Face_Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bz2\n",
    "import os\n",
    "\n",
    "from urllib.request import urlopen\n",
    "\n",
    "def download_landmarks(dst_file):\n",
    "    url = 'http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2'\n",
    "    decompressor = bz2.BZ2Decompressor()\n",
    "    \n",
    "    with urlopen(url) as src, open(dst_file, 'wb') as dst:\n",
    "        data = src.read(1024)\n",
    "        while len(data) > 0:\n",
    "            dst.write(decompressor.decompress(data))\n",
    "            data = src.read(1024)\n",
    "\n",
    "dst_dir = 'models'\n",
    "dst_file = os.path.join(dst_dir, 'landmarks.dat')\n",
    "\n",
    "if not os.path.exists(dst_file):\n",
    "    os.makedirs(dst_dir)\n",
    "    download_landmarks(dst_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import create_model\n",
    "nn4_small2 = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_6 (InputLayer)            (None, 96, 96, 3)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_47 (ZeroPadding2 (None, 102, 102, 3)  0           input_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 48, 48, 64)   9472        zero_padding2d_47[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "bn1 (BatchNormalization)        (None, 48, 48, 64)   256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 48, 48, 64)   0           bn1[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_48 (ZeroPadding2 (None, 50, 50, 64)   0           activation_75[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling2D) (None, 24, 24, 64)   0           zero_padding2d_48[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lrn_1 (Lambda)                  (None, 24, 24, 64)   0           max_pooling2d_13[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (Conv2D)                  (None, 24, 24, 64)   4160        lrn_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bn2 (BatchNormalization)        (None, 24, 24, 64)   256         conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 24, 24, 64)   0           bn2[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_49 (ZeroPadding2 (None, 26, 26, 64)   0           activation_76[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3 (Conv2D)                  (None, 24, 24, 192)  110784      zero_padding2d_49[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "bn3 (BatchNormalization)        (None, 24, 24, 192)  768         conv3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 24, 24, 192)  0           bn3[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "lrn_2 (Lambda)                  (None, 24, 24, 192)  0           activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_50 (ZeroPadding2 (None, 26, 26, 192)  0           lrn_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling2D) (None, 12, 12, 192)  0           zero_padding2d_50[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_3x3_conv1 (Conv2D) (None, 12, 12, 96)   18528       max_pooling2d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_5x5_conv1 (Conv2D) (None, 12, 12, 16)   3088        max_pooling2d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_3x3_bn1 (BatchNorm (None, 12, 12, 96)   384         inception_3a_3x3_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_5x5_bn1 (BatchNorm (None, 12, 12, 16)   64          inception_3a_5x5_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 12, 12, 96)   0           inception_3a_3x3_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 12, 12, 16)   0           inception_3a_5x5_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling2D) (None, 5, 5, 192)    0           max_pooling2d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_51 (ZeroPadding2 (None, 14, 14, 96)   0           activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_52 (ZeroPadding2 (None, 16, 16, 16)   0           activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_pool_conv (Conv2D) (None, 5, 5, 32)     6176        max_pooling2d_15[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_3x3_conv2 (Conv2D) (None, 12, 12, 128)  110720      zero_padding2d_51[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_5x5_conv2 (Conv2D) (None, 12, 12, 32)   12832       zero_padding2d_52[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_pool_bn (BatchNorm (None, 5, 5, 32)     128         inception_3a_pool_conv[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_1x1_conv (Conv2D)  (None, 12, 12, 64)   12352       max_pooling2d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_3x3_bn2 (BatchNorm (None, 12, 12, 128)  512         inception_3a_3x3_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_5x5_bn2 (BatchNorm (None, 12, 12, 32)   128         inception_3a_5x5_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 5, 5, 32)     0           inception_3a_pool_bn[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "inception_3a_1x1_bn (BatchNorma (None, 12, 12, 64)   256         inception_3a_1x1_conv[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 12, 12, 128)  0           inception_3a_3x3_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 12, 12, 32)   0           inception_3a_5x5_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_53 (ZeroPadding2 (None, 12, 12, 32)   0           activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 12, 12, 64)   0           inception_3a_1x1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_15 (Concatenate)    (None, 12, 12, 256)  0           activation_79[0][0]              \n",
      "                                                                 activation_81[0][0]              \n",
      "                                                                 zero_padding2d_53[0][0]          \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_3x3_conv1 (Conv2D) (None, 12, 12, 96)   24672       concatenate_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_5x5_conv1 (Conv2D) (None, 12, 12, 32)   8224        concatenate_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_3x3_bn1 (BatchNorm (None, 12, 12, 96)   384         inception_3b_3x3_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_5x5_bn1 (BatchNorm (None, 12, 12, 32)   128         inception_3b_5x5_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 12, 12, 96)   0           inception_3b_3x3_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 12, 12, 32)   0           inception_3b_5x5_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePoo (None, 4, 4, 256)    0           concatenate_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_54 (ZeroPadding2 (None, 14, 14, 96)   0           activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_55 (ZeroPadding2 (None, 16, 16, 32)   0           activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_pool_conv (Conv2D) (None, 4, 4, 64)     16448       average_pooling2d_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_3x3_conv2 (Conv2D) (None, 12, 12, 128)  110720      zero_padding2d_54[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_5x5_conv2 (Conv2D) (None, 12, 12, 64)   51264       zero_padding2d_55[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_pool_bn (BatchNorm (None, 4, 4, 64)     256         inception_3b_pool_conv[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_1x1_conv (Conv2D)  (None, 12, 12, 64)   16448       concatenate_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_3x3_bn2 (BatchNorm (None, 12, 12, 128)  512         inception_3b_3x3_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_5x5_bn2 (BatchNorm (None, 12, 12, 64)   256         inception_3b_5x5_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 4, 4, 64)     0           inception_3b_pool_bn[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "inception_3b_1x1_bn (BatchNorma (None, 12, 12, 64)   256         inception_3b_1x1_conv[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 12, 12, 128)  0           inception_3b_3x3_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 12, 12, 64)   0           inception_3b_5x5_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_56 (ZeroPadding2 (None, 12, 12, 64)   0           activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 12, 12, 64)   0           inception_3b_1x1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_16 (Concatenate)    (None, 12, 12, 320)  0           activation_85[0][0]              \n",
      "                                                                 activation_87[0][0]              \n",
      "                                                                 zero_padding2d_56[0][0]          \n",
      "                                                                 activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "inception_3c_3x3_conv1 (Conv2D) (None, 12, 12, 128)  41088       concatenate_16[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_3c_5x5_conv1 (Conv2D) (None, 12, 12, 32)   10272       concatenate_16[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_3c_3x3_bn1 (BatchNorm (None, 12, 12, 128)  512         inception_3c_3x3_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_3c_5x5_bn1 (BatchNorm (None, 12, 12, 32)   128         inception_3c_5x5_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 12, 12, 128)  0           inception_3c_3x3_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 12, 12, 32)   0           inception_3c_5x5_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_57 (ZeroPadding2 (None, 14, 14, 128)  0           activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_58 (ZeroPadding2 (None, 16, 16, 32)   0           activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "inception_3c_3x3_conv2 (Conv2D) (None, 6, 6, 256)    295168      zero_padding2d_57[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_3c_5x5_conv2 (Conv2D) (None, 6, 6, 64)     51264       zero_padding2d_58[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_3c_3x3_bn2 (BatchNorm (None, 6, 6, 256)    1024        inception_3c_3x3_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_3c_5x5_bn2 (BatchNorm (None, 6, 6, 64)     256         inception_3c_5x5_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_16 (MaxPooling2D) (None, 5, 5, 320)    0           concatenate_16[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 6, 6, 256)    0           inception_3c_3x3_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 6, 6, 64)     0           inception_3c_5x5_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_59 (ZeroPadding2 (None, 6, 6, 320)    0           max_pooling2d_16[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_17 (Concatenate)    (None, 6, 6, 640)    0           activation_91[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "                                                                 zero_padding2d_59[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_3x3_conv1 (Conv2D) (None, 6, 6, 96)     61536       concatenate_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_5x5_conv1 (Conv2D) (None, 6, 6, 32)     20512       concatenate_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_3x3_bn1 (BatchNorm (None, 6, 6, 96)     384         inception_4a_3x3_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_5x5_bn1 (BatchNorm (None, 6, 6, 32)     128         inception_4a_5x5_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, 6, 6, 96)     0           inception_4a_3x3_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_96 (Activation)      (None, 6, 6, 32)     0           inception_4a_5x5_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_10 (AveragePo (None, 2, 2, 640)    0           concatenate_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_60 (ZeroPadding2 (None, 8, 8, 96)     0           activation_94[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_61 (ZeroPadding2 (None, 10, 10, 32)   0           activation_96[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_pool_conv (Conv2D) (None, 2, 2, 128)    82048       average_pooling2d_10[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_3x3_conv2 (Conv2D) (None, 6, 6, 192)    166080      zero_padding2d_60[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_5x5_conv2 (Conv2D) (None, 6, 6, 64)     51264       zero_padding2d_61[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_pool_bn (BatchNorm (None, 2, 2, 128)    512         inception_4a_pool_conv[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_1x1_conv (Conv2D)  (None, 6, 6, 256)    164096      concatenate_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_3x3_bn2 (BatchNorm (None, 6, 6, 192)    768         inception_4a_3x3_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_5x5_bn2 (BatchNorm (None, 6, 6, 64)     256         inception_4a_5x5_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_98 (Activation)      (None, 2, 2, 128)    0           inception_4a_pool_bn[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "inception_4a_1x1_bn (BatchNorma (None, 6, 6, 256)    1024        inception_4a_1x1_conv[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_95 (Activation)      (None, 6, 6, 192)    0           inception_4a_3x3_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_97 (Activation)      (None, 6, 6, 64)     0           inception_4a_5x5_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_62 (ZeroPadding2 (None, 6, 6, 128)    0           activation_98[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_99 (Activation)      (None, 6, 6, 256)    0           inception_4a_1x1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_18 (Concatenate)    (None, 6, 6, 640)    0           activation_95[0][0]              \n",
      "                                                                 activation_97[0][0]              \n",
      "                                                                 zero_padding2d_62[0][0]          \n",
      "                                                                 activation_99[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "inception_4e_3x3_conv1 (Conv2D) (None, 6, 6, 160)    102560      concatenate_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_4e_5x5_conv1 (Conv2D) (None, 6, 6, 64)     41024       concatenate_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_4e_3x3_bn1 (BatchNorm (None, 6, 6, 160)    640         inception_4e_3x3_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_4e_5x5_bn1 (BatchNorm (None, 6, 6, 64)     256         inception_4e_5x5_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_100 (Activation)     (None, 6, 6, 160)    0           inception_4e_3x3_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_102 (Activation)     (None, 6, 6, 64)     0           inception_4e_5x5_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_63 (ZeroPadding2 (None, 8, 8, 160)    0           activation_100[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_64 (ZeroPadding2 (None, 10, 10, 64)   0           activation_102[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_4e_3x3_conv2 (Conv2D) (None, 3, 3, 256)    368896      zero_padding2d_63[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_4e_5x5_conv2 (Conv2D) (None, 3, 3, 128)    204928      zero_padding2d_64[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_4e_3x3_bn2 (BatchNorm (None, 3, 3, 256)    1024        inception_4e_3x3_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_4e_5x5_bn2 (BatchNorm (None, 3, 3, 128)    512         inception_4e_5x5_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_17 (MaxPooling2D) (None, 2, 2, 640)    0           concatenate_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_101 (Activation)     (None, 3, 3, 256)    0           inception_4e_3x3_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "activation_103 (Activation)     (None, 3, 3, 128)    0           inception_4e_5x5_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_65 (ZeroPadding2 (None, 3, 3, 640)    0           max_pooling2d_17[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_19 (Concatenate)    (None, 3, 3, 1024)   0           activation_101[0][0]             \n",
      "                                                                 activation_103[0][0]             \n",
      "                                                                 zero_padding2d_65[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_5a_3x3_conv1 (Conv2D) (None, 3, 3, 96)     98400       concatenate_19[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_5a_3x3_bn1 (BatchNorm (None, 3, 3, 96)     384         inception_5a_3x3_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_104 (Activation)     (None, 3, 3, 96)     0           inception_5a_3x3_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_11 (AveragePo (None, 1, 1, 1024)   0           concatenate_19[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_66 (ZeroPadding2 (None, 5, 5, 96)     0           activation_104[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_5a_pool_conv (Conv2D) (None, 1, 1, 96)     98400       average_pooling2d_11[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "inception_5a_3x3_conv2 (Conv2D) (None, 3, 3, 384)    332160      zero_padding2d_66[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_5a_pool_bn (BatchNorm (None, 1, 1, 96)     384         inception_5a_pool_conv[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_5a_1x1_conv (Conv2D)  (None, 3, 3, 256)    262400      concatenate_19[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_5a_3x3_bn2 (BatchNorm (None, 3, 3, 384)    1536        inception_5a_3x3_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_106 (Activation)     (None, 1, 1, 96)     0           inception_5a_pool_bn[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "inception_5a_1x1_bn (BatchNorma (None, 3, 3, 256)    1024        inception_5a_1x1_conv[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_105 (Activation)     (None, 3, 3, 384)    0           inception_5a_3x3_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_67 (ZeroPadding2 (None, 3, 3, 96)     0           activation_106[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_107 (Activation)     (None, 3, 3, 256)    0           inception_5a_1x1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_20 (Concatenate)    (None, 3, 3, 736)    0           activation_105[0][0]             \n",
      "                                                                 zero_padding2d_67[0][0]          \n",
      "                                                                 activation_107[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_5b_3x3_conv1 (Conv2D) (None, 3, 3, 96)     70752       concatenate_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_5b_3x3_bn1 (BatchNorm (None, 3, 3, 96)     384         inception_5b_3x3_conv1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_108 (Activation)     (None, 3, 3, 96)     0           inception_5b_3x3_bn1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_18 (MaxPooling2D) (None, 1, 1, 736)    0           concatenate_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_68 (ZeroPadding2 (None, 5, 5, 96)     0           activation_108[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_5b_pool_conv (Conv2D) (None, 1, 1, 96)     70752       max_pooling2d_18[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception_5b_3x3_conv2 (Conv2D) (None, 3, 3, 384)    332160      zero_padding2d_68[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "inception_5b_pool_bn (BatchNorm (None, 1, 1, 96)     384         inception_5b_pool_conv[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "inception_5b_1x1_conv (Conv2D)  (None, 3, 3, 256)    188672      concatenate_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "inception_5b_3x3_bn2 (BatchNorm (None, 3, 3, 384)    1536        inception_5b_3x3_conv2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_110 (Activation)     (None, 1, 1, 96)     0           inception_5b_pool_bn[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "inception_5b_1x1_bn (BatchNorma (None, 3, 3, 256)    1024        inception_5b_1x1_conv[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_109 (Activation)     (None, 3, 3, 384)    0           inception_5b_3x3_bn2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_69 (ZeroPadding2 (None, 3, 3, 96)     0           activation_110[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_111 (Activation)     (None, 3, 3, 256)    0           inception_5b_1x1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_21 (Concatenate)    (None, 3, 3, 736)    0           activation_109[0][0]             \n",
      "                                                                 zero_padding2d_69[0][0]          \n",
      "                                                                 activation_111[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_12 (AveragePo (None, 1, 1, 736)    0           concatenate_21[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 736)          0           average_pooling2d_12[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "dense_layer (Dense)             (None, 128)          94336       flatten_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "norm_layer (Lambda)             (None, 128)          0           dense_layer[0][0]                \n",
      "==================================================================================================\n",
      "Total params: 3,743,280\n",
      "Trainable params: 3,733,968\n",
      "Non-trainable params: 9,312\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "nn4_small2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "from keras.models import Model\n",
    "from keras.layers import Input,Layer\n",
    "\n",
    "in_a = Input(shape=(96,96,3))\n",
    "in_p = Input(shape=(96,96,3))\n",
    "in_n = Input(shape=(96,96,3))\n",
    "\n",
    "emb_a = nn4_small2(in_a)\n",
    "emb_p = nn4_small2(in_p)\n",
    "emb_n = nn4_small2(in_n)\n",
    "\n",
    "class TripletLossLayer(Layer):\n",
    "    def __init__(self, alpha, **kwargs):\n",
    "        self.alpha = alpha\n",
    "        super(TripletLossLayer, self).__init__(**kwargs)\n",
    "    \n",
    "    def triplet_loss(self, inputs):\n",
    "        a, p, n = inputs\n",
    "        p_dist = K.sum(K.square(a-p), axis=-1)\n",
    "        n_dist = K.sum(K.square(a-n), axis=-1)\n",
    "        return K.sum(K.maximum(p_dist - n_dist + self.alpha, 0), axis=0)\n",
    "    \n",
    "    def call(self, inputs):\n",
    "        loss = self.triplet_loss(inputs)\n",
    "        self.add_loss(loss)\n",
    "        return loss\n",
    "\n",
    "# Layer that computes the triplet loss from anchor, positive and negative embedding vectors\n",
    "triplet_loss_layer = TripletLossLayer(alpha=0.2, name='triplet_loss_layer')([emb_a, emb_p, emb_n])\n",
    "\n",
    "# Model that can be trained with anchor, positive negative images\n",
    "nn4_small2_train = Model([in_a, in_p, in_n], triplet_loss_layer)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: UserWarning: Output \"triplet_loss_layer\" missing from loss dictionary. We assume this was done on purpose, and we will not be expecting any data to be passed to \"triplet_loss_layer\" during training.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "100/100 [==============================] - 300s 3s/step - loss: 0.7999\n",
      "Epoch 2/10\n",
      "100/100 [==============================] - 261s 3s/step - loss: 0.8031\n",
      "Epoch 3/10\n",
      "100/100 [==============================] - 260s 3s/step - loss: 0.8014\n",
      "Epoch 4/10\n",
      "100/100 [==============================] - 246s 2s/step - loss: 0.8007\n",
      "Epoch 5/10\n",
      "100/100 [==============================] - 237s 2s/step - loss: 0.8004\n",
      "Epoch 6/10\n",
      "100/100 [==============================] - 249s 2s/step - loss: 0.7999\n",
      "Epoch 7/10\n",
      "100/100 [==============================] - 288s 3s/step - loss: 0.7999\n",
      "Epoch 8/10\n",
      "100/100 [==============================] - 262s 3s/step - loss: 0.8008\n",
      "Epoch 9/10\n",
      "100/100 [==============================] - 274s 3s/step - loss: 0.8025\n",
      "Epoch 10/10\n",
      "100/100 [==============================] - 279s 3s/step - loss: 0.8004\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f06ab28df98>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from data import triplet_generator\n",
    "\n",
    "# triplet_generator() creates a generator that continuously returns \n",
    "# ([a_batch, p_batch, n_batch], None) tuples where a_batch, p_batch \n",
    "# and n_batch are batches of anchor, positive and negative RGB images \n",
    "# each having a shape of (batch_size, 96, 96, 3).\n",
    "generator = triplet_generator() \n",
    "\n",
    "nn4_small2_train.compile(loss=None, optimizer='adam')\n",
    "nn4_small2_train.fit_generator(generator, epochs=10, steps_per_epoch=100)\n",
    "\n",
    "# Please note that the current implementation of the generator only generates \n",
    "# random image data. The main goal of this code snippet is to demonstrate \n",
    "# the general setup for model training. In the following, we will anyway \n",
    "# use a pre-trained model so we don't need a generator here that operates \n",
    "# on real training data. I'll maybe provide a fully functional generator\n",
    "# later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn4_small2_pretrained = create_model()\n",
    "nn4_small2_pretrained.load_weights('weights/nn4.small2.v1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os.path\n",
    "\n",
    "class IdentityMetadata():\n",
    "    def __init__(self, base, name, file):\n",
    "        # dataset base directory\n",
    "        self.base = base\n",
    "        # identity name\n",
    "        self.name = name\n",
    "        # image file name\n",
    "        self.file = file\n",
    "\n",
    "    def __repr__(self):\n",
    "        return self.image_path()\n",
    "\n",
    "    def image_path(self):\n",
    "        return os.path.join(self.base, self.name, self.file) \n",
    "    \n",
    "def load_metadata(path):\n",
    "    metadata = []\n",
    "    for i in os.listdir(path):\n",
    "        for f in os.listdir(os.path.join(path, i)):\n",
    "            # Check file extension. Allow only jpg/jpeg' files.\n",
    "            ext = os.path.splitext(f)[1]\n",
    "            if ext == '.jpg' or ext == '.jpeg':\n",
    "                metadata.append(IdentityMetadata(path, i, f))\n",
    "    return np.array(metadata)\n",
    "\n",
    "metadata = load_metadata('Dataset')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "from align import AlignDlib\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "def load_image(path):\n",
    "    img = cv2.imread(path, 1)\n",
    "    # OpenCV loads images with color channels\n",
    "    # in BGR order. So we need to reverse them\n",
    "    return img[...,::-1]\n",
    "\n",
    "# Initialize the OpenFace face alignment utility\n",
    "alignment = AlignDlib('models/landmarks.dat')\n",
    "\n",
    "# Load an image of Jacques Chirac\n",
    "jc_orig = load_image(metadata[100].image_path())\n",
    "\n",
    "# Detect face and return bounding box\n",
    "bb = alignment.getLargestFaceBoundingBox(jc_orig)\n",
    "\n",
    "# Transform image using specified face landmark indices and crop image to 96x96\n",
    "jc_aligned = alignment.align(96, jc_orig, bb, landmarkIndices=AlignDlib.OUTER_EYES_AND_NOSE)\n",
    "\n",
    "# Show original image\n",
    "plt.subplot(131)\n",
    "plt.imshow(jc_orig)\n",
    "\n",
    "# Show original image with bounding box\n",
    "plt.subplot(132)\n",
    "plt.imshow(jc_orig)\n",
    "plt.gca().add_patch(patches.Rectangle((bb.left(), bb.top()), bb.width(), bb.height(), fill=False, color='red'))\n",
    "\n",
    "# Show aligned image\n",
    "plt.subplot(133)\n",
    "plt.imshow(jc_aligned);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def align_image(img):\n",
    "    return alignment.align(96, img, alignment.getLargestFaceBoundingBox(img), \n",
    "                           landmarkIndices=AlignDlib.OUTER_EYES_AND_NOSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3808,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "embedded = np.zeros((metadata.shape[0], 128))\n",
    "corrupt = []\n",
    "for i, m in enumerate(metadata):\n",
    "    img = load_image(m.image_path())\n",
    "    img = align_image(img)\n",
    "    if img is not None:\n",
    "        # scale RGB values to interval [0,1]\n",
    "        img = (img / 255.).astype(np.float32)\n",
    "    # obtain embedding vector for image\n",
    "    try:\n",
    "        embedded[i] = nn4_small2_pretrained.predict(np.expand_dims(img, axis=0))[0]\n",
    "    except Exception as e:\n",
    "        corrupt.append(i)\n",
    "        print(i)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedded = np.delete(embedded,corrupt,0)\n",
    "metadata = np.delete(metadata,corrupt,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3439,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def  distance (emb1, emb2):\n",
    "    return np.sum(np.square(emb1 - emb2))\n",
    "\n",
    "def show_pair(idx1, idx2):\n",
    "    plt.figure(figsize=(8,3))\n",
    "    plt.suptitle(f'Distance = {distance(embedded[idx1], embedded[idx2]):.2f}')\n",
    "    plt.subplot(121)\n",
    "    plt.imshow(load_image(metadata[idx1].image_path()))\n",
    "    plt.subplot(122)\n",
    "    plt.imshow(load_image(metadata[idx2].image_path()));    \n",
    "\n",
    "show_pair(4, 4)\n",
    "show_pair(4, 212)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
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      "3\n",
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      "10\n",
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      "37\n",
      "38\n",
      "39\n",
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    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "\n",
    "distances = [] # squared L2 distance between pairs\n",
    "identical = [] # 1 if same identity, 0 otherwise\n",
    "\n",
    "num = len(metadata)\n",
    "\n",
    "for i in range(num - 1):\n",
    "    print(i)\n",
    "    for j in range(1, num):\n",
    "        distances.append(distance(embedded[i], embedded[j]))\n",
    "        identical.append(1 if metadata[i].name == metadata[j].name else 0)\n",
    "        \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "distances = np.array(distances)\n",
    "identical = np.array(identical)\n",
    "\n",
    "thresholds = np.arange(0.3, 1.0, 0.01)\n",
    "\n",
    "f1_scores = [f1_score(identical, distances < t) for t in thresholds]\n",
    "acc_scores = [accuracy_score(identical, distances < t) for t in thresholds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt_idx = np.argmax(f1_scores)\n",
    "# Threshold at maximal F1 score\n",
    "opt_tau = thresholds[opt_idx]\n",
    "# Accuracy at maximal F1 score\n",
    "opt_acc = accuracy_score(identical, distances < opt_tau)\n",
    "\n",
    "# Plot F1 score and accuracy as function of distance threshold\n",
    "plt.plot(thresholds, f1_scores, label='F1 score');\n",
    "plt.plot(thresholds, acc_scores, label='Accuracy');\n",
    "plt.axvline(x=opt_tau, linestyle='--', lw=1, c='lightgrey', label='Threshold')\n",
    "plt.title(f'Accuracy at threshold {opt_tau:.2f} = {opt_acc:.3f}');\n",
    "plt.xlabel('Distance threshold')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_pos = distances[identical == 1]\n",
    "dist_neg = distances[identical == 0]\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.hist(dist_pos)\n",
    "plt.axvline(x=opt_tau, linestyle='--', lw=1, c='lightgrey', label='Threshold')\n",
    "plt.title('Distances (pos. pairs)')\n",
    "plt.legend();\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.hist(dist_neg)\n",
    "plt.axvline(x=opt_tau, linestyle='--', lw=1, c='lightgrey', label='Threshold')\n",
    "plt.title('Distances (neg. pairs)')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy = 0.9575581395348837, SVM accuracy = 0.9627906976744186\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "targets = np.array([m.name for m in metadata])\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(targets)\n",
    "\n",
    "# Numerical encoding of identities\n",
    "y = encoder.transform(targets)\n",
    "\n",
    "train_idx = np.arange(metadata.shape[0]) % 2 != 0\n",
    "test_idx = np.arange(metadata.shape[0]) % 2 == 0\n",
    "\n",
    "# 50 train examples of 10 identities (5 examples each)\n",
    "X_train = embedded[train_idx]\n",
    "# 50 test examples of 10 identities (5 examples each)\n",
    "X_test = embedded[test_idx]\n",
    "\n",
    "y_train = y[train_idx]\n",
    "y_test = y[test_idx]\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=1, metric='euclidean')\n",
    "svc = LinearSVC()\n",
    "\n",
    "knn.fit(X_train, y_train)\n",
    "svc.fit(X_train, y_train)\n",
    "\n",
    "acc_knn = accuracy_score(y_test, knn.predict(X_test))\n",
    "acc_svc = accuracy_score(y_test, svc.predict(X_test))\n",
    "\n",
    "print(f'KNN accuracy = {acc_knn}, SVM accuracy = {acc_svc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "# Suppress LabelEncoder warning\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "example_idx = 29\n",
    "\n",
    "example_image = load_image(metadata[test_idx][example_idx].image_path())\n",
    "example_prediction = svc.predict([embedded[test_idx][example_idx]])\n",
    "example_identity = encoder.inverse_transform(example_prediction)[0]\n",
    "\n",
    "plt.imshow(example_image)\n",
    "plt.title(f'Recognized as {example_identity}');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "X_embedded = TSNE(n_components=2).fit_transform(embedded)\n",
    "\n",
    "for i, t in enumerate(set(targets)):\n",
    "    idx = targets == t\n",
    "    plt.scatter(X_embedded[idx, 0], X_embedded[idx, 1], label=t)   \n",
    "\n",
    "plt.legend(bbox_to_anchor=(1, 1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "face = np.load('embedded_face.npy')\n",
    "label = np.load('meta_face.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "celeb_class = ['n000234','n003257','n004063','n004516','n004605','n005053','n006414','n006552','n008598']\n",
    "labels = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in label:\n",
    "    k=str(i)\n",
    "    k=k.split('/')[1]\n",
    "    if(k=='n000234'):\n",
    "        labels.append(0)\n",
    "    elif(k=='n003257'):\n",
    "        labels.append(1)\n",
    "    elif(k=='n004063'):\n",
    "        labels.append(2)\n",
    "    elif(k=='n004516'):\n",
    "        labels.append(3)\n",
    "    elif(k=='n004605'):\n",
    "        labels.append(4)\n",
    "    elif(k=='n005053'):\n",
    "        labels.append(5)\n",
    "    elif(k=='n006414'):\n",
    "        labels.append(6)\n",
    "    elif(k=='n006552'):\n",
    "        labels.append(7)\n",
    "    elif(k=='n008598'):\n",
    "        labels.append(8)\n",
    "    else:\n",
    "        continue\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3439"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('embedded_face_labels.npy',labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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