embeddings-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "from keras import backend as K\n",
    "from keras.applications import ResNet50\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "from keras.preprocessing import image \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 55, 55, 64)   0           activation_50[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 55, 55, 64)   4160        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 55, 55, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 55, 55, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 55, 55, 256)  16640       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 55, 55, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, 55, 55, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 55, 55, 256)  0           add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 55, 55, 64)   16448       activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 55, 55, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 55, 55, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    (None, 55, 55, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 55, 55, 256)  0           add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 55, 55, 64)   16448       activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 55, 55, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 55, 55, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_58[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    (None, 55, 55, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 55, 55, 256)  0           add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_60[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_20 (Add)                    (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 28, 28, 512)  0           add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_63[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_21 (Add)                    (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 28, 28, 512)  0           add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_22 (Add)                    (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 28, 28, 512)  0           add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_68[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_69[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_23 (Add)                    (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_68[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 28, 28, 512)  0           add_23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_71[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_72[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_71[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_24 (Add)                    (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 14, 14, 1024) 0           add_24[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_75[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_76[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_25 (Add)                    (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 14, 14, 1024) 0           add_25[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_26 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 14, 14, 1024) 0           add_26[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_27 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 14, 14, 1024) 0           add_27[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_85[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_28 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 14, 14, 1024) 0           add_28[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_29 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 14, 14, 1024) 0           add_29[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_30 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 7, 7, 2048)   0           add_30[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_94[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_31 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_95 (Activation)      (None, 7, 7, 2048)   0           add_31[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_95[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_96 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_96[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_97 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_97[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_32 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_95[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_98 (Activation)      (None, 7, 7, 2048)   0           add_32[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_98[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 2048)         0           avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "fc1000 (Dense)                  (None, 1000)         2049000     flatten_2[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 25,636,712\n",
      "Trainable params: 25,583,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build model and load pretrained weights\n",
    "model = ResNet50()\n",
    "\n",
    "model.summary() # prints the summary of model with layer names and sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "\n",
    "# choose from one of the layer names \n",
    "layer_name = 'flatten_2'\n",
    "emb_model = Model(inputs=model.input, outputs=model.get_layer(layer_name).output)\n",
    "\n",
    "# Create a model which returns embedding at a particular layer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "def display_image(img_path):\n",
    "    img = cv2.imread(img_path)\n",
    "    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    plt.imshow(cv_rgb)\n",
    "    plt.show()\n",
    "    \n",
    "def preprocess_input_image(img_path):\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    x = image.img_to_array(img)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    x = preprocess_input(x)\n",
    "    return x\n",
    "\n",
    "def predict_label(img_path):\n",
    "    x = preprocess_input_image(img_path)\n",
    "    prediction = model.predict(x)\n",
    "    print(np.argmax(prediction))\n",
    "    print('Predicted:', decode_predictions(prediction, top=3)[0])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.0030324   0.31756642  0.         ...,  0.11003964  0.79779387\n",
      "   0.01895883]]\n"
     ]
    }
   ],
   "source": [
    "img_path='images/car2.jpeg'\n",
    "test_image = preprocess_input_image(img_path)\n",
    "# extract embedding\n",
    "# embeddings=[]\n",
    "embedding = emb_model.predict(test_image)\n",
    "print(embedding)\n",
    "# embeddings.append(embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2048)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt                        \n",
    "%matplotlib inline \n",
    "print(embedding.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['parrot25.jpeg', 'dog5.jpg', 'box10.jpeg', 'box3.jpeg', 'face7.jpeg', 'parrot3.jpeg', 'gun4.jpeg', 'fruit7.jpeg', 'parrot13.jpeg', 'face27.jpeg', 'cat1.jpeg', 'fruit6.jpeg', 'parrot12.jpeg', 'face26.jpeg', 'gun5.jpeg', 'parrot2.jpeg', 'face6.jpeg', 'box2.jpeg', 'face10.jpeg', 'parrot24.jpeg', 'box5.jpeg', 'face1.jpeg', 'parrot5.jpeg', 'gun2.jpeg', 'dog1.jpeg', 'parrot15.jpeg', 'fruit1.jpeg', 'face21.jpeg', 'face17.jpeg', 'parrot23.jpeg', 'box9.jpeg', 'parrot9.jpeg', 'car7.jpeg', 'parrot19.jpeg', 'parrot18.jpeg', 'car6.jpeg', 'parrot8.jpeg', 'box8.jpeg', 'face16.jpeg', 'parrot22.jpeg', 'parrot14.jpeg', 'face20.jpeg', 'face101.jpeg', 'dog.jpeg', 'gun3.jpeg', 'parrot4.jpeg', 'box4.jpeg', 'cat4.jpeg', 'parrot17.jpeg', 'fruit3.jpeg', 'face23.jpeg', 'dog3.jpeg', 'parrot7.jpeg', 'face3.jpeg', 'face19.jpeg', 'box7.jpeg', 'car5.jpeg', 'parrot21.jpeg', 'face14.jpeg', 'parrot20.jpeg', 'car4.jpeg', 'box6.jpeg', 'face2.jpeg', 'parrot6.jpeg', 'gun1.jpeg', 'dog2.jpeg', 'parrot16.jpeg', 'fruit2.jpeg', 'face22.jpeg', 'car3.jpeg', 'face9.jpeg', 'face13.jpeg', 'cat2.jpeg', 'fruit5.jpeg', 'parrot11.jpeg', 'face25.jpeg', 'gun6.jpeg', 'parrot1.jpeg', 'face5.jpeg', 'box1.jpeg', 'gun7.jpeg', 'dog4.jpeg', 'parrot10.jpeg', 'face24.jpeg', 'cat3.jpeg', 'face12.jpeg', 'car2.jpeg', 'fruit8.jpeg']\n"
     ]
    }
   ],
   "source": [
    "img_width = 224\n",
    "img_height = 224\n",
    "#vgg_path = '/Users/gene/Learn/keras-rtst/vgg16_weights.h5'\n",
    "images_path = '/Users/anshu/meet-up/internship/embedding/images/'\n",
    "# tsne_path = '/Users/anshu/meet-up/internship/embedding/tsne_points.txt'\n",
    "\n",
    "# get images\n",
    "images = [f for f in listdir(images_path) if isfile(join(images_path, f))]\n",
    "images_all = [images[i]  for i in range(1,len(images), 1) ] \n",
    "\n",
    "images =[]\n",
    "for im in images_all:\n",
    "    if(im!='.DS_Store'):\n",
    "        images.append(im) \n",
    "print(images)        \n",
    "\n",
    "features = np.zeros([len(images), 2048])\n",
    "i=0\n",
    "for im in images:\n",
    "    if(im=='.DS_Store'):\n",
    "        continue\n",
    "    test_image = preprocess_input_image(images_path+im)\n",
    "    \n",
    "    embedding = emb_model.predict(test_image)\n",
    "    features[i, :] = emb_model.predict(test_image)\n",
    "    i=i+1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88\n"
     ]
    }
   ],
   "source": [
    "no_of_images=len(images)\n",
    "print(no_of_images)\n",
    "import pickle\n",
    "with open(\"features\", 'wb') as f:\n",
    "    pickle.dump((features, images), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=2, random_state=0)\n",
    "reduced = tsne.fit_transform(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1836555f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(8, 8))\n",
    "# plt.scatter(x = reduced[:,0], y=reduced[:,1])\n",
    "# plt.show()\n",
    "from collections import Counter\n",
    "\n",
    "target=[None]*len(images)\n",
    "for idx,im in enumerate(images):\n",
    "    if('parrot' in im):\n",
    "        target[idx]=1\n",
    "    elif 'dog' in im:\n",
    "        target[idx]=2\n",
    "    elif 'cat' in im:\n",
    "        target[idx]=3\n",
    "    elif 'gun' in im:\n",
    "        target[idx]=4\n",
    "    elif 'fruit' in im:\n",
    "        target[idx]=5\n",
    "    elif 'car' in im:\n",
    "        target[idx] =6\n",
    "    elif 'face' in im:\n",
    "        target[idx] = 7\n",
    "    elif 'box' in im:\n",
    "        target[idx] = 8\n",
    "    else:\n",
    "        target[idx] = 9\n",
    "\n",
    "vis_x = reduced[:, 0]\n",
    "vis_y = reduced[:, 1]\n",
    "\n",
    "plt.scatter(vis_x, vis_y, c=target, cmap=plt.cm.get_cmap(\"jet\", 10))\n",
    "plt.colorbar(ticks=range(10))\n",
    "plt.clim(-0.5, 9.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(88,)\n"
     ]
    }
   ],
   "source": [
    "reduced_transformed = reduced - np.min(reduced, axis=0)\n",
    "reduced_transformed /= np.max(reduced_transformed, axis=0)\n",
    "image_xindex_sorted = np.argsort(np.sum(reduced_transformed, axis=1))\n",
    "print(image_xindex_sorted.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a51e50b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw all images in a merged image\n",
    "no_of_images=200\n",
    "from PIL import Image\n",
    "image_width = 500\n",
    "merged_width = int(np.ceil(np.sqrt(no_of_images))*image_width)\n",
    "merged_image = np.zeros((merged_width, merged_width, 3), dtype='uint8')\n",
    "ellipside =True\n",
    "for counter, index in enumerate(image_xindex_sorted):\n",
    "    # set location\n",
    "    if ellipside:\n",
    "        a = np.ceil(reduced_transformed[counter, 0] * (merged_width-image_width-1)+1)\n",
    "        b = np.ceil(reduced_transformed[counter, 1] * (merged_width-image_width-1)+1)\n",
    "        a = int(a - np.mod(a-1,image_width) + 1)\n",
    "        b = int(b - np.mod(b-1,image_width) + 1)\n",
    "        if merged_image[a,b,0] != 0:\n",
    "            continue\n",
    "        image_address = images[counter]\n",
    "        img = np.asarray(Image.open('images/'+image_address).resize((image_width, image_width)))\n",
    "        merged_image[a:a+image_width, b:b+image_width,:] = img[:,:,:3]\n",
    "    else:\n",
    "        b = int(np.mod(counter, np.sqrt(no_of_images)))\n",
    "        a = int(np.mod(counter//np.sqrt(no_of_images), np.sqrt(no_of_images)))\n",
    "        image_address = images[index]\n",
    "        print(image_address)\n",
    "        img = np.asarray(Image.open('images/'+image_address).resize((image_width, image_width)))\n",
    "        merged_image[a*image_width:(a+1)*image_width, b*image_width:(b+1)*image_width,:] = img[:,:,:3]\n",
    "\n",
    "plt.imshow(merged_image)\n",
    "plt.show()\n",
    "merged_image = Image.fromarray(merged_image)\n",
    "if ellipside:\n",
    "    merged_image.save('merged-%s-ellipsoide-resnet50.png'%images_path.split('/')[-2])\n",
    "else:\n",
    "    merged_image.save('merged-%s.png'%images_path.split('/')[-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.contrib.tensorboard.plugins import projector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings_path = '/Users/anshu/meet-up/internship/embedding/'\n",
    "LOG_DIR = '/Users/anshu/meet-up/internship/embedding/embeddings-logs/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(88, 2048)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('feature_vectors_400_samples.txt',features)\n",
    "#feature_vectors = np.loadtxt('feature_vectors.txt')\n",
    "pickle.dump(features, open('feature_vectors_400_samples.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['parrot25.jpeg', 'dog5.jpg', 'box10.jpeg', 'box3.jpeg', 'face7.jpeg', 'parrot3.jpeg', 'gun4.jpeg', 'fruit7.jpeg', 'parrot13.jpeg', 'face27.jpeg', 'cat1.jpeg', 'fruit6.jpeg', 'parrot12.jpeg', 'face26.jpeg', 'gun5.jpeg', 'parrot2.jpeg', 'face6.jpeg', 'box2.jpeg', 'face10.jpeg', 'parrot24.jpeg', 'box5.jpeg', 'face1.jpeg', 'parrot5.jpeg', 'gun2.jpeg', 'dog1.jpeg', 'parrot15.jpeg', 'fruit1.jpeg', 'face21.jpeg', 'face17.jpeg', 'parrot23.jpeg', 'box9.jpeg', 'parrot9.jpeg', 'car7.jpeg', 'parrot19.jpeg', 'parrot18.jpeg', 'car6.jpeg', 'parrot8.jpeg', 'box8.jpeg', 'face16.jpeg', 'parrot22.jpeg', 'parrot14.jpeg', 'face20.jpeg', 'face101.jpeg', 'dog.jpeg', 'gun3.jpeg', 'parrot4.jpeg', 'box4.jpeg', 'cat4.jpeg', 'parrot17.jpeg', 'fruit3.jpeg', 'face23.jpeg', 'dog3.jpeg', 'parrot7.jpeg', 'face3.jpeg', 'face19.jpeg', 'box7.jpeg', 'car5.jpeg', 'parrot21.jpeg', 'face14.jpeg', 'parrot20.jpeg', 'car4.jpeg', 'box6.jpeg', 'face2.jpeg', 'parrot6.jpeg', 'gun1.jpeg', 'dog2.jpeg', 'parrot16.jpeg', 'fruit2.jpeg', 'face22.jpeg', 'car3.jpeg', 'face9.jpeg', 'face13.jpeg', 'cat2.jpeg', 'fruit5.jpeg', 'parrot11.jpeg', 'face25.jpeg', 'gun6.jpeg', 'parrot1.jpeg', 'face5.jpeg', 'box1.jpeg', 'gun7.jpeg', 'dog4.jpeg', 'parrot10.jpeg', 'face24.jpeg', 'cat3.jpeg', 'face12.jpeg', 'car2.jpeg', 'fruit8.jpeg']\n"
     ]
    }
   ],
   "source": [
    "img_data=[]\n",
    "print(images)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "for img in images:\n",
    "    input_img=cv2.imread('images/'+img )\n",
    "    input_img_resize=cv2.resize(input_img,(224,224))\n",
    "    img_data.append(input_img_resize)\n",
    "    \n",
    "                \n",
    "img_data = np.array(img_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature_vectors_shape: (88, 2048)\n",
      "num of images: 88\n",
      "size of individual feature vector: 2048\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "feature_vectors = np.loadtxt('feature_vectors_400_samples.txt')\n",
    "print (\"feature_vectors_shape:\",feature_vectors.shape)\n",
    "print (\"num of images:\",feature_vectors.shape[0])\n",
    "print (\"size of individual feature vector:\",feature_vectors.shape[1])\n",
    "features = tf.Variable(feature_vectors, name='features')\n",
    "target\n",
    "images_name = ['parrot','dog','cat','gun','fruit','car','face','box','others']\n",
    "\n",
    "\n",
    "metadata_file = open(os.path.join(LOG_DIR, 'metadata_4_classes.tsv'), 'w')\n",
    "metadata_file.write('Class\\tName\\n')\n",
    "\n",
    "for i,label in enumerate(target):\n",
    "    c = images_name[target[i]-1]\n",
    "    metadata_file.write('{}\\t{}\\n'.format(target[i],c))\n",
    "    \n",
    "metadata_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def images_to_sprite(data):\n",
    "    \"\"\"Creates the sprite image along with any necessary padding\n",
    "\n",
    "    Args:\n",
    "      data: NxHxW[x3] tensor containing the images.\n",
    "\n",
    "    Returns:\n",
    "      data: Properly shaped HxWx3 image with any necessary padding.\n",
    "    \"\"\"\n",
    "    if len(data.shape) == 3:\n",
    "        data = np.tile(data[...,np.newaxis], (1,1,1,3))\n",
    "    data = data.astype(np.float32)\n",
    "    min = np.min(data.reshape((data.shape[0], -1)), axis=1)\n",
    "    data = (data.transpose(1,2,3,0) - min).transpose(3,0,1,2)\n",
    "    max = np.max(data.reshape((data.shape[0], -1)), axis=1)\n",
    "    data = (data.transpose(1,2,3,0) / max).transpose(3,0,1,2)\n",
    "    # Inverting the colors seems to look better for MNIST\n",
    "    #data = 1 - data\n",
    "\n",
    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
    "    padding = ((0, n ** 2 - data.shape[0]), (0, 0),\n",
    "            (0, 0)) + ((0, 0),) * (data.ndim - 3)\n",
    "    data = np.pad(data, padding, mode='constant',\n",
    "            constant_values=0)\n",
    "    # Tile the individual thumbnails into an image.\n",
    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3)\n",
    "            + tuple(range(4, data.ndim + 1)))\n",
    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
    "    data = (data * 255).astype(np.uint8)\n",
    "    return data\n",
    "#%%\n",
    "sprite = images_to_sprite(img_data)\n",
    "cv2.imwrite(os.path.join(LOG_DIR, 'sprite_4_classes.png'), sprite)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver = tf.train.Saver([features])\n",
    "\n",
    "    sess.run(features.initializer)\n",
    "    saver.save(sess, os.path.join(LOG_DIR, 'images_4_classes.ckpt'))\n",
    "    \n",
    "    config = projector.ProjectorConfig()\n",
    "    # One can add multiple embeddings.\n",
    "    embedding = config.embeddings.add()\n",
    "    embedding.tensor_name = features.name\n",
    "    # Link this tensor to its metadata file (e.g. labels).\n",
    "    embedding.metadata_path = os.path.join(LOG_DIR, 'metadata_4_classes.tsv')\n",
    "    # Comment out if you don't want sprites\n",
    "    embedding.sprite.image_path = os.path.join(LOG_DIR, 'sprite_4_classes.png')\n",
    "    embedding.sprite.single_image_dim.extend([img_data.shape[1], img_data.shape[1]])\n",
    "    # Saves a config file that TensorBoard will read during startup.\n",
    "    projector.visualize_embeddings(tf.summary.FileWriter(LOG_DIR), config)"
   ]
  }
 ],
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