzZcsWFBYWoqioCIcOHUJmZiby8/PR0NDQ6/UVFRV49tlnsXv3blRVVSE5ORmTJ0/G119/7XPdlClTUFtbqx6bN2/W4+3cH2Z+iIiAEJvvpA9mfkgDUoOft956Cz//+c8xe/ZsjBo1CuvXr0e/fv3whz/8odfr33//fcydOxdjxozByJEjsWHDBrjdbpSXl/tcZ7PZEB8frx4xMTF6vJ37o25twcwPEZmcd90PMz+kAWnBT2dnJ6qrq+F0OnsaExQEp9OJqqqqu7pHa2srurq6EBsb6/N8RUUFBg0ahPT0dLzwwgu4cuXKHe/T0dGBpqYmn0N3avATf+friIgCnfp70AJYubcX9T1pwc/ly5fhcrngcPiO7zocDtTV1d3VPRYvXozExESfAGrKlCnYtGkTysvLsWLFClRWVuLxxx+Hy+W67X1KSkoQFRWlHsnJyff2pu6VEF7DXsz8EJHJeYIfWwSX/iBN+O32FqWlpSgrK0NFRQXCwsLU52fMmKGeP/TQQ8jIyMADDzyAiooK5OXl9XqvV155BYWFherXTU1N+gZAHU1AV4tyzqnuRGR2nt+DrPchjUgLqePi4hAcHIz6+nqf5+vr6xEff+ehnzfffBOlpaXYsWMHMjIy7njt0KFDERcXhzNnztz2GpvNhsjISJ9DV56sT1gUdw0nIvJkfljvQxqRFvxYrVaMGzfOp1jZU7ycm5t72+9buXIliouLsX37dmRlZX3rv/Pvf/8bV65cQUKCgTMqnOZORNRjwHDlMUrnEgQyDanDXoWFhSgoKEBWVhays7Px9ttvo6WlBbNnzwYAzJo1C0lJSSgpKQEArFixAsuXL8cHH3yA1NRUtTbIbrfDbrfj+vXreP311/HMM88gPj4eZ8+excsvv4xhw4YhPz9f2vv8VpzmTkTUI+1HwIzNQOIY2S2hACU1+Jk+fTouXbqE5cuXo66uDmPGjMH27dvVIugLFy4gyKvYbd26dejs7MRPfvITn/sUFRXhtddeQ3BwMI4ePYo//elPuHbtGhITEzF58mQUFxfDZrPp+t6+E3WmF4MfIiJYLMDI/y67FRTALEIIIbsRRtPU1ISoqCg0NjbqU/+zbSFwYAMwcRGQt0z7f4+IiCgA3e3fb84hNAIOexEREemGwY8RsOCZiIhINwx+jICZHyIiIt0w+JHNdQNo6d7IlZkfIiIizTH4ka2lARBuwBIM9I+T3RoiIqKAx+BHtiavDU2DguW2hYiIyAQY/MimFjuz3oeIiEgPDH5kY7EzERGRrhj8yMZp7kRERLpi8CMbMz9ERES6YvAjm7qvFzM/REREemDwI1uz12wvIiIi0hyDH9nUYS9mfoiIiPTA4Eemjmags1k551R3IiIiXTD4kcmT9bFFAja73LYQERGZBIMfmbjAIRERke4Y/MjUXKc8cpo7ERGRbhj8yNTEzA8REZHeGPzIpE5zZ/BDRESkFwY/MnkyP5zmTkREpBsGPzIx80NERKQ7Bj8ycV8vIiIi3TH4kcXtAq7XK+fc14uIiEg3DH5kabkECBdgCQL6D5TdGiIiItNg8COLp9jZ7gCCQ+S2hYiIyEQY/MjCYmciIiIpGPzIwmnuREREUjD4kYWZHyIiIikY/MjCfb2IiIikYPAjC/f1IiIikoLBjywc9iIiIpKCwY8s6urOLHgmIiLSE4MfGTpbgI5G5ZyZHyIiIl0x+JHBk/Wx2oGwSLltISIiMhkGPzKw3oeIiEgaBj8yqMFPvNx2EBERmRCDHxm4ujMREZE0DH5k4LAXERGRNAx+ZGDmh4iISBoGPzIw80NERCQNgx8Z1H29mPkhIiLSG4MfvbndnO1FREQkEYMfvbVeBtw3AFgAu0N2a4iIiEyHwY/ePMXO9kFAcKjcthAREZkQgx+9sdiZiIhIKgY/euM0dyIiIqkY/OjNM9OLmR8iIiIpGPzordmT+WHwQ0REJAODH701seaHiIhIJgY/emPBMxERkVQMfvTGgmciIiKpGPzoqasNaL+mnDPzQ0REJAWDHz15hrxC+wFhUXLbQkREZFIMfvTkXexsschtCxERkUkx+NETi52JiIikY/Cjpyau8UNERCQbgx89MfNDREQkHYMfPbk6gWArp7kTERFJZBFCCNmNMJqmpiZERUWhsbERkZGRfXtzIQD3DSA4tG/vS0REZHJ3+/ebmR+9WSwMfIiIiCRi8ENERESmwuCHiIiITIXBDxEREZkKgx8iIiIyFQY/REREZCrSg5+1a9ciNTUVYWFhyMnJwf79+2977XvvvYeJEyciJiYGMTExcDqdt1wvhMDy5cuRkJCA8PBwOJ1OnD59Wuu3QURERH5CavCzZcsWFBYWoqioCIcOHUJmZiby8/PR0NDQ6/UVFRV49tlnsXv3blRVVSE5ORmTJ0/G119/rV6zcuVKrF69GuvXr8e+ffvQv39/5Ofno729Xa+3RURERAYmdZHDnJwcjB8/HmvWrAEAuN1uJCcn45e//CWWLFnyrd/vcrkQExODNWvWYNasWRBCIDExEQsXLsSiRYsAAI2NjXA4HNi4cSNmzJhxV+3SdJFDIiIi0oThFzns7OxEdXU1nE5nT2OCguB0OlFVVXVX92htbUVXVxdiY2MBAOfOnUNdXZ3PPaOiopCTk3PHe3Z0dKCpqcnnICIiosAkLfi5fPkyXC4XHA6Hz/MOhwN1dXV3dY/FixcjMTFRDXY83/dd71lSUoKoqCj1SE5O/i5vhYiIiPyI9ILne1VaWoqysjJs3boVYWFh93WvV155BY2NjepRU1PTR60kIiIiowmR9Q/HxcUhODgY9fX1Ps/X19cjPj7+jt/75ptvorS0FLt27UJGRob6vOf76uvrkZCQ4HPPMWPG3PZ+NpsNNpvtHt4FERER+RtpmR+r1Ypx48ahvLxcfc7tdqO8vBy5ubm3/b6VK1eiuLgY27dvR1ZWls9raWlpiI+P97lnU1MT9u3bd8d7EhERkXlIy/wAQGFhIQoKCpCVlYXs7Gy8/fbbaGlpwezZswEAs2bNQlJSEkpKSgAAK1aswPLly/HBBx8gNTVVreOx2+2w2+2wWCx46aWX8Otf/xrDhw9HWloali1bhsTEREydOvWu2+WZAMfCZyIiIv/h+bv9rRPZhWTvvPOOSElJEVarVWRnZ4u9e/eqr02aNEkUFBSoXw8ZMkQAuOUoKipSr3G73WLZsmXC4XAIm80m8vLyxKlTp75Tm2pqanr9d3jw4MGDBw8exj9qamru+Hde6jo/RuV2u3Hx4kVERETAYrH02X2bmpqQnJyMmpoarh+kA/a3vtjf+mOf64v9ra976W8hBJqbm5GYmIigoNtX9kgd9jKqoKAgDB48WLP7R0ZG8n8cHbG/9cX+1h/7XF/sb3191/6Oior61mv8dqo7ERER0b1g8ENERESmwuBHRzabDUVFRVxTSCfsb32xv/XHPtcX+1tfWvY3C56JiIjIVJj5ISIiIlNh8ENERESmwuCHiIiITIXBDxEREZkKgx8drV27FqmpqQgLC0NOTg72798vu0kB4R//+AeefPJJJCYmwmKx4JNPPvF5XQiB5cuXIyEhAeHh4XA6nTh9+rScxgaAkpISjB8/HhERERg0aBCmTp2KU6dO+VzT3t6OefPmYcCAAbDb7XjmmWdQX18vqcX+bd26dcjIyFAXesvNzcXf//539XX2tXZKS0vVPSM92N9967XXXoPFYvE5Ro4cqb6uVX8z+NHJli1bUFhYiKKiIhw6dAiZmZnIz89HQ0OD7Kb5vZaWFmRmZmLt2rW9vr5y5UqsXr0a69evx759+9C/f3/k5+ejvb1d55YGhsrKSsybNw979+7Fzp070dXVhcmTJ6OlpUW9ZsGCBfj000/x0UcfobKyEhcvXsSPf/xjia32X4MHD0ZpaSmqq6tx8OBBPProo3jqqadw4sQJAOxrrRw4cADvvvsuMjIyfJ5nf/e90aNHo7a2Vj0+//xz9TXN+vs77fhJ9yw7O1vMmzdP/drlconExERRUlIisVWBB4DYunWr+rXb7Rbx8fHiN7/5jfrctWvXhM1mE5s3b5bQwsDT0NAgAIjKykohhNK/oaGh4qOPPlKvOXnypAAgqqqqZDUzoMTExIgNGzawrzXS3Nwshg8fLnbu3CkmTZok5s+fL4Tgz7YWioqKRGZmZq+vadnfzPzooLOzE9XV1XA6nepzQUFBcDqdqKqqktiywHfu3DnU1dX59H1UVBRycnLY932ksbERABAbGwsAqK6uRldXl0+fjxw5EikpKezz++RyuVBWVoaWlhbk5uayrzUyb948PPHEEz79CvBnWyunT59GYmIihg4dipkzZ+LChQsAtO1vbmyqg8uXL8PlcsHhcPg873A48K9//UtSq8yhrq4OAHrte89rdO/cbjdeeuklTJgwAd/73vcAKH1utVoRHR3tcy37/N4dO3YMubm5aG9vh91ux9atWzFq1CgcOXKEfd3HysrKcOjQIRw4cOCW1/iz3fdycnKwceNGpKeno7a2Fq+//jomTpyI48ePa9rfDH6I6J7NmzcPx48f9xmjp76Xnp6OI0eOoLGxER9//DEKCgpQWVkpu1kBp6amBvPnz8fOnTsRFhYmuzmm8Pjjj6vnGRkZyMnJwZAhQ/Dhhx8iPDxcs3+Xw146iIuLQ3Bw8C0V6vX19YiPj5fUKnPw9C/7vu+9+OKL+Otf/4rdu3dj8ODB6vPx8fHo7OzEtWvXfK5nn987q9WKYcOGYdy4cSgpKUFmZiZ++9vfsq/7WHV1NRoaGjB27FiEhIQgJCQElZWVWL16NUJCQuBwONjfGouOjsaIESNw5swZTX++GfzowGq1Yty4cSgvL1efc7vdKC8vR25ursSWBb60tDTEx8f79H1TUxP27dvHvr9HQgi8+OKL2Lp1Kz777DOkpaX5vD5u3DiEhob69PmpU6dw4cIF9nkfcbvd6OjoYF/3sby8PBw7dgxHjhxRj6ysLMycOVM9Z39r6/r16zh79iwSEhK0/fm+r3JpumtlZWXCZrOJjRs3in/+85/i+eefF9HR0aKurk520/xec3OzOHz4sDh8+LAAIN566y1x+PBhcf78eSGEEKWlpSI6Olr85S9/EUePHhVPPfWUSEtLE21tbZJb7p9eeOEFERUVJSoqKkRtba16tLa2qtf84he/ECkpKeKzzz4TBw8eFLm5uSI3N1diq/3XkiVLRGVlpTh37pw4evSoWLJkibBYLGLHjh1CCPa11rxnewnB/u5rCxcuFBUVFeLcuXNiz549wul0iri4ONHQ0CCE0K6/Gfzo6J133hEpKSnCarWK7OxssXfvXtlNCgi7d+8WAG45CgoKhBDKdPdly5YJh8MhbDabyMvLE6dOnZLbaD/WW18DEH/84x/Va9ra2sTcuXNFTEyM6Nevn3j66adFbW2tvEb7sTlz5oghQ4YIq9UqBg4cKPLy8tTARwj2tdZuDn7Y331r+vTpIiEhQVitVpGUlCSmT58uzpw5o76uVX9bhBDi/nJHRERERP6DNT9ERERkKgx+iIiIyFQY/BAREZGpMPghIiIiU2HwQ0RERKbC4IeIiIhMhcEPERERmQqDHyKiu1BRUQGLxXLLPkNE5H8Y/BAREZGpMPghIiIiU2HwQ0R+we12o6SkBGlpaQgPD0dmZiY+/vhjAD1DUtu2bUNGRgbCwsLwgx/8AMePH/e5x5///GeMHj0aNpsNqampWLVqlc/rHR0dWLx4MZKTk2Gz2TBs2DD8/ve/97mmuroaWVlZ6NevH374wx/i1KlT2r5xIupzDH6IyC+UlJRg06ZNWL9+PU6cOIEFCxbgpz/9KSorK9VrfvWrX2HVqlU4cOAABg4ciCeffBJdXV0AlKBl2rRpmDFjBo4dO4bXXnsNy5Ytw8aNG9XvnzVrFjZv3ozVq1fj5MmTePfdd2G3233a8eqrr2LVqlU4ePAgQkJCMGfOHF3ePxH1HW5sSkSG19HRgdjYWOzatQu5ubnq8z/72c/Q2tqK559/Ho888gjKysowffp0AMA333yDwYMHY+PGjZg2bRpmzpyJS5cuYceOHer3v/zyy9i2bRtOnDiBL774Aunp6di5cyecTuctbaioqMAjjzyCXbt2IS8vDwDwt7/9DU888QTa2toQFhamcS8QUV9h5oeIDO/MmTNobW3FY489Brvdrh6bNm3C2bNn1eu8A6PY2Fikp6fj5MmTAICTJ09iwoQJPvedMGECTp8+DZfLhSNHjiA4OBiTJk26Y1syMjLU84SEBABAQ0PDfb9HItJPiOwGEBF9m+vXrwMAtm3bhqSkJJ/XbDabTwB0r8LDw+/qutDQUPXcYrEAUOqRiMh/MPNDRIY3atQo2Gw2XLhwAcOGDfM5kpOT1ev27t2rnl+9ehVffPEFHnzwQQDAgw8+iD179vjcd8+ePRgxYgSCg4Px0EMPwe12+9QQEVFgYuaHiAwvIiICixYtwoIFC+B2u/Hwww+jsbERe/bsQWRkJIYMGQIAeOONNzBgwAA4HA68+uqriIuLw9SpUwEACxcuxPjx41FcXIzp06ejqqoKa9aswe9+9zsAQGpqKgoKCjBnzhysXr0amZmZOH/+PBoaGjBt2jRZb52INMDgh4j8QnFxMQYOHIiSkhJ8+eWXiI6OxtixY7F06VJ12Km0tBTz58/H6dOnMWbMGHz66aewWq0AgLFjx+LDDz/E8uXLUVxcjISEBLzxxht47rnn1H9j3bp1WLp0KebOnYsrV64gJSUFS5culfF2iUhDnO1FRH7PMxPr6tWriI6Olt0cIjI41vwQERGRqTD4ISIiIlPhsBcRERGZCjM/REREZCoMfoiIiMhUGPwQERGRqTD4ISIiIlNh8ENERESmwuCHiIiITIXBDxEREZkKgx8iIiIyFQY/REREZCr/H7brsVcu2bs7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy of the complex CNN model: 0.3340857923030853\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Plotting accuracy trajectory\n",
    "plt.plot(complex_cnn_model_results.history['accuracy'])\n",
    "plt.plot(complex_cnn_model_results.history['val_accuracy'])\n",
    "plt.title('Complex CNN model accuracy trajectory')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plotting loss trajectory\n",
    "plt.plot(complex_cnn_model_results.history['loss'],'o')\n",
    "plt.plot(complex_cnn_model_results.history['val_loss'],'o')\n",
    "plt.title('Complex CNN model loss trajectory')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "## Testing the basic CNN model\n",
    "\n",
    "cnn_score = complex_cnn_model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test accuracy of the complex CNN model:',cnn_score[1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Residual Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 500, 1, 22)  0           []                               \n",
      "                                ]                                                                 \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)             (None, 500, 1, 64)   14144       ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " max_pooling2d_15 (MaxPooling2D  (None, 167, 1, 64)  0           ['conv2d_16[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " dropout_15 (Dropout)           (None, 167, 1, 64)   0           ['max_pooling2d_15[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)             (None, 167, 1, 64)   41024       ['dropout_15[0][0]']             \n",
      "                                                                                                  \n",
      " batch_normalization_15 (BatchN  (None, 167, 1, 64)  256         ['conv2d_17[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 167, 1, 64)   0           ['batch_normalization_15[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)             (None, 167, 1, 64)   41024       ['activation[0][0]']             \n",