Train_VCTK_Keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
    "from tensorflow.keras.utils import Sequence, to_categorical\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from models.vggish import VGGish\n",
    "from utils import apply_melspectrogram_to_file\n",
    "import math\n",
    "import numpy as np\n",
    "from models.model_keras_dropout import vgg_att\n",
    "from dataloaders.DatagenVoxCeleb import get_keras_datagens"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '/media/data/bbekci/voxceleb2/data/dev/pkls/'\n",
    "tr_txt = 'txts/tr_voxceleb_video_pkl_paths.txt'\n",
    "val_txt = 'txts/val_voxceleb_video_pkl_paths.txt' \n",
    "batch_size = 32"
=======
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_per_person = 10\n",
    "batch_size = 64\n",
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 14,
>>>>>>> 90355144ce4a7d9932e7274ea8bfbec292c5288f
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      "(32, 300, 40)\n",
      "(32,)\n",
      "[2294 3948 4615 1574  895 1372 4011  953 4626 3281 5046 1608 5479  423\n",
      " 5978 3912 3937 1279 1736 1776  199 2369 3253 3875 5321 5246 1519 4917\n",
      "  476 4864 3615 2043]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[4786 2930 3706  708 2729 3284 2379  386 3209 4378 2932 1065 3705 4294\n",
      " 2543 3480 3212 1096 1276 1647  296 3358 2869  452 5359 4246  217 3913\n",
      " 2562 5458  722  271]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[5231 3077  638 4236  684 4811 2340 1711 1529 2670 5241 2441 4323 4454\n",
      " 4932  949 3732 1162 2303 1735 1286 1550 1206 2597 5475  374 5359 4260\n",
      " 2299 4582 5812  540]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[4385 2804 4568 1458 5795 5045 4435 3505  333 5505 2574   73  297 1435\n",
      " 1235  145 3353  957  264 5515 4702 3003 4582 5405 4100   66 4318 5746\n",
      " 2945  278 5961 5214]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[ 634 3600  150 4284 3940 5927  678 5864 2909 4071   75  321 2792 3570\n",
      " 3684  873  935  399 2643 4663  778   14  792 1087 1635 3153 4206 4977\n",
      " 2522  945 3775  200]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[2755 2963  722 4825 1496  913 3147 1605 5418 1351 1624 5974  666 5877\n",
      "  752 4059 5890 2279 1292 1178 2138  444  818  302 5446 5079  178 3121\n",
      "  651 4236 1654 1526]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[5991 2766 2676 5237 3394 2436  634 5548 4894 5152 4117  535 5859 3103\n",
      " 3913 3568  728 2756 5001  634 5004 4125 5511 3099 4309 2374 3322 4704\n",
      " 5078 1519 5433 5081]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[ 207 4008 4204  533  708 4729 2042 3947 2001  780 3408 1905 1655 4009\n",
      "  920 3961 4301 3717 2997 4932 1188 5477 2312  638 3033 4710 1336 2665\n",
      " 1087  387  628 3209]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[ 294 1534 3437 5799 3804 3799 4602 1545 3803 4847 4614 3918 4164 4580\n",
      " 2542 2923 2213 1661 2209 1688 1096 2109  117 3110 4033 4147 4302  950\n",
      " 2815 2250 4789 3708]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1161 3240   17 2386 2645 5650 4380 2310  715 2690 5166 5120  825  204\n",
      " 2843  705 3575 1159 4170 4507 4283 1458 4679 3134 4508 2498 2954 4007\n",
      " 5312 3398  400  651]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[2091 2504  679 1083 1169 1774  245 4941  994 4524 5580 5154 2096 1770\n",
      "  560 1398 5472 2084 4282 2772   89 4093 1736 1495 2608 4955 4424 4694\n",
      " 5226 5059 2294 1366]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1949 5378 1852 1306  137  301 1832  306 3095 5473 4356  603 2668 5400\n",
      " 5638 5384 3041 5239 3065 2525 2403 4874 5704 4584 2285 3926 2450 4210\n",
      " 4084  397 3326 3512]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[5076 3205 3806 1100 1439 2772 5558 4131 1599 4062 1426 5952 4425 5897\n",
      " 4690 3607 5359  124 4867 3745 5046 3013 1577 1635 4146 3883 2050 2642\n",
      " 1918 4083  440 3374]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1526 2685 4633 1977 4800 2790 1291 1846 1243 2007 3848  220 2450 4702\n",
      " 2639   95  213 5060  816 4132 5337 4509  501 2562 2667  404 3263 1041\n",
      " 2433 5858 2522 3913]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1263 5384 1220 2572 2878 3625 3303 3392 1713 1832 5525 5610 5274 1151\n",
      " 5352 1865 5758  592 3065 2608 1597 2043 3905 4308 4283   32 3238 4852\n",
      " 4580 3588 5489 3355]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1913 1620 1081 3014 4811 3598 1037 5039 5480 5159  231 4908 2498 5356\n",
      " 2129 2001 1670  519 1114 4448 3646 4617 4159 5020  464 1935 1172 3618\n",
      " 1306  100 2857 2876]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[ 797 5504  581 2585 5700 5216 2883 5774 1407 5524   47  862 3713 4751\n",
      "  931 1233 2951 5582 1456  935 5455 5280 2107 5899   18 2202 2406  639\n",
      " 3184 4990 5241 3339]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[4773 1652 4685 5229 2739 2199 1695 1884 5921 5698 5504 3755 3831 2020\n",
      " 3283  670  440   73 3157 5693 1072 3129 4806  410 2836  793 5782  913\n",
      " 1064 2876 3490  533]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[5185 5325 2814 4384 5707 3024 3329 1425 4919 1300 3135 2488 5237 2524\n",
      " 5584  121 5959 5688 2014 5647 2306 3430 3528 2784 5919 3557 3298 3728\n",
      " 1892 3540 5409 3097]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[4264 1790 3400 4448 1256 1801 2454  907 3558 5730 4789 1964 2743 1048\n",
      " 3636 2237 5208 4025 4627 2803 5702 2695 4396 3917 1614 1299  917 4422\n",
      " 1514 5833 5366 4557]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[2687 3173 4197 1818 3587 1083 1832 2347  699 4943 1527 5828 5747 5891\n",
      " 2769 2698 5971 1168 1942 1964 1548 4178  844 2871 5953 2573 4303 4620\n",
      "  318 2994 4981 4862]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[5739 4362 4887 2920 3406  954 5682 3917 1435 1997 4612 2734  159 3988\n",
      " 1793 3934 5872 5198 2568 2429 1473 2342 4048 4906 4461 3636 5210  921\n",
      " 4164 5765 3560 1505]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1113  914  859   80 4011 5355 3517 2715  661  838  381 5644 3031 4380\n",
      " 4900 3031  942 3074 5990 3156 5288  472 3618 4135 4272 5738  553 1880\n",
      "  254 4271  270 4595]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[4814 5383 1389 4446 3732 3259 5970 3419  940  949 3422  470 3326   11\n",
      " 1721 5698 3083 3884 1936 4886 3788 5561  697  675  797  774 4246  560\n",
      " 3235 3623 1122 5396]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1764   91 1599 3841 1796 4898 2715 4650  264 2386 4843 1086 4804 1587\n",
      " 1122 4197 2750 5339 5352 1087 3086 2626 1376   98 4176 3772 2142 4249\n",
      " 1505 4016 5614 5115]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[4736 1702 5331 5438 1584  405  679 2042 4971 4350 5598 3359 3802 3396\n",
      " 5856 1758 3144   89 5245  935 4617 3769  134 3994 3301 4351 3168 4347\n",
      " 3645 5169 4322 3618]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1194 2643 1737 3779 5839 3699 3964 4947 5717 2217 2054 5242 3171 5980\n",
      " 5207 2905 4928 5332 2605 5565 5827 3207 4270 2708  245 4538  851 4841\n",
      " 4041 2500 2654 1856]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[3499 4801 2870 1522  260 3407 3065 3908 1617 2104  352 2865 2579 2573\n",
      " 2705  440 1667 3418 3798 1103 5168 4935 5237 3077 4595  796 5667 3105\n",
      " 2606 3970 5452 1866]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[2713 5100 3346 1595 4766 2325 2235 5425  382  522 3493 2498  207 4207\n",
      " 4511 4366  258 3071  465 3831  379 4868 2882 3592 4525 4145 2865 1425\n",
      " 1915 2524 2803  973]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1312 2971 4193 3001  978 1606 5458 3287 3080  690 5785 4748 2398 1774\n",
      "  507 5034 5831 2547  885  480 5458 5093 2895  463 5617 4989 2876 3027\n",
      " 1221 5716 2519 3929]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[3709 3976 1851 5014 5229 3248 4345 3909 2647 3675 5643 1986 2769 3988\n",
      "  417 1914 5582 1062  400  767 5931 4063  706 4166 5237 3995 4264 5525\n",
      " 4863  581 4080 5460]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[5439 5042 3780 2113 1602 3194 1802 3559 2772 5286 4725 2854 4078 4576\n",
      " 3874 1929 3215 1703 2150  198  188  960 2109  855 2848 1995  364 1300\n",
      " 5427  781 2688 1053]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1421 3191 5780  139 1656  250  604 1664 1461 5768 5143 4828 2870 4326\n",
      " 2613 5558 5955 1807 3874 5381 1666 1905 1074 1653  946 5034 5790 2093\n",
      " 2007 1207  765 5736]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[4248 5104 2962 5350 5724 4081 2802 2034 2046 4208 5304 2531 1622 2976\n",
      " 3034 3953 3812 1608 3929 4726 3576 1981 4666 1945 1227 3512 4494  406\n",
      " 1009 1090  965 5459]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[5799 2311 5727 2747   89 5565 5349 4063 1261  105 4096 3069   22 2489\n",
      "  881 2007 4607  522 4038  954 3147 5374  129 5176 5622 2898 5575 3141\n",
      " 3230  836 4192 5644]\n",
      "(32, 300, 40)\n",
      "(32,)\n",
      "[1207 3663 3471  508 1999 5930 5584 2180  217 3548 1929 5857  472 4375\n",
      " 4041 2186 4009 1861  950  633 2150 1371 3410 4343 3780 2638 1654 2118\n",
      " 2021 1435  608 5680]\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-977ada5cc803>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtr_gen\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/utils/data_utils.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    469\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m     \u001b[0;34m\"\"\"Create a generator that iterate over the Sequence.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 471\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    472\u001b[0m       \u001b[0;32myield\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/utils/data_utils.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    469\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m     \u001b[0;34m\"\"\"Create a generator that iterate over the Sequence.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 471\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    472\u001b[0m       \u001b[0;32myield\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/inzpeech/dataloaders/DatagenVoxCeleb.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatagen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_batch_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/inzpeech/dataloaders/DatagenVoxCeleb.py\u001b[0m in \u001b[0;36mget_batch_sample\u001b[0;34m(self, idx, batch_size)\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_pickle_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpickle_load\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m                 \u001b[0mloaded_sample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpickle_load\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0midname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvideoname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloaded_sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36m_frombuffer\u001b[0;34m(buf, dtype, shape, order)\u001b[0m\n\u001b[1;32m   1810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1812\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m_frombuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1813\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfrombuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1814\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
=======
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 100, 40, 1)\n",
      "(64, 109)\n"
>>>>>>> 90355144ce4a7d9932e7274ea8bfbec292c5288f
     ]
    }
   ],
   "source": [
<<<<<<< HEAD
    "for x,y in tr_gen:\n",
    "    print(x.shape)\n",
    "    print(y.shape)\n",
    "    print(y)"
=======
    "for x, y in tr_gen:\n",
    "    print(x.shape)\n",
    "    print(y.shape)\n",
    "    break"
>>>>>>> 90355144ce4a7d9932e7274ea8bfbec292c5288f
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_per_person = 300\n",
    "batch_size = 10\n",
    "num_class = 1251 #or 109\n",
    "input_shape = (100, 40, 1)\n",
    "persons_to_include = ['p228', 'p227', 'p225', 'p245', 'p247', 'p246', 'p228', 'p250', 'p251', 'p248', 'p231', 'p253']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'tr_gen, val_gen, te_gen = get_datagen(sample_per_person, batch_size, apply_melspectrogram_to_file, include_person=None)'"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "\"\"\"tr_gen, val_gen, te_gen = get_datagen(sample_per_person, batch_size, apply_melspectrogram_to_file, include_person=None)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'for x, y in tr_gen:\\n    print(x.shape)\\n    print(y.shape)\\n    print(np.unique(np.argmax(y, axis=1)))\\n    break'"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "\"\"\"for x, y in tr_gen:\n",
    "    print(x.shape)\n",
    "    print(y.shape)\n",
    "    print(np.unique(np.argmax(y, axis=1)))\n",
    "    break\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(None, 150, 20, 64)\n(None, 75, 10, 128)\n(None, 38, 5, 256)\n(None, 19, 3, 512)\nafter reshape\n(None, 19, 1536)\nafter attention\n(None, 4, 1536)\nafter avgpool\n(None, 1, 1536)\nModel: \"model_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_2 (InputLayer)         [(None, 300, 40, 1)]      0         \n_________________________________________________________________\nblock1_conv1 (Conv2D)        (None, 300, 40, 64)       640       \n_________________________________________________________________\nblock1_conv2 (Conv2D)        (None, 300, 40, 64)       36928     \n_________________________________________________________________\nbatch_normalization_4 (Batch (None, 300, 40, 64)       256       \n_________________________________________________________________\nmax_pooling2d_4 (MaxPooling2 (None, 150, 20, 64)       0         \n_________________________________________________________________\nblock2_conv1 (Conv2D)        (None, 150, 20, 128)      73856     \n_________________________________________________________________\nblock2_conv2 (Conv2D)        (None, 150, 20, 128)      147584    \n_________________________________________________________________\nbatch_normalization_5 (Batch (None, 150, 20, 128)      512       \n_________________________________________________________________\nmax_pooling2d_5 (MaxPooling2 (None, 75, 10, 128)       0         \n_________________________________________________________________\nblock3_conv1 (Conv2D)        (None, 75, 10, 256)       295168    \n_________________________________________________________________\nblock3_conv2 (Conv2D)        (None, 75, 10, 256)       590080    \n_________________________________________________________________\nbatch_normalization_6 (Batch (None, 75, 10, 256)       1024      \n_________________________________________________________________\nmax_pooling2d_6 (MaxPooling2 (None, 38, 5, 256)        0         \n_________________________________________________________________\nblock4_conv1 (Conv2D)        (None, 38, 5, 512)        1180160   \n_________________________________________________________________\nblock4_conv2 (Conv2D)        (None, 38, 5, 512)        2359808   \n_________________________________________________________________\nbatch_normalization_7 (Batch (None, 38, 5, 512)        2048      \n_________________________________________________________________\nmax_pooling2d_7 (MaxPooling2 (None, 19, 3, 512)        0         \n_________________________________________________________________\nreshape_1 (Reshape)          (None, 19, 1536)          0         \n_________________________________________________________________\nself_attention_1 (SelfAttent (None, 4, 1536)           394240    \n_________________________________________________________________\naverage_pooling1d_1 (Average (None, 1, 1536)           0         \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 1536)              0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 256)               393472    \n_________________________________________________________________\ndense_3 (Dense)              (None, 1251)              321507    \n=================================================================\nTotal params: 5,797,283\nTrainable params: 5,795,363\nNon-trainable params: 1,920\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = vgg_att(num_class)"
=======
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = VGGish(input_shape, 109)"
>>>>>>> 90355144ce4a7d9932e7274ea8bfbec292c5288f
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = Adam(lr=1e-3)\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])"
=======
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = Adam(lr=1e-4)\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
>>>>>>> 90355144ce4a7d9932e7274ea8bfbec292c5288f
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "in user code:\n\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:571 train_function  *\n        outputs = self.distribute_strategy.run(\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica\n        return fn(*args, **kwargs)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:532 train_step  **\n        loss = self.compiled_loss(\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:143 __call__\n        losses = self.call(y_true, y_pred)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:246 call\n        return self.fn(y_true, y_pred, **self._fn_kwargs)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy\n        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with\n        raise ValueError(\"Shapes %s and %s are incompatible\" % (self, other))\n\n    ValueError: Shapes (None, 1) and (None, 1251) are incompatible\n",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-fc52f26b8f63>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;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[1;32m    846\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    847\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    849\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    625\u001b[0m       \u001b[0;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m       \u001b[0minitializers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 627\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    628\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m       \u001b[0;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m    503\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_deleter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFunctionDeleter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lifted_initializer_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m     self._concrete_stateful_fn = (\n\u001b[0;32m--> 505\u001b[0;31m         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m    506\u001b[0m             *args, **kwds))\n\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2444\u001b[0m       \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2445\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2446\u001b[0;31m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2447\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   2775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2776\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2777\u001b[0;31m       \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2778\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2779\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   2655\u001b[0m     \u001b[0marg_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase_arg_names\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmissing_arg_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2656\u001b[0m     graph_function = ConcreteFunction(\n\u001b[0;32m-> 2657\u001b[0;31m         func_graph_module.func_graph_from_py_func(\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2659\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_python_function\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m    979\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m       \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    439\u001b[0m         \u001b[0;31m# __wrapped__ allows AutoGraph to swap in a converted function. We give\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m         \u001b[0;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    442\u001b[0m     \u001b[0mweak_wrapped_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    966\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    969\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:571 train_function  *\n        outputs = self.distribute_strategy.run(\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica\n        return fn(*args, **kwargs)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:532 train_step  **\n        loss = self.compiled_loss(\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:143 __call__\n        losses = self.call(y_true, y_pred)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:246 call\n        return self.fn(y_true, y_pred, **self._fn_kwargs)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy\n        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n    /home/bbekci/miniconda3/envs/inzpeech/lib/python3.8/site-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with\n        raise ValueError(\"Shapes %s and %s are incompatible\" % (self, other))\n\n    ValueError: Shapes (None, 1) and (None, 1251) are incompatible\n"
=======
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "14/14 [==============================] - 31s 2s/step - loss: 9.0328 - accuracy: 0.0238 - val_loss: 32030.5371 - val_accuracy: 0.0202\n",
      "Epoch 2/20\n",
      "14/14 [==============================] - 31s 2s/step - loss: 5.8168 - accuracy: 0.0306 - val_loss: 12789.2061 - val_accuracy: 0.0202\n",
      "Epoch 3/20\n",
      "14/14 [==============================] - 32s 2s/step - loss: 5.0801 - accuracy: 0.0522 - val_loss: 4288.9995 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/20\n",
      "14/14 [==============================] - ETA: 0s - loss: 5.0360 - accuracy: 0.0782"
>>>>>>> 90355144ce4a7d9932e7274ea8bfbec292c5288f
     ]
    }
   ],
   "source": [
    "model.fit(tr_gen, validation_data=val_gen, epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "name": "Python 3.8.5 64-bit ('inzpeech': conda)",
   "display_name": "Python 3.8.5 64-bit ('inzpeech': conda)",
   "metadata": {
    "interpreter": {
     "hash": "fcc15a4440aa802b6aa76ba989d07fd1e1f9e303ad2563ebf174689c6e63879d"
    }
   }
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
>>>>>>> 90355144ce4a7d9932e7274ea8bfbec292c5288f
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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   "version": "3.8.5-final"
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   "version": "3.8.5"
>>>>>>> 90355144ce4a7d9932e7274ea8bfbec292c5288f
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
<<<<<<< HEAD
}
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}
>>>>>>> 90355144ce4a7d9932e7274ea8bfbec292c5288f