Spaces:
Configuration error
Configuration error
File size: 99,037 Bytes
2bab8da |
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 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"id": "aac52aaf",
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.applications import InceptionV3\n",
"from tensorflow.keras.models import Model\n",
"from tensorflow.keras.layers import Dropout,Input,Flatten,Dense,MaxPooling2D\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator #data Augmentation"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "54ba6829",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\Ashu\\AppData\\Local\\Temp/ipykernel_948/337460670.py:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.config.list_physical_devices('GPU')` instead.\n"
]
},
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.test.is_gpu_available()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "5d25419e",
"metadata": {},
"outputs": [],
"source": [
"\n",
"batchsize=8"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "97b30652",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 61128 images belonging to 2 classes.\n",
"Found 15281 images belonging to 2 classes.\n"
]
}
],
"source": [
"train_datagen = ImageDataGenerator(rescale=1./255,rotation_range=0.2,shear_range=0.2,zoom_range=0.2,width_shift_range=0.2,height_shift_range=0.2\n",
" ,validation_split=0.2)\n",
"train_data = train_datagen.flow_from_directory(r'E:\\study\\Sem-7\\finalProject\\Drivers Drowsiness Detection using Deep Learning\\mrlEyes_2018_01\\Prepared_Data\\Train'\n",
" ,target_size=(80,80),batch_size=8,class_mode='categorical',subset='training')\n",
"validation_data = train_datagen.flow_from_directory(r'E:\\study\\Sem-7\\finalProject\\Drivers Drowsiness Detection using Deep Learning\\mrlEyes_2018_01\\Prepared_Data\\Train'\n",
" ,target_size=(80,80),batch_size=8,class_mode='categorical'\n",
" ,subset='validation')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "f6f3df3a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 8489 images belonging to 2 classes.\n"
]
}
],
"source": [
"test_datagen = ImageDataGenerator(rescale=1./255)\n",
"test_data = train_datagen.flow_from_directory(r'E:\\study\\Sem-7\\finalProject\\Drivers Drowsiness Detection using Deep Learning\\mrlEyes_2018_01\\Prepared_Data\\Test'\n",
" ,target_size=(80,80),batch_size=8,class_mode='categorical')\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "3e3d4286",
"metadata": {},
"outputs": [],
"source": [
"bmodel = InceptionV3(include_top = False, weights = 'imagenet',input_tensor = Input(shape = (80,80,3),batch_size=8 ))\n",
"hmodel = bmodel.output"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "022a0cde",
"metadata": {},
"outputs": [],
"source": [
"hmodel = Flatten()(hmodel)\n",
"hmodel = Dense(64,activation='relu')(hmodel)\n",
"hmodel = Dropout(0.5)(hmodel)\n",
"hmodel = Dense(2,activation= 'Softmax')(hmodel)\n",
"\n",
"model = Model(inputs = bmodel.input, outputs=hmodel)\n",
"for layer in bmodel.layers:\n",
" layer.trainable = False"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "ebfe3cd9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"model_1\"\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_5 (InputLayer) [(8, 80, 80, 3)] 0 \n",
"__________________________________________________________________________________________________\n",
"conv2d_376 (Conv2D) (8, 39, 39, 32) 864 input_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_376 (BatchN (8, 39, 39, 32) 96 conv2d_376[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_376 (Activation) (8, 39, 39, 32) 0 batch_normalization_376[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_377 (Conv2D) (8, 37, 37, 32) 9216 activation_376[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_377 (BatchN (8, 37, 37, 32) 96 conv2d_377[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_377 (Activation) (8, 37, 37, 32) 0 batch_normalization_377[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_378 (Conv2D) (8, 37, 37, 64) 18432 activation_377[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_378 (BatchN (8, 37, 37, 64) 192 conv2d_378[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_378 (Activation) (8, 37, 37, 64) 0 batch_normalization_378[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_16 (MaxPooling2D) (8, 18, 18, 64) 0 activation_378[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_379 (Conv2D) (8, 18, 18, 80) 5120 max_pooling2d_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_379 (BatchN (8, 18, 18, 80) 240 conv2d_379[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_379 (Activation) (8, 18, 18, 80) 0 batch_normalization_379[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_380 (Conv2D) (8, 16, 16, 192) 138240 activation_379[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_380 (BatchN (8, 16, 16, 192) 576 conv2d_380[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_380 (Activation) (8, 16, 16, 192) 0 batch_normalization_380[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_17 (MaxPooling2D) (8, 7, 7, 192) 0 activation_380[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_384 (Conv2D) (8, 7, 7, 64) 12288 max_pooling2d_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_384 (BatchN (8, 7, 7, 64) 192 conv2d_384[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_384 (Activation) (8, 7, 7, 64) 0 batch_normalization_384[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_382 (Conv2D) (8, 7, 7, 48) 9216 max_pooling2d_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_385 (Conv2D) (8, 7, 7, 96) 55296 activation_384[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_382 (BatchN (8, 7, 7, 48) 144 conv2d_382[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_385 (BatchN (8, 7, 7, 96) 288 conv2d_385[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_382 (Activation) (8, 7, 7, 48) 0 batch_normalization_382[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_385 (Activation) (8, 7, 7, 96) 0 batch_normalization_385[0][0] \n",
"__________________________________________________________________________________________________\n",
"average_pooling2d_36 (AveragePo (8, 7, 7, 192) 0 max_pooling2d_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_381 (Conv2D) (8, 7, 7, 64) 12288 max_pooling2d_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_383 (Conv2D) (8, 7, 7, 64) 76800 activation_382[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_386 (Conv2D) (8, 7, 7, 96) 82944 activation_385[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_387 (Conv2D) (8, 7, 7, 32) 6144 average_pooling2d_36[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_381 (BatchN (8, 7, 7, 64) 192 conv2d_381[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_383 (BatchN (8, 7, 7, 64) 192 conv2d_383[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_386 (BatchN (8, 7, 7, 96) 288 conv2d_386[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_387 (BatchN (8, 7, 7, 32) 96 conv2d_387[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_381 (Activation) (8, 7, 7, 64) 0 batch_normalization_381[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_383 (Activation) (8, 7, 7, 64) 0 batch_normalization_383[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_386 (Activation) (8, 7, 7, 96) 0 batch_normalization_386[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_387 (Activation) (8, 7, 7, 32) 0 batch_normalization_387[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed0 (Concatenate) (8, 7, 7, 256) 0 activation_381[0][0] \n",
" activation_383[0][0] \n",
" activation_386[0][0] \n",
" activation_387[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_391 (Conv2D) (8, 7, 7, 64) 16384 mixed0[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_391 (BatchN (8, 7, 7, 64) 192 conv2d_391[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_391 (Activation) (8, 7, 7, 64) 0 batch_normalization_391[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_389 (Conv2D) (8, 7, 7, 48) 12288 mixed0[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_392 (Conv2D) (8, 7, 7, 96) 55296 activation_391[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_389 (BatchN (8, 7, 7, 48) 144 conv2d_389[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_392 (BatchN (8, 7, 7, 96) 288 conv2d_392[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_389 (Activation) (8, 7, 7, 48) 0 batch_normalization_389[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_392 (Activation) (8, 7, 7, 96) 0 batch_normalization_392[0][0] \n",
"__________________________________________________________________________________________________\n",
"average_pooling2d_37 (AveragePo (8, 7, 7, 256) 0 mixed0[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_388 (Conv2D) (8, 7, 7, 64) 16384 mixed0[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_390 (Conv2D) (8, 7, 7, 64) 76800 activation_389[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_393 (Conv2D) (8, 7, 7, 96) 82944 activation_392[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_394 (Conv2D) (8, 7, 7, 64) 16384 average_pooling2d_37[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_388 (BatchN (8, 7, 7, 64) 192 conv2d_388[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_390 (BatchN (8, 7, 7, 64) 192 conv2d_390[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_393 (BatchN (8, 7, 7, 96) 288 conv2d_393[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_394 (BatchN (8, 7, 7, 64) 192 conv2d_394[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_388 (Activation) (8, 7, 7, 64) 0 batch_normalization_388[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_390 (Activation) (8, 7, 7, 64) 0 batch_normalization_390[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_393 (Activation) (8, 7, 7, 96) 0 batch_normalization_393[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_394 (Activation) (8, 7, 7, 64) 0 batch_normalization_394[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed1 (Concatenate) (8, 7, 7, 288) 0 activation_388[0][0] \n",
" activation_390[0][0] \n",
" activation_393[0][0] \n",
" activation_394[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_398 (Conv2D) (8, 7, 7, 64) 18432 mixed1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_398 (BatchN (8, 7, 7, 64) 192 conv2d_398[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_398 (Activation) (8, 7, 7, 64) 0 batch_normalization_398[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_396 (Conv2D) (8, 7, 7, 48) 13824 mixed1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_399 (Conv2D) (8, 7, 7, 96) 55296 activation_398[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_396 (BatchN (8, 7, 7, 48) 144 conv2d_396[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_399 (BatchN (8, 7, 7, 96) 288 conv2d_399[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_396 (Activation) (8, 7, 7, 48) 0 batch_normalization_396[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_399 (Activation) (8, 7, 7, 96) 0 batch_normalization_399[0][0] \n",
"__________________________________________________________________________________________________\n",
"average_pooling2d_38 (AveragePo (8, 7, 7, 288) 0 mixed1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_395 (Conv2D) (8, 7, 7, 64) 18432 mixed1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_397 (Conv2D) (8, 7, 7, 64) 76800 activation_396[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_400 (Conv2D) (8, 7, 7, 96) 82944 activation_399[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_401 (Conv2D) (8, 7, 7, 64) 18432 average_pooling2d_38[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_395 (BatchN (8, 7, 7, 64) 192 conv2d_395[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_397 (BatchN (8, 7, 7, 64) 192 conv2d_397[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_400 (BatchN (8, 7, 7, 96) 288 conv2d_400[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_401 (BatchN (8, 7, 7, 64) 192 conv2d_401[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_395 (Activation) (8, 7, 7, 64) 0 batch_normalization_395[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_397 (Activation) (8, 7, 7, 64) 0 batch_normalization_397[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_400 (Activation) (8, 7, 7, 96) 0 batch_normalization_400[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_401 (Activation) (8, 7, 7, 64) 0 batch_normalization_401[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed2 (Concatenate) (8, 7, 7, 288) 0 activation_395[0][0] \n",
" activation_397[0][0] \n",
" activation_400[0][0] \n",
" activation_401[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_403 (Conv2D) (8, 7, 7, 64) 18432 mixed2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_403 (BatchN (8, 7, 7, 64) 192 conv2d_403[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_403 (Activation) (8, 7, 7, 64) 0 batch_normalization_403[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_404 (Conv2D) (8, 7, 7, 96) 55296 activation_403[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_404 (BatchN (8, 7, 7, 96) 288 conv2d_404[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_404 (Activation) (8, 7, 7, 96) 0 batch_normalization_404[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_402 (Conv2D) (8, 3, 3, 384) 995328 mixed2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_405 (Conv2D) (8, 3, 3, 96) 82944 activation_404[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_402 (BatchN (8, 3, 3, 384) 1152 conv2d_402[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_405 (BatchN (8, 3, 3, 96) 288 conv2d_405[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_402 (Activation) (8, 3, 3, 384) 0 batch_normalization_402[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_405 (Activation) (8, 3, 3, 96) 0 batch_normalization_405[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_18 (MaxPooling2D) (8, 3, 3, 288) 0 mixed2[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed3 (Concatenate) (8, 3, 3, 768) 0 activation_402[0][0] \n",
" activation_405[0][0] \n",
" max_pooling2d_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_410 (Conv2D) (8, 3, 3, 128) 98304 mixed3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_410 (BatchN (8, 3, 3, 128) 384 conv2d_410[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_410 (Activation) (8, 3, 3, 128) 0 batch_normalization_410[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_411 (Conv2D) (8, 3, 3, 128) 114688 activation_410[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_411 (BatchN (8, 3, 3, 128) 384 conv2d_411[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_411 (Activation) (8, 3, 3, 128) 0 batch_normalization_411[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_407 (Conv2D) (8, 3, 3, 128) 98304 mixed3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_412 (Conv2D) (8, 3, 3, 128) 114688 activation_411[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_407 (BatchN (8, 3, 3, 128) 384 conv2d_407[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_412 (BatchN (8, 3, 3, 128) 384 conv2d_412[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_407 (Activation) (8, 3, 3, 128) 0 batch_normalization_407[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_412 (Activation) (8, 3, 3, 128) 0 batch_normalization_412[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_408 (Conv2D) (8, 3, 3, 128) 114688 activation_407[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_413 (Conv2D) (8, 3, 3, 128) 114688 activation_412[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_408 (BatchN (8, 3, 3, 128) 384 conv2d_408[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_413 (BatchN (8, 3, 3, 128) 384 conv2d_413[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_408 (Activation) (8, 3, 3, 128) 0 batch_normalization_408[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_413 (Activation) (8, 3, 3, 128) 0 batch_normalization_413[0][0] \n",
"__________________________________________________________________________________________________\n",
"average_pooling2d_39 (AveragePo (8, 3, 3, 768) 0 mixed3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_406 (Conv2D) (8, 3, 3, 192) 147456 mixed3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_409 (Conv2D) (8, 3, 3, 192) 172032 activation_408[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_414 (Conv2D) (8, 3, 3, 192) 172032 activation_413[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_415 (Conv2D) (8, 3, 3, 192) 147456 average_pooling2d_39[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_406 (BatchN (8, 3, 3, 192) 576 conv2d_406[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_409 (BatchN (8, 3, 3, 192) 576 conv2d_409[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_414 (BatchN (8, 3, 3, 192) 576 conv2d_414[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_415 (BatchN (8, 3, 3, 192) 576 conv2d_415[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_406 (Activation) (8, 3, 3, 192) 0 batch_normalization_406[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_409 (Activation) (8, 3, 3, 192) 0 batch_normalization_409[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_414 (Activation) (8, 3, 3, 192) 0 batch_normalization_414[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_415 (Activation) (8, 3, 3, 192) 0 batch_normalization_415[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed4 (Concatenate) (8, 3, 3, 768) 0 activation_406[0][0] \n",
" activation_409[0][0] \n",
" activation_414[0][0] \n",
" activation_415[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_420 (Conv2D) (8, 3, 3, 160) 122880 mixed4[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_420 (BatchN (8, 3, 3, 160) 480 conv2d_420[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_420 (Activation) (8, 3, 3, 160) 0 batch_normalization_420[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_421 (Conv2D) (8, 3, 3, 160) 179200 activation_420[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_421 (BatchN (8, 3, 3, 160) 480 conv2d_421[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_421 (Activation) (8, 3, 3, 160) 0 batch_normalization_421[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_417 (Conv2D) (8, 3, 3, 160) 122880 mixed4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_422 (Conv2D) (8, 3, 3, 160) 179200 activation_421[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_417 (BatchN (8, 3, 3, 160) 480 conv2d_417[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_422 (BatchN (8, 3, 3, 160) 480 conv2d_422[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_417 (Activation) (8, 3, 3, 160) 0 batch_normalization_417[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_422 (Activation) (8, 3, 3, 160) 0 batch_normalization_422[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_418 (Conv2D) (8, 3, 3, 160) 179200 activation_417[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_423 (Conv2D) (8, 3, 3, 160) 179200 activation_422[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_418 (BatchN (8, 3, 3, 160) 480 conv2d_418[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_423 (BatchN (8, 3, 3, 160) 480 conv2d_423[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_418 (Activation) (8, 3, 3, 160) 0 batch_normalization_418[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_423 (Activation) (8, 3, 3, 160) 0 batch_normalization_423[0][0] \n",
"__________________________________________________________________________________________________\n",
"average_pooling2d_40 (AveragePo (8, 3, 3, 768) 0 mixed4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_416 (Conv2D) (8, 3, 3, 192) 147456 mixed4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_419 (Conv2D) (8, 3, 3, 192) 215040 activation_418[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_424 (Conv2D) (8, 3, 3, 192) 215040 activation_423[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_425 (Conv2D) (8, 3, 3, 192) 147456 average_pooling2d_40[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_416 (BatchN (8, 3, 3, 192) 576 conv2d_416[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_419 (BatchN (8, 3, 3, 192) 576 conv2d_419[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_424 (BatchN (8, 3, 3, 192) 576 conv2d_424[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_425 (BatchN (8, 3, 3, 192) 576 conv2d_425[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_416 (Activation) (8, 3, 3, 192) 0 batch_normalization_416[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_419 (Activation) (8, 3, 3, 192) 0 batch_normalization_419[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_424 (Activation) (8, 3, 3, 192) 0 batch_normalization_424[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_425 (Activation) (8, 3, 3, 192) 0 batch_normalization_425[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed5 (Concatenate) (8, 3, 3, 768) 0 activation_416[0][0] \n",
" activation_419[0][0] \n",
" activation_424[0][0] \n",
" activation_425[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_430 (Conv2D) (8, 3, 3, 160) 122880 mixed5[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_430 (BatchN (8, 3, 3, 160) 480 conv2d_430[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_430 (Activation) (8, 3, 3, 160) 0 batch_normalization_430[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_431 (Conv2D) (8, 3, 3, 160) 179200 activation_430[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_431 (BatchN (8, 3, 3, 160) 480 conv2d_431[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_431 (Activation) (8, 3, 3, 160) 0 batch_normalization_431[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_427 (Conv2D) (8, 3, 3, 160) 122880 mixed5[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_432 (Conv2D) (8, 3, 3, 160) 179200 activation_431[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_427 (BatchN (8, 3, 3, 160) 480 conv2d_427[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_432 (BatchN (8, 3, 3, 160) 480 conv2d_432[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_427 (Activation) (8, 3, 3, 160) 0 batch_normalization_427[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_432 (Activation) (8, 3, 3, 160) 0 batch_normalization_432[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_428 (Conv2D) (8, 3, 3, 160) 179200 activation_427[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_433 (Conv2D) (8, 3, 3, 160) 179200 activation_432[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_428 (BatchN (8, 3, 3, 160) 480 conv2d_428[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_433 (BatchN (8, 3, 3, 160) 480 conv2d_433[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_428 (Activation) (8, 3, 3, 160) 0 batch_normalization_428[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_433 (Activation) (8, 3, 3, 160) 0 batch_normalization_433[0][0] \n",
"__________________________________________________________________________________________________\n",
"average_pooling2d_41 (AveragePo (8, 3, 3, 768) 0 mixed5[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_426 (Conv2D) (8, 3, 3, 192) 147456 mixed5[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_429 (Conv2D) (8, 3, 3, 192) 215040 activation_428[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_434 (Conv2D) (8, 3, 3, 192) 215040 activation_433[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_435 (Conv2D) (8, 3, 3, 192) 147456 average_pooling2d_41[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_426 (BatchN (8, 3, 3, 192) 576 conv2d_426[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_429 (BatchN (8, 3, 3, 192) 576 conv2d_429[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_434 (BatchN (8, 3, 3, 192) 576 conv2d_434[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_435 (BatchN (8, 3, 3, 192) 576 conv2d_435[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_426 (Activation) (8, 3, 3, 192) 0 batch_normalization_426[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_429 (Activation) (8, 3, 3, 192) 0 batch_normalization_429[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_434 (Activation) (8, 3, 3, 192) 0 batch_normalization_434[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_435 (Activation) (8, 3, 3, 192) 0 batch_normalization_435[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed6 (Concatenate) (8, 3, 3, 768) 0 activation_426[0][0] \n",
" activation_429[0][0] \n",
" activation_434[0][0] \n",
" activation_435[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_440 (Conv2D) (8, 3, 3, 192) 147456 mixed6[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_440 (BatchN (8, 3, 3, 192) 576 conv2d_440[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_440 (Activation) (8, 3, 3, 192) 0 batch_normalization_440[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_441 (Conv2D) (8, 3, 3, 192) 258048 activation_440[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_441 (BatchN (8, 3, 3, 192) 576 conv2d_441[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_441 (Activation) (8, 3, 3, 192) 0 batch_normalization_441[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_437 (Conv2D) (8, 3, 3, 192) 147456 mixed6[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_442 (Conv2D) (8, 3, 3, 192) 258048 activation_441[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_437 (BatchN (8, 3, 3, 192) 576 conv2d_437[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_442 (BatchN (8, 3, 3, 192) 576 conv2d_442[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_437 (Activation) (8, 3, 3, 192) 0 batch_normalization_437[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_442 (Activation) (8, 3, 3, 192) 0 batch_normalization_442[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_438 (Conv2D) (8, 3, 3, 192) 258048 activation_437[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_443 (Conv2D) (8, 3, 3, 192) 258048 activation_442[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_438 (BatchN (8, 3, 3, 192) 576 conv2d_438[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_443 (BatchN (8, 3, 3, 192) 576 conv2d_443[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_438 (Activation) (8, 3, 3, 192) 0 batch_normalization_438[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_443 (Activation) (8, 3, 3, 192) 0 batch_normalization_443[0][0] \n",
"__________________________________________________________________________________________________\n",
"average_pooling2d_42 (AveragePo (8, 3, 3, 768) 0 mixed6[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_436 (Conv2D) (8, 3, 3, 192) 147456 mixed6[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_439 (Conv2D) (8, 3, 3, 192) 258048 activation_438[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_444 (Conv2D) (8, 3, 3, 192) 258048 activation_443[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_445 (Conv2D) (8, 3, 3, 192) 147456 average_pooling2d_42[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_436 (BatchN (8, 3, 3, 192) 576 conv2d_436[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_439 (BatchN (8, 3, 3, 192) 576 conv2d_439[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_444 (BatchN (8, 3, 3, 192) 576 conv2d_444[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_445 (BatchN (8, 3, 3, 192) 576 conv2d_445[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_436 (Activation) (8, 3, 3, 192) 0 batch_normalization_436[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_439 (Activation) (8, 3, 3, 192) 0 batch_normalization_439[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_444 (Activation) (8, 3, 3, 192) 0 batch_normalization_444[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_445 (Activation) (8, 3, 3, 192) 0 batch_normalization_445[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed7 (Concatenate) (8, 3, 3, 768) 0 activation_436[0][0] \n",
" activation_439[0][0] \n",
" activation_444[0][0] \n",
" activation_445[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_448 (Conv2D) (8, 3, 3, 192) 147456 mixed7[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_448 (BatchN (8, 3, 3, 192) 576 conv2d_448[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_448 (Activation) (8, 3, 3, 192) 0 batch_normalization_448[0][0] \n",
"__________________________________________________________________________________________________\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"conv2d_449 (Conv2D) (8, 3, 3, 192) 258048 activation_448[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_449 (BatchN (8, 3, 3, 192) 576 conv2d_449[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_449 (Activation) (8, 3, 3, 192) 0 batch_normalization_449[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_446 (Conv2D) (8, 3, 3, 192) 147456 mixed7[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_450 (Conv2D) (8, 3, 3, 192) 258048 activation_449[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_446 (BatchN (8, 3, 3, 192) 576 conv2d_446[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_450 (BatchN (8, 3, 3, 192) 576 conv2d_450[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_446 (Activation) (8, 3, 3, 192) 0 batch_normalization_446[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_450 (Activation) (8, 3, 3, 192) 0 batch_normalization_450[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_447 (Conv2D) (8, 1, 1, 320) 552960 activation_446[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_451 (Conv2D) (8, 1, 1, 192) 331776 activation_450[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_447 (BatchN (8, 1, 1, 320) 960 conv2d_447[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_451 (BatchN (8, 1, 1, 192) 576 conv2d_451[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_447 (Activation) (8, 1, 1, 320) 0 batch_normalization_447[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_451 (Activation) (8, 1, 1, 192) 0 batch_normalization_451[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_19 (MaxPooling2D) (8, 1, 1, 768) 0 mixed7[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed8 (Concatenate) (8, 1, 1, 1280) 0 activation_447[0][0] \n",
" activation_451[0][0] \n",
" max_pooling2d_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_456 (Conv2D) (8, 1, 1, 448) 573440 mixed8[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_456 (BatchN (8, 1, 1, 448) 1344 conv2d_456[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_456 (Activation) (8, 1, 1, 448) 0 batch_normalization_456[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_453 (Conv2D) (8, 1, 1, 384) 491520 mixed8[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_457 (Conv2D) (8, 1, 1, 384) 1548288 activation_456[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_453 (BatchN (8, 1, 1, 384) 1152 conv2d_453[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_457 (BatchN (8, 1, 1, 384) 1152 conv2d_457[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_453 (Activation) (8, 1, 1, 384) 0 batch_normalization_453[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_457 (Activation) (8, 1, 1, 384) 0 batch_normalization_457[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_454 (Conv2D) (8, 1, 1, 384) 442368 activation_453[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_455 (Conv2D) (8, 1, 1, 384) 442368 activation_453[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_458 (Conv2D) (8, 1, 1, 384) 442368 activation_457[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_459 (Conv2D) (8, 1, 1, 384) 442368 activation_457[0][0] \n",
"__________________________________________________________________________________________________\n",
"average_pooling2d_43 (AveragePo (8, 1, 1, 1280) 0 mixed8[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_452 (Conv2D) (8, 1, 1, 320) 409600 mixed8[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_454 (BatchN (8, 1, 1, 384) 1152 conv2d_454[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_455 (BatchN (8, 1, 1, 384) 1152 conv2d_455[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_458 (BatchN (8, 1, 1, 384) 1152 conv2d_458[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_459 (BatchN (8, 1, 1, 384) 1152 conv2d_459[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_460 (Conv2D) (8, 1, 1, 192) 245760 average_pooling2d_43[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_452 (BatchN (8, 1, 1, 320) 960 conv2d_452[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_454 (Activation) (8, 1, 1, 384) 0 batch_normalization_454[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_455 (Activation) (8, 1, 1, 384) 0 batch_normalization_455[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_458 (Activation) (8, 1, 1, 384) 0 batch_normalization_458[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_459 (Activation) (8, 1, 1, 384) 0 batch_normalization_459[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_460 (BatchN (8, 1, 1, 192) 576 conv2d_460[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_452 (Activation) (8, 1, 1, 320) 0 batch_normalization_452[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed9_0 (Concatenate) (8, 1, 1, 768) 0 activation_454[0][0] \n",
" activation_455[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_8 (Concatenate) (8, 1, 1, 768) 0 activation_458[0][0] \n",
" activation_459[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_460 (Activation) (8, 1, 1, 192) 0 batch_normalization_460[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed9 (Concatenate) (8, 1, 1, 2048) 0 activation_452[0][0] \n",
" mixed9_0[0][0] \n",
" concatenate_8[0][0] \n",
" activation_460[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_465 (Conv2D) (8, 1, 1, 448) 917504 mixed9[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_465 (BatchN (8, 1, 1, 448) 1344 conv2d_465[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_465 (Activation) (8, 1, 1, 448) 0 batch_normalization_465[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_462 (Conv2D) (8, 1, 1, 384) 786432 mixed9[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_466 (Conv2D) (8, 1, 1, 384) 1548288 activation_465[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_462 (BatchN (8, 1, 1, 384) 1152 conv2d_462[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_466 (BatchN (8, 1, 1, 384) 1152 conv2d_466[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_462 (Activation) (8, 1, 1, 384) 0 batch_normalization_462[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_466 (Activation) (8, 1, 1, 384) 0 batch_normalization_466[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_463 (Conv2D) (8, 1, 1, 384) 442368 activation_462[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_464 (Conv2D) (8, 1, 1, 384) 442368 activation_462[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_467 (Conv2D) (8, 1, 1, 384) 442368 activation_466[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_468 (Conv2D) (8, 1, 1, 384) 442368 activation_466[0][0] \n",
"__________________________________________________________________________________________________\n",
"average_pooling2d_44 (AveragePo (8, 1, 1, 2048) 0 mixed9[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_461 (Conv2D) (8, 1, 1, 320) 655360 mixed9[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_463 (BatchN (8, 1, 1, 384) 1152 conv2d_463[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_464 (BatchN (8, 1, 1, 384) 1152 conv2d_464[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_467 (BatchN (8, 1, 1, 384) 1152 conv2d_467[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_468 (BatchN (8, 1, 1, 384) 1152 conv2d_468[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_469 (Conv2D) (8, 1, 1, 192) 393216 average_pooling2d_44[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_461 (BatchN (8, 1, 1, 320) 960 conv2d_461[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_463 (Activation) (8, 1, 1, 384) 0 batch_normalization_463[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_464 (Activation) (8, 1, 1, 384) 0 batch_normalization_464[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_467 (Activation) (8, 1, 1, 384) 0 batch_normalization_467[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_468 (Activation) (8, 1, 1, 384) 0 batch_normalization_468[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_469 (BatchN (8, 1, 1, 192) 576 conv2d_469[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_461 (Activation) (8, 1, 1, 320) 0 batch_normalization_461[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed9_1 (Concatenate) (8, 1, 1, 768) 0 activation_463[0][0] \n",
" activation_464[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_9 (Concatenate) (8, 1, 1, 768) 0 activation_467[0][0] \n",
" activation_468[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_469 (Activation) (8, 1, 1, 192) 0 batch_normalization_469[0][0] \n",
"__________________________________________________________________________________________________\n",
"mixed10 (Concatenate) (8, 1, 1, 2048) 0 activation_461[0][0] \n",
" mixed9_1[0][0] \n",
" concatenate_9[0][0] \n",
" activation_469[0][0] \n",
"__________________________________________________________________________________________________\n",
"flatten_2 (Flatten) (8, 2048) 0 mixed10[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_4 (Dense) (8, 64) 131136 flatten_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_2 (Dropout) (8, 64) 0 dense_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_5 (Dense) (8, 2) 130 dropout_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"flatten_3 (Flatten) (8, 2) 0 dense_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_6 (Dense) (8, 64) 192 flatten_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_3 (Dropout) (8, 64) 0 dense_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_7 (Dense) (8, 2) 130 dropout_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"flatten_4 (Flatten) (8, 2) 0 dense_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_8 (Dense) (8, 64) 192 flatten_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_4 (Dropout) (8, 64) 0 dense_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_9 (Dense) (8, 2) 130 dropout_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"flatten_5 (Flatten) (8, 2) 0 dense_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_10 (Dense) (8, 64) 192 flatten_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_5 (Dropout) (8, 64) 0 dense_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_11 (Dense) (8, 2) 130 dropout_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"flatten_6 (Flatten) (8, 2) 0 dense_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_12 (Dense) (8, 64) 192 flatten_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_6 (Dropout) (8, 64) 0 dense_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_13 (Dense) (8, 2) 130 dropout_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"flatten_7 (Flatten) (8, 2) 0 dense_13[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_14 (Dense) (8, 64) 192 flatten_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_7 (Dropout) (8, 64) 0 dense_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_15 (Dense) (8, 2) 130 dropout_7[0][0] \n",
"==================================================================================================\n",
"Total params: 21,935,660\n",
"Trainable params: 132,876\n",
"Non-trainable params: 21,802,784\n",
"__________________________________________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "225b6a54",
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, ReduceLROnPlateau"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "c0fbd7f6",
"metadata": {},
"outputs": [],
"source": [
"checkpoint = ModelCheckpoint(r'E:\\study\\Sem-7\\finalProject\\Drivers Drowsiness Detection using Deep Learning\\Models',\n",
" monitor = 'val_loss',save_best_only = True,verbose = 3)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "5c998a2c",
"metadata": {},
"outputs": [],
"source": [
"earlystop = EarlyStopping(monitor = 'val_loss' , patience=7,verbose = 3, restore_best_weights=True)\n",
"learning_rate= ReduceLROnPlateau(monitor='val_loss',patience=3,verbose=3)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "e658c8c1",
"metadata": {},
"outputs": [],
"source": [
"callbacks=[checkpoint,earlystop,learning_rate]\n"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "99ab0e6b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:1940: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
" warnings.warn('`Model.fit_generator` is deprecated and '\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/50\n",
"7641/7641 [==============================] - 2656s 347ms/step - loss: 0.4920 - accuracy: 0.7714 - val_loss: 0.4017 - val_accuracy: 0.8552\n",
"\n",
"Epoch 00001: val_loss improved from inf to 0.40173, saving model to E:\\study\\Sem-7\\finalProject\\Drivers Drowsiness Detection using Deep Learning\\Models\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:Found untraced functions such as softmax_2_layer_call_fn, softmax_2_layer_call_and_return_conditional_losses, softmax_3_layer_call_fn, softmax_3_layer_call_and_return_conditional_losses, softmax_4_layer_call_fn while saving (showing 5 of 30). These functions will not be directly callable after loading.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: E:\\study\\Sem-7\\finalProject\\Drivers Drowsiness Detection using Deep Learning\\Models\\assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: E:\\study\\Sem-7\\finalProject\\Drivers Drowsiness Detection using Deep Learning\\Models\\assets\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 2/50\n",
"7641/7641 [==============================] - 2455s 321ms/step - loss: 0.4202 - accuracy: 0.8507 - val_loss: 0.4206 - val_accuracy: 0.8512\n",
"\n",
"Epoch 00002: val_loss did not improve from 0.40173\n",
"Epoch 3/50\n",
"7641/7641 [==============================] - 3070s 402ms/step - loss: 0.3943 - accuracy: 0.8605 - val_loss: 0.3772 - val_accuracy: 0.8706\n",
"\n",
"Epoch 00003: val_loss improved from 0.40173 to 0.37724, saving model to E:\\study\\Sem-7\\finalProject\\Drivers Drowsiness Detection using Deep Learning\\Models\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:Found untraced functions such as softmax_2_layer_call_fn, softmax_2_layer_call_and_return_conditional_losses, softmax_3_layer_call_fn, softmax_3_layer_call_and_return_conditional_losses, softmax_4_layer_call_fn while saving (showing 5 of 30). These functions will not be directly callable after loading.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: E:\\study\\Sem-7\\finalProject\\Drivers Drowsiness Detection using Deep Learning\\Models\\assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: E:\\study\\Sem-7\\finalProject\\Drivers Drowsiness Detection using Deep Learning\\Models\\assets\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 4/50\n",
" 577/7641 [=>............................] - ETA: 59:35 - loss: 0.3983 - accuracy: 0.8681"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_948/567684715.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Adam'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'categorical_crossentropy'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m model.fit_generator(train_data,steps_per_epoch=train_data.samples//batchsize,\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msamples\u001b[0m\u001b[1;33m//\u001b[0m\u001b[0mbatchsize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m 1941\u001b[0m \u001b[1;34m'will be removed in a future version. '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1942\u001b[0m 'Please use `Model.fit`, which supports generators.')\n\u001b[1;32m-> 1943\u001b[1;33m return self.fit(\n\u001b[0m\u001b[0;32m 1944\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1945\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m 1181\u001b[0m _r=1):\n\u001b[0;32m 1182\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1183\u001b[1;33m \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1184\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1185\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 887\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 888\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 889\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 891\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 915\u001b[0m \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 916\u001b[0m \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 917\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 918\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 919\u001b[0m \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 3021\u001b[0m (graph_function,\n\u001b[0;32m 3022\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m-> 3023\u001b[1;33m return graph_function._call_flat(\n\u001b[0m\u001b[0;32m 3024\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[0;32m 3025\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m 1958\u001b[0m and executing_eagerly):\n\u001b[0;32m 1959\u001b[0m \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1960\u001b[1;33m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m 1961\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m 1962\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m 589\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 590\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 591\u001b[1;33m outputs = execute.execute(\n\u001b[0m\u001b[0;32m 592\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 593\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m 60\u001b[0m inputs, attrs, num_outputs)\n\u001b[0;32m 61\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"model.compile(optimizer='Adam', loss='categorical_crossentropy',metrics=['accuracy'])\n",
"\n",
"model.fit_generator(train_data,steps_per_epoch=train_data.samples//batchsize,\n",
" validation_data=validation_data,\n",
" validation_steps=validation_data.samples//batchsize,\n",
" callbacks=callbacks,\n",
" epochs=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87271003",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "07d5b272",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|