dsgn-video-stream.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dict = {\n",
    "    'race_id': {\n",
    "        0: 'white', \n",
    "        1: 'black', \n",
    "        2: 'asian', \n",
    "        3: 'indian', \n",
    "        4: 'others'\n",
    "    },\n",
    "    'gender_id': {\n",
    "        0: 'male',\n",
    "        1: 'female'\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicity_distribution = {\n",
    "    'Cashier': [3.5080048e-01, 3.1121464e-05, 6.4722931e-01, 2.5344267e-04,\n",
    "        1.6856192e-03], \n",
    "    'CEO': [9.9974424e-01, 1.3011397e-04, 2.5828704e-05, 4.5370639e-06,\n",
    "        9.5260068e-05],\n",
    "    'Construction Worker': [0.9618115 , 0.00803938, 0.00211852, 0.01608352, 0.01194708],\n",
    "    'Drug Dealer': [0.6143627 , 0.19683151, 0.00415425, 0.00151483, 0.18313669],\n",
    "    'Doctor': [3.7060756e-02, 3.3610930e-05, 5.0900042e-01, 4.4761142e-01,\n",
    "        6.2938030e-03],\n",
    "    'Service Worker': [9.9973005e-01, 1.8429040e-05, 2.3945526e-04, 4.7824483e-06,\n",
    "        7.3214278e-06],\n",
    "    'Inmate': [0.35789937, 0.01828072, 0.5011195 , 0.10793434, 0.01476599], \n",
    "    'Lawyer': [5.6974422e-02, 4.8700431e-06, 2.2556496e-04, 9.2834878e-01,\n",
    "        1.4446383e-02],\n",
    "    'Politician': [9.9654490e-01, 3.2715327e-03, 1.6118442e-05, 4.4877459e-05,\n",
    "        1.2260668e-04],\n",
    "    'Teacher': [0.05923579, 0.00195845, 0.08559094, 0.6998868 , 0.15332802]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_distribution = {\n",
    "    'Cashier': [1.9956619e-04, 9.9978673e-01], \n",
    "    'CEO': [9.9910378e-01, 8.5834874e-04],\n",
    "    'Construction Worker': [9.9944746e-01, 5.4820825e-04],\n",
    "    'Drug Dealer': [9.995867e-01, 3.731928e-04],\n",
    "    'Doctor': [0.8458318 , 0.15971448],\n",
    "    'Service Worker': [0.00136237, 0.9986067],\n",
    "    'Inmate': [9.9970573e-01, 3.0194136e-04], \n",
    "    'Lawyer': [0.9957363 , 0.00391406],\n",
    "    'Politician': [9.9999958e-01, 3.9245296e-07],\n",
    "    'Teacher': [1.0662096e-04, 9.9989194e-01]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kailyliu/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"face_net\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"face_net\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_4        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_8        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalization │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ activation[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ activation_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">198</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ activation_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_6     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │ dropout[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │ dropout_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │ dropout_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_5        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_9        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ activation_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ activation_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ activation_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_4     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_7     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ dropout_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ dropout_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ dropout_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_6        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_10       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ activation_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ activation_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ activation_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_5     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_8     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_10          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8192</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8192</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8192</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,048,704</span> │ flatten[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,048,704</span> │ flatten_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,048,704</span> │ flatten_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_7        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_11       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ activation_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ activation_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ activation_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_11          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │ dropout_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">645</span> │ dropout_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">258</span> │ dropout_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ age_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ race_output         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ gender_output       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │                   │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m198\u001b[0m, \u001b[38;5;34m198\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │ \u001b[38;5;34m3\u001b[0m)                │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m198\u001b[0m, \u001b[38;5;34m198\u001b[0m,  │        \u001b[38;5;34m448\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m198\u001b[0m, \u001b[38;5;34m198\u001b[0m,  │        \u001b[38;5;34m448\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m198\u001b[0m, \u001b[38;5;34m198\u001b[0m,  │        \u001b[38;5;34m448\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m198\u001b[0m, \u001b[38;5;34m198\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_4        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m198\u001b[0m, \u001b[38;5;34m198\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ conv2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_8        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m198\u001b[0m, \u001b[38;5;34m198\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m198\u001b[0m, \u001b[38;5;34m198\u001b[0m,  │         \u001b[38;5;34m64\u001b[0m │ activation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m198\u001b[0m, \u001b[38;5;34m198\u001b[0m,  │         \u001b[38;5;34m64\u001b[0m │ activation_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m198\u001b[0m, \u001b[38;5;34m198\u001b[0m,  │         \u001b[38;5;34m64\u001b[0m │ activation_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_6     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d_3[\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d_6[\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │      \u001b[38;5;34m4,640\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │      \u001b[38;5;34m4,640\u001b[0m │ dropout_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │      \u001b[38;5;34m4,640\u001b[0m │ dropout_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_5        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_9        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │        \u001b[38;5;34m128\u001b[0m │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │        \u001b[38;5;34m128\u001b[0m │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m, \u001b[38;5;34m66\u001b[0m,    │        \u001b[38;5;34m128\u001b[0m │ activation_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_4     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_7     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d_4[\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d_7[\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │      \u001b[38;5;34m9,248\u001b[0m │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │      \u001b[38;5;34m9,248\u001b[0m │ dropout_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │      \u001b[38;5;34m9,248\u001b[0m │ dropout_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_6        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_10       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │        \u001b[38;5;34m128\u001b[0m │ activation_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │        \u001b[38;5;34m128\u001b[0m │ activation_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m33\u001b[0m,    │        \u001b[38;5;34m128\u001b[0m │ activation_10[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_5     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d_8     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d_2[\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d_5[\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_10          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d_8[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8192\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8192\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ dropout_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8192\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ dropout_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │  \u001b[38;5;34m1,048,704\u001b[0m │ flatten[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │  \u001b[38;5;34m1,048,704\u001b[0m │ flatten_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │  \u001b[38;5;34m1,048,704\u001b[0m │ flatten_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_7        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ activation_11       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ dense_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m512\u001b[0m │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m512\u001b[0m │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m512\u001b[0m │ activation_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_11          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │        \u001b[38;5;34m129\u001b[0m │ dropout_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)         │        \u001b[38;5;34m645\u001b[0m │ dropout_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)         │        \u001b[38;5;34m258\u001b[0m │ dropout_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ age_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │          \u001b[38;5;34m0\u001b[0m │ dense_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ race_output         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)         │          \u001b[38;5;34m0\u001b[0m │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ gender_output       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)         │          \u001b[38;5;34m0\u001b[0m │ dense_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │                   │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,575,450</span> (36.53 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,575,450\u001b[0m (36.53 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,191,400</span> (12.17 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,191,400\u001b[0m (12.17 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,248</span> (4.88 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,248\u001b[0m (4.88 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,382,802</span> (24.35 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m6,382,802\u001b[0m (24.35 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = load_model(\"model_checkpoint.keras\")\n",
    "# THIS ONE DOESNT WORK / NEEDS TO BE TRAINED\n",
    "# model = load_model(\"final-model.keras\") \n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_occupation(predicted_gender, predicted_race):\n",
    "    gender_weight = 2\n",
    "    ethnicity_weight = 1\n",
    "\n",
    "    # Calculate scores with weights\n",
    "    occupation_scores = {}\n",
    "    for occupation, gender_dist in gender_distribution.items():\n",
    "        race_dist = ethnicity_distribution[occupation]\n",
    "        gender_score = np.dot(predicted_gender, gender_dist) * gender_weight\n",
    "        race_score = np.dot(predicted_race, race_dist) * ethnicity_weight\n",
    "        total_score = gender_score + race_score  # Weighted sum of scores\n",
    "        occupation_scores[occupation] = total_score\n",
    "\n",
    "    # Find the occupation with the highest score\n",
    "    best_occupation = max(occupation_scores, key=occupation_scores.get)\n",
    "\n",
    "    return best_occupation, occupation_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.39685004]), 'CEO': array([2.85294752]), 'Construction Worker': array([2.82095776]), 'Drug Dealer': array([2.52079023]), 'Doctor': array([1.7679863]), 'Service Worker': array([0.89681984]), 'Inmate': array([2.34772353]), 'Lawyer': array([2.02690001]), 'Politician': array([2.8519225]), 'Teacher': array([0.08677793])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.60576569]), 'CEO': array([2.49696043]), 'Construction Worker': array([2.47388903]), 'Drug Dealer': array([2.2546443]), 'Doctor': array([1.78011453]), 'Service Worker': array([0.8205649]), 'Inmate': array([2.24339239]), 'Lawyer': array([1.88084886]), 'Politician': array([2.49645656]), 'Teacher': array([0.23794316])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "Best matched occupation: Politician\n",
      "Scores per occupation: {'Cashier': array([0.23489241]), 'CEO': array([2.38690683]), 'Construction Worker': array([2.37796445]), 'Drug Dealer': array([2.23859313]), 'Doctor': array([1.84749674]), 'Service Worker': array([0.53172447]), 'Inmate': array([2.14271697]), 'Lawyer': array([2.33089117]), 'Politician': array([2.38748926]), 'Teacher': array([0.39032708])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.58506922]), 'CEO': array([0.81935212]), 'Construction Worker': array([0.81629054]), 'Drug Dealer': array([0.79805884]), 'Doctor': array([0.82191052]), 'Service Worker': array([1.76623965]), 'Inmate': array([0.6751972]), 'Lawyer': array([0.80477764]), 'Politician': array([0.81900294]), 'Teacher': array([1.69354585])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.55311314]), 'CEO': array([0.79524636]), 'Construction Worker': array([0.79586518]), 'Drug Dealer': array([0.83131665]), 'Doctor': array([0.87978623]), 'Service Worker': array([1.59944645]), 'Inmate': array([0.75939509]), 'Lawyer': array([0.77722682]), 'Politician': array([0.79525271]), 'Teacher': array([1.57512785])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.96480158]), 'CEO': array([0.2366807]), 'Construction Worker': array([0.2413021]), 'Drug Dealer': array([0.297294]), 'Doctor': array([0.58141179]), 'Service Worker': array([1.98296528]), 'Inmate': array([0.24562793]), 'Lawyer': array([0.41176137]), 'Politician': array([0.23629462]), 'Teacher': array([2.10517117])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "Best matched occupation: Inmate\n",
      "Scores per occupation: {'Cashier': array([1.18050477]), 'CEO': array([1.32554042]), 'Construction Worker': array([1.32298028]), 'Drug Dealer': array([1.31226727]), 'Doctor': array([1.27310365]), 'Service Worker': array([1.15237655]), 'Inmate': array([1.33387584]), 'Lawyer': array([1.2138819]), 'Politician': array([1.32570798]), 'Teacher': array([1.06277253])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Inmate\n",
      "Scores per occupation: {'Cashier': array([1.25368319]), 'CEO': array([1.19082275]), 'Construction Worker': array([1.19408354]), 'Drug Dealer': array([1.22048321]), 'Doctor': array([1.39886315]), 'Service Worker': array([0.97394713]), 'Inmate': array([1.41366557]), 'Lawyer': array([1.22387475]), 'Politician': array([1.19111742]), 'Teacher': array([1.05466564])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.97204114]), 'CEO': array([0.55767022]), 'Construction Worker': array([0.54998781]), 'Drug Dealer': array([0.50995071]), 'Doctor': array([0.55750516]), 'Service Worker': array([2.17201842]), 'Inmate': array([0.37351262]), 'Lawyer': array([0.35492952]), 'Politician': array([0.5563236]), 'Teacher': array([1.95351327])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.2304064]), 'CEO': array([1.40195502]), 'Construction Worker': array([1.39245465]), 'Drug Dealer': array([1.28009466]), 'Doctor': array([1.1475638]), 'Service Worker': array([1.51140655]), 'Inmate': array([1.16154926]), 'Lawyer': array([1.28390013]), 'Politician': array([1.40116283]), 'Teacher': array([1.32708965])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.53134584]), 'CEO': array([1.82026572]), 'Construction Worker': array([1.82249895]), 'Drug Dealer': array([1.7563581]), 'Doctor': array([1.68869988]), 'Service Worker': array([0.68884463]), 'Inmate': array([1.73427283]), 'Lawyer': array([2.15277274]), 'Politician': array([1.82092195]), 'Teacher': array([0.88937045])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.34796854]), 'CEO': array([0.89011546]), 'Construction Worker': array([0.89595294]), 'Drug Dealer': array([0.90563835]), 'Doctor': array([1.09092545]), 'Service Worker': array([1.40856239]), 'Inmate': array([0.88325535]), 'Lawyer': array([1.2277079]), 'Politician': array([0.89015526]), 'Teacher': array([1.64552979])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.97302628]), 'CEO': array([1.59055508]), 'Construction Worker': array([1.58304873]), 'Drug Dealer': array([1.45456958]), 'Doctor': array([1.33832597]), 'Service Worker': array([1.24555344]), 'Inmate': array([1.37197223]), 'Lawyer': array([1.60858893]), 'Politician': array([1.58991813]), 'Teacher': array([1.1661402])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.6898378]), 'CEO': array([0.50334368]), 'Construction Worker': array([0.50859773]), 'Drug Dealer': array([0.57668615]), 'Doctor': array([0.76166929]), 'Service Worker': array([1.69718584]), 'Inmate': array([0.52020169]), 'Lawyer': array([0.65814753]), 'Politician': array([0.50347968]), 'Teacher': array([1.81418177])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([1.12979734]), 'CEO': array([1.32716801]), 'Construction Worker': array([1.32399709]), 'Drug Dealer': array([1.34171707]), 'Doctor': array([1.16429273]), 'Service Worker': array([1.21286325]), 'Inmate': array([1.24444341]), 'Lawyer': array([1.14867357]), 'Politician': array([1.32789453]), 'Teacher': array([1.05100209])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.90742114]), 'CEO': array([0.590382]), 'Construction Worker': array([0.58449461]), 'Drug Dealer': array([0.54111074]), 'Doctor': array([0.61959599]), 'Service Worker': array([2.10685174]), 'Inmate': array([0.41699734]), 'Lawyer': array([0.47917716]), 'Politician': array([0.58893317]), 'Teacher': array([1.96550089])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.7164001]), 'CEO': array([0.85115355]), 'Construction Worker': array([0.84580217]), 'Drug Dealer': array([0.82549762]), 'Doctor': array([0.84591261]), 'Service Worker': array([1.78165495]), 'Inmate': array([0.77203689]), 'Lawyer': array([0.6372534]), 'Politician': array([0.85006705]), 'Teacher': array([1.60619566])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.80445104]), 'CEO': array([0.28420536]), 'Construction Worker': array([0.2952729]), 'Drug Dealer': array([0.35103487]), 'Doctor': array([0.74358486]), 'Service Worker': array([1.80269179]), 'Inmate': array([0.34041267]), 'Lawyer': array([0.73852964]), 'Politician': array([0.28327587]), 'Teacher': array([2.17930639])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.94334215]), 'CEO': array([0.76471847]), 'Construction Worker': array([0.7504257]), 'Drug Dealer': array([0.62543632]), 'Doctor': array([0.6023305]), 'Service Worker': array([2.26953337]), 'Inmate': array([0.46457752]), 'Lawyer': array([0.4654191]), 'Politician': array([0.7626513]), 'Teacher': array([1.93011847])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.00090391]), 'CEO': array([0.53851563]), 'Construction Worker': array([0.53046432]), 'Drug Dealer': array([0.50258225]), 'Doctor': array([0.52541286]), 'Service Worker': array([2.17741457]), 'Inmate': array([0.3647186]), 'Lawyer': array([0.26316782]), 'Politician': array([0.53709311]), 'Teacher': array([1.91892102])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.21361305]), 'CEO': array([0.60397588]), 'Construction Worker': array([0.58709716]), 'Drug Dealer': array([0.45771684]), 'Doctor': array([0.47862469]), 'Service Worker': array([2.45866517]), 'Inmate': array([0.34544628]), 'Lawyer': array([0.13784275]), 'Politician': array([0.60116525]), 'Teacher': array([2.01889332])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.20425969]), 'CEO': array([0.85810389]), 'Construction Worker': array([0.83118307]), 'Drug Dealer': array([0.60428164]), 'Doctor': array([0.46193402]), 'Service Worker': array([2.644219]), 'Inmate': array([0.4146609]), 'Lawyer': array([0.16869216]), 'Politician': array([0.85454893]), 'Teacher': array([1.96607154])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Inmate\n",
      "Scores per occupation: {'Cashier': array([1.27796664]), 'CEO': array([1.33659377]), 'Construction Worker': array([1.33564535]), 'Drug Dealer': array([1.33338081]), 'Doctor': array([1.46270648]), 'Service Worker': array([0.98809795]), 'Inmate': array([1.54822517]), 'Lawyer': array([1.2244893]), 'Politician': array([1.33679056]), 'Teacher': array([0.94526436])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Inmate\n",
      "Scores per occupation: {'Cashier': array([0.64123778]), 'CEO': array([1.82595808]), 'Construction Worker': array([1.82769552]), 'Drug Dealer': array([1.85929363]), 'Doctor': array([1.76444541]), 'Service Worker': array([0.40364206]), 'Inmate': array([2.00667511]), 'Lawyer': array([1.75334581]), 'Politician': array([1.82747756]), 'Teacher': array([0.39528721])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.81616806]), 'CEO': array([0.52581105]), 'Construction Worker': array([0.52711736]), 'Drug Dealer': array([0.57617945]), 'Doctor': array([0.65822062]), 'Service Worker': array([1.85663711]), 'Inmate': array([0.48397812]), 'Lawyer': array([0.4309465]), 'Politician': array([0.5244816]), 'Teacher': array([1.82810767])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.21416443]), 'CEO': array([0.75218732]), 'Construction Worker': array([0.73508176]), 'Drug Dealer': array([0.57945306]), 'Doctor': array([0.75740178]), 'Service Worker': array([2.23141112]), 'Inmate': array([0.67072717]), 'Lawyer': array([0.30477385]), 'Politician': array([0.74946013]), 'Teacher': array([1.82600411])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.40123526]), 'CEO': array([0.42937971]), 'Construction Worker': array([0.42141182]), 'Drug Dealer': array([0.34180704]), 'Doctor': array([0.83471567]), 'Service Worker': array([2.08028852]), 'Inmate': array([0.64209269]), 'Lawyer': array([0.20633584]), 'Politician': array([0.42721322]), 'Teacher': array([1.91897481])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.90776366]), 'CEO': array([1.13357628]), 'Construction Worker': array([1.10780317]), 'Drug Dealer': array([0.89559404]), 'Doctor': array([0.65685335]), 'Service Worker': array([2.34602085]), 'Inmate': array([0.69541612]), 'Lawyer': array([0.45745482]), 'Politician': array([1.13072968]), 'Teacher': array([1.6811931])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.0820273]), 'CEO': array([0.66296992]), 'Construction Worker': array([0.6599919]), 'Drug Dealer': array([0.62473891]), 'Doctor': array([1.11702375]), 'Service Worker': array([1.62972465]), 'Inmate': array([0.9947292]), 'Lawyer': array([0.54566157]), 'Politician': array([0.66180561]), 'Teacher': array([1.58467412])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.06503388]), 'CEO': array([1.05008045]), 'Construction Worker': array([1.02602298]), 'Drug Dealer': array([0.80832285]), 'Doctor': array([0.77197489]), 'Service Worker': array([2.29734187]), 'Inmate': array([0.77823902]), 'Lawyer': array([0.43893147]), 'Politician': array([1.0470203]), 'Teacher': array([1.71338151])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.9970655]), 'CEO': array([1.03554481]), 'Construction Worker': array([1.01648875]), 'Drug Dealer': array([0.84217449]), 'Doctor': array([0.90769717]), 'Service Worker': array([2.06692736]), 'Inmate': array([0.90713268]), 'Lawyer': array([0.54050205]), 'Politician': array([1.03309385]), 'Teacher': array([1.6088843])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.40313224]), 'CEO': array([1.61845316]), 'Construction Worker': array([1.59922713]), 'Drug Dealer': array([1.42514207]), 'Doctor': array([1.25845317]), 'Service Worker': array([1.53622903]), 'Inmate': array([1.4404879]), 'Lawyer': array([1.10058133]), 'Politician': array([1.61697985]), 'Teacher': array([1.05412269])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.54934379]), 'CEO': array([2.75502613]), 'Construction Worker': array([2.72106517]), 'Drug Dealer': array([2.40429647]), 'Doctor': array([1.65598481]), 'Service Worker': array([1.09940658]), 'Inmate': array([2.20396113]), 'Lawyer': array([1.8852117]), 'Politician': array([2.75359178]), 'Teacher': array([0.24223342])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.9232996]), 'CEO': array([1.9486918]), 'Construction Worker': array([1.93360809]), 'Drug Dealer': array([1.7974925]), 'Doctor': array([1.49542564]), 'Service Worker': array([1.07972397]), 'Inmate': array([1.7684807]), 'Lawyer': array([1.52729423]), 'Politician': array([1.94819455]), 'Teacher': array([0.6874433])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Politician\n",
      "Scores per occupation: {'Cashier': array([1.17227051]), 'CEO': array([1.39159299]), 'Construction Worker': array([1.38658442]), 'Drug Dealer': array([1.38165491]), 'Doctor': array([1.26301868]), 'Service Worker': array([1.15742284]), 'Inmate': array([1.37711768]), 'Lawyer': array([1.16248606]), 'Politician': array([1.39223541]), 'Teacher': array([0.95977747])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.58169326]), 'CEO': array([0.90332543]), 'Construction Worker': array([0.89795586]), 'Drug Dealer': array([0.90921168]), 'Doctor': array([0.80871206]), 'Service Worker': array([1.71206265]), 'Inmate': array([0.77277525]), 'Lawyer': array([0.64879208]), 'Politician': array([0.90315381]), 'Teacher': array([1.4834169])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([1.98335774]), 'CEO': array([0.25629831]), 'Construction Worker': array([0.26145544]), 'Drug Dealer': array([0.35641519]), 'Doctor': array([0.55356118]), 'Service Worker': array([1.92185452]), 'Inmate': array([0.30480976]), 'Lawyer': array([0.23070287]), 'Politician': array([0.25513645]), 'Teacher': array([1.97477143])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([2.00650684]), 'CEO': array([0.18486812]), 'Construction Worker': array([0.19013591]), 'Drug Dealer': array([0.29777273]), 'Doctor': array([0.46304682]), 'Service Worker': array([1.98989254]), 'Inmate': array([0.19834687]), 'Lawyer': array([0.14840874]), 'Politician': array([0.18381532]), 'Teacher': array([2.02762045])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.87055707]), 'CEO': array([1.10701581]), 'Construction Worker': array([1.08379648]), 'Drug Dealer': array([0.89915384]), 'Doctor': array([0.67572169]), 'Service Worker': array([2.27057227]), 'Inmate': array([0.70639106]), 'Lawyer': array([0.4787229]), 'Politician': array([1.10442589]), 'Teacher': array([1.65997816])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.08534397]), 'CEO': array([0.60538427]), 'Construction Worker': array([0.59281162]), 'Drug Dealer': array([0.51724866]), 'Doctor': array([0.51553235]), 'Service Worker': array([2.2934756]), 'Inmate': array([0.3881041]), 'Lawyer': array([0.20646364]), 'Politician': array([0.60320827]), 'Teacher': array([1.9311746])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.91382901]), 'CEO': array([0.24573776]), 'Construction Worker': array([0.25201329]), 'Drug Dealer': array([0.36229855]), 'Doctor': array([0.51745937]), 'Service Worker': array([1.91279697]), 'Inmate': array([0.25436611]), 'Lawyer': array([0.2652405]), 'Politician': array([0.24494113]), 'Teacher': array([1.98725743])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.96163169]), 'CEO': array([1.27259029]), 'Construction Worker': array([1.27483974]), 'Drug Dealer': array([1.38270112]), 'Doctor': array([1.13682942]), 'Service Worker': array([1.0279156]), 'Inmate': array([1.21980295]), 'Lawyer': array([1.16491388]), 'Politician': array([1.27477985]), 'Teacher': array([0.93133127])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.33036856]), 'CEO': array([0.83962843]), 'Construction Worker': array([0.84307114]), 'Drug Dealer': array([0.97585929]), 'Doctor': array([0.84985556]), 'Service Worker': array([1.38069947]), 'Inmate': array([0.8032466]), 'Lawyer': array([0.75784009]), 'Politician': array([0.84159204]), 'Teacher': array([1.30289758])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([2.00224744]), 'CEO': array([0.18785078]), 'Construction Worker': array([0.19423663]), 'Drug Dealer': array([0.30479069]), 'Doctor': array([0.5366154]), 'Service Worker': array([1.92344808]), 'Inmate': array([0.25818031]), 'Lawyer': array([0.2181002]), 'Politician': array([0.18719428]), 'Teacher': array([2.00633969])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.76597804]), 'CEO': array([0.2946658]), 'Construction Worker': array([0.30294157]), 'Drug Dealer': array([0.43800837]), 'Doctor': array([0.60618141]), 'Service Worker': array([1.75942636]), 'Inmate': array([0.3281131]), 'Lawyer': array([0.4508378]), 'Politician': array([0.2955177]), 'Teacher': array([1.88763067])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.17027888]), 'CEO': array([1.06696504]), 'Construction Worker': array([1.07032257]), 'Drug Dealer': array([1.17756977]), 'Doctor': array([1.03902113]), 'Service Worker': array([1.20028616]), 'Inmate': array([1.0462786]), 'Lawyer': array([1.00335381]), 'Politician': array([1.06858484]), 'Teacher': array([1.15309303])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.77874827]), 'CEO': array([0.33983196]), 'Construction Worker': array([0.34537143]), 'Drug Dealer': array([0.48555003]), 'Doctor': array([0.56820669]), 'Service Worker': array([1.77587282]), 'Inmate': array([0.35260011]), 'Lawyer': array([0.33857536]), 'Politician': array([0.34082312]), 'Teacher': array([1.78116189])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.04194536]), 'CEO': array([1.06186585]), 'Construction Worker': array([1.03432403]), 'Drug Dealer': array([0.79674729]), 'Doctor': array([0.59979]), 'Service Worker': array([2.48315892]), 'Inmate': array([0.61493917]), 'Lawyer': array([0.35422713]), 'Politician': array([1.05843642]), 'Teacher': array([1.79204192])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.19135082]), 'CEO': array([0.69195195]), 'Construction Worker': array([0.67304438]), 'Drug Dealer': array([0.52537568]), 'Doctor': array([0.52474485]), 'Service Worker': array([2.43822353]), 'Inmate': array([0.42505798]), 'Lawyer': array([0.17617303]), 'Politician': array([0.68917692]), 'Teacher': array([1.9478114])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.32146865]), 'CEO': array([0.75384684]), 'Construction Worker': array([0.72880278]), 'Drug Dealer': array([0.5087597]), 'Doctor': array([0.51317008]), 'Service Worker': array([2.61259957]), 'Inmate': array([0.4326653]), 'Lawyer': array([0.12114851]), 'Politician': array([0.75026197]), 'Teacher': array([2.00113832])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.0645814]), 'CEO': array([1.11433013]), 'Construction Worker': array([1.08466092]), 'Drug Dealer': array([0.82076735]), 'Doctor': array([0.63361032]), 'Service Worker': array([2.49885617]), 'Inmate': array([0.6661715]), 'Lawyer': array([0.36809654]), 'Politician': array([1.11071715]), 'Teacher': array([1.76766502])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.74650478]), 'CEO': array([0.92162166]), 'Construction Worker': array([0.9093743]), 'Drug Dealer': array([0.84215846]), 'Doctor': array([0.73476822]), 'Service Worker': array([1.95896904]), 'Inmate': array([0.70508471]), 'Lawyer': array([0.53589321]), 'Politician': array([0.92047786]), 'Teacher': array([1.5911698])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.28252938]), 'CEO': array([1.6021737]), 'Construction Worker': array([1.58340976]), 'Drug Dealer': array([1.43889288]), 'Doctor': array([1.10785729]), 'Service Worker': array([1.58480961]), 'Inmate': array([1.29304615]), 'Lawyer': array([1.06126766]), 'Politician': array([1.60102028]), 'Teacher': array([1.06921736])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.13260196]), 'CEO': array([1.77264325]), 'Construction Worker': array([1.75335626]), 'Drug Dealer': array([1.59635273]), 'Doctor': array([1.22306724]), 'Service Worker': array([1.43291262]), 'Inmate': array([1.46256067]), 'Lawyer': array([1.2170286]), 'Politician': array([1.77147327]), 'Teacher': array([0.9110831])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.59899617]), 'CEO': array([1.4163283]), 'Construction Worker': array([1.39261162]), 'Drug Dealer': array([1.19365695]), 'Doctor': array([0.90594542]), 'Service Worker': array([1.9743116]), 'Inmate': array([1.03329222]), 'Lawyer': array([0.77708163]), 'Politician': array([1.41403187]), 'Teacher': array([1.35906872])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.52332737]), 'CEO': array([1.55011109]), 'Construction Worker': array([1.52578341]), 'Drug Dealer': array([1.31433675]), 'Doctor': array([1.03750981]), 'Service Worker': array([1.85020938]), 'Inmate': array([1.20398904]), 'Lawyer': array([0.90924257]), 'Politician': array([1.54802608]), 'Teacher': array([1.23055091])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.61192107]), 'CEO': array([1.48701179]), 'Construction Worker': array([1.4610326]), 'Drug Dealer': array([1.23598978]), 'Doctor': array([0.949821]), 'Service Worker': array([1.98745107]), 'Inmate': array([1.09756735]), 'Lawyer': array([0.81033121]), 'Politician': array([1.48465191]), 'Teacher': array([1.32727686])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.51594944]), 'CEO': array([1.5269432]), 'Construction Worker': array([1.50638467]), 'Drug Dealer': array([1.31798292]), 'Doctor': array([1.17700284]), 'Service Worker': array([1.66347745]), 'Inmate': array([1.33318336]), 'Lawyer': array([0.98357577]), 'Politician': array([1.52512579]), 'Teacher': array([1.15517587])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.7857048]), 'CEO': array([1.285041]), 'Construction Worker': array([1.26344891]), 'Drug Dealer': array([1.06439817]), 'Doctor': array([1.00556305]), 'Service Worker': array([1.93561976]), 'Inmate': array([1.08592481]), 'Lawyer': array([0.72746581]), 'Politician': array([1.2826731]), 'Teacher': array([1.41143853])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.44548161]), 'CEO': array([0.50093814]), 'Construction Worker': array([0.48542332]), 'Drug Dealer': array([0.34006231]), 'Doctor': array([0.66458177]), 'Service Worker': array([2.36639779]), 'Inmate': array([0.49985887]), 'Lawyer': array([0.10189314]), 'Politician': array([0.49796103]), 'Teacher': array([2.01422189])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.3542683]), 'CEO': array([0.79372931]), 'Construction Worker': array([0.7668479]), 'Drug Dealer': array([0.5220806]), 'Doctor': array([0.53809509]), 'Service Worker': array([2.64205382]), 'Inmate': array([0.46908763]), 'Lawyer': array([0.12764425]), 'Politician': array([0.78986976]), 'Teacher': array([1.9987515])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.10255713]), 'CEO': array([2.08995372]), 'Construction Worker': array([2.06041266]), 'Drug Dealer': array([1.7954555]), 'Doctor': array([1.28410635]), 'Service Worker': array([1.56541276]), 'Inmate': array([1.62109]), 'Lawyer': array([1.32531337]), 'Politician': array([2.08808282]), 'Teacher': array([0.81465895])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.74249695]), 'CEO': array([2.22029979]), 'Construction Worker': array([2.20269213]), 'Drug Dealer': array([2.0399696]), 'Doctor': array([1.70020914]), 'Service Worker': array([0.83834568]), 'Inmate': array([2.07718325]), 'Lawyer': array([1.72496039]), 'Politician': array([2.22007905]), 'Teacher': array([0.38393552])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.16357217]), 'CEO': array([2.02388187]), 'Construction Worker': array([1.99699402]), 'Drug Dealer': array([1.74724908]), 'Doctor': array([1.35434747]), 'Service Worker': array([1.49507119]), 'Inmate': array([1.66604758]), 'Lawyer': array([1.32935075]), 'Politician': array([2.02205869]), 'Teacher': array([0.82567527])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.54065438]), 'CEO': array([2.73742063]), 'Construction Worker': array([2.70403814]), 'Drug Dealer': array([2.39552926]), 'Doctor': array([1.63940481]), 'Service Worker': array([1.09880679]), 'Inmate': array([2.18193784]), 'Lawyer': array([1.87758832]), 'Politician': array([2.73605833]), 'Teacher': array([0.2508488])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.36931668]), 'CEO': array([0.42508976]), 'Construction Worker': array([0.41780546]), 'Drug Dealer': array([0.34861171]), 'Doctor': array([0.82099037]), 'Service Worker': array([2.06373925]), 'Inmate': array([0.62792272]), 'Lawyer': array([0.21159725]), 'Politician': array([0.42303524]), 'Teacher': array([1.91150423])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.20221997]), 'CEO': array([0.60817506]), 'Construction Worker': array([0.59967915]), 'Drug Dealer': array([0.52342964]), 'Doctor': array([0.88746594]), 'Service Worker': array([1.96784862]), 'Inmate': array([0.75154159]), 'Lawyer': array([0.35783699]), 'Politician': array([0.6063312]), 'Teacher': array([1.7742934])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.16432572]), 'CEO': array([0.75407263]), 'Construction Worker': array([0.73898203]), 'Drug Dealer': array([0.60555686]), 'Doctor': array([0.79042985]), 'Service Worker': array([2.15054216]), 'Inmate': array([0.70344997]), 'Lawyer': array([0.34724926]), 'Politician': array([0.75165266]), 'Teacher': array([1.78739322])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.24565174]), 'CEO': array([0.64844949]), 'Construction Worker': array([0.63424696]), 'Drug Dealer': array([0.50925439]), 'Doctor': array([0.74564496]), 'Service Worker': array([2.19916612]), 'Inmate': array([0.62638417]), 'Lawyer': array([0.26533748]), 'Politician': array([0.64599697]), 'Teacher': array([1.86019954])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.17439512]), 'CEO': array([0.72081332]), 'Construction Worker': array([0.70694709]), 'Drug Dealer': array([0.58504692]), 'Doctor': array([0.79933465]), 'Service Worker': array([2.12543485]), 'Inmate': array([0.70161106]), 'Lawyer': array([0.34159591]), 'Politician': array([0.71849491]), 'Teacher': array([1.7922769])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.3159363]), 'CEO': array([0.6874218]), 'Construction Worker': array([0.66715509]), 'Drug Dealer': array([0.48702487]), 'Doctor': array([0.61184327]), 'Service Worker': array([2.4455994]), 'Inmate': array([0.50779476]), 'Lawyer': array([0.16642235]), 'Politician': array([0.68425537]), 'Teacher': array([1.95793625])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.33358827]), 'CEO': array([0.69684031]), 'Construction Worker': array([0.67409523]), 'Drug Dealer': array([0.47437524]), 'Doctor': array([0.53013341]), 'Service Worker': array([2.56291513]), 'Inmate': array([0.43101953]), 'Lawyer': array([0.11476215]), 'Politician': array([0.69337528]), 'Teacher': array([2.00906177])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.09844041]), 'CEO': array([0.82789456]), 'Construction Worker': array([0.80981654]), 'Drug Dealer': array([0.65998719]), 'Doctor': array([0.70994965]), 'Service Worker': array([2.23514185]), 'Inmate': array([0.65041391]), 'Lawyer': array([0.3426823]), 'Politician': array([0.82544737]), 'Teacher': array([1.77950639])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([1.96816694]), 'CEO': array([0.89688955]), 'Construction Worker': array([0.88556215]), 'Drug Dealer': array([0.78366201]), 'Doctor': array([0.99486839]), 'Service Worker': array([1.83757803]), 'Inmate': array([0.9499025]), 'Lawyer': array([0.57323455]), 'Politician': array([0.89514877]), 'Teacher': array([1.56651912])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([1.5259445]), 'CEO': array([1.42422845]), 'Construction Worker': array([1.41033948]), 'Drug Dealer': array([1.27755531]), 'Doctor': array([1.3120846]), 'Service Worker': array([1.4443053]), 'Inmate': array([1.43111658]), 'Lawyer': array([1.03813887]), 'Politician': array([1.42298699]), 'Teacher': array([1.10959778])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.21851207]), 'CEO': array([0.71183161]), 'Construction Worker': array([0.69763507]), 'Drug Dealer': array([0.5660677]), 'Doctor': array([0.8242734]), 'Service Worker': array([2.13072926]), 'Inmate': array([0.72209849]), 'Lawyer': array([0.33500097]), 'Politician': array([0.70939099]), 'Teacher': array([1.8017228])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.18836678]), 'CEO': array([0.43748723]), 'Construction Worker': array([0.43265908]), 'Drug Dealer': array([0.41387072]), 'Doctor': array([0.74074414]), 'Service Worker': array([2.00163207]), 'Inmate': array([0.55608947]), 'Lawyer': array([0.24973794]), 'Politician': array([0.4362113]), 'Teacher': array([1.8608229])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.36815251]), 'CEO': array([0.27587875]), 'Construction Worker': array([0.27162187]), 'Drug Dealer': array([0.24783962]), 'Doctor': array([0.69614102]), 'Service Worker': array([2.09966033]), 'Inmate': array([0.45978309]), 'Lawyer': array([0.11475905]), 'Politician': array([0.27419957]), 'Teacher': array([1.9940132])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.05855556]), 'CEO': array([0.35690571]), 'Construction Worker': array([0.35529562]), 'Drug Dealer': array([0.39982339]), 'Doctor': array([0.5815025]), 'Service Worker': array([2.01194449]), 'Inmate': array([0.37687394]), 'Lawyer': array([0.2059593]), 'Politician': array([0.35638609]), 'Teacher': array([1.89283341])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.29737287]), 'CEO': array([0.45942581]), 'Construction Worker': array([0.44848264]), 'Drug Dealer': array([0.36816305]), 'Doctor': array([0.61959953]), 'Service Worker': array([2.2586368]), 'Inmate': array([0.44238131]), 'Lawyer': array([0.14065118]), 'Politician': array([0.45729464]), 'Teacher': array([1.9784501])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.34154745]), 'CEO': array([1.06382958]), 'Construction Worker': array([1.06330589]), 'Drug Dealer': array([1.1113488]), 'Doctor': array([1.0404141]), 'Service Worker': array([1.34397216]), 'Inmate': array([1.04878117]), 'Lawyer': array([0.91777185]), 'Politician': array([1.06427464]), 'Teacher': array([1.23909066])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.95641847]), 'CEO': array([1.3830479]), 'Construction Worker': array([1.38403869]), 'Drug Dealer': array([1.44260154]), 'Doctor': array([1.2217534]), 'Service Worker': array([1.01904424]), 'Inmate': array([1.32504792]), 'Lawyer': array([1.25557343]), 'Politician': array([1.38380756]), 'Teacher': array([0.9428262])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.35775933]), 'CEO': array([1.08123425]), 'Construction Worker': array([1.07982273]), 'Drug Dealer': array([1.11429376]), 'Doctor': array([1.02317152]), 'Service Worker': array([1.38944767]), 'Inmate': array([1.0379185]), 'Lawyer': array([0.90239841]), 'Politician': array([1.08118318]), 'Teacher': array([1.26763133])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.31057673]), 'CEO': array([1.06911358]), 'Construction Worker': array([1.06700357]), 'Drug Dealer': array([1.1186291]), 'Doctor': array([0.96294283]), 'Service Worker': array([1.40010481]), 'Inmate': array([0.98210151]), 'Lawyer': array([0.87672899]), 'Politician': array([1.06995828]), 'Teacher': array([1.22925676])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.22414897]), 'CEO': array([1.25560556]), 'Construction Worker': array([1.25149015]), 'Drug Dealer': array([1.26502561]), 'Doctor': array([1.08404551]), 'Service Worker': array([1.31862741]), 'Inmate': array([1.15640448]), 'Lawyer': array([1.02141931]), 'Politician': array([1.25587601]), 'Teacher': array([1.12365905])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.97235296]), 'CEO': array([0.32256548]), 'Construction Worker': array([0.32357274]), 'Drug Dealer': array([0.37526308]), 'Doctor': array([0.52009657]), 'Service Worker': array([2.04236656]), 'Inmate': array([0.26552479]), 'Lawyer': array([0.28572383]), 'Politician': array([0.3217335]), 'Teacher': array([2.01467274])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.12414642]), 'CEO': array([0.6408561]), 'Construction Worker': array([0.63055552]), 'Drug Dealer': array([0.55212459]), 'Doctor': array([0.75732925]), 'Service Worker': array([2.07241379]), 'Inmate': array([0.63510911]), 'Lawyer': array([0.32782266]), 'Politician': array([0.63890134]), 'Teacher': array([1.80764748])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.18185413]), 'CEO': array([0.41954603]), 'Construction Worker': array([0.4134854]), 'Drug Dealer': array([0.39274958]), 'Doctor': array([0.6388914]), 'Service Worker': array([2.10118144]), 'Inmate': array([0.45118616]), 'Lawyer': array([0.19319585]), 'Politician': array([0.41818325]), 'Teacher': array([1.91447469])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.16221023]), 'CEO': array([0.4011733]), 'Construction Worker': array([0.39470463]), 'Drug Dealer': array([0.38174025]), 'Doctor': array([0.5638012]), 'Service Worker': array([2.15777022]), 'Inmate': array([0.37253383]), 'Lawyer': array([0.15745973]), 'Politician': array([0.39997422]), 'Teacher': array([1.94293353])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.01141033]), 'CEO': array([0.534242]), 'Construction Worker': array([0.53343424]), 'Drug Dealer': array([0.55496305]), 'Doctor': array([0.88669647]), 'Service Worker': array([1.74257665]), 'Inmate': array([0.73009066]), 'Lawyer': array([0.4286098]), 'Politician': array([0.53367184]), 'Teacher': array([1.6901559])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([1.99912043]), 'CEO': array([0.7012457]), 'Construction Worker': array([0.69677611]), 'Drug Dealer': array([0.66749558]), 'Doctor': array([0.9913559]), 'Service Worker': array([1.73311747]), 'Inmate': array([0.88263894]), 'Lawyer': array([0.52680527]), 'Politician': array([0.70019142]), 'Teacher': array([1.61684197])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([1.82008165]), 'CEO': array([0.88352519]), 'Construction Worker': array([0.87774538]), 'Drug Dealer': array([0.84117386]), 'Doctor': array([1.04279625]), 'Service Worker': array([1.64260475]), 'Inmate': array([0.99161052]), 'Lawyer': array([0.66907257]), 'Politician': array([0.8826567]), 'Teacher': array([1.48110387])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.30809221]), 'CEO': array([0.90808848]), 'Construction Worker': array([0.87484968]), 'Drug Dealer': array([0.57956776]), 'Doctor': array([0.38792486]), 'Service Worker': array([2.8643913]), 'Inmate': array([0.35417513]), 'Lawyer': array([0.09614531]), 'Politician': array([0.90374421]), 'Teacher': array([2.05135891])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.4739309]), 'CEO': array([1.97688796]), 'Construction Worker': array([1.97458795]), 'Drug Dealer': array([2.00175683]), 'Doctor': array([1.65886488]), 'Service Worker': array([0.46796917]), 'Inmate': array([1.96398956]), 'Lawyer': array([1.77687703]), 'Politician': array([1.97894791]), 'Teacher': array([0.30521359])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Politician\n",
      "Scores per occupation: {'Cashier': array([0.77626265]), 'CEO': array([1.97067092]), 'Construction Worker': array([1.95683063]), 'Drug Dealer': array([1.86433804]), 'Doctor': array([1.42796076]), 'Service Worker': array([1.01676681]), 'Inmate': array([1.72720732]), 'Lawyer': array([1.5281155]), 'Politician': array([1.97095778]), 'Teacher': array([0.59880063])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.08968914]), 'CEO': array([0.7206083]), 'Construction Worker': array([0.71164986]), 'Drug Dealer': array([0.63190797]), 'Doctor': array([0.94340985]), 'Service Worker': array([1.87943605]), 'Inmate': array([0.84380137]), 'Lawyer': array([0.45653105]), 'Politician': array([0.71892543]), 'Teacher': array([1.67030387])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([1.92687126]), 'CEO': array([0.92974947]), 'Construction Worker': array([0.91696527]), 'Drug Dealer': array([0.8083109]), 'Doctor': array([0.94365791]), 'Service Worker': array([1.88241244]), 'Inmate': array([0.91119929]), 'Lawyer': array([0.56822378]), 'Politician': array([0.92802161]), 'Teacher': array([1.56432469])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([1.58810808]), 'CEO': array([1.29396413]), 'Construction Worker': array([1.28056171]), 'Drug Dealer': array([1.16440623]), 'Doctor': array([1.17157321]), 'Service Worker': array([1.57154609]), 'Inmate': array([1.25187546]), 'Lawyer': array([0.91064053]), 'Politician': array([1.29275287]), 'Teacher': array([1.23147643])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.98074064]), 'CEO': array([0.98573807]), 'Construction Worker': array([0.96772847]), 'Drug Dealer': array([0.81030419]), 'Doctor': array([0.85413816]), 'Service Worker': array([2.07671378]), 'Inmate': array([0.84033975]), 'Lawyer': array([0.50670614]), 'Politician': array([0.98342323]), 'Teacher': array([1.63233109])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.55642999]), 'CEO': array([1.42172363]), 'Construction Worker': array([1.40426193]), 'Drug Dealer': array([1.24675724]), 'Doctor': array([1.17835869]), 'Service Worker': array([1.62506535]), 'Inmate': array([1.29994514]), 'Lawyer': array([0.94967156]), 'Politician': array([1.4201855]), 'Teacher': array([1.19075063])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.23303035]), 'CEO': array([1.79983304]), 'Construction Worker': array([1.77797264]), 'Drug Dealer': array([1.58598286]), 'Doctor': array([1.27452934]), 'Service Worker': array([1.48955803]), 'Inmate': array([1.51728029]), 'Lawyer': array([1.21297948]), 'Politician': array([1.79847203]), 'Teacher': array([0.92819852])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.19828643]), 'CEO': array([0.7698393]), 'Construction Worker': array([0.75221976]), 'Drug Dealer': array([0.59485509]), 'Doctor': array([0.74655554]), 'Service Worker': array([2.24278175]), 'Inmate': array([0.66576519]), 'Lawyer': array([0.30963399]), 'Politician': array([0.76716946]), 'Teacher': array([1.82001785])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.45173478]), 'CEO': array([1.55562179]), 'Construction Worker': array([1.53558202]), 'Drug Dealer': array([1.35811051]), 'Doctor': array([1.16856625]), 'Service Worker': array([1.63653709]), 'Inmate': array([1.33422253]), 'Lawyer': array([1.01393436]), 'Politician': array([1.55395838]), 'Teacher': array([1.12874974])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.42646685]), 'CEO': array([1.71060797]), 'Construction Worker': array([1.68565546]), 'Drug Dealer': array([1.45759132]), 'Doctor': array([1.17932031]), 'Service Worker': array([1.71508915]), 'Inmate': array([1.39228524]), 'Lawyer': array([1.06285441]), 'Politician': array([1.70852466]), 'Teacher': array([1.0943238])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.29017624]), 'CEO': array([1.05664748]), 'Construction Worker': array([1.01863842]), 'Drug Dealer': array([0.67574963]), 'Doctor': array([0.40634426]), 'Service Worker': array([2.925329]), 'Inmate': array([0.4244439]), 'Lawyer': array([0.13081594]), 'Politician': array([1.05188968]), 'Teacher': array([1.99918649])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.29361841]), 'CEO': array([1.02749534]), 'Construction Worker': array([0.99054508]), 'Drug Dealer': array([0.65787318]), 'Doctor': array([0.40837626]), 'Service Worker': array([2.90573219]), 'Inmate': array([0.41679691]), 'Lawyer': array([0.1258488]), 'Politician': array([1.02282291]), 'Teacher': array([2.00503469])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Politician\n",
      "Scores per occupation: {'Cashier': array([0.53665846]), 'CEO': array([2.41450889]), 'Construction Worker': array([2.39856341]), 'Drug Dealer': array([2.24229927]), 'Doctor': array([1.90084921]), 'Service Worker': array([0.55665824]), 'Inmate': array([2.33628989]), 'Lawyer': array([1.96099266]), 'Politician': array([2.41467795]), 'Teacher': array([0.15154909])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.21723578]), 'CEO': array([1.11759057]), 'Construction Worker': array([1.08088037]), 'Drug Dealer': array([0.7489429]), 'Doctor': array([0.48420711]), 'Service Worker': array([2.81459096]), 'Inmate': array([0.51919229]), 'Lawyer': array([0.22010043]), 'Politician': array([1.1130775]), 'Teacher': array([1.91983011])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.52296068]), 'CEO': array([0.33637096]), 'Construction Worker': array([0.32529111]), 'Drug Dealer': array([0.21949079]), 'Doctor': array([0.67685843]), 'Service Worker': array([2.30302127]), 'Inmate': array([0.45980059]), 'Lawyer': array([0.04258769]), 'Politician': array([0.33367385]), 'Teacher': array([2.06455998])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.33449658]), 'CEO': array([0.97916388]), 'Construction Worker': array([0.94261086]), 'Drug Dealer': array([0.61306962]), 'Doctor': array([0.39083715]), 'Service Worker': array([2.92765121]), 'Inmate': array([0.38450902]), 'Lawyer': array([0.08809145]), 'Politician': array([0.97445555]), 'Teacher': array([2.03876969])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.21831118]), 'CEO': array([0.8293218]), 'Construction Worker': array([0.80707213]), 'Drug Dealer': array([0.60884817]), 'Doctor': array([0.65416146]), 'Service Worker': array([2.41070144]), 'Inmate': array([0.59432588]), 'Lawyer': array([0.26026797]), 'Politician': array([0.82614909]), 'Teacher': array([1.87112713])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.25808867]), 'CEO': array([1.01448997]), 'Construction Worker': array([0.98041093]), 'Drug Dealer': array([0.6731037]), 'Doctor': array([0.47678889]), 'Service Worker': array([2.78553422]), 'Inmate': array([0.47929441]), 'Lawyer': array([0.17813974]), 'Politician': array([1.01013598]), 'Teacher': array([1.95616685])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.25059008]), 'CEO': array([0.77735278]), 'Construction Worker': array([0.75514069]), 'Drug Dealer': array([0.56057623]), 'Doctor': array([0.60277704]), 'Service Worker': array([2.46180358]), 'Inmate': array([0.52783315]), 'Lawyer': array([0.20733805]), 'Politician': array([0.77415185]), 'Teacher': array([1.91890936])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.43323068]), 'CEO': array([0.51967383]), 'Construction Worker': array([0.50220871]), 'Drug Dealer': array([0.3450623]), 'Doctor': array([0.58863594]), 'Service Worker': array([2.45675929]), 'Inmate': array([0.43196342]), 'Lawyer': array([0.06860003]), 'Politician': array([0.51654129]), 'Teacher': array([2.04602848])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.46207515]), 'CEO': array([0.42060763]), 'Construction Worker': array([0.40719289]), 'Drug Dealer': array([0.28536224]), 'Doctor': array([0.64080673]), 'Service Worker': array([2.35059648]), 'Inmate': array([0.45167382]), 'Lawyer': array([0.06569862]), 'Politician': array([0.4178198]), 'Teacher': array([2.04392894])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.3526585]), 'CEO': array([0.85744036]), 'Construction Worker': array([0.82630742]), 'Drug Dealer': array([0.54614286]), 'Doctor': array([0.44891842]), 'Service Worker': array([2.78961242]), 'Inmate': array([0.40238105]), 'Lawyer': array([0.09035582]), 'Politician': array([0.85319078]), 'Teacher': array([2.0354426])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.34085026]), 'CEO': array([0.70519572]), 'Construction Worker': array([0.68173393]), 'Drug Dealer': array([0.47417391]), 'Doctor': array([0.52327988]), 'Service Worker': array([2.58575463]), 'Inmate': array([0.42540433]), 'Lawyer': array([0.11222731]), 'Politician': array([0.70167394]), 'Teacher': array([2.01712686])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.33820425]), 'CEO': array([0.72287092]), 'Construction Worker': array([0.69809141]), 'Drug Dealer': array([0.48057169]), 'Doctor': array([0.49025945]), 'Service Worker': array([2.63496901]), 'Inmate': array([0.39848339]), 'Lawyer': array([0.09911064]), 'Politician': array([0.71926124]), 'Teacher': array([2.03060911])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.42164965]), 'CEO': array([0.68010062]), 'Construction Worker': array([0.655235]), 'Drug Dealer': array([0.42897455]), 'Doctor': array([0.51463085]), 'Service Worker': array([2.64715149]), 'Inmate': array([0.41060162]), 'Lawyer': array([0.06180779]), 'Politician': array([0.67630548]), 'Teacher': array([2.0555653])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.23481018]), 'CEO': array([1.10944325]), 'Construction Worker': array([1.07159578]), 'Drug Dealer': array([0.73028552]), 'Doctor': array([0.44341579]), 'Service Worker': array([2.87054384]), 'Inmate': array([0.47744747]), 'Lawyer': array([0.18628983]), 'Politician': array([1.10480194]), 'Teacher': array([1.94675296])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.23211348]), 'CEO': array([1.11541566]), 'Construction Worker': array([1.07773519]), 'Drug Dealer': array([0.73747113]), 'Doctor': array([0.45514224]), 'Service Worker': array([2.85955743]), 'Inmate': array([0.49039525]), 'Lawyer': array([0.19608665]), 'Politician': array([1.11079314]), 'Teacher': array([1.9404806])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.11260291]), 'CEO': array([1.24083745]), 'Construction Worker': array([1.20336524]), 'Drug Dealer': array([0.86443635]), 'Doctor': array([0.54437669]), 'Service Worker': array([2.73945117]), 'Inmate': array([0.61711912]), 'Lawyer': array([0.32383112]), 'Politician': array([1.23644403]), 'Teacher': array([1.8230046])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.5048388]), 'CEO': array([0.32612151]), 'Construction Worker': array([0.31692553]), 'Drug Dealer': array([0.22740167]), 'Doctor': array([0.72347227]), 'Service Worker': array([2.22621415]), 'Inmate': array([0.50157646]), 'Lawyer': array([0.07598966]), 'Politician': array([0.32362881]), 'Teacher': array([2.03532914])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.46174222]), 'CEO': array([0.38411511]), 'Construction Worker': array([0.37383383]), 'Drug Dealer': array([0.27613165]), 'Doctor': array([0.72296034]), 'Service Worker': array([2.22997533]), 'Inmate': array([0.51947784]), 'Lawyer': array([0.10625001]), 'Politician': array([0.38160526]), 'Teacher': array([2.00818475])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.00744912]), 'CEO': array([0.89518189]), 'Construction Worker': array([0.88226971]), 'Drug Dealer': array([0.76406123]), 'Doctor': array([0.95568364]), 'Service Worker': array([1.92013168]), 'Inmate': array([0.90908119]), 'Lawyer': array([0.53732783]), 'Politician': array([0.89316543]), 'Teacher': array([1.61347377])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.4063351]), 'CEO': array([0.52980351]), 'Construction Worker': array([0.5145441]), 'Drug Dealer': array([0.37295701]), 'Doctor': array([0.68123233]), 'Service Worker': array([2.33279432]), 'Inmate': array([0.52531638]), 'Lawyer': array([0.1344265]), 'Politician': array([0.52692979]), 'Teacher': array([1.98355811])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.03498398]), 'CEO': array([0.86787555]), 'Construction Worker': array([0.85513296]), 'Drug Dealer': array([0.73840807]), 'Doctor': array([0.9430876]), 'Service Worker': array([1.94250264]), 'Inmate': array([0.8865538]), 'Lawyer': array([0.51622421]), 'Politician': array([0.86584257]), 'Teacher': array([1.64111642])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.31735678]), 'CEO': array([0.66376451]), 'Construction Worker': array([0.64652489]), 'Drug Dealer': array([0.48712881]), 'Doctor': array([0.71656803]), 'Service Worker': array([2.30860921]), 'Inmate': array([0.60221524]), 'Lawyer': array([0.21973453]), 'Politician': array([0.66086322]), 'Teacher': array([1.90719725])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.29881863]), 'CEO': array([0.56066598]), 'Construction Worker': array([0.54996099]), 'Drug Dealer': array([0.44843227]), 'Doctor': array([0.81928588]), 'Service Worker': array([2.09971802]), 'Inmate': array([0.66915672]), 'Lawyer': array([0.26757184]), 'Politician': array([0.55836319]), 'Teacher': array([1.86231361])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.30216337]), 'CEO': array([0.6454098]), 'Construction Worker': array([0.6307584]), 'Drug Dealer': array([0.49290821]), 'Doctor': array([0.7839313]), 'Service Worker': array([2.20492972]), 'Inmate': array([0.66145121]), 'Lawyer': array([0.26231852]), 'Politician': array([0.64277352]), 'Teacher': array([1.87046346])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.43503787]), 'CEO': array([0.3979902]), 'Construction Worker': array([0.38931951]), 'Drug Dealer': array([0.30392655]), 'Doctor': array([0.78992453]), 'Service Worker': array([2.13903729]), 'Inmate': array([0.58886496]), 'Lawyer': array([0.15904385]), 'Politician': array([0.39568498]), 'Teacher': array([1.96028054])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.37674258]), 'CEO': array([0.56941867]), 'Construction Worker': array([0.55147818]), 'Drug Dealer': array([0.39100399]), 'Doctor': array([0.58659509]), 'Service Worker': array([2.45216895]), 'Inmate': array([0.44076817]), 'Lawyer': array([0.11282915]), 'Politician': array([0.56634228]), 'Teacher': array([2.02740052])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.39367965]), 'CEO': array([0.37686889]), 'Construction Worker': array([0.36750471]), 'Drug Dealer': array([0.28817971]), 'Doctor': array([0.66633933]), 'Service Worker': array([2.23621481]), 'Inmate': array([0.45804071]), 'Lawyer': array([0.11361776]), 'Politician': array([0.37458591]), 'Teacher': array([2.0196454])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.15264182]), 'CEO': array([0.78774662]), 'Construction Worker': array([0.76783385]), 'Drug Dealer': array([0.5982626]), 'Doctor': array([0.58755052]), 'Service Worker': array([2.41459372]), 'Inmate': array([0.50659747]), 'Lawyer': array([0.26619701]), 'Politician': array([0.78470048]), 'Teacher': array([1.91831122])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.10856386]), 'CEO': array([0.57343216]), 'Construction Worker': array([0.56430942]), 'Drug Dealer': array([0.49800288]), 'Doctor': array([0.66356309]), 'Service Worker': array([2.14699158]), 'Inmate': array([0.50417911]), 'Lawyer': array([0.30765989]), 'Politician': array([0.57147667]), 'Teacher': array([1.91082666])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.003499]), 'CEO': array([0.74922987]), 'Construction Worker': array([0.73706493]), 'Drug Dealer': array([0.64780636]), 'Doctor': array([0.70472375]), 'Service Worker': array([2.11669481]), 'Inmate': array([0.60977349]), 'Lawyer': array([0.39162999]), 'Politician': array([0.74722773]), 'Teacher': array([1.79265577])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.08307137]), 'CEO': array([0.69281276]), 'Construction Worker': array([0.68347733]), 'Drug Dealer': array([0.60479869]), 'Doctor': array([0.85954465]), 'Service Worker': array([1.96247528]), 'Inmate': array([0.74585872]), 'Lawyer': array([0.42329968]), 'Politician': array([0.69105714]), 'Teacher': array([1.73718793])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.05662009]), 'CEO': array([0.67466802]), 'Construction Worker': array([0.66441339]), 'Drug Dealer': array([0.5951299]), 'Doctor': array([0.74636097]), 'Service Worker': array([2.05058652]), 'Inmate': array([0.63465543]), 'Lawyer': array([0.36086833]), 'Politician': array([0.6730473]), 'Teacher': array([1.77292773])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.36712299]), 'CEO': array([0.3881813]), 'Construction Worker': array([0.38154009]), 'Drug Dealer': array([0.322349]), 'Doctor': array([0.78628727]), 'Service Worker': array([2.07563796]), 'Inmate': array([0.58232228]), 'Lawyer': array([0.18677831]), 'Politician': array([0.38621073]), 'Teacher': array([1.93290249])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Cashier\n",
      "Scores per occupation: {'Cashier': array([2.11266545]), 'CEO': array([0.62773457]), 'Construction Worker': array([0.61927445]), 'Drug Dealer': array([0.55851242]), 'Doctor': array([0.81882464]), 'Service Worker': array([1.98327901]), 'Inmate': array([0.69020477]), 'Lawyer': array([0.36694967]), 'Politician': array([0.62622102]), 'Teacher': array([1.76495827])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.65581329]), 'CEO': array([2.43165002]), 'Construction Worker': array([2.40875402]), 'Drug Dealer': array([2.18358273]), 'Doctor': array([1.69958266]), 'Service Worker': array([0.95424597]), 'Inmate': array([2.11722411]), 'Lawyer': array([1.85674853]), 'Politician': array([2.43088036]), 'Teacher': array([0.39192647])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Politician\n",
      "Scores per occupation: {'Cashier': array([0.56618819]), 'CEO': array([2.35394242]), 'Construction Worker': array([2.34012762]), 'Drug Dealer': array([2.19854243]), 'Doctor': array([1.92964142]), 'Service Worker': array([0.51639597]), 'Inmate': array([2.33863899]), 'Lawyer': array([1.96556912]), 'Politician': array([2.3542661]), 'Teacher': array([0.17405414])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.13989541]), 'CEO': array([0.90189563]), 'Construction Worker': array([0.87538318]), 'Drug Dealer': array([0.65504518]), 'Doctor': array([0.4851576]), 'Service Worker': array([2.59910275]), 'Inmate': array([0.44649846]), 'Lawyer': array([0.22943469]), 'Politician': array([0.89862787]), 'Teacher': array([1.92674794])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.5274388]), 'CEO': array([1.34042019]), 'Construction Worker': array([1.32193357]), 'Drug Dealer': array([1.17184002]), 'Doctor': array([0.94373351]), 'Service Worker': array([1.80839154]), 'Inmate': array([1.04750392]), 'Lawyer': array([0.81279724]), 'Politician': array([1.33849917]), 'Teacher': array([1.31740821])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.0602401]), 'CEO': array([0.84974607]), 'Construction Worker': array([0.83020578]), 'Drug Dealer': array([0.66735373]), 'Doctor': array([0.61872677]), 'Service Worker': array([2.33321077]), 'Inmate': array([0.56089634]), 'Lawyer': array([0.32815658]), 'Politician': array([0.8468925]), 'Teacher': array([1.83796821])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.94515442]), 'CEO': array([1.07614488]), 'Construction Worker': array([1.05242375]), 'Drug Dealer': array([0.84524058]), 'Doctor': array([0.68037315]), 'Service Worker': array([2.32632926]), 'Inmate': array([0.68944023]), 'Lawyer': array([0.46328679]), 'Politician': array([1.07301759]), 'Teacher': array([1.73395618])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.27308385]), 'CEO': array([1.3641174]), 'Construction Worker': array([1.35425656]), 'Drug Dealer': array([1.27592178]), 'Doctor': array([1.08453783]), 'Service Worker': array([1.53131309]), 'Inmate': array([1.13171161]), 'Lawyer': array([1.1381919]), 'Politician': array([1.36374009]), 'Teacher': array([1.27225875])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.75110571]), 'CEO': array([1.00838598]), 'Construction Worker': array([0.99295721]), 'Drug Dealer': array([0.87565438]), 'Doctor': array([0.73375115]), 'Service Worker': array([2.08837677]), 'Inmate': array([0.69202254]), 'Lawyer': array([0.6296082]), 'Politician': array([1.00689782]), 'Teacher': array([1.68318229])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.98878488]), 'CEO': array([0.51888131]), 'Construction Worker': array([0.51491091]), 'Drug Dealer': array([0.49141109]), 'Doctor': array([0.61740533]), 'Service Worker': array([2.08027714]), 'Inmate': array([0.42386574]), 'Lawyer': array([0.36248731]), 'Politician': array([0.51669237]), 'Teacher': array([1.96944535])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.43272238]), 'CEO': array([1.45298913]), 'Construction Worker': array([1.43653396]), 'Drug Dealer': array([1.29575559]), 'Doctor': array([1.05769699]), 'Service Worker': array([1.70350767]), 'Inmate': array([1.17525045]), 'Lawyer': array([0.98124219]), 'Politician': array([1.45109819]), 'Teacher': array([1.27295032])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.55420196]), 'CEO': array([0.94657286]), 'Construction Worker': array([0.94320946]), 'Drug Dealer': array([0.93698332]), 'Doctor': array([0.8793139]), 'Service Worker': array([1.66284705]), 'Inmate': array([0.83565664]), 'Lawyer': array([0.74443018]), 'Politician': array([0.94512182]), 'Teacher': array([1.53039082])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.0743553]), 'CEO': array([1.75858775]), 'Construction Worker': array([1.74358322]), 'Drug Dealer': array([1.6113698]), 'Doctor': array([1.24800087]), 'Service Worker': array([1.40196469]), 'Inmate': array([1.44420019]), 'Lawyer': array([1.33437957]), 'Politician': array([1.75727479]), 'Teacher': array([1.00640491])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.59360482]), 'CEO': array([0.91171241]), 'Construction Worker': array([0.90927997]), 'Drug Dealer': array([0.89760951]), 'Doctor': array([0.92065384]), 'Service Worker': array([1.67042551]), 'Inmate': array([0.83626757]), 'Lawyer': array([0.80153214]), 'Politician': array([0.91052895]), 'Teacher': array([1.59304228])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.51327786]), 'CEO': array([1.16722327]), 'Construction Worker': array([1.15824638]), 'Drug Dealer': array([1.09327393]), 'Doctor': array([1.00204545]), 'Service Worker': array([1.63497433]), 'Inmate': array([1.03026328]), 'Lawyer': array([0.86128835]), 'Politician': array([1.16584002]), 'Teacher': array([1.37958592])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.77148281]), 'CEO': array([1.002413]), 'Construction Worker': array([0.99114289]), 'Drug Dealer': array([0.90274403]), 'Doctor': array([0.88369705]), 'Service Worker': array([1.88185292]), 'Inmate': array([0.86511271]), 'Lawyer': array([0.64261026]), 'Politician': array([1.00040334]), 'Teacher': array([1.5795192])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.35591295]), 'CEO': array([0.97316547]), 'Construction Worker': array([0.97879333]), 'Drug Dealer': array([1.0185985]), 'Doctor': array([1.1861129]), 'Service Worker': array([1.24646197]), 'Inmate': array([1.07984144]), 'Lawyer': array([1.13762759]), 'Politician': array([0.97320496]), 'Teacher': array([1.40835546])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.04423804]), 'CEO': array([1.62484912]), 'Construction Worker': array([1.61786993]), 'Drug Dealer': array([1.55464461]), 'Doctor': array([1.38789424]), 'Service Worker': array([1.12677715]), 'Inmate': array([1.53166586]), 'Lawyer': array([1.41419864]), 'Politician': array([1.62454783]), 'Teacher': array([0.94078143])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Inmate\n",
      "Scores per occupation: {'Cashier': array([1.20027838]), 'CEO': array([1.23201818]), 'Construction Worker': array([1.23584744]), 'Drug Dealer': array([1.27255962]), 'Doctor': array([1.42669956]), 'Service Worker': array([0.93578779]), 'Inmate': array([1.44706597]), 'Lawyer': array([1.28720637]), 'Politician': array([1.23271924]), 'Teacher': array([1.02360714])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.15681134]), 'CEO': array([2.01078039]), 'Construction Worker': array([1.98718403]), 'Drug Dealer': array([1.76253881]), 'Doctor': array([1.43378909]), 'Service Worker': array([1.41497855]), 'Inmate': array([1.72295129]), 'Lawyer': array([1.40998979]), 'Politician': array([2.00926573]), 'Teacher': array([0.83585184])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.35954885]), 'CEO': array([1.93667032]), 'Construction Worker': array([1.94205556]), 'Drug Dealer': array([1.88810691]), 'Doctor': array([1.83619409]), 'Service Worker': array([0.48018984]), 'Inmate': array([1.89326094]), 'Lawyer': array([2.37773671]), 'Politician': array([1.93755681]), 'Teacher': array([0.78232366])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.44897901]), 'CEO': array([1.699351]), 'Construction Worker': array([1.70948201]), 'Drug Dealer': array([1.75073156]), 'Doctor': array([1.73905209]), 'Service Worker': array([0.42846712]), 'Inmate': array([1.77382298]), 'Lawyer': array([2.14200527]), 'Politician': array([1.70141826]), 'Teacher': array([0.76266981])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Politician\n",
      "Scores per occupation: {'Cashier': array([0.88687693]), 'CEO': array([1.67907082]), 'Construction Worker': array([1.67187696]), 'Drug Dealer': array([1.6591881]), 'Doctor': array([1.35629031]), 'Service Worker': array([0.9907688]), 'Inmate': array([1.56571794]), 'Lawyer': array([1.38535725]), 'Politician': array([1.68029373]), 'Teacher': array([0.71323175])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.52240424]), 'CEO': array([1.57043182]), 'Construction Worker': array([1.5810587]), 'Drug Dealer': array([1.64524252]), 'Doctor': array([1.6169101]), 'Service Worker': array([0.51743748]), 'Inmate': array([1.63093629]), 'Lawyer': array([1.98319802]), 'Politician': array([1.57255077]), 'Teacher': array([0.83350254])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.57933502]), 'CEO': array([1.50256291]), 'Construction Worker': array([1.51750413]), 'Drug Dealer': array([1.50673537]), 'Doctor': array([1.76310172]), 'Service Worker': array([0.5827633]), 'Inmate': array([1.59785309]), 'Lawyer': array([2.33052811]), 'Politician': array([1.50340209]), 'Teacher': array([1.21197459])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([1.08443971]), 'CEO': array([0.99052219]), 'Construction Worker': array([1.00560117]), 'Drug Dealer': array([1.03423714]), 'Doctor': array([1.3551282]), 'Service Worker': array([1.08385324]), 'Inmate': array([1.07651021]), 'Lawyer': array([1.70495411]), 'Politician': array([0.99051674]), 'Teacher': array([1.65578308])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.13379614]), 'CEO': array([0.92206037]), 'Construction Worker': array([0.93762244]), 'Drug Dealer': array([0.94094335]), 'Doctor': array([1.36412862]), 'Service Worker': array([1.13368274]), 'Inmate': array([1.02019196]), 'Lawyer': array([1.75973136]), 'Politician': array([0.92196674]), 'Teacher': array([1.77818507])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.11411668]), 'CEO': array([1.89511985]), 'Construction Worker': array([1.91125268]), 'Drug Dealer': array([1.91041536]), 'Doctor': array([2.02997372]), 'Service Worker': array([0.11805065]), 'Inmate': array([1.99403293]), 'Lawyer': array([2.7373865]), 'Politician': array([1.8967564]), 'Teacher': array([0.76533417])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.64983316]), 'CEO': array([1.3562955]), 'Construction Worker': array([1.37230532]), 'Drug Dealer': array([1.36850396]), 'Doctor': array([1.67143898]), 'Service Worker': array([0.65083505]), 'Inmate': array([1.45846336]), 'Lawyer': array([2.22549568]), 'Politician': array([1.35700334]), 'Teacher': array([1.31476908])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.29421768]), 'CEO': array([1.71907096]), 'Construction Worker': array([1.73506919]), 'Drug Dealer': array([1.73447812]), 'Doctor': array([1.91081658]), 'Service Worker': array([0.2934953]), 'Inmate': array([1.82073794]), 'Lawyer': array([2.55951031]), 'Politician': array([1.72039739]), 'Teacher': array([0.93980984])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.53371738]), 'CEO': array([1.61445245]), 'Construction Worker': array([1.62237582]), 'Drug Dealer': array([1.67651505]), 'Doctor': array([1.51528127]), 'Service Worker': array([0.58894182]), 'Inmate': array([1.60068785]), 'Lawyer': array([1.81622551]), 'Politician': array([1.61522631]), 'Teacher': array([0.79216734])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.434863]), 'CEO': array([1.90010508]), 'Construction Worker': array([1.9005443]), 'Drug Dealer': array([1.92376358]), 'Doctor': array([1.52125475]), 'Service Worker': array([0.58633721]), 'Inmate': array([1.77049492]), 'Lawyer': array([1.77062378]), 'Politician': array([1.90055228]), 'Teacher': array([0.52570047])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.96243884]), 'CEO': array([1.33985948]), 'Construction Worker': array([1.3425139]), 'Drug Dealer': array([1.33277562]), 'Doctor': array([1.28646975]), 'Service Worker': array([1.07177723]), 'Inmate': array([1.27078972]), 'Lawyer': array([1.4819554]), 'Politician': array([1.33957134]), 'Teacher': array([1.18741891])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.33075199]), 'CEO': array([0.78428221]), 'Construction Worker': array([0.79348475]), 'Drug Dealer': array([0.85845236]), 'Doctor': array([0.97821826]), 'Service Worker': array([1.36448311]), 'Inmate': array([0.79201722]), 'Lawyer': array([1.04648077]), 'Politician': array([0.7836615]), 'Teacher': array([1.62323931])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.07211198]), 'CEO': array([1.54269859]), 'Construction Worker': array([1.53239872]), 'Drug Dealer': array([1.44898243]), 'Doctor': array([1.138972]), 'Service Worker': array([1.35447228]), 'Inmate': array([1.27799443]), 'Lawyer': array([1.21390984]), 'Politician': array([1.54151725]), 'Teacher': array([1.06480149])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Politician\n",
      "Scores per occupation: {'Cashier': array([0.43674803]), 'CEO': array([1.99645087]), 'Construction Worker': array([1.99339514]), 'Drug Dealer': array([1.96556758]), 'Doctor': array([1.56959317]), 'Service Worker': array([0.63489047]), 'Inmate': array([1.82596351]), 'Lawyer': array([1.85778363]), 'Politician': array([1.99677207]), 'Teacher': array([0.52683868])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.34830105]), 'CEO': array([0.79918526]), 'Construction Worker': array([0.8071031]), 'Drug Dealer': array([0.85460319]), 'Doctor': array([0.98136136]), 'Service Worker': array([1.39653645]), 'Inmate': array([0.79362503]), 'Lawyer': array([1.05483539]), 'Politician': array([0.79850313]), 'Teacher': array([1.63603782])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.20150202]), 'CEO': array([2.01914913]), 'Construction Worker': array([2.02435159]), 'Drug Dealer': array([2.07804718]), 'Doctor': array([1.69663798]), 'Service Worker': array([0.29611614]), 'Inmate': array([1.95509534]), 'Lawyer': array([2.04483833]), 'Politician': array([2.02027303]), 'Teacher': array([0.37240855])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Politician\n",
      "Scores per occupation: {'Cashier': array([0.41355947]), 'CEO': array([2.14219816]), 'Construction Worker': array([2.13483802]), 'Drug Dealer': array([2.07821226]), 'Doctor': array([1.5941968]), 'Service Worker': array([0.64779845]), 'Inmate': array([1.9239259]), 'Lawyer': array([1.85315839]), 'Politician': array([2.14232682]), 'Teacher': array([0.41086378])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.22600656]), 'CEO': array([1.86771097]), 'Construction Worker': array([1.87782577]), 'Drug Dealer': array([1.96920831]), 'Doctor': array([1.67941368]), 'Service Worker': array([0.25965338]), 'Inmate': array([1.87039135]), 'Lawyer': array([2.03626464]), 'Politician': array([1.86900373]), 'Teacher': array([0.47167387])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.12585775]), 'CEO': array([2.11558873]), 'Construction Worker': array([2.11983678]), 'Drug Dealer': array([2.17831862]), 'Doctor': array([1.73166374]), 'Service Worker': array([0.22212736]), 'Inmate': array([2.04272005]), 'Lawyer': array([2.07103702]), 'Politician': array([2.11688995]), 'Teacher': array([0.24702809])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.6404683]), 'CEO': array([1.57414699]), 'Construction Worker': array([1.57859578]), 'Drug Dealer': array([1.65817774]), 'Doctor': array([1.33140694]), 'Service Worker': array([0.73235555]), 'Inmate': array([1.49828319]), 'Lawyer': array([1.48006072]), 'Politician': array([1.57448004]), 'Teacher': array([0.73828861])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.43629218]), 'CEO': array([0.92196982]), 'Construction Worker': array([0.9215862]), 'Drug Dealer': array([0.90957608]), 'Doctor': array([0.94381625]), 'Service Worker': array([1.56989348]), 'Inmate': array([0.82154559]), 'Lawyer': array([0.96641096]), 'Politician': array([0.9213001]), 'Teacher': array([1.58324821])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.51393878]), 'CEO': array([0.80467762]), 'Construction Worker': array([0.80626226]), 'Drug Dealer': array([0.77858168]), 'Doctor': array([0.94036864]), 'Service Worker': array([1.63882534]), 'Inmate': array([0.72849628]), 'Lawyer': array([1.01141238]), 'Politician': array([0.8037915]), 'Teacher': array([1.76739262])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.26006838]), 'CEO': array([0.8492352]), 'Construction Worker': array([0.85930713]), 'Drug Dealer': array([0.89692887]), 'Doctor': array([1.116996]), 'Service Worker': array([1.28018477]), 'Inmate': array([0.88739116]), 'Lawyer': array([1.28883709]), 'Politician': array([0.84915843]), 'Teacher': array([1.63390333])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.3592552]), 'CEO': array([1.93330159]), 'Construction Worker': array([1.93628225]), 'Drug Dealer': array([1.94017529]), 'Doctor': array([1.65448846]), 'Service Worker': array([0.49067092]), 'Inmate': array([1.8436038]), 'Lawyer': array([2.01919129]), 'Politician': array([1.93400901]), 'Teacher': array([0.57493279])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.16149386]), 'CEO': array([1.85714363]), 'Construction Worker': array([1.87203693]), 'Drug Dealer': array([1.90087516]), 'Doctor': array([1.92362837]), 'Service Worker': array([0.1580387]), 'Inmate': array([1.94276526]), 'Lawyer': array([2.52132815]), 'Politician': array([1.85868645]), 'Teacher': array([0.69530482])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.12861925]), 'CEO': array([0.94577452]), 'Construction Worker': array([0.95812869]), 'Drug Dealer': array([0.97934044]), 'Doctor': array([1.26020734]), 'Service Worker': array([1.13533171]), 'Inmate': array([1.01095849]), 'Lawyer': array([1.53755788]), 'Politician': array([0.94561759]), 'Teacher': array([1.60889641])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([1.06942547]), 'CEO': array([1.05748099]), 'Construction Worker': array([1.06866527]), 'Drug Dealer': array([1.1098912]), 'Doctor': array([1.31160676]), 'Service Worker': array([1.01857437]), 'Inmate': array([1.15205989]), 'Lawyer': array([1.5034566]), 'Politician': array([1.05758743]), 'Teacher': array([1.40131793])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.48244295]), 'CEO': array([1.6970467]), 'Construction Worker': array([1.70465393]), 'Drug Dealer': array([1.76421536]), 'Doctor': array([1.55781267]), 'Service Worker': array([0.52768702]), 'Inmate': array([1.68518149]), 'Lawyer': array([1.84556415]), 'Politician': array([1.69781064]), 'Teacher': array([0.70274339])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.98674244]), 'CEO': array([1.60903031]), 'Construction Worker': array([1.60030327]), 'Drug Dealer': array([1.52370476]), 'Doctor': array([1.23323612]), 'Service Worker': array([1.24274272]), 'Inmate': array([1.37429569]), 'Lawyer': array([1.35181605]), 'Politician': array([1.60823475]), 'Teacher': array([1.00605944])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.41720119]), 'CEO': array([1.70286039]), 'Construction Worker': array([1.71245704]), 'Drug Dealer': array([1.7879482]), 'Doctor': array([1.59734592]), 'Service Worker': array([0.43258099]), 'Inmate': array([1.7219086]), 'Lawyer': array([1.90512384]), 'Politician': array([1.70384819]), 'Teacher': array([0.65954877])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.32678177]), 'CEO': array([2.65619407]), 'Construction Worker': array([2.63443933]), 'Drug Dealer': array([2.42020922]), 'Doctor': array([1.80630363]), 'Service Worker': array([0.69135308]), 'Inmate': array([2.29446483]), 'Lawyer': array([2.10103311]), 'Politician': array([2.65582146]), 'Teacher': array([0.14700297])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.12422806]), 'CEO': array([2.1780607]), 'Construction Worker': array([2.18059634]), 'Drug Dealer': array([2.19833167]), 'Doctor': array([1.785824]), 'Service Worker': array([0.24893713]), 'Inmate': array([2.08471948]), 'Lawyer': array([2.17344346]), 'Politician': array([2.17914424]), 'Teacher': array([0.27918928])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.86606012]), 'CEO': array([1.26755599]), 'Construction Worker': array([1.27561499]), 'Drug Dealer': array([1.36465639]), 'Doctor': array([1.23324251]), 'Service Worker': array([0.89897942]), 'Inmate': array([1.25924525]), 'Lawyer': array([1.35845096]), 'Politician': array([1.26775619]), 'Teacher': array([1.04804132])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.29942842]), 'CEO': array([1.09436907]), 'Construction Worker': array([1.09242776]), 'Drug Dealer': array([1.08412336]), 'Doctor': array([0.99055844]), 'Service Worker': array([1.45732388]), 'Inmate': array([0.96016857]), 'Lawyer': array([1.00931487]), 'Politician': array([1.09360112]), 'Teacher': array([1.38873664])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.75420353]), 'CEO': array([1.64310561]), 'Construction Worker': array([1.64127791]), 'Drug Dealer': array([1.65614318]), 'Doctor': array([1.31027308]), 'Service Worker': array([0.91573255]), 'Inmate': array([1.49523975]), 'Lawyer': array([1.43786368]), 'Politician': array([1.64306903]), 'Teacher': array([0.78625906])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.71703293]), 'CEO': array([0.61048549]), 'Construction Worker': array([0.61196623]), 'Drug Dealer': array([0.61645533]), 'Doctor': array([0.7613499]), 'Service Worker': array([1.80536279]), 'Inmate': array([0.54707085]), 'Lawyer': array([0.67487307]), 'Politician': array([0.60904824]), 'Teacher': array([1.86728174])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.15419739]), 'CEO': array([1.5406664]), 'Construction Worker': array([1.52797601]), 'Drug Dealer': array([1.42450297]), 'Doctor': array([1.10762006]), 'Service Worker': array([1.45738806]), 'Inmate': array([1.25094849]), 'Lawyer': array([1.15108522]), 'Politician': array([1.53926825]), 'Teacher': array([1.10756076])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.32034545]), 'CEO': array([2.04917585]), 'Construction Worker': array([2.04893154]), 'Drug Dealer': array([2.05628259]), 'Doctor': array([1.63835125]), 'Service Worker': array([0.46841087]), 'Inmate': array([1.92309792]), 'Lawyer': array([1.93270222]), 'Politician': array([2.04983073]), 'Teacher': array([0.40659341])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.50945607]), 'CEO': array([0.67345863]), 'Construction Worker': array([0.679239]), 'Drug Dealer': array([0.77394399]), 'Doctor': array([0.76663443]), 'Service Worker': array([1.54168096]), 'Inmate': array([0.64815281]), 'Lawyer': array([0.6329904]), 'Politician': array([0.67242561]), 'Teacher': array([1.60029473])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.83434203]), 'CEO': array([1.56201463]), 'Construction Worker': array([1.56067984]), 'Drug Dealer': array([1.56807368]), 'Doctor': array([1.29270088]), 'Service Worker': array([0.9733141]), 'Inmate': array([1.43671422]), 'Lawyer': array([1.40736753]), 'Politician': array([1.56182292]), 'Teacher': array([0.8808724])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.65043257]), 'CEO': array([1.41794074]), 'Construction Worker': array([1.42814539]), 'Drug Dealer': array([1.55735446]), 'Doctor': array([1.31619683]), 'Service Worker': array([0.67257783]), 'Inmate': array([1.41725208]), 'Lawyer': array([1.47255226]), 'Politician': array([1.41852842]), 'Teacher': array([0.82643215])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.33649546]), 'CEO': array([1.70946438]), 'Construction Worker': array([1.72157022]), 'Drug Dealer': array([1.82008591]), 'Doctor': array([1.62977116]), 'Service Worker': array([0.33580223]), 'Inmate': array([1.75010692]), 'Lawyer': array([1.97245166]), 'Politician': array([1.71071081]), 'Teacher': array([0.62261631])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.94019986]), 'CEO': array([0.9765485]), 'Construction Worker': array([0.95440329]), 'Drug Dealer': array([0.77114238]), 'Doctor': array([0.58070123]), 'Service Worker': array([2.36601782]), 'Inmate': array([0.56041191]), 'Lawyer': array([0.39226944]), 'Politician': array([0.97354729]), 'Teacher': array([1.79739563])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([2.09374557]), 'CEO': array([0.34097403]), 'Construction Worker': array([0.33879847]), 'Drug Dealer': array([0.2820787]), 'Doctor': array([0.59511283]), 'Service Worker': array([2.20806094]), 'Inmate': array([0.25668795]), 'Lawyer': array([0.4459515]), 'Politician': array([0.33889201]), 'Teacher': array([2.24844413])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.20105843]), 'CEO': array([0.87538207]), 'Construction Worker': array([0.84715325]), 'Drug Dealer': array([0.60758243]), 'Doctor': array([0.42912399]), 'Service Worker': array([2.69149503]), 'Inmate': array([0.38797959]), 'Lawyer': array([0.15482934]), 'Politician': array([0.87161528]), 'Teacher': array([1.98147625])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.22940993]), 'CEO': array([0.71162195]), 'Construction Worker': array([0.68824507]), 'Drug Dealer': array([0.49240672]), 'Doctor': array([0.40320432]), 'Service Worker': array([2.65365651]), 'Inmate': array([0.2952651]), 'Lawyer': array([0.12922383]), 'Politician': array([0.70826746]), 'Teacher': array([2.06888536])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.25060735]), 'CEO': array([0.81305537]), 'Construction Worker': array([0.78524901]), 'Drug Dealer': array([0.55106458]), 'Doctor': array([0.39857149]), 'Service Worker': array([2.72775673]), 'Inmate': array([0.33780955]), 'Lawyer': array([0.10523189]), 'Politician': array([0.80931741]), 'Teacher': array([2.02788527])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.27654763]), 'CEO': array([0.86126181]), 'Construction Worker': array([0.83069479]), 'Drug Dealer': array([0.5682771]), 'Doctor': array([0.38712325]), 'Service Worker': array([2.79448083]), 'Inmate': array([0.3445657]), 'Lawyer': array([0.09001217]), 'Politician': array([0.85721573]), 'Teacher': array([2.03141873])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.91440896]), 'CEO': array([1.31708893]), 'Construction Worker': array([1.28404445]), 'Drug Dealer': array([0.99282977]), 'Doctor': array([0.65974411]), 'Service Worker': array([2.48101563]), 'Inmate': array([0.75529147]), 'Lawyer': array([0.49031209]), 'Politician': array([1.31349187]), 'Teacher': array([1.65712411])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.14551117]), 'CEO': array([2.14088137]), 'Construction Worker': array([2.10727175]), 'Drug Dealer': array([1.80106789]), 'Doctor': array([1.21646132]), 'Service Worker': array([1.72949225]), 'Inmate': array([1.56451016]), 'Lawyer': array([1.29128061]), 'Politician': array([2.13845992]), 'Teacher': array([0.8855865])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.67296388]), 'CEO': array([1.62427558]), 'Construction Worker': array([1.58866111]), 'Drug Dealer': array([1.2667962]), 'Doctor': array([0.82734694]), 'Service Worker': array([2.28051023]), 'Inmate': array([1.02190608]), 'Lawyer': array([0.73666885]), 'Politician': array([1.62076373]), 'Teacher': array([1.39744689])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.18934334]), 'CEO': array([1.04811558]), 'Construction Worker': array([1.0141163]), 'Drug Dealer': array([0.7126979]), 'Doctor': array([0.46085699]), 'Service Worker': array([2.76271087]), 'Inmate': array([0.47676237]), 'Lawyer': array([0.2020792]), 'Politician': array([1.04382719]), 'Teacher': array([1.92419931])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.28638587]), 'CEO': array([0.89586422]), 'Construction Worker': array([0.86383512]), 'Drug Dealer': array([0.58481749]), 'Doctor': array([0.38966541]), 'Service Worker': array([2.82521372]), 'Inmate': array([0.35787055]), 'Lawyer': array([0.09327745]), 'Politician': array([0.89162676]), 'Teacher': array([2.03114719])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.16155107]), 'CEO': array([0.48421411]), 'Construction Worker': array([0.47121994]), 'Drug Dealer': array([0.39269333]), 'Doctor': array([0.38958119]), 'Service Worker': array([2.44040256]), 'Inmate': array([0.21406804]), 'Lawyer': array([0.10493787]), 'Politician': array([0.4819743]), 'Teacher': array([2.0727687])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.19227677]), 'CEO': array([0.80967386]), 'Construction Worker': array([0.78350016]), 'Drug Dealer': array([0.57454959]), 'Doctor': array([0.40169446]), 'Service Worker': array([2.66460518]), 'Inmate': array([0.34351449]), 'Lawyer': array([0.12665281]), 'Politician': array([0.80635691]), 'Teacher': array([1.98733115])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.12436047]), 'CEO': array([0.77497999]), 'Construction Worker': array([0.75294372]), 'Drug Dealer': array([0.58624009]), 'Doctor': array([0.44323362]), 'Service Worker': array([2.53057044]), 'Inmate': array([0.37327284]), 'Lawyer': array([0.17456093]), 'Politician': array([0.77206479]), 'Teacher': array([1.94520634])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.13447669]), 'CEO': array([0.44860475]), 'Construction Worker': array([0.43664946]), 'Drug Dealer': array([0.38941011]), 'Doctor': array([0.36081878]), 'Service Worker': array([2.40628958]), 'Inmate': array([0.18749803]), 'Lawyer': array([0.07127544]), 'Politician': array([0.44709453]), 'Teacher': array([2.03093755])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.30173013]), 'CEO': array([0.9319647]), 'Construction Worker': array([0.89773629]), 'Drug Dealer': array([0.5968054]), 'Doctor': array([0.37313897]), 'Service Worker': array([2.88388457]), 'Inmate': array([0.35321012]), 'Lawyer': array([0.08338565]), 'Politician': array([0.92753098]), 'Teacher': array([2.0392637])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.92645421]), 'CEO': array([0.80786609]), 'Construction Worker': array([0.79150047]), 'Drug Dealer': array([0.69016198]), 'Doctor': array([0.5583488]), 'Service Worker': array([2.24205211]), 'Inmate': array([0.49767501]), 'Lawyer': array([0.33314044]), 'Politician': array([0.80618088]), 'Teacher': array([1.77305454])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.22508961]), 'CEO': array([0.78648028]), 'Construction Worker': array([0.7603374]), 'Drug Dealer': array([0.5475751]), 'Doctor': array([0.39743986]), 'Service Worker': array([2.68174164]), 'Inmate': array([0.33197964]), 'Lawyer': array([0.1044411]), 'Politician': array([0.78297421]), 'Teacher': array([2.01162621])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.21809897]), 'CEO': array([0.73104907]), 'Construction Worker': array([0.70727023]), 'Drug Dealer': array([0.51806234]), 'Doctor': array([0.39015467]), 'Service Worker': array([2.64564111]), 'Inmate': array([0.3063049]), 'Lawyer': array([0.09566118]), 'Politician': array([0.7276387]), 'Teacher': array([2.02925446])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.21822205]), 'CEO': array([0.50895501]), 'Construction Worker': array([0.49394812]), 'Drug Dealer': array([0.39171158]), 'Doctor': array([0.40603163]), 'Service Worker': array([2.47920402]), 'Inmate': array([0.2445055]), 'Lawyer': array([0.07867642]), 'Politician': array([0.50638903]), 'Teacher': array([2.0703488])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.24538782]), 'CEO': array([0.73586241]), 'Construction Worker': array([0.71070253]), 'Drug Dealer': array([0.50956133]), 'Doctor': array([0.3740485]), 'Service Worker': array([2.69088815]), 'Inmate': array([0.29312732]), 'Lawyer': array([0.07316155]), 'Politician': array([0.73241495]), 'Teacher': array([2.0412179])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.19791193]), 'CEO': array([0.73028875]), 'Construction Worker': array([0.70757146]), 'Drug Dealer': array([0.52598876]), 'Doctor': array([0.40879613]), 'Service Worker': array([2.60602518]), 'Inmate': array([0.32357879]), 'Lawyer': array([0.11613701]), 'Politician': array([0.72686195]), 'Teacher': array([2.01741499])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.18199973]), 'CEO': array([0.64423301]), 'Construction Worker': array([0.62456589]), 'Drug Dealer': array([0.4857547]), 'Doctor': array([0.40820705]), 'Service Worker': array([2.53221452]), 'Inmate': array([0.29603957]), 'Lawyer': array([0.10872425]), 'Politician': array([0.64173879]), 'Teacher': array([2.00045521])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.22729178]), 'CEO': array([0.58212163]), 'Construction Worker': array([0.56391477]), 'Drug Dealer': array([0.42782921]), 'Doctor': array([0.39872411]), 'Service Worker': array([2.54825845]), 'Inmate': array([0.26037974]), 'Lawyer': array([0.08370778]), 'Politician': array([0.57921767]), 'Teacher': array([2.06792315])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.12892229]), 'CEO': array([1.04523456]), 'Construction Worker': array([1.01346524]), 'Drug Dealer': array([0.73798975]), 'Doctor': array([0.4931576]), 'Service Worker': array([2.67029077]), 'Inmate': array([0.50700229]), 'Lawyer': array([0.24868156]), 'Politician': array([1.04139267]), 'Teacher': array([1.87751269])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.14751761]), 'CEO': array([0.77688334]), 'Construction Worker': array([0.75407571]), 'Drug Dealer': array([0.57726082]), 'Doctor': array([0.44899298]), 'Service Worker': array([2.54594532]), 'Inmate': array([0.37934433]), 'Lawyer': array([0.16729939]), 'Politician': array([0.77391379]), 'Teacher': array([1.9481508])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.14262623]), 'CEO': array([0.90701739]), 'Construction Worker': array([0.87970525]), 'Drug Dealer': array([0.65300311]), 'Doctor': array([0.46664764]), 'Service Worker': array([2.61401382]), 'Inmate': array([0.43769133]), 'Lawyer': array([0.20244142]), 'Politician': array([0.90357079]), 'Teacher': array([1.91849531])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.55641754]), 'CEO': array([1.08477115]), 'Construction Worker': array([1.07234223]), 'Drug Dealer': array([1.0049133]), 'Doctor': array([0.81202135]), 'Service Worker': array([1.81152273]), 'Inmate': array([0.83467032]), 'Lawyer': array([0.6897046]), 'Politician': array([1.08389036]), 'Teacher': array([1.43174931])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.83869117]), 'CEO': array([0.96382196]), 'Construction Worker': array([0.9457009]), 'Drug Dealer': array([0.81812527]), 'Doctor': array([0.65146805]), 'Service Worker': array([2.16368776]), 'Inmate': array([0.63953455]), 'Lawyer': array([0.44890788]), 'Politician': array([0.96189022]), 'Teacher': array([1.66336389])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.89112677]), 'CEO': array([0.9973199]), 'Construction Worker': array([0.9760908]), 'Drug Dealer': array([0.81686279]), 'Doctor': array([0.61727322]), 'Service Worker': array([2.27837689]), 'Inmate': array([0.61439236]), 'Lawyer': array([0.41717304]), 'Politician': array([0.99502135]), 'Teacher': array([1.70796795])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.65537449]), 'CEO': array([1.16041949]), 'Construction Worker': array([1.14161648]), 'Drug Dealer': array([1.00946375]), 'Doctor': array([0.76388668]), 'Service Worker': array([2.0074433]), 'Inmate': array([0.81350493]), 'Lawyer': array([0.62748941]), 'Politician': array([1.1588567]), 'Teacher': array([1.48346421])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.48266992]), 'CEO': array([1.29517162]), 'Construction Worker': array([1.2784536]), 'Drug Dealer': array([1.15591901]), 'Doctor': array([0.89260193]), 'Service Worker': array([1.80412005]), 'Inmate': array([0.97825198]), 'Lawyer': array([0.80835074]), 'Politician': array([1.2936748]), 'Teacher': array([1.33949184])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.57615541]), 'CEO': array([1.12666326]), 'Construction Worker': array([1.11190699]), 'Drug Dealer': array([1.02440461]), 'Doctor': array([0.80256586]), 'Service Worker': array([1.8687577]), 'Inmate': array([0.8401013]), 'Lawyer': array([0.68087103]), 'Politician': array([1.12571175]), 'Teacher': array([1.43117223])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.05753351]), 'CEO': array([0.70783028]), 'Construction Worker': array([0.69153345]), 'Drug Dealer': array([0.582761]), 'Doctor': array([0.5331143]), 'Service Worker': array([2.31915481]), 'Inmate': array([0.43953472]), 'Lawyer': array([0.24373104]), 'Politician': array([0.70580808]), 'Teacher': array([1.86782368])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.1493625]), 'CEO': array([0.82203192]), 'Construction Worker': array([0.79808129]), 'Drug Dealer': array([0.60362352]), 'Doctor': array([0.46734905]), 'Service Worker': array([2.573882]), 'Inmate': array([0.40191923]), 'Lawyer': array([0.20465995]), 'Politician': array([0.81890501]), 'Teacher': array([1.9605788])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.55864938]), 'CEO': array([2.36379811]), 'Construction Worker': array([2.34643283]), 'Drug Dealer': array([2.18552391]), 'Doctor': array([1.71722954]), 'Service Worker': array([0.77895718]), 'Inmate': array([2.12515233]), 'Lawyer': array([1.87418205]), 'Politician': array([2.36355851]), 'Teacher': array([0.32456537])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.33972025]), 'CEO': array([2.0716452]), 'Construction Worker': array([2.07733928]), 'Drug Dealer': array([2.06072627]), 'Doctor': array([2.10535415]), 'Service Worker': array([0.07640343]), 'Inmate': array([2.30890574]), 'Lawyer': array([2.33732397]), 'Politician': array([2.07322401]), 'Teacher': array([0.31392635])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Inmate\n",
      "Scores per occupation: {'Cashier': array([0.55012107]), 'CEO': array([2.28970821]), 'Construction Worker': array([2.27987015]), 'Drug Dealer': array([2.17600948]), 'Doctor': array([1.99614089]), 'Service Worker': array([0.36769077]), 'Inmate': array([2.39003667]), 'Lawyer': array([1.98364734]), 'Politician': array([2.29044679]), 'Teacher': array([0.12044671])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.30662348]), 'CEO': array([1.19840163]), 'Construction Worker': array([1.19027659]), 'Drug Dealer': array([1.1481744]), 'Doctor': array([0.96564927]), 'Service Worker': array([1.54638112]), 'Inmate': array([0.98452026]), 'Lawyer': array([0.98215861]), 'Politician': array([1.19843305]), 'Teacher': array([1.29900374])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.24497186]), 'CEO': array([0.82516085]), 'Construction Worker': array([0.83513873]), 'Drug Dealer': array([0.89130875]), 'Doctor': array([1.09512564]), 'Service Worker': array([1.26499605]), 'Inmate': array([0.86620366]), 'Lawyer': array([1.26555696]), 'Politician': array([0.82594977]), 'Teacher': array([1.59144434])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.46510972]), 'CEO': array([1.5545706]), 'Construction Worker': array([1.56892732]), 'Drug Dealer': array([1.58568098]), 'Doctor': array([1.74545971]), 'Service Worker': array([0.46723394]), 'Inmate': array([1.64304162]), 'Lawyer': array([2.28957309]), 'Politician': array([1.556071]), 'Teacher': array([1.02277182])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Lawyer\n",
      "Scores per occupation: {'Cashier': array([0.75356555]), 'CEO': array([1.27617461]), 'Construction Worker': array([1.28497022]), 'Drug Dealer': array([1.41901178]), 'Doctor': array([1.27600237]), 'Service Worker': array([0.76538045]), 'Inmate': array([1.3010321]), 'Lawyer': array([1.44636843]), 'Politician': array([1.27899558]), 'Teacher': array([0.89197334])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.50084674]), 'CEO': array([1.25143602]), 'Construction Worker': array([1.23904493]), 'Drug Dealer': array([1.10308503]), 'Doctor': array([1.07960518]), 'Service Worker': array([1.65132326]), 'Inmate': array([1.09327878]), 'Lawyer': array([0.99936006]), 'Politician': array([1.24980824]), 'Teacher': array([1.38934087])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.1902279]), 'CEO': array([1.22352093]), 'Construction Worker': array([1.21971226]), 'Drug Dealer': array([1.18986892]), 'Doctor': array([1.10567479]), 'Service Worker': array([1.3435405]), 'Inmate': array([1.09781025]), 'Lawyer': array([1.17790785]), 'Politician': array([1.22375849]), 'Teacher': array([1.24874197])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.15899703]), 'CEO': array([1.31017158]), 'Construction Worker': array([1.30155698]), 'Drug Dealer': array([1.29032778]), 'Doctor': array([0.98855226]), 'Service Worker': array([1.39325208]), 'Inmate': array([1.09119066]), 'Lawyer': array([0.98535916]), 'Politician': array([1.31088286]), 'Teacher': array([1.06701601])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.37624991]), 'CEO': array([1.77506272]), 'Construction Worker': array([1.74305337]), 'Drug Dealer': array([1.45982678]), 'Doctor': array([0.97490369]), 'Service Worker': array([1.93911806]), 'Inmate': array([1.22081246]), 'Lawyer': array([0.96556161]), 'Politician': array([1.77248156]), 'Teacher': array([1.13003075])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.11938291]), 'CEO': array([1.56038172]), 'Construction Worker': array([1.54901014]), 'Drug Dealer': array([1.41488095]), 'Doctor': array([1.22995067]), 'Service Worker': array([1.42411811]), 'Inmate': array([1.2954404]), 'Lawyer': array([1.40158739]), 'Politician': array([1.55958983]), 'Teacher': array([1.19818956])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.01104292]), 'CEO': array([2.07768445]), 'Construction Worker': array([2.0504722]), 'Drug Dealer': array([1.79409013]), 'Doctor': array([1.28360472]), 'Service Worker': array([1.52361546]), 'Inmate': array([1.58469523]), 'Lawyer': array([1.42814379]), 'Politician': array([2.0759926]), 'Teacher': array([0.8546114])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.63161237]), 'CEO': array([1.50291294]), 'Construction Worker': array([1.4731519]), 'Drug Dealer': array([1.21153239]), 'Doctor': array([0.83582512]), 'Service Worker': array([2.16484396]), 'Inmate': array([0.98060705]), 'Lawyer': array([0.7620351]), 'Politician': array([1.50014208]), 'Teacher': array([1.41757213])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.33616962]), 'CEO': array([1.86170579]), 'Construction Worker': array([1.82903871]), 'Drug Dealer': array([1.52816317]), 'Doctor': array([1.03569964]), 'Service Worker': array([1.91653326]), 'Inmate': array([1.29363231]), 'Lawyer': array([1.06181519]), 'Politician': array([1.85898177]), 'Teacher': array([1.10983428])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.21716624]), 'CEO': array([2.07648539]), 'Construction Worker': array([2.04012036]), 'Drug Dealer': array([1.70560574]), 'Doctor': array([1.1176418]), 'Service Worker': array([1.84690766]), 'Inmate': array([1.45558507]), 'Lawyer': array([1.17207688]), 'Politician': array([2.07365613]), 'Teacher': array([0.94329188])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.15562632]), 'CEO': array([1.18152228]), 'Construction Worker': array([1.14347593]), 'Drug Dealer': array([0.79962613]), 'Doctor': array([0.48561663]), 'Service Worker': array([2.79719981]), 'Inmate': array([0.5443541]), 'Lawyer': array([0.25240926]), 'Politician': array([1.17697906]), 'Teacher': array([1.86758838])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.80543126]), 'CEO': array([1.48676032]), 'Construction Worker': array([1.45004341]), 'Drug Dealer': array([1.11806793]), 'Doctor': array([0.71132442]), 'Service Worker': array([2.43025999]), 'Inmate': array([0.86746685]), 'Lawyer': array([0.58020648]), 'Politician': array([1.48293449]), 'Teacher': array([1.52460813])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.95801444]), 'CEO': array([1.36617707]), 'Construction Worker': array([1.32898187]), 'Drug Dealer': array([0.99237946]), 'Doctor': array([0.62461429]), 'Service Worker': array([2.58926413]), 'Inmate': array([0.739934]), 'Lawyer': array([0.45095651]), 'Politician': array([1.36202859]), 'Teacher': array([1.67575053])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.52974255]), 'CEO': array([1.72726661]), 'Construction Worker': array([1.6920071]), 'Drug Dealer': array([1.37062731]), 'Doctor': array([0.89885006]), 'Service Worker': array([2.13854793]), 'Inmate': array([1.12622844]), 'Lawyer': array([0.85478458]), 'Politician': array([1.72399867]), 'Teacher': array([1.26620417])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.73975177]), 'CEO': array([1.55527269]), 'Construction Worker': array([1.51851158]), 'Drug Dealer': array([1.1862634]), 'Doctor': array([0.75636197]), 'Service Worker': array([2.36614912]), 'Inmate': array([0.93454841]), 'Lawyer': array([0.64491546]), 'Politician': array([1.55156423]), 'Teacher': array([1.45726537])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.02337991]), 'CEO': array([1.26710931]), 'Construction Worker': array([1.2306044]), 'Drug Dealer': array([0.90086153]), 'Doctor': array([0.56720993]), 'Service Worker': array([2.64305999]), 'Inmate': array([0.65232908]), 'Lawyer': array([0.37079853]), 'Politician': array([1.26289065]), 'Teacher': array([1.74793093])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.89787261]), 'CEO': array([1.37941667]), 'Construction Worker': array([1.34320753]), 'Drug Dealer': array([1.01818253]), 'Doctor': array([0.64563305]), 'Service Worker': array([2.51447852]), 'Inmate': array([0.76814095]), 'Lawyer': array([0.4847182]), 'Politician': array([1.37550883]), 'Teacher': array([1.62030044])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.04132448]), 'CEO': array([1.27855519]), 'Construction Worker': array([1.24138602]), 'Drug Dealer': array([0.9057761]), 'Doctor': array([0.56808896]), 'Service Worker': array([2.66679633]), 'Inmate': array([0.65686438]), 'Lawyer': array([0.36568604]), 'Politician': array([1.2742866]), 'Teacher': array([1.75521391])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.01820437]), 'CEO': array([1.21186828]), 'Construction Worker': array([1.1776719]), 'Drug Dealer': array([0.87235483]), 'Doctor': array([0.55905032]), 'Service Worker': array([2.60788446]), 'Inmate': array([0.62718026]), 'Lawyer': array([0.35828134]), 'Politician': array([1.20777324]), 'Teacher': array([1.76288765])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.29742304]), 'CEO': array([0.95347959]), 'Construction Worker': array([0.91853233]), 'Drug Dealer': array([0.61058452]), 'Doctor': array([0.37819235]), 'Service Worker': array([2.88955786]), 'Inmate': array([0.36500977]), 'Lawyer': array([0.09193739]), 'Politician': array([0.94904652]), 'Teacher': array([2.02848443])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.31427634]), 'CEO': array([1.0102224]), 'Construction Worker': array([0.97263684]), 'Drug Dealer': array([0.63536529]), 'Doctor': array([0.37654237]), 'Service Worker': array([2.9470182]), 'Inmate': array([0.38129298]), 'Lawyer': array([0.09327916]), 'Politician': array([1.00547062]), 'Teacher': array([2.02936252])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.79179444]), 'CEO': array([1.50072123]), 'Construction Worker': array([1.46417637]), 'Drug Dealer': array([1.13443492]), 'Doctor': array([0.72209987]), 'Service Worker': array([2.41532926]), 'Inmate': array([0.88270185]), 'Lawyer': array([0.59469516]), 'Politician': array([1.49692367]), 'Teacher': array([1.51201891])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.54480988]), 'CEO': array([1.78701672]), 'Construction Worker': array([1.74957251]), 'Drug Dealer': array([1.4083368]), 'Doctor': array([0.90557318]), 'Service Worker': array([2.18376641]), 'Inmate': array([1.15437958]), 'Lawyer': array([0.85801364]), 'Politician': array([1.78357568]), 'Teacher': array([1.25671854])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.93622007]), 'CEO': array([2.39752666]), 'Construction Worker': array([2.36153332]), 'Drug Dealer': array([2.02976514]), 'Doctor': array([1.35551899]), 'Service Worker': array([1.54820378]), 'Inmate': array([1.79130912]), 'Lawyer': array([1.48934136]), 'Politician': array([2.39526484]), 'Teacher': array([0.6459287])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.55547059]), 'CEO': array([2.63196217]), 'Construction Worker': array([2.60180054]), 'Drug Dealer': array([2.32341518]), 'Doctor': array([1.63802768]), 'Service Worker': array([1.03485518]), 'Inmate': array([2.14863381]), 'Lawyer': array([1.84678766]), 'Politician': array([2.63083295]), 'Teacher': array([0.26619546])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.74857353]), 'CEO': array([2.36752505]), 'Construction Worker': array([2.33862522]), 'Drug Dealer': array([2.08078631]), 'Doctor': array([1.45182286]), 'Service Worker': array([1.23361867]), 'Inmate': array([1.88355554]), 'Lawyer': array([1.60620622]), 'Politician': array([2.36623195]), 'Teacher': array([0.48712653])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([1.2434056]), 'CEO': array([1.65800409]), 'Construction Worker': array([1.63592942]), 'Drug Dealer': array([1.45878758]), 'Doctor': array([1.05580845]), 'Service Worker': array([1.63882878]), 'Inmate': array([1.26565205]), 'Lawyer': array([1.04578089]), 'Politician': array([1.65653144]), 'Teacher': array([1.04498795])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.20311285]), 'CEO': array([0.75271603]), 'Construction Worker': array([0.72915145]), 'Drug Dealer': array([0.53503954]), 'Doctor': array([0.4264341]), 'Service Worker': array([2.60863889]), 'Inmate': array([0.34611169]), 'Lawyer': array([0.13057633]), 'Politician': array([0.74921968]), 'Teacher': array([2.00806076])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.93357942]), 'CEO': array([0.396618]), 'Construction Worker': array([0.39530791]), 'Drug Dealer': array([0.4357973]), 'Doctor': array([0.47510025]), 'Service Worker': array([2.05954803]), 'Inmate': array([0.27839401]), 'Lawyer': array([0.22309555]), 'Politician': array([0.39512402]), 'Teacher': array([1.94558886])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([2.14108361]), 'CEO': array([0.49876007]), 'Construction Worker': array([0.4868311]), 'Drug Dealer': array([0.41401242]), 'Doctor': array([0.41646881]), 'Service Worker': array([2.3879079]), 'Inmate': array([0.25270721]), 'Lawyer': array([0.11499507]), 'Politician': array([0.49617004]), 'Teacher': array([2.04612748])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.70960439]), 'CEO': array([0.48464711]), 'Construction Worker': array([0.48987781]), 'Drug Dealer': array([0.55558241]), 'Doctor': array([0.68063159]), 'Service Worker': array([1.75066636]), 'Inmate': array([0.46173637]), 'Lawyer': array([0.54399965]), 'Politician': array([0.48342094]), 'Teacher': array([1.85235951])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.67509135]), 'CEO': array([0.46113874]), 'Construction Worker': array([0.46788114]), 'Drug Dealer': array([0.56721331]), 'Doctor': array([0.65888673]), 'Service Worker': array([1.6996147]), 'Inmate': array([0.4523191]), 'Lawyer': array([0.50623557]), 'Politician': array([0.46030079]), 'Teacher': array([1.80258122])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.89429838]), 'CEO': array([0.30515887]), 'Construction Worker': array([0.30939216]), 'Drug Dealer': array([0.37222993]), 'Doctor': array([0.54662059]), 'Service Worker': array([1.96025075]), 'Inmate': array([0.26404165]), 'Lawyer': array([0.37439196]), 'Politician': array([0.30407918]), 'Teacher': array([2.03920481])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.78855039]), 'CEO': array([0.43558141]), 'Construction Worker': array([0.43982366]), 'Drug Dealer': array([0.46663414]), 'Doctor': array([0.68255466]), 'Service Worker': array([1.8808004]), 'Inmate': array([0.38592423]), 'Lawyer': array([0.62551863]), 'Politician': array([0.43454123]), 'Teacher': array([2.02651146])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Drug Dealer\n",
      "Scores per occupation: {'Cashier': array([0.973051]), 'CEO': array([1.22520591]), 'Construction Worker': array([1.22874282]), 'Drug Dealer': array([1.28961422]), 'Doctor': array([1.14874302]), 'Service Worker': array([1.03860668]), 'Inmate': array([1.17919923]), 'Lawyer': array([1.22258186]), 'Politician': array([1.22570374]), 'Teacher': array([1.06494387])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.16676498]), 'CEO': array([0.99515887]), 'Construction Worker': array([1.00015937]), 'Drug Dealer': array([1.06522995]), 'Doctor': array([1.03357948]), 'Service Worker': array([1.22142505]), 'Inmate': array([0.96782542]), 'Lawyer': array([1.08104034]), 'Politician': array([0.99550562]), 'Teacher': array([1.30973341])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.77822383]), 'CEO': array([0.45896156]), 'Construction Worker': array([0.46202088]), 'Drug Dealer': array([0.53050215]), 'Doctor': array([0.59352146]), 'Service Worker': array([1.84023379]), 'Inmate': array([0.40444384]), 'Lawyer': array([0.38839553]), 'Politician': array([0.45754194]), 'Teacher': array([1.84607249])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Teacher\n",
      "Scores per occupation: {'Cashier': array([1.38703291]), 'CEO': array([1.04895237]), 'Construction Worker': array([1.04669575]), 'Drug Dealer': array([0.98922116]), 'Doctor': array([1.02186336]), 'Service Worker': array([1.58017996]), 'Inmate': array([0.90252961]), 'Lawyer': array([1.1258019]), 'Politician': array([1.04790573]), 'Teacher': array([1.59672516])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.7478807]), 'CEO': array([2.31602328]), 'Construction Worker': array([2.28914292]), 'Drug Dealer': array([2.04654814]), 'Doctor': array([1.42895938]), 'Service Worker': array([1.25499089]), 'Inmate': array([1.82459676]), 'Lawyer': array([1.6268921]), 'Politician': array([2.31482945]), 'Teacher': array([0.56578238])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: CEO\n",
      "Scores per occupation: {'Cashier': array([0.79750694]), 'CEO': array([2.01027137]), 'Construction Worker': array([1.99258254]), 'Drug Dealer': array([1.8467353]), 'Doctor': array([1.36258434]), 'Service Worker': array([1.17725689]), 'Inmate': array([1.65081426]), 'Lawyer': array([1.53802956]), 'Politician': array([2.00994916]), 'Teacher': array([0.69871603])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.41115129]), 'CEO': array([1.87171022]), 'Construction Worker': array([1.83682574]), 'Drug Dealer': array([1.52065453]), 'Doctor': array([1.00506976]), 'Service Worker': array([2.01780338]), 'Inmate': array([1.27351056]), 'Lawyer': array([1.00090535]), 'Politician': array([1.86875129]), 'Teacher': array([1.14797922])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.28127086]), 'CEO': array([1.72764875]), 'Construction Worker': array([1.70167189]), 'Drug Dealer': array([1.4848303]), 'Doctor': array([1.02895862]), 'Service Worker': array([1.7653081]), 'Inmate': array([1.25467316]), 'Lawyer': array([1.0409668]), 'Politician': array([1.72583601]), 'Teacher': array([1.08393728])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.75438082]), 'CEO': array([1.09095273]), 'Construction Worker': array([1.0708922]), 'Drug Dealer': array([0.92088422]), 'Doctor': array([0.69174328]), 'Service Worker': array([2.1504117]), 'Inmate': array([0.70794281]), 'Lawyer': array([0.55607248]), 'Politician': array([1.08907341]), 'Teacher': array([1.61053261])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.62051847]), 'CEO': array([0.97688785]), 'Construction Worker': array([0.96493884]), 'Drug Dealer': array([0.90859676]), 'Doctor': array([0.71246676]), 'Service Worker': array([1.90433659]), 'Inmate': array([0.70509064]), 'Lawyer': array([0.58207033]), 'Politician': array([0.97590425]), 'Teacher': array([1.52644779])}\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "Best matched occupation: Service Worker\n",
      "Scores per occupation: {'Cashier': array([1.93286506]), 'CEO': array([0.92008074]), 'Construction Worker': array([0.89982307]), 'Drug Dealer': array([0.73470744]), 'Doctor': array([0.5743215]), 'Service Worker': array([2.32890372]), 'Inmate': array([0.53434425]), 'Lawyer': array([0.38518089]), 'Politician': array([0.91727813]), 'Teacher': array([1.80653151])}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the pre-trained face cascade classifier\n",
    "face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "\n",
    "# Initialize video capture\n",
    "cap = cv2.VideoCapture(1)  # Changed to capture from the default camera (index 0)\n",
    "\n",
    "# Check if video capture is opened successfully\n",
    "if not cap.isOpened():\n",
    "    print(\"Error: Failed to open video capture.\")\n",
    "    exit()\n",
    "\n",
    "# Get the screen resolution\n",
    "# screen_width = 1921  # Change this to your screen's width resolution\n",
    "# screen_height = 1202  # Change this to your screen's height resolution\n",
    "\n",
    "# Set the display window size\n",
    "# cv2.namedWindow(\"Fullscreen\", cv2.WINDOW_NORMAL)\n",
    "# cv2.setWindowProperty(\"Fullscreen\", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)\n",
    "\n",
    "while True:\n",
    "    # Capture frame-by-frame\n",
    "    ret, frame = cap.read()\n",
    "    frame = cv2.flip(frame, 1)\n",
    "\n",
    "    # Check if frame is read successfully\n",
    "    if not ret:\n",
    "        print(\"Error: Failed to read frame from video capture.\")\n",
    "        break\n",
    "    \n",
    "    # Resize the frame to match the screen resolution\n",
    "    # frame = cv2.resize(frame, (screen_width, screen_height))\n",
    "\n",
    "    # Detect faces in the original color frame\n",
    "    faces = face_cascade.detectMultiScale(frame, scaleFactor=1.2, minNeighbors=8, minSize=(300, 300))\n",
    "\n",
    "    # Process each detected face\n",
    "    if len(faces) > 0:\n",
    "        for i, (x, y, w, h) in enumerate(faces):\n",
    "            \n",
    "            # Adjust the face rectangle to zoom into the central part\n",
    "            adjust_factor = 0.2  # Percentage to zoom\n",
    "            new_w = int(w * (1 - adjust_factor))\n",
    "            new_h = int(h * (1 - adjust_factor))\n",
    "            new_x = x + int(w * adjust_factor / 2)\n",
    "            new_y = y + int(h * adjust_factor / 2)\n",
    "\n",
    "            cv2.rectangle(frame, (new_x, new_y), (new_x + new_w, new_y + new_h), (255, 255, 255), 2)\n",
    "            face_roi_color = frame[new_y:new_y + new_h, new_x:new_x + new_w]\n",
    "\n",
    "            # Resize the zoomed face region to match the input shape of the model\n",
    "            resized_face = cv2.resize(face_roi_color, (198, 198))\n",
    "\n",
    "            # Normalize the face image\n",
    "            normalized_face = np.array(resized_face) / 255.0  # Corrected normalization of resized_face\n",
    "\n",
    "            # Reshape the face image to match the input shape of the model\n",
    "            input_data = np.expand_dims(normalized_face, axis=0)\n",
    "            \n",
    "            # Make predictions on the face image (assuming 'model' and 'dataset_dict' are defined elsewhere)\n",
    "            predictions = model.predict(input_data)\n",
    "            \n",
    "            # Extract predictions\n",
    "            predicted_ethnicity_distribution = predictions[1]\n",
    "            predicted_gender_distribution = predictions[2]\n",
    "\n",
    "            best_occupation, occupation_scores = assign_occupation(predicted_gender_distribution, predicted_ethnicity_distribution)\n",
    "            \n",
    "            # Output the results\n",
    "            print(\"Best matched occupation:\", best_occupation)\n",
    "            print(\"Scores per occupation:\", occupation_scores)\n",
    "\n",
    "            cv2.putText(frame, best_occupation, (new_x, new_y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2)\n",
    "\n",
    "    # Display the resulting frame\n",
    "    cv2.imshow(\"\", frame)\n",
    "    \n",
    "    # Exit the loop if 'Esc' key is pressed\n",
    "    if cv2.waitKey(1) == 27:\n",
    "        break\n",
    "\n",
    "# Release the video capture and close all windows\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()\n",
    "cv2.waitKey(1)"
   ]
  }
 ],
 "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",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}