test-image.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Pillow in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (7.1.1)\n",
      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 20.0.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: nb_black in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (1.0.7)\n",
      "Requirement already satisfied: black>='19.3'; python_version >= \"3.6\" in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from nb_black) (19.10b0)\n",
      "Requirement already satisfied: ipython in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from nb_black) (7.13.0)\n",
      "Requirement already satisfied: pathspec<1,>=0.6 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from black>='19.3'; python_version >= \"3.6\"->nb_black) (0.8.0)\n",
      "Requirement already satisfied: typed-ast>=1.4.0 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from black>='19.3'; python_version >= \"3.6\"->nb_black) (1.4.1)\n",
      "Requirement already satisfied: attrs>=18.1.0 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from black>='19.3'; python_version >= \"3.6\"->nb_black) (19.3.0)\n",
      "Requirement already satisfied: regex in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from black>='19.3'; python_version >= \"3.6\"->nb_black) (2020.4.4)\n",
      "Requirement already satisfied: toml>=0.9.4 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from black>='19.3'; python_version >= \"3.6\"->nb_black) (0.10.0)\n",
      "Requirement already satisfied: appdirs in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from black>='19.3'; python_version >= \"3.6\"->nb_black) (1.4.3)\n",
      "Requirement already satisfied: click>=6.5 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from black>='19.3'; python_version >= \"3.6\"->nb_black) (7.1.1)\n",
      "Requirement already satisfied: setuptools>=18.5 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from ipython->nb_black) (41.2.0)\n",
      "Requirement already satisfied: backcall in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from ipython->nb_black) (0.1.0)\n",
      "Requirement already satisfied: pygments in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from ipython->nb_black) (2.6.1)\n",
      "Requirement already satisfied: pickleshare in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from ipython->nb_black) (0.7.5)\n",
      "Requirement already satisfied: jedi>=0.10 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from ipython->nb_black) (0.16.0)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from ipython->nb_black) (3.0.4)\n",
      "Requirement already satisfied: pexpect; sys_platform != \"win32\" in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from ipython->nb_black) (4.8.0)\n",
      "Requirement already satisfied: traitlets>=4.2 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from ipython->nb_black) (4.3.3)\n",
      "Requirement already satisfied: decorator in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from ipython->nb_black) (4.4.2)\n",
      "Requirement already satisfied: parso>=0.5.2 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from jedi>=0.10->ipython->nb_black) (0.6.2)\n",
      "Requirement already satisfied: wcwidth in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->nb_black) (0.1.9)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from pexpect; sys_platform != \"win32\"->ipython->nb_black) (0.6.0)\n",
      "Requirement already satisfied: ipython-genutils in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from traitlets>=4.2->ipython->nb_black) (0.2.0)\n",
      "Requirement already satisfied: six in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from traitlets>=4.2->ipython->nb_black) (1.14.0)\n",
      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 20.0.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 1;\n",
       "                var nbb_unformatted_code = \"# Pre requisites for this notebook\\n!pip install Pillow\\n!pip install nb_black\\n%load_ext nb_black\";\n",
       "                var nbb_formatted_code = \"# Pre requisites for this notebook\\n!pip install Pillow\\n!pip install nb_black\\n%load_ext nb_black\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre requisites for this notebook\n",
    "!pip install Pillow\n",
    "!pip install nb_black\n",
    "%load_ext nb_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"import os\\nfrom requests import get\\nfrom pathlib import Path\\nimport gc\\nimport tensorflow as tf\\nfrom concurrent.futures import ProcessPoolExecutor as PoolExecutor\\n\\nfrom tensorflow.keras.callbacks import EarlyStopping\\nfrom tensorflow_addons.optimizers import RectifiedAdam, Lookahead\\nfrom tensorflow_addons.activations import mish\\n\\nimport pandas as pd\\nimport numpy as np\\nfrom sklearn.metrics import roc_auc_score\\n\\nnp.random.seed(0)\\n\\nfrom PIL import Image, ImageDraw, ImageFont\\nfrom matplotlib.pyplot import imshow\\nimport matplotlib.pyplot as plt\\n\\n%matplotlib inline\";\n",
       "                var nbb_formatted_code = \"import os\\nfrom requests import get\\nfrom pathlib import Path\\nimport gc\\nimport tensorflow as tf\\nfrom concurrent.futures import ProcessPoolExecutor as PoolExecutor\\n\\nfrom tensorflow.keras.callbacks import EarlyStopping\\nfrom tensorflow_addons.optimizers import RectifiedAdam, Lookahead\\nfrom tensorflow_addons.activations import mish\\n\\nimport pandas as pd\\nimport numpy as np\\nfrom sklearn.metrics import roc_auc_score\\n\\nnp.random.seed(0)\\n\\nfrom PIL import Image, ImageDraw, ImageFont\\nfrom matplotlib.pyplot import imshow\\nimport matplotlib.pyplot as plt\\n\\n%matplotlib inline\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "from requests import get\n",
    "from pathlib import Path\n",
    "import gc\n",
    "import tensorflow as tf\n",
    "from concurrent.futures import ProcessPoolExecutor as PoolExecutor\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow_addons.optimizers import RectifiedAdam, Lookahead\n",
    "from tensorflow_addons.activations import mish\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "from PIL import Image, ImageDraw, ImageFont\n",
    "from matplotlib.pyplot import imshow\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"def download(url, out, force=False, verify=True):\\n    out.parent.mkdir(parents=True, exist_ok=True)\\n    if force:\\n        print(f\\\"Removing file at {str(out)}\\\")\\n        out.unlink()\\n\\n    if out.exists():\\n        print(\\\"File already exists.\\\")\\n        return\\n    print(f\\\"Downloading {url} at {str(out)} ...\\\")\\n    # open in binary mode\\n    with out.open(mode=\\\"wb\\\") as file:\\n        # get request\\n        response = get(url, verify=verify)\\n        for chunk in response.iter_content(100000):\\n            # write to file\\n            file.write(chunk)\";\n",
       "                var nbb_formatted_code = \"def download(url, out, force=False, verify=True):\\n    out.parent.mkdir(parents=True, exist_ok=True)\\n    if force:\\n        print(f\\\"Removing file at {str(out)}\\\")\\n        out.unlink()\\n\\n    if out.exists():\\n        print(\\\"File already exists.\\\")\\n        return\\n    print(f\\\"Downloading {url} at {str(out)} ...\\\")\\n    # open in binary mode\\n    with out.open(mode=\\\"wb\\\") as file:\\n        # get request\\n        response = get(url, verify=verify)\\n        for chunk in response.iter_content(100000):\\n            # write to file\\n            file.write(chunk)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def download(url, out, force=False, verify=True):\n",
    "    out.parent.mkdir(parents=True, exist_ok=True)\n",
    "    if force:\n",
    "        print(f\"Removing file at {str(out)}\")\n",
    "        out.unlink()\n",
    "\n",
    "    if out.exists():\n",
    "        print(\"File already exists.\")\n",
    "        return\n",
    "    print(f\"Downloading {url} at {str(out)} ...\")\n",
    "    # open in binary mode\n",
    "    with out.open(mode=\"wb\") as file:\n",
    "        # get request\n",
    "        response = get(url, verify=verify)\n",
    "        for chunk in response.iter_content(100000):\n",
    "            # write to file\n",
    "            file.write(chunk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download census-income dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File already exists.\n",
      "File already exists.\n",
      "File already exists.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"url = \\\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\\\"\\nurl_test = \\\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\\\"\\nfont_url = \\\"https://ff.static.1001fonts.net/r/o/roboto-condensed.regular.ttf\\\"\\n\\ndataset_name = \\\"census-income\\\"\\nout = Path(os.getcwd()) / f\\\"data/{dataset_name}.csv\\\"\\nout_test = Path(os.getcwd()) / f\\\"data/{dataset_name}_test.csv\\\"\\nout_font = Path(os.getcwd()) / f\\\"RobotoCondensed-Regular.ttf\\\"\\n\\ndownload(url, out)\\ndownload(url_test, out_test)\\ndownload(font_url, out_font)\";\n",
       "                var nbb_formatted_code = \"url = \\\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\\\"\\nurl_test = \\\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\\\"\\nfont_url = \\\"https://ff.static.1001fonts.net/r/o/roboto-condensed.regular.ttf\\\"\\n\\ndataset_name = \\\"census-income\\\"\\nout = Path(os.getcwd()) / f\\\"data/{dataset_name}.csv\\\"\\nout_test = Path(os.getcwd()) / f\\\"data/{dataset_name}_test.csv\\\"\\nout_font = Path(os.getcwd()) / f\\\"RobotoCondensed-Regular.ttf\\\"\\n\\ndownload(url, out)\\ndownload(url_test, out_test)\\ndownload(font_url, out_font)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\"\n",
    "url_test = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\"\n",
    "font_url = \"https://ff.static.1001fonts.net/r/o/roboto-condensed.regular.ttf\"\n",
    "\n",
    "dataset_name = \"census-income\"\n",
    "out = Path(os.getcwd()) / f\"data/{dataset_name}.csv\"\n",
    "out_test = Path(os.getcwd()) / f\"data/{dataset_name}_test.csv\"\n",
    "out_font = Path(os.getcwd()) / f\"RobotoCondensed-Regular.ttf\"\n",
    "\n",
    "download(url, out)\n",
    "download(url_test, out_test)\n",
    "download(font_url, out_font)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load census-income as a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32561, 15)\n",
      "(16280, 15)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"cols = [\\n    \\\"age\\\",\\n    \\\"workclass\\\",\\n    \\\"fnlwgt\\\",\\n    \\\"education\\\",\\n    \\\"education-num\\\",\\n    \\\"marital-status\\\",\\n    \\\"occupation\\\",\\n    \\\"relationship\\\",\\n    \\\"race\\\",\\n    \\\"sex\\\",\\n    \\\"capital-gain\\\",\\n    \\\"capital-loss\\\",\\n    \\\"hours-per-week\\\",\\n    \\\"native-country\\\",\\n    \\\"target\\\",\\n]\\ntrain = pd.read_csv(out, names=cols)\\ntrain[\\\"target\\\"] = train[\\\"target\\\"].str.strip()\\nprint(train.shape)\\ntest = pd.read_csv(out_test, names=cols, skiprows=2)\\ntest[\\\"target\\\"] = test[\\\"target\\\"].str.strip().str.strip(\\\".\\\")\\nprint(test.shape)\";\n",
       "                var nbb_formatted_code = \"cols = [\\n    \\\"age\\\",\\n    \\\"workclass\\\",\\n    \\\"fnlwgt\\\",\\n    \\\"education\\\",\\n    \\\"education-num\\\",\\n    \\\"marital-status\\\",\\n    \\\"occupation\\\",\\n    \\\"relationship\\\",\\n    \\\"race\\\",\\n    \\\"sex\\\",\\n    \\\"capital-gain\\\",\\n    \\\"capital-loss\\\",\\n    \\\"hours-per-week\\\",\\n    \\\"native-country\\\",\\n    \\\"target\\\",\\n]\\ntrain = pd.read_csv(out, names=cols)\\ntrain[\\\"target\\\"] = train[\\\"target\\\"].str.strip()\\nprint(train.shape)\\ntest = pd.read_csv(out_test, names=cols, skiprows=2)\\ntest[\\\"target\\\"] = test[\\\"target\\\"].str.strip().str.strip(\\\".\\\")\\nprint(test.shape)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = [\n",
    "    \"age\",\n",
    "    \"workclass\",\n",
    "    \"fnlwgt\",\n",
    "    \"education\",\n",
    "    \"education-num\",\n",
    "    \"marital-status\",\n",
    "    \"occupation\",\n",
    "    \"relationship\",\n",
    "    \"race\",\n",
    "    \"sex\",\n",
    "    \"capital-gain\",\n",
    "    \"capital-loss\",\n",
    "    \"hours-per-week\",\n",
    "    \"native-country\",\n",
    "    \"target\",\n",
    "]\n",
    "train = pd.read_csv(out, names=cols)\n",
    "train[\"target\"] = train[\"target\"].str.strip()\n",
    "print(train.shape)\n",
    "test = pd.read_csv(out_test, names=cols, skiprows=2)\n",
    "test[\"target\"] = test[\"target\"].str.strip().str.strip(\".\")\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"if \\\"Set\\\" not in train.columns:\\n    train[\\\"Set\\\"] = np.random.choice(\\n        [\\\"train\\\", \\\"valid\\\"], p=[0.8, 0.2], size=(train.shape[0],)\\n    )\\n\\ntrain_indices = train[train.Set == \\\"train\\\"].index\\nvalid_indices = train[train.Set == \\\"valid\\\"].index\";\n",
       "                var nbb_formatted_code = \"if \\\"Set\\\" not in train.columns:\\n    train[\\\"Set\\\"] = np.random.choice(\\n        [\\\"train\\\", \\\"valid\\\"], p=[0.8, 0.2], size=(train.shape[0],)\\n    )\\n\\ntrain_indices = train[train.Set == \\\"train\\\"].index\\nvalid_indices = train[train.Set == \\\"valid\\\"].index\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if \"Set\" not in train.columns:\n",
    "    train[\"Set\"] = np.random.choice(\n",
    "        [\"train\", \"valid\"], p=[0.8, 0.2], size=(train.shape[0],)\n",
    "    )\n",
    "\n",
    "train_indices = train[train.Set == \"train\"].index\n",
    "valid_indices = train[train.Set == \"valid\"].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encode target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32561, 1)\n",
      "(16280, 1)\n",
      "(32561, 14)\n",
      "(16280, 14)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"from sklearn import preprocessing\\n\\n# label_encoder object knows how to understand word labels.\\nlabel_encoder = preprocessing.LabelEncoder()\\n\\n# Encode labels in column 'species'.\\nY = (\\n    label_encoder.fit_transform(train[[\\\"target\\\"]].values.reshape(-1))\\n    .reshape(-1, 1)\\n    .astype(\\\"uint8\\\")\\n)\\nprint(Y.shape)\\n\\nY_test = (\\n    label_encoder.transform(test[[\\\"target\\\"]].values.reshape(-1))\\n    .reshape(-1, 1)\\n    .astype(\\\"uint8\\\")\\n)\\nprint(Y_test.shape)\\n\\nX = train.drop(columns=[\\\"target\\\", \\\"Set\\\"]).values.astype(\\\"str\\\")\\nprint(X.shape)\\n\\nX_test = test.drop(columns=[\\\"target\\\"]).values.astype(\\\"str\\\")\\nprint(X_test.shape)\\n\\ndel train\\ngc.collect()\";\n",
       "                var nbb_formatted_code = \"from sklearn import preprocessing\\n\\n# label_encoder object knows how to understand word labels.\\nlabel_encoder = preprocessing.LabelEncoder()\\n\\n# Encode labels in column 'species'.\\nY = (\\n    label_encoder.fit_transform(train[[\\\"target\\\"]].values.reshape(-1))\\n    .reshape(-1, 1)\\n    .astype(\\\"uint8\\\")\\n)\\nprint(Y.shape)\\n\\nY_test = (\\n    label_encoder.transform(test[[\\\"target\\\"]].values.reshape(-1))\\n    .reshape(-1, 1)\\n    .astype(\\\"uint8\\\")\\n)\\nprint(Y_test.shape)\\n\\nX = train.drop(columns=[\\\"target\\\", \\\"Set\\\"]).values.astype(\\\"str\\\")\\nprint(X.shape)\\n\\nX_test = test.drop(columns=[\\\"target\\\"]).values.astype(\\\"str\\\")\\nprint(X_test.shape)\\n\\ndel train\\ngc.collect()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# label_encoder object knows how to understand word labels.\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "\n",
    "# Encode labels in column 'species'.\n",
    "Y = (\n",
    "    label_encoder.fit_transform(train[[\"target\"]].values.reshape(-1))\n",
    "    .reshape(-1, 1)\n",
    "    .astype(\"uint8\")\n",
    ")\n",
    "print(Y.shape)\n",
    "\n",
    "Y_test = (\n",
    "    label_encoder.transform(test[[\"target\"]].values.reshape(-1))\n",
    "    .reshape(-1, 1)\n",
    "    .astype(\"uint8\")\n",
    ")\n",
    "print(Y_test.shape)\n",
    "\n",
    "X = train.drop(columns=[\"target\", \"Set\"]).values.astype(\"str\")\n",
    "print(X.shape)\n",
    "\n",
    "X_test = test.drop(columns=[\"target\"]).values.astype(\"str\")\n",
    "print(X_test.shape)\n",
    "\n",
    "del train\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"# https://he-arc.github.io/livre-python/pillow/index.html#methodes-de-dessin\\n# https://stackoverflow.com/questions/26649716/how-to-show-pil-image-in-ipython-notebook\\n# https://stackoverflow.com/questions/384759/how-to-convert-a-pil-image-into-a-numpy-array\\n# line = np.array(pic, dtypes=\\\"uint8\\\")\\n# from https://arxiv.org/pdf/1902.02160.pdf page 2\";\n",
       "                var nbb_formatted_code = \"# https://he-arc.github.io/livre-python/pillow/index.html#methodes-de-dessin\\n# https://stackoverflow.com/questions/26649716/how-to-show-pil-image-in-ipython-notebook\\n# https://stackoverflow.com/questions/384759/how-to-convert-a-pil-image-into-a-numpy-array\\n# line = np.array(pic, dtypes=\\\"uint8\\\")\\n# from https://arxiv.org/pdf/1902.02160.pdf page 2\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://he-arc.github.io/livre-python/pillow/index.html#methodes-de-dessin\n",
    "# https://stackoverflow.com/questions/26649716/how-to-show-pil-image-in-ipython-notebook\n",
    "# https://stackoverflow.com/questions/384759/how-to-convert-a-pil-image-into-a-numpy-array\n",
    "# line = np.array(pic, dtypes=\"uint8\")\n",
    "# from https://arxiv.org/pdf/1902.02160.pdf page 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"WHITE = (255, 255, 255)\\nBLACK = (0, 0, 0)\\n\\n\\ndef word_to_square_image(text, size, cut_length=None):\\n\\n    truncated = text[:cut_length] if cut_length is not None else text\\n    max_x = np.ceil(np.sqrt(len(truncated))).astype(\\\"int\\\")\\n    character_size = np.floor(size / max_x).astype(\\\"int\\\")\\n    padding = np.floor((size - (max_x * character_size)) / 2).astype(\\\"int\\\")\\n    # Do we need pt to px conversion ? Seems like not\\n    # font_size =  int(np.floor(character_size*0.75))\\n    font_size = character_size\\n\\n    fnt = ImageFont.truetype(out_font.as_posix(), font_size)\\n    image = Image.new(\\\"RGB\\\", (size, size), BLACK)\\n    # Obtention du contexte graphique\\n    draw = ImageDraw.Draw(image)\\n    x = 0\\n    y = 0\\n    for letter in text:\\n        draw.text(\\n            (padding + x * character_size, padding + y * character_size),\\n            letter,\\n            font=fnt,\\n            fill=WHITE,\\n        )\\n        if x + 1 < max_x:\\n            x += 1\\n        else:\\n            y += 1\\n            x = 0\\n    return np.array(image)\";\n",
       "                var nbb_formatted_code = \"WHITE = (255, 255, 255)\\nBLACK = (0, 0, 0)\\n\\n\\ndef word_to_square_image(text, size, cut_length=None):\\n\\n    truncated = text[:cut_length] if cut_length is not None else text\\n    max_x = np.ceil(np.sqrt(len(truncated))).astype(\\\"int\\\")\\n    character_size = np.floor(size / max_x).astype(\\\"int\\\")\\n    padding = np.floor((size - (max_x * character_size)) / 2).astype(\\\"int\\\")\\n    # Do we need pt to px conversion ? Seems like not\\n    # font_size =  int(np.floor(character_size*0.75))\\n    font_size = character_size\\n\\n    fnt = ImageFont.truetype(out_font.as_posix(), font_size)\\n    image = Image.new(\\\"RGB\\\", (size, size), BLACK)\\n    # Obtention du contexte graphique\\n    draw = ImageDraw.Draw(image)\\n    x = 0\\n    y = 0\\n    for letter in text:\\n        draw.text(\\n            (padding + x * character_size, padding + y * character_size),\\n            letter,\\n            font=fnt,\\n            fill=WHITE,\\n        )\\n        if x + 1 < max_x:\\n            x += 1\\n        else:\\n            y += 1\\n            x = 0\\n    return np.array(image)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WHITE = (255, 255, 255)\n",
    "BLACK = (0, 0, 0)\n",
    "\n",
    "\n",
    "def word_to_square_image(text, size, cut_length=None):\n",
    "\n",
    "    truncated = text[:cut_length] if cut_length is not None else text\n",
    "    max_x = np.ceil(np.sqrt(len(truncated))).astype(\"int\")\n",
    "    character_size = np.floor(size / max_x).astype(\"int\")\n",
    "    padding = np.floor((size - (max_x * character_size)) / 2).astype(\"int\")\n",
    "    # Do we need pt to px conversion ? Seems like not\n",
    "    # font_size =  int(np.floor(character_size*0.75))\n",
    "    font_size = character_size\n",
    "\n",
    "    fnt = ImageFont.truetype(out_font.as_posix(), font_size)\n",
    "    image = Image.new(\"RGB\", (size, size), BLACK)\n",
    "    # Obtention du contexte graphique\n",
    "    draw = ImageDraw.Draw(image)\n",
    "    x = 0\n",
    "    y = 0\n",
    "    for letter in text:\n",
    "        draw.text(\n",
    "            (padding + x * character_size, padding + y * character_size),\n",
    "            letter,\n",
    "            font=fnt,\n",
    "            fill=WHITE,\n",
    "        )\n",
    "        if x + 1 < max_x:\n",
    "            x += 1\n",
    "        else:\n",
    "            y += 1\n",
    "            x = 0\n",
    "    return np.array(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"def text_to_square_image(features, image_size=299, cut_length=None):\\n    square_nb = np.ceil(np.sqrt(len(features))).astype(\\\"int\\\")\\n    word_size = np.floor(image_size / square_nb).astype(\\\"int\\\")\\n    max_features = len(features)\\n    padding = np.floor((image_size - square_nb * word_size) / 2).astype(\\\"int\\\")\\n    result_image = np.zeros((image_size, image_size, 3), dtype=\\\"uint8\\\")\\n    results = []\\n    i_feature = 0\\n    for x in range(0, square_nb):\\n        if i_feature is None:\\n            break\\n        for y in range(0, square_nb):\\n            i_feature = x * (square_nb) + y\\n            if i_feature >= max_features:\\n                i_feature = None\\n                break\\n            x_pos = x * word_size + padding\\n            y_pos = y * word_size + padding\\n            result_image[\\n                x_pos : x_pos + word_size, y_pos : y_pos + word_size\\n            ] = word_to_square_image(\\n                features[i_feature], size=word_size, cut_length=cut_length\\n            )\\n    return result_image\";\n",
       "                var nbb_formatted_code = \"def text_to_square_image(features, image_size=299, cut_length=None):\\n    square_nb = np.ceil(np.sqrt(len(features))).astype(\\\"int\\\")\\n    word_size = np.floor(image_size / square_nb).astype(\\\"int\\\")\\n    max_features = len(features)\\n    padding = np.floor((image_size - square_nb * word_size) / 2).astype(\\\"int\\\")\\n    result_image = np.zeros((image_size, image_size, 3), dtype=\\\"uint8\\\")\\n    results = []\\n    i_feature = 0\\n    for x in range(0, square_nb):\\n        if i_feature is None:\\n            break\\n        for y in range(0, square_nb):\\n            i_feature = x * (square_nb) + y\\n            if i_feature >= max_features:\\n                i_feature = None\\n                break\\n            x_pos = x * word_size + padding\\n            y_pos = y * word_size + padding\\n            result_image[\\n                x_pos : x_pos + word_size, y_pos : y_pos + word_size\\n            ] = word_to_square_image(\\n                features[i_feature], size=word_size, cut_length=cut_length\\n            )\\n    return result_image\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def text_to_square_image(features, image_size=299, cut_length=None):\n",
    "    square_nb = np.ceil(np.sqrt(len(features))).astype(\"int\")\n",
    "    word_size = np.floor(image_size / square_nb).astype(\"int\")\n",
    "    max_features = len(features)\n",
    "    padding = np.floor((image_size - square_nb * word_size) / 2).astype(\"int\")\n",
    "    result_image = np.zeros((image_size, image_size, 3), dtype=\"uint8\")\n",
    "    results = []\n",
    "    i_feature = 0\n",
    "    for x in range(0, square_nb):\n",
    "        if i_feature is None:\n",
    "            break\n",
    "        for y in range(0, square_nb):\n",
    "            i_feature = x * (square_nb) + y\n",
    "            if i_feature >= max_features:\n",
    "                i_feature = None\n",
    "                break\n",
    "            x_pos = x * word_size + padding\n",
    "            y_pos = y * word_size + padding\n",
    "            result_image[\n",
    "                x_pos : x_pos + word_size, y_pos : y_pos + word_size\n",
    "            ] = word_to_square_image(\n",
    "                features[i_feature], size=word_size, cut_length=cut_length\n",
    "            )\n",
    "    return result_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 299, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO2deXxTVfr/Pydp0p1udKcoLRRoy15qQQUBZZN9EBkYcABB5oeyOSqMooLyHf0qrl9QQdmEih3ZHBYRKgybUhaFQktpS1u670uSptnu8/ujaSalKUuTNKE579frefX23Jt7nnuTfHLuOc85DyMicDgcx0Vkawc4HI5t4SLA4Tg4XAQ4HAeHiwCH4+BwEeBwHBwuAhyOg2M1EWCMjWaMpTPGMhljK6xVD4fDMQ9mjTgBxpgYwA0ATwHIB3AewJ+JKNXilXE4HLOwVksgDkAmEd0kIjWAXQAmWqkuDodjBk5WOm8ogDyj//MBPNLSwYwxHrbI4VifciLyv73QWiJwVxhjCwAssFX9HI4Dkmuq0FoiUAAgzOj/TvoyA0S0EcBGgLcEOBxbYq0+gfMAujHGujDGpACmA/jRSnVxOBwzsEpLgIi0jLEXARwBIAawmYiuWaMuDodjHlYZIrxvJ/jjAIfTFlwkotjbC23WMch58HFxccHw4cPBGMPhw4chCIKtXeK0Ai4CnFYTEhKCDRs2QCwWIzY2FiUlJbZ2idMKuAhwWg1jDE5OThCLxRCLxbZ2h9NK7E4E+vTpg2effRYAkJKSgr1796K+vt7GXnFMUVhYiMWLF0MkEqG4uNjW7nBaid2JQHR0NFauXAkASExMxOHDh7kI2ClqtRonTpwA0NAqsCY+Pj5gjKG2thYA4OrqCicnJxAR1Go16urqLF6nSCSCm5sbJBIJAECr1UKpVEKr1VqsjsbrksvlUKvVTfZ5enpCIpFApVJBoVBYrM7b4VOJOa2me/fuKC0tRX5+PqKioqxWD2MMN27cQGFhIcaMGYOXX34Zp0+fRl5eHlJTU7Fz504MGzYMIpHlPs7e3t6YOXMmDh06hKysLOTm5uLEiRNYsWIFQkJCLFKHRCJBTk4OSktLMWnSpGb7t2/fjtLSUnz44Ydwc3OzSJ2msLuWAOfBgTEGsVgMkUhk1ZYAYwyMMTg7O2P58uUYOHAgamtrUVVVhZCQEEycOBHdu3fHCy+8gFOnTpldn6urKxYsWICVK1eiQ4cOqKyshEKhQL9+/dC7d2+Eh4dj+fLlqK6uNrsukUgEsVhs8v417rP2/eUtAc4DRa9evbB06VLExsYiLi4Ozz33HLKzs9G9e3csWLAAPj4+ZtcRERGBF198Ee7u7li3bh1iY2MxaNAgvP3226irq8PkyZMxfvx4C1yNfcBFgPNAsWnTJmzevBmFhYUoKipCQkICNm3aBI1Gg/j4eHTu3NnsOsLDwxEWFgaVSoV169YhNzcX+fn5+OqrrwwtjZ49exr6Ch50+OPAPSKRSNCvXz+MHTsWERERkEqlKCgoQHJyMg4fPoyamhqr+7Bs2TIMGzYMQMOXITk5Gc8++yzi4uKQl5eHzz//HIWFhVb3w5ZkZmbCOMpVEAQcOXIEr732GsLCwuDv32ym7H2j0+lARGCMwd/f3xD/UFZWhpUrV8Lf3x8FBQXQ6XRm12UPcBG4B6KiorBq1SqMGTMG7u7uhmc0QRCg1Wpx48YNrF69GocOHYJSqbSaH/369TM0Q2/cuIEVK1YgNjYWTk5OSE1Nxffff9/uRcAUWVlZ0Gq1kEqlCAgIAGMM5oTDp6enIyUlBTExMdiyZQvef/99nD59GiUlJUhJSbGg5/YBfxy4C5GRkfjiiy8wffp0eHl5QafTITMzE1euXIFMJoOzszN69eqFzz//HDNmzGizoJnnnnsO8fHxkEqlBlGy9jCdvaJQKCAIAhhjcHd3N/t8ubm5WLNmDdLT0zFgwABs27YNmzdvxoIFCyw2MmBPcBG4A97e3njttdcwePBgEBFu3bqFOXPmYPz48ZgyZQomT56M/fv3Q6PRIDg4GKtXr0bPnj3bxDc/Pz/k5+dj1apVGD58OObOnYubN2+2Sd32hvGcBUsME2o0Guzfvx/PPPMMvvzyS9TW1uLJJ5/EBx98gN27d+PRRx9tV4LLReAOPP744xg3bhzEYjEKCgowf/587Nq1Czdu3MDNmzdx4sQJzJkzB8ePHwcABAcHY/ny5W3SYVRRUYHZs2dj7dq1OH78OJKTk9ukX8IecXZ2NnwpVSqVRc6p1Wpx7do1LFq0CIMHD8YHH3yA8vJyxMXFYdu2bRg4cKBF6rEHuAi0gFQqxdChQ+Hv7w8iwuHDh3HmzJlmz5pVVVX48MMPoVarwRhDfHw8Hn74Yav7t2/fPpP+OCL+/v4Qi8UgIlRVVZl9T/z8/ODv7w9nZ2cQEbKzs/HWW2/h1VdfRW5uLh566CE8/fTTFnn0a4w+tOXcCy4CLeDi4oLBgweDMQaZTIYTJ060GLqZkpKClJQUQ29yTEyM1f27du2aQ07ddXZ2blYWHx8PZ2dnVFZWorS01Kzzi8ViHDx4EBcvXsSsWbMMLQytVovDhw8jNTUVTk5OCAwMhFQqNasuAKisrAQAhIaGNil3dXU1ea3WgItAC0gkEoSHhwNo6HjKyMho8ViFQoHr168DADw8PNqk88hRWwDTpk1DREQEgIZIwm7dumHevHlwdXXF5cuXkZ+fb9b5G3/5w8LCMHv2bEPcAWMMkZGRCAkJgVarRVlZWbNY//tFEARcunQJADBhwgT4+fkBaBCA5557Dn379jXr/PcKHyJsAZFIhA4dOgBo6Ci60/O2Vqs1hJBKJBJ4eHi0iY+OSHx8PLZu3YqLFy9Cp9Nh0KBB6Nu3L+rq6nDgwAEUFRWZdX5BELB582YMGzYMgwYNwrZt2/DHH3+AMYZHHnkEUVFRKCkpQVJSktlxAoIgYP/+/Rg/fjzi4uLwzTffICMjA507d8Zjjz2GgIAAs85/r3ARaIHGWHWg4ddBo9G0eGzjTDagQTzaSySZPbJhwwY8/vjjmDVrFtzd3UFEqKysxI4dO/Dll19aZIbfiRMnsHTpUrzyyivo27cvBg0aBABQKpWG/oGTJ0+aXQ8R4dChQ9i4cSOmT5+OsWPHQhAEyOVy/Prrr+jatSt69Ohhdj13g4vAHTBuct9tSMh4v6M01cvKyvDuu+9Cq9W22apCqamp+PTTTzFs2DB06tQJdXV1SElJwYkTJ+4o1PeDRqPBrl27cO7cOQwdOtTwvJ6fn49ff/0VGRkZFnuPKysr8cYbb+CXX35BdHQ0iAiZmZk4fvw4Ro4ciR49euD8+fNmP3rcCS4CLSAIAurq6uDp6QmRSAQXF5cWjzXer9VqHWb9g9LSUqxatapN62yM19i6davV68rOzkZ2drbV66mtrcW+ffuwb9++JuU7d+60et0A7xhsEZ1OZ+hpdnFxQVBQUIvHSiQSQ8x6fX09qqqq2sRHDscScBFoAbVajdTUhiTKHh4e6NWrV4vHurm5oXv37gAaVD0nJ6ctXORwLAIXgRaor6/Hf/7zH2i1Wri6umLkyJHNxnIbeeSRR9CjRw8QEfLz83HlypU29pbDaT1cBFpAp9Ph6NGjuHatIXHSiBEjsHjxYsNYLtAQWDJgwACsXr0aTk5O0Ol0+Pbbb1FeXm4rtzmc+8auOwYffvhh/PnPf77rIotlZWU4fPiwxeu/fv06tm3bhrVr18LV1RWLFi1C9+7dcfbsWdTW1iIyMhIjRoww9OqeOnUKiYmJFvfD0REEAQsWLIBUKkVycrLNR18aR4Js7YfFICKbGwBqtBkzZlAjWq2W6urq7mq//PILGZ/Dkubs7Exr166liooK0ul0JAgCqVQqUiqVpNFoSBAEqq+vpyNHjlCvXr2s5gcA2r59u+HeLFmyhEQikVXr49bcRCIR3bhxg0pLS2nmzJmkT6H3oNgFU98/u24JiMViuLq63vU4a8ZYq1QqvPPOO7hy5QqmTJmC2NhYBAYGQiKRoKKiAllZWfjpp5+wfft25OaaTP/OaWf4+vrCz8+vzWL7rY1ZCUkZYzkAZAB0ALREFMsY8wXwPYCHAeQAmEZEdxwzM05IGhAQcN/LV9fW1hpisK0FYwy+vr4IDQ01xA4olUqUl5ejoKDAYoEqd6Jnz54IDAwE0LDMlrlx8pz7RyQSobS0FH5+fpg3bx62bNnyID0WWC0h6TAiMu4JWwEgiYjeY4yt0P//2r2erLS01OyZYNaAiFBRUYGKigqb+ZCWloa0tDSb1c9pn1hjdGAigG367W0AmmdV4HA4doO5LQEC8LO+Of8VEW0EEEhEjVO5igEEmnohY2wBgAVm1s+xIR07doRIJIJMJoNGo4G7uzukUikEQYBSqTSkBhOLxYZ9AAxptex9PQQnJ6dm12SplYtM4ebmBhcXF4jFYuh0OqunHzNgZq9+qP5vAIDLAIYAqL7tmKr7GR3g9uBYUVERCYJAq1atopUrV9KlS5dILpdTcXExJSYm0sCBA8nb25vmzp1LZ8+epZqaGqqpqaFjx47RM888QxKJxObX0JIFBATQsmXL6Pfffye5XE7l5eWUmJhIw4cPp6qqKiIimjt3rkVGB5ycnGjYsGG0c+dOysnJIaVSSfn5+bRnzx4aM2YMubi4WOq6TI4OWHKY720AfweQDiBYXxYMIJ2LQPu04uJiIiLKysqiuro6KigooJKSEhIEgQRBoN9++43WrVtHlZWVVFlZSTk5OaRSqUgQBMrLy6O4uDibX4Mp8/DwoLVr15JcLidBEKiqqoqysrJILpdTamoqqdVqi4mASCSiKVOmUEZGBgmCQAqFgrKyskgmk5EgCJSfn09z5861lGBaVgQAuAPwNNo+C2A0gA8ArNCXrwDwv1wE2qc1ikBlZSXNnj2b/P39KSgoiFasWEE1NTWk0+lIqVTSv//9b+rSpQv5+fnRkCFDKDc3lwRBoC1bttj8GkzZkCFDqKqqitRqNX3++ecUHh5Ofn5+NGDAADp06BBptVqLiUB0dDRdvnyZtFotHTp0iAYMGEB+fn4UExNDu3btIo1GQ7du3aKBAwda4tosLgLhaHgEuAzgGoDX9eV+AJIAZAA4BsCXi0D7tEYR+Pjjj0ksFhvKQ0JC6OjRo4ZWgvEvvpOTE61Zs4YEQaC0tDSSSqU2vw5jY4zRhx9+SERE165dIy8vryb7IyMjqbq62iIiwBijl19+2dAy6tatW5P9QUFBdO3aNSIi+uSTTyzx6GFSBFo9OkBEN4moj96iiWitvryCiEYQUTciepKIKltbB+fBID09vUknX2lpKYqLiwE0LAJiHESl1WqRnp5uSBTi7e3d5v7eicZlxAAgKSkJtbW1TfZnZmZaZPUioKHDdOTIkWCMISkpCZmZmU32l5SUYM+ePQCA0aNHW21FYj6BiGNxtFqtIXhKLpc3W2SlMVUbY+yOi7XYAsYYHnroIQAN4mbtuhqT1ZiaE0FE+O233wA05LSwRMZlU3AR4LQ5t3/Y7Y3GVGYKhcLqvjYuZtu4UO3tNC5QwxgzHGtpuAhwOLfRmMqsLeIYGpv4La1c3FjOGLNIijVTcBHgcG6j8fGlLSYINQZUtZRItXECHREZjrU0XAQ4nNtoXBSmMc25tSAiQ56Exn6I22lMZKNUKq22diUXAQ7HCCLC77//DgAYPHgwnJyaRta7ublZrFkuCIIhn+SYMWPg5ubWZL+TkxOeeuopAA0dh9ZaxZqLAIdjhCAIOHDgANRqNYYOHYqxY8ca5jx4e3tj6dKlzb6s5tS1Z88eVFZWonfv3njxxRfh5eUFoOFRZMyYMZg0aRLq6uqwfft2q/VR2PWiIo2dIcZNMkEQ2mziCWPM0HFDRNDpdM18IiIIgtCsF1kkEjX5xWhLvznmcebMGZw8eRLDhw/HZ599hjNnzqC4uBiRkZGIi4uz6CPC+fPnsWvXLjz//PN47bXX8PjjjyMjIwPBwcEYPHgwPDw88MMPP+D48eMWq/N27LYlEB4ejoULF2L//v3IyspCQUEBUlJSsG3bNkydOrXJgp/W4plnnkFlZSUqKyvx9ddfIzIyEnPmzMGPP/6IzMxM5ObmIikpCQsWLDDkjXN3d8dTTz2Fb775BteuXUNBQQEuX76MdevWYeDAgVbr4eVYjoKCArz66qv4+eef4enpialTp2LhwoWIiYnB+vXrcePGDYvVVVtbizfeeAOfffYZamtrMWzYMCxatAgTJkyASCTC5s2bsXz5cqsuXmvWykIWc8JoZSHGGIYMGYK1a9ciPj7eZJSUUqnErl27sGrVKhQUFFjNr5kzZ2LHjh0AGp7JSktLMWrUqGa5BlUqFXbs2IE1a9Zg/vz5eOGFFwzJSIxJT0/H/PnzcerUKav53JYsX74cbm5uOHz4MC5evNhk3/jx49GnTx+kpqbi4MGDTabg9ujRA1OnToVMJsPWrVvvmOzVlvj7+2PEiBEICwuDWq3GpUuXcOHCBUybNg1hYWE4cOAA/vjjD4vUJRaLERcXh169esHX1xe1tbW4fv06Tp8+bckUZCZXFrLYLEJzDEbxzf369aPffvuNdDodaTQaKigooP3791NCQgIlJydTXV2dYbHPjz/+mDw8PKwWRz5z5kxqRK1Wk0qlotzcXNq3bx/t3buXCgoKDJNJFAoF/fzzzySTyai+vp6Sk5Pp+++/p99++41kMhkREQmCQMeOHSMfHx+bx8hzc0iz7lRiS4iAq6srbdu2zbCC76ZNmyg0NJREIhExxkgikdBf/vIXunXrFgmCQCUlJTRixAir3bTbRSAhIYECAwNJJBKRSCSirl270oEDB0ir1Rqmz1ZXV9PLL79M7u7uxBgjkUhE06ZNo+rqahIEgcrKymjy5Mm2/jBwc0yzfxGIioqiyspKEgSBUlNT6aGHHmp2Ia6urvTpp58alvt+6623msxgs6QZi8Dvv/9ucknxiRMnGmaVCYJAP/30U7Nf+g4dOtDu3buJiEij0dA//vGPB22pam7twyw7i9AajB492jCr7NChQyaX8FYqlTh27BgUCgUYYxg0aFCzsVxrkJGRYXJ1319//dUQySUIAg4ePNgsqEOhUBiemcViMYKCggzDThyOrbErEYiLizNs32kJ8ZycHENnSbdu3aw2xdIYnU5ncjJJRUWFYWopEZnMQ6jT6ZpMBHFzc2vWucjh2Aq7ihPo1KmTYfuRRx5pcZppUFCQIa7bx8fHqqGdd+P2iR8tLURpfJxYLLapz5akLSfbcKyDXYmAp6enYXvhwoUtTuNkjBl+Sd3d3dvNF+pBo0ePHjh79iw0Gg2GDx9uSN7KebCwKxEwjsS712dmLgC2QywWw8fHByqVqk0eyTjWwa5EAIDh13/58uVITk6+p+OtNcWSw3EE7EoEjBMt5Obm4uzZszb0hsNxDOxqdMA4z19oaKgNPeFwHAe7EoGUlBTD9mOPPXbXyTb8OdQxcXd3h7+/P4KDgxEYGIgOHTpYtG8oODgYoaGhcHNzg4eHB4KCgkzOBWkv2JUIHD16FFqtFowxDB06FIMHD27xze3VqxeeffbZFpdl4rQ/nJycMHToUHz11Vc4f/48srOz8ccffyAhIQFTpkyxyDx/sViMtLQ05OXl4Y033sDWrVuRmpqKEydOmH8BdopdicClS5dw8eJFEBH8/f2xdu1aPP7444YWAWMMrq6uGD9+PL788kusW7cO48eP59NzHQCRSISxY8fiq6++wowZM9CxY0fk5eXB1dUVY8eOxYYNGzB37lyLrQvIGMO8efMwfvx4yGQyq07ltTV29e2prq7Gu+++i9zcXIhEIjz22GP497//jZMnT2L79u1ITEzE+fPn8d1332HQoEHw8vJCTEwMFwEHICIiAmvWrEHXrl1x9OhRDB06FLGxsRg4cCA2b94MHx8fvP766xg8eLDF6pRIJHj++efRr18/jBs3zmLntTtsPXnIeAIRAJJKpfTMM8/Q5cuXSRAEMoUgCFRcXEzvvvsuBQQEWG3ChfEEou+++468vb1NHnfr1i3D5KD4+HiTxyxYsMBwri1btpCnp6etJ5OYbdHR0UREVF9fT71797ZqXYsWLTK87z179myyz8/Pjy5cuEBERF9//TU5OTm1uh6xWGyYEPZ///d/JBKJbH6fLWgmJxDZ1RAhAKjVauzevRtpaWkYNWoUnnzySURFRcHLywtqtRqZmZk4c+YM9u3bh8uXL0Mul1vNF51OZ4hBUKvVLUYwKpVK1NXVQavVthg+q9Vqm5yLc+8wxjBq1CgwxvCf//yn2co+FRUV+Ne//oUBAwbgqaeegpOTk0VShV24cMExwqHv4Vd6M4BSAFeNynwBHEVD0tGjAHz05QzAZwAyAVwB0P9+WwLGJhKJyMnJiaRSqcEkEkmbqbNIJCJnZ2dydna+46+LVCo1HNfSFOF7PdeDZG3VEmCMUUZGBhERvfLKKybv8dChQ4mISKlUUmhoaKvrMm4JzJ492+b32MLW6qnEW9GQctyYFQCSiKgbGjIQr9CXjwHQTW8LAHxxD+dvEUEQoNVqoVarDabRaNpMnQVBgEqlgkqluuMvi1qtNhzXUmvhXs/FMU3jKrwtLUXWmMbLmum62it3FQEiOgng9szCEwFs029vAzDJqHy7/tH3NwDejLFgSznLcUyMU3C19ANw+yxNzr3T2m71QCIq0m8XAwjUb4cCyDM6Ll9f1gzG2ALG2AXG2IVW+sBxEIjIEFLeUiyAcblx+Dnn7pg9tkYN7V/TbeA7v24jEcWSqdVPOZzbKCwsBNCQrstUAFljmHl9fT0qK29vuHLuRGtFoKSxma//W6ovLwAQZnRcJ30Zh9NqiAgnT54EEWH06NFN1p0AGgKJRo9u6LZKTk7ms0rvk9aKwI8AntNvPwdgv1H5bNZAPIAao8cGDqfV7N27F2VlZYiMjMTy5cvh6+sLoGHdidGjR2PKlCmoq6vDzp07ecfrfXLXOAHG2HcAngDQkTGWD+AtAO8BSGSMzQOQC2Ca/vBDAMaiYYiwDsAcK/jMcUCuXLmCLVu2YPHixViyZAkGDx6M9PR0BAYGIi4uDl5eXti/fz9++umnFkdoOC1g6ei/1hhsP37KrRXWlhGDAMjT05NWrVpFN27coNraWlKr1aRQKOjWrVu0ceNGCg4ONrsOR4wTsLs0ZJwHh44dO2Lu3LkQBAHbtm1DWVmZ1esUiUTo378/YmJi4OvrC7lcjoyMDJw9e7bFRV7vB8YYlixZAqlUioMHD7a3dRNNpiHjIsDhOA4mRYBPv+NwHBwuAhyOg8NFgMNxcLgIcDgODhcBDsfB4SLA4Tg4XAQ4HAeHiwCH4+BwEeBwHBwuAhyOg8NFgMNxcLgIcDgODhcBDsfB4SLA4Tg4XAQ4HAeHiwCH4+BwEeBwHBwuAhyOg8NFgMNxcLgIcDgODhcBDsfB4SLA4Tg4XAQ4HAeHiwCH4+BwEeBwHJy7igBjbDNjrJQxdtWo7G3GWAFj7A+9jTXat5IxlskYS2eMjbKW4xwOxzLcS0tgK4DRJso/JqK+ejsEAIyxKADTAUTrX7OBMSa2lLMcDsfy3FUEiOgkgMp7PN9EALuISEVE2WhIUR5nhn8cDsfKmNMn8CJj7Ir+ccFHXxYKIM/omHx9GYfDsVNaKwJfAIgA0BdAEYB193sCxtgCxtgFxtiFVvrA4XAsQKtEgIhKiEhHRAKATfhvk78AQJjRoZ30ZabOsZGIYk2lSuZwOG1Hq0SAMRZs9O9kAI0jBz8CmM4Yc2aMdQHQDUCyeS5yOBxr4nS3Axhj3wF4AkBHxlg+gLcAPMEY6wuAAOQAeAEAiOgaYywRQCoALYBFRKSzjuscDscSMCKytQ9gjNneCQ6n/XPR1OM3jxjkcBwcLgIcjoPDRYDDcXC4CNyBkJAQfP311zh16hSGDBlia3c4HKtw19EBRyYuLg7PPvss3N3dMWPGDCQnJ6O+vt6mPvn6+mLmzJkAgLKyMhw4cAByudymPkmlUsTGxiI8PByenp5QKBTIyMjAxYsXoVarLVoXYwy9e/dGz5494ePjA7lcjqysLFy4cMEidTHGsGDBAkilUhw9ehQlJSV45JFH0LlzZ2g0GqSlpeH8+fPQ6Sw76OXl5YVBgwahU6dOEIlEKCoqwsWLF1FYWGjRekxCRDY3NAw12p1NmDCBqqurSafT0fr168nZ2dnmPkVFRVEjly5dotDQUJv6ExQUROvWraOMjAySyWSk0WhILpdTRkYGvffee+Tp6Wmxutzc3Ojll1+mlJQUqq6uJrVaTTKZjDIyMujjjz8mf39/s+sQi8VUXV1NREQrVqygH374gQoKCkilUlFdXR3duHGD5syZY9F72L17d0pISKCCggKqr68nlUpFJSUldPz4cYqLi7NkXRdMfv9sLQD2LAJ+fn60evVqSkhIoP79+9vcH8C+RMDLy4s2b95MarWaSktL6aeffqKvv/6akpKSqKqqiurq6mjt2rXk6upqdl2urq60evVqksvlJJPJ6PTp07R582Y6evQolZeXk1qtph07dpgtBMYiUFNTQ3l5ebRv3z5KTEykmzdvklarpczMTOrXr59F7mFgYCDt27ePtFot5ebm0u7duykhIYFycnJIq9XS77//Tt27d7fUe8ZFoD2YPYnAU089RXK5nJRKJT333HOGL7uXlxf985//pPr6ekpNTaU+ffqYXVdcXBwVFhaSRqOh999/3/Bl9/T0pIULF5JcLie5XE4zZ84kfdxJq8xYBIqLi2n8+PEklUpJJBLRhAkTKCcnh+rq6mjx4sUWuYdDhw4lmUxGCoWCpkyZQmKxmMRiMU2aNIlyc3Oprq6OlixZYtY1GZlJEbDbjkHGGFxcXODp6Qlvb294eXnB3d0dYnHbLU8gkUjg7e0Nb29vuLm5gTHWZnUbwxgz+NGhQwdDuVgshpeXF7y9veHu7t7mfk2cOBHu7u44c+YMEhMToVQqAQA1NTX45ptvUFtbi27duiE8PNwidQUFBeHmzZt4//33UVZWBgCQyWTYsmULkpKSDH03Tk6W6eravHkzDh06BLVaDUEQcPLkSVy/fhw+6NsAACAASURBVB0uLi54+OGHIZFIzK4jJCQEHh4eUKlUSEpKgk6ng06nw7Fjx3Ds2DHcvHkTLi4uFqmrRWzdCjDVEvDy8qJnnnmGEhMTKTs7mxQKBVVUVNCZM2fo1Vdfpe7du1tKGe9oM2bMIK1WS1qtlhISEsjb29smv7gBAQGk0WgMvjQiCEIT/9rar+TkZCIiWrNmDYnF4ma/qJs2baLdu3fTU089Zdb7xRijM2fOEBHRJ598QiKRqNkxs2bNIiKiqqoqs/ohjFsCs2fPbubHDz/8QEREGzduJDc3N7Pv4YgRI6iuro7q6upo7ty5TXxnjJFIJLLkZ91kS8DuRgf8/f3xyiuv4IUXXmjyq+fm5obBgwdj4MCBmDRpEhYtWoTff//dqr4wxgwtD1u1Aoz9uN0HY/9EorZv1AUEBAAAysvLG8XcgE6nw/z58y1WV6dOnQAA2dnZzeoCgMzMTACAq6sr/Pz8IJPJLFZ3I0QEQRAANLTCLHHPU1NTcfz4cYwePRpr167Fo48+iu3bt+PMmTPQarUmr9XS2JUISCQSzJ8/Hy+99BKcnZ1RW1uL8+fPIzU1FaGhoRg8eDACAwMRHx+PDRs2YMKECYZmYXtGoVDggw8+AGMMfn5+mDt3LgCgpKQEe/bsgVwux+XLl9vcLxcXFwCAWq226oeVMWZ43FEoFCaPMR4mtcWjUWspLi7GkiVL8M4772DUqFGYNWsWJkyYgHPnzuGTTz7B6dOnrT8sbetHAePHAX9/f8rJySEiotraWlq6dCm5uLgYmkZjxoyhW7duERGRXC6nv/3tb1Zt7s6cOdPQ9P7uu+9s9jhgbPbUMVhUVERERH/729+s+njGGKPS0lIiIlqwYIHJunr37k1ERPX19dSzZ89W13WnxwEAlJiYSERE33zzDXl4eFjsGiUSCU2aNIm2bt1K+fn5REQkk8loxYoVlqzH/jsGQ0NDERbWsCZJWVkZdu3aZVBBIsKxY8ewc+dOCIIAFxcX9OnTB1Kp1JYuOzR1dXUAAGdnZ5OPS4wxiz1GNTbvPT09Te738PBoduyDhEajwb59+7B48WL89a9/xZEjR+Dq6oqlS5fiySeftGrddvU4IJVKDR8aIoJWq22yX6PRYNeuXYbylJQUmz6rOzo5OTkIDw9HWFhYs/fByckJK1euhKenJ3744QckJ5u3tsz169cRHh6Ofv36QSQSNYvYi46OBtDw41FRUWFWXW0FYwxjxoyBVCrFxYsXkZeXh9raWhw7dgy3bt3Czp07MWDAADz66KM4ePAgNBqNVfywq5ZAYWEhqqqqADR0EM6dOxdeXl5Njrl8+TJWrVqFVatWITExESqVyhaucgCcOXMGAPDEE0/Azc2tyb7o6GgsWbIEL730EoKDg029/J4hIvz8888gIgwfPrzZkKOzszP+9Kc/AQCSkpKs9mWxNE5OTvjoo4+wd+9eQz9PI43fBcYYJBKJVTt+7UoEysvL8d1330Gr1cLT0xMrV65EQkICnnzySbi7u9ukB5zTMvv370dZWRliYmKwcuVKBAUFwc3NDREREfjHP/4BLy8vXL58GWlpaWbXdeTIEaSlpSEgIAAffPABoqKi4ObmhsDAQLzyyit47LHHUFZWhoSEhGYtSHtFp9Ph4MGDEAQBf/3rXzFs2DB4eHigQ4cOGD9+PKKioqBUKpGVlWXxORhNsHWnoHHHIADq1KkTffnll1RTU0NEDWPhSqWSzpw5Q/Pnz6eHH37Y5DixNYx3DN7ZnJ2dadmyZVRWVkZarZZKS0spNTWVqqqqSKfT0c2bN2nq1KkW6TQUiUT0zDPPUFZWFul0OqqpqaHU1FQqLi4mnU5H5eXl9Pe//93Qkdxaa+uOwa5du1JSUhKp1WpSKpWUlZVF2dnZpFKpSKvV0oEDBygsLMxS79mDEzbs4+ND8+bNo9OnT5NKpTJ86FUqFSUlJdHMmTNJKpVa/UPOReDu5unpSTNmzKCff/6ZiouLSaVSUXFxMe3fv5/GjRtHTk5OFqtLIpHQyJEj6V//+hfl5eWRSqWi0tJSOnbsGM2aNYvc3d3NrqOtRYAxRjExMfTRRx9Renq6IXAoNTWVPvvsM+rRo4cl368HRwSABuUPCAig8ePH09GjRw2z+QRBoOrqanrttddIIpFY9QPOReDeTCQSkZ+fH3Xu3JnCw8Opc+fO5OPjY7VhQy8vLwoLCzPU5efnZ9HWYZcuXSgiIsJk5GFgYCBFRERQQECARa/P1dWVQkJCqEuXLtSlSxcKDg62yMSr2+zBEgFjc3FxoQkTJtDPP/9MOp2OiIgqKipo7NixVv1wcxHg1s7M/uMEGseVbx9uqq+vx48//ohFixbh5MmTABoWYRg1apTFJotwOI6KXYnAwYMHUVJSgpKSEowbN67Z/szMTCQkJABoiN0ODAxsNjTlCOhbTwBsO6eB0z6wKxEoKSlBx44d0bFjR4wcObLZB1wkEhkixogISqXSukMndoggCIZxcLFYbN0pphyHwK5EYPfu3YZIsD/96U+YPn063N3dIZVK4ezsjNjYWMybNw9AwyPCtWvXbL7mX1ujVqtRXl4OoCGgKiYmBlKplD8WcVqPrTsFjTsG3d3dafPmzaRUKkkQBKqvr6eUlBQ6ePAg/fbbbySTyUgQBBIEgc6ePWv1TjF77Bj09fWlffv2Ge5DUVER/fTTT7R69Wqb+8bN7s3+1xNQKBRYvXo16urqMGfOHLi5uSEmJgYxMTGGY7RaLc6dO4fXXnsNBQUmEx63a6qrq7Fr1y4MGjQIAQEBCAoKQlBQEKqrq23tGucBxa5EAAByc3OxcuVK7N69G9OmTcOAAQPg7+8PpVKJtLQ07Nu3D7/88kubLMWsVCpRVFQEoOHL17ighC0RBAH79u2DUqnE4sWLERERAZlMhnPnztnatXaFh4cHPDw8oFar7ea9txr30FQPA3AcDZmGrwFYoi/3BXAUQIb+r4++nAH4DEAmgCsA+psbJ8CNW1vbm2++SYIg0IkTJyyylLmdWKvjBLQAXiaiKADxABYxxqIArACQRETdACTp/weAMQC66W0BgC/uoQ4Ox65oKWalPXJXESCiIiK6pN+WAUgDEApgIoBt+sO2AZik354IYLu+P+03AN6MMfPmknI4bYxxLEZ75776BBhjDwPoB+AcgEAiKtLvKgYQqN8OBZBn9LJ8fVmRURkYYwvQ0FLgPOBIpVLEx8cjMjISbm5uqKqqwpUrV3D16lWLp+tydnZGfHw8IiIi4OnpCZlMhvT0dCQnJ1tkHYFJkyYhIiICjz76KICGBU7/3//7fygqKsLGjRvNPn9LuLi4YPDgwYiIiICrqysqKipw+fJlXLt2zfqCdB/DeB4ALgKYov+/+rb9Vfq/BwA8ZlSeBCCW9wm0T/P396ePPvrIsDS8Wq2mmpoaSktLo5deeqnZUuTmWGhoKK1fv56ys7NJLpeTWq0muVxOWVlZ9P7771OHDh3MrmPPnj2k1WoNc1QEQSCdTkfZ2dlWu4dBQUH01VdfUW5uruEeVldXU0pKCs2ZM8eSk6NaP4EIgATAEQDLjcrSAQTrt4MBpOu3vwLwZ1PHcRFoX+bh4UHr168nlUpFubm5tHfvXvryyy/p5MmTJJPJqKKiwuyMQI3m5+dHO3fuJI1GQ0VFRfTjjz/Shg0b6MiRI4aUZ++++67ZM+/mzJlD69ato7NnzxIRUV5eHn3xxRe0atUqq9xDX19f2rZtG6nVasrKyqLdu3fTV199RWfPniWFQkFFRUU0adIkq2YguhcBYAC2A/jktvIPAKzQb68A8L/67acBHNa/Lh5AMh8daJ/26KOPUlVVFVVVVdHYsWMNawcEBQXRrl27SKvV0u7du6ljx45m1/X000+TQqEghUJB06dPNywe4uXlRe+99x6pVCq6du0aRUdHW+Ta3nzzTSIi+s9//mPV0YGxY8dSTU0NFRcX0xNPPGFoOXXq1IkOHjxIWq2Wtm7dSl5eXpaor9Ui8Jj+BFcA/KG3sQD80NDUzwBwDICvkWisB5AFIAV3eRTgIvDg2nvvvUdERImJic0WeZkyZQrV1tbS9evXqW/fvmbVwxijTZs2ERHR4cOHm2WH7tatG1VWVpJGo6Fx48ZZ5NraQgREIhFt2LCBiIi+/vrrZs3+WbNmUX19PV25coUiIiIsUWfrIgaJ6DQavtimGGHieAKw6G7n5Tz4PPbYYwCACxcuNFvX78aNG1Cr1QgODoaPj4/ZdcXGxgIAzp4926wDMDs7G4mJifD09ERtba3ZdbUVIpEIgwcPBgAkJyc3C0hKS0uDVqtFWFhYkyXVLY3dRQxyHhyCgoIAAFOmTEFUVFSTXmxPT0+4u7vDycnJIrkh7pTyTKvVYuHChWbXYUxbxAcwxgz38C9/+QsGDRrUZL+3tzekUimkUqlVZ4tyEeC0msZ0X3379m0yv6ORxgy75sIYg7OzMwC02XLitwuNtWi8h3Fxcejfv3+z/W0xVZ6LAMds9u7di9OnT7f4xbl+/brZdbTVl7KtabyuhIQEXLhwocXjbt26ZTUfuAhwWo1SqQQAnD9/Hl988YXVJtkQUZOUZ6ZozElhKR/aKly4vr4enp6eOHXqFLZs2dImdd6OXS0qwnmwyM/PBwBERkZaPTFMTk4OAOChhx5q9gWVSCRYs2YN1q1bZ+hANJe2aHkQkeEe9ujRw2bzFLgIcFpNYxqyESNGoGPHjs329+jRAz179jS7HiIy1DVs2LBm60r27t0bL774IhYuXGjSj9YgCAIEQQBjzGoCJwgCfv31VwDAqFGj4O3t3eyYmJgYdO3a1Sr1G7iXiEFrG+xgzJvb/dvAgQOpoqKCtFotffvttxQZGUmenp7k4+ND48aNo99//50SEhIoMDDQ7Lri4uKopKSEVCoV/c///A+FhISQp6cn9ezZk/bu3UsajYZOnTpF4eHhFrm2F198kerq6ig9PZ2GDBlikcQmpuzJJ5+kqqoqUqvV9OWXXxryHfj6+tLUqVPp2rVrtGnTJvLx8bFEfQ9u3gF7sQ4dOlBsbCyNHTuWJk+eTGPGjKGBAwe22bJjAwcOpKlTp9LUqVOpa9euJBaLqVOnTvTEE0/QhAkTaNy4cTRgwIBmmXF8fHxo0KBB9PTTT9PEiRPpiSeeoLCwMLNDUV1cXOjll1+m8vJyEgSBZDIZpaenU2FhIWk0GlIoFPTFF19QQECA2dfu4uJCS5cupdLSUkPasfT0dKqtrSVBEOjGjRs0fvx4iyUEiY+Pp6ysLBIEgUpKSujUqVNWeU/d3d3pzTffpOrqahIEgWpqauj69etUXFxMGo2Gamtr6aOPPrLUZ4yLQGtNJBLRo48+Slu3bqX09HSSyWSGN+jGjRu0Y8cOGjhwoNUy7jTa9u2NM7SJli1bRvPmzaOjR49SUVER1dfXk0KhoOvXr9Nnn31GDz30EIlEIho0aBDt2LHDMOlGpVJRUVERHTt2jJ5++mmzferQoQP9+c9/pp9++omKi4tJrVZTVVUVnTx5kpYsWUJBQUEWu35PT0969tlnDXWpVCoqLCykH374gZ566imLTlZydnaml156iXJycqi+vp7OnTtntffVx8eH5s6dS7/88guVlpaSWq2miooKSkpKohdeeMGSEYtcBFpjjDGaMmUK5eXlGWaXKRQKqqmpofr6esMss0uXLtHAgQOt6ouxCFy9epVqampIpVJRbW0tKRQKw8w3tVpNe/bsofHjx1NGRgZptVpSKBQkk8lIrVYTUcPsuIyMDHryySfNFi+RSETe3t4UEhJCYWFhFBoaSr6+vhbNQ3h7XcHBwRQWFkbBwcEWmT1oyqRSKQUFBVFYWJhFWjN3uy4fHx/DPQwJCSEfHx+LChtaEAGm/xLaFP2H0C6Jjo7GoUOH0LlzZ2i1Whw4cABHjx5FVVUVunXrhqlTpyI6OhoikQhbtmzBwoULrRbgsX37dsyaNQsAQES4fv069uzZg/T0dHh7e2PkyJEYOXIkpFIpBEFAWVkZ/Pz88Msvv2D//v2oq6tDt27dMG3aNHTt2hVEhF27dmHRokWoqqqyis8cu+IiETUfPrF1K8CeWwKMMXrnnXcMv55Hjhxpssy5WCymyZMnG7LYFhcXW2q2l0kzbgmUlJTQkCFDmvzahoSEGKbANv7aHz9+vElmWycnJ5o/fz5VVFQQEdHNmzcpKirK5veaW5uY/ecitDfc3Nzg5eWFzMxMpKWl4fPPP2+yzLlOp8PRo0chl8sBAL6+vggObpuV1LZu3YqzZ882mbhTWFiIr7/+2vB/bW0ttm3bhvT0dEOZVqvFkSNHDGWdO3c2xK9zHBMeMXgHlEol3n33XXz00UcAgOLi4mbHyOVyQ5QaYwwdOnRoE98KCwtNRsdduXLFsF1eXo4rV640trYMlJSUoLKyEkBDKrPGyTkcx4SLwB0QBAGlpaWG/0UiEZydneHk5NRkJVrjSC9rR87dDZlMZthWKpWGlGXGqFSqJhNx3NzcwBhrJhYcx4CLwD0gFovRtWtXjBgxAmPGjEF0dDQ6duwIFxcXALCrPIDGrQOjPpc7Ymvh4tgW+/n02jETJ07EihUr0KdPH4vMjedw7AkuAndh+PDh+PTTTxEaGgoAqKqqQm5uLq5evYqKigoIgoDnn3/ekDLd0fD09IS3tzfUajXKysrad7qudgoXgTvg7++PpUuXGgTg3LlzePPNN3Hy5EmoVCrDcVOnTnVYEZg/fz4+/PBDXLx4EePHjzfZecqxb7gI3IEePXqgd+/eYIwhPz8fb731Fo4ePWprt+wOR0jV1Z7hPUJ3ICAgAIGBDYmVLl++jMuXL9vYIw7H8vCWwB1wcnIy9PyrVKpmK+oCDQtaOOIv4YQJExAdHW1YcTg4OBiLFy9GaWkpPvnkE4vXt2zZMri4uODQoUPNxLjRl6tXr+LIkSNmhW0zxrBs2TI4Ozvj0KFDcHd3x4ABA6BQKLB582ZzL8Mu4SJwB+RyOWQyGXx8fBASEgJfX19UVFQY9kskEkyfPh1eXl429NI2/OlPf8LMmTMNAhgSEoJXX30V5eXl+PTTTy0acyASifD666/Dz88PpaWlzQKgpk2bhpkzZ+Lbb7/FiRMnzBIBkUiEN998E15eXujfvz/69++P4OBg5OfncxFwRLKyspCZmYnY2FgMGDAAL730Er799lsolUp06NABkydPxl//+tcma8I7yhDioUOHUFRUhIEDB2L48OEoLi7G7t27UVpa2m6CjiZNmoSrV68iKSkJRUVFd3/BAwoXgTuQlZWFPXv2oHfv3nB2dsaiRYswYcIEyGQy+Pn5ISgoCFlZWdDpdAgMDARjDJ06dbK1223C999/j++//x7Lly/H8OHDUVBQgLVr17ar0YGMjAzMnj0bV69ebTfCZgreMXgHNBoN1q9fj88//xyVlZXQaDQICwtDz5494eXlhYsXL2LRokX45ZdfoFAooFQqLbbQJcf2bN68ud0LAMBbAndFJpNhxYoVSExMxPDhw/Hwww9Do9Hg6tWr2LdvH0pLS1FUVITDhw8DQJO5Bpbm9OnThmCc1NRUkx/OxpmDQMNqwAqFwuS5jh8/jpqaGgANv3jt/YPeGtrTo80duYe5/mEAjgNIBXANwBJ9+dsACmCUpNToNSsBZKIhLfmoB3U9AW53t+XLlxMR0fnz5y26lJixiUQiKi8vJyKiefPmNVsJaceOHUREtH37dvL09DSrLrFYbFgfYvbs2Ta/vxa21iUkBaAF8DIRXWKMeQK4yBhrjJj5mIg+ND6YMRYFYDqAaAAhAI4xxiKJyPx8VBwOx+LctU+AiIqI6JJ+WwYgDUDoHV4yEcAuIlIRUTYaWgRxlnCWw+FYnvvqGGSMPQygH4Bz+qIXGWNXGGObGWON+adDAeQZvSwfdxYNDueeccTALGtzzyLAGPMAsBvAUiKqBfAFgAgAfQEUAVh3PxUzxhYwxi4wxlrOwsjhACD6by7C27MPAVwYzOWeRIAxJkGDAOwkoj0AQEQlRKQjIgHAJvy3yV+Ahs7ERjrpy5pARBuJKNbk6qecB4a2SNcF/DfvYePKzo106tQJISEhVqvXEbjru8YaZPYbAGlE9JFRufGKmpMBXNVv/whgOmPMmTHWBUA3AMmWc5ljT9TU1ECpVMLf3x99+vQxmU/PXIgIJ06cABFh0qRJGDRoEHx8fBAeHo41a9YgPj7e4nU6EvcyOvAogFkAUhhjf+jL/gHgz4yxvmgYesgB8AIAENE1xlgiGoYUtQAW8ZGB9ssff/yB3Nxc9OzZE99++y1yc3MRGxtr8fH1hIQETJkyBZGRkThy5AhKSkrQsWNHCIIAtVptWOqN0wpskWeAxwm0H5NKpTRv3jzKyMggpVJJly9ftko6NolEQuPGjaOff/6ZZDIZyeVyunDhAi1atIgOHz5MgiBYLE6gqqqKBEGgWbNm2fz+Wth4BiKOdXBycoKXlxckEgk0Gk2TmZaWRCQSwcPDA66urgAAtVoNmUxmKFMqlaipqTG7FRIUFATGGKqrq6FUKi3hur1gMgMRFwEOx3EwKQJ87sA9IJFIDNtardYx4sk5DgOfRXgPpKWlobKyEiUlJejXr5+t3eFwLApvCdwD7u7u8PDwgFqt5ok6OO0O/onmcBwcLgIcjoNjt48DIpEI3bt3x5AhQxAZGQlnZ2fU1NQgJSUFJ0+eRGFhoVXrf/vttw1JRxozDYvFYrz++usoLy+HXC7H8uXLeSch58HH1oFCpoKFfH196cMPP6SCggJSKBSk1WpJp9ORWq2m2tpaun79Os2aNYucnZ2tFliRkpJCd6K8vNwqQTHcuFnRWr2oSJsSHByM9957DzNmzICTkxPq6+uRnZ0NtVqNjh07wt/fH5GRkfj8888hkUjw7bffNkmzbSlu3LgBnU4Hxhh69uwJiUQCIkJmZibq6uoMS3NxOA88tm4FGLcEnJycaPny5SSXy0kQBLpy5QrNmTOHBgwYQH369KFhw4bRtm3bqL6+ngRBoEuXLlFUVJRVVLNr164UHR1NMTExVFZWRkREarWann32WYqOjqYePXrYWtW5cbtfM9kSsLkAGItAcHAwZWZmkiAIlJ+fT0OHDiWRSNTkQgIDA+mHH34gnU5HOp2OFi5c2OwYS1tRUREREalUKoqNjbX1G8mNW2vNpAjY1ejA0KFDER4eDqBhZd1Tp041S3VdUlKCgwcPQqlUQiQSYejQoYZUYRwO5/6xKxEYMmSIYfvEiRMt5rq/du0a6uvrAQBRUVEQi8Vt4h+H0x6xq5/Q7t27G7YlEgliYmJMHhcQENAkBx6P4uNwWo9diYCPj49he9myZXj++edNHieVSg1j925ubnyNOQ7HDOxKBBpn6zHG0KVLl/t6DYfDaR12JQJAgwAQEQ4fPoyCgmbrkzZDp9NZJU6Aw3EU7EoENBqNIQx3w4YNOHjwoI094nDaP3bVo1ZVVWXY7tixow094XAcB7sSgZs3bxq2+/fvb0NPOBzHwa5E4NdffzU8DgwbNgwBAQEtHuvq6oqwsLA2GRnQ6f67YjofjuS0N+zqE3348GHk5eWBMYbIyEi8+eab6NSpU5NjxGIxunTpgn/+85/Yu3cv4uKsn+u0srISQIMANEY0cjjtBbvqGCwvL8fGjRvx+uuvw83NDXPmzEFERASOHTuG/Px8uLi4oGvXrhg6dCji4+Ph5OSEqVOn4uLFi9BqtVbzKyUlBb169YJYLMaiRYvg7u4OpVKJhIQEq9XJ4bQZtp48ZDyBCAD5+/vT+vXrqba2lgRBIEEQSKlUUlVVFdXU1JBarSZBEEitVtPZs2fpkUcesfrEi6effpoUCgUREWm1WqqtraWcnBy+ngC3B83sfxZhozk7O9MLL7xAR44coZs3b5JcLietVksqlYoKCwvp119/pbVr11Lnzp3b5Oa5u7vTG2+8QZmZmaRWq0mlUtHVq1e5CHB70OzBykDEGENQUBC6dOmCjh07wtnZGTqdDlVVVcjLy0N2dnaTDjtr4+HhgZiYGMOSY8XFxThz5kyb1c/hWACegYjDcXBMioBdjQ5wOJy2h4sAh+Pg2MsQYTkAhf6vvdAR3J+7YW8+cX/uzEOmCu2iTwAAGGMXTD2v2Aruz92xN5+4P62DPw5wOA4OFwEOx8GxJxHYaGsHboP7c3fszSfuTyuwmz4BDodjG+ypJcDhcGyAzUWAMTaaMZbOGMtkjK2wkQ85jLEUxtgfjLEL+jJfxthRxliG/q/P3c5jpg+bGWOljLGrRmUmfWANfKa/Z1cYYxZfgaUFf95mjBXo79MfjLGxRvtW6v1JZ4yNsoI/YYyx44yxVMbYNcbYEn25Le9RSz7Z7D61ChtPHBIDyAIQDkAK4DKAKBv4kQOg421l/wtghX57BYD3rezDEAD9AVy9mw8AxgI4DIABiAdwro38eRvA300cG6V/75wBdNG/p2IL+xMMoL9+2xPADX29trxHLflks/vUGrN1SyAOQCYR3SQiNYBdACba2KdGJgLYpt/eBmCSNSsjopMAKu/Rh4kAtlMDvwHwZowFt4E/LTERwC4iUhFRNoBMNLy3lvSniIgu6bdlANIAhMK296gln1rC6vepNdhaBEIB5Bn9n48730RrQQB+ZoxdZIwt0JcFElGRfrsYQKAN/GrJB1vetxf1zevNRo9IbeoPY+xhAP0AnIOd3KPbfALs4D7dK7YWAXvhMSLqD2AMgEWMsSHGO6mhLWfTYRR78AHAFwAiAPQFUARgXVs7wBjzALAbwFIiqjXeZ6t7ZMInm9+n+8HWIlAAIMzo/076sjaFiAr0f0sB7EVDE62ksfmo/1va1n7dwQeb3DciKiEiHREJADbhv03ZNvGHMSZBw5dtJxHt0Rfb9B6Z8snW9+l+sbUInAfQjTHWhTEmBTAdwI9t6QBjzJ0x5tm4DWAkgKt62WcZ8AAAAOxJREFUP57TH/YcgP1t6Zeelnz4EcBsfQ94PIAaoyax1bjtmXoyGu5Toz/TGWPOjLEuALoBSLZw3QzANwDSiOgjo102u0ct+WTL+9QqbN0ziYZe3Bto6Cl93Qb1h6Ohx/YygGuNPgDwA5AEIAPAMQC+VvbjOzQ0HTVoeFac15IPaOjxXq+/ZykAYtvIn2/19V1Bwwc62Oj41/X+pAMYYwV/HkNDU/8KgD/0NtbG96gln2x2n1pjPGKQw3FwbP04wOFwbAwXAQ7HweEiwOE4OFwEOBwHh4sAh+PgcBHgcBwcLgIcjoPDRYDDcXD+P/cQhEe29+9lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"example1 = text_to_square_image([\\\"Lorem\\\", \\\"ipsum dolor\\\", \\\"sit amet\\\", \\\"consectetur\\\"])\\nimshow(example1)\\nexample1.shape\";\n",
       "                var nbb_formatted_code = \"example1 = text_to_square_image([\\\"Lorem\\\", \\\"ipsum dolor\\\", \\\"sit amet\\\", \\\"consectetur\\\"])\\nimshow(example1)\\nexample1.shape\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example1 = text_to_square_image([\"Lorem\", \"ipsum dolor\", \"sit amet\", \"consectetur\"])\n",
    "imshow(example1)\n",
    "example1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"def preprocess_data(data, map_func):\\n    preprocessed_df = None\\n    with PoolExecutor() as executor:\\n        preprocessed_df = np.stack(list(executor.map(map_func, data)), axis=0)\\n    print(preprocessed_df.shape)\\n    print(preprocessed_df.nbytes / (1024 * 1024))  # Memory size in RAM\\n    gc.collect()\\n    return preprocessed_df\";\n",
       "                var nbb_formatted_code = \"def preprocess_data(data, map_func):\\n    preprocessed_df = None\\n    with PoolExecutor() as executor:\\n        preprocessed_df = np.stack(list(executor.map(map_func, data)), axis=0)\\n    print(preprocessed_df.shape)\\n    print(preprocessed_df.nbytes / (1024 * 1024))  # Memory size in RAM\\n    gc.collect()\\n    return preprocessed_df\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def preprocess_data(data, map_func):\n",
    "    preprocessed_df = None\n",
    "    with PoolExecutor() as executor:\n",
    "        preprocessed_df = np.stack(list(executor.map(map_func, data)), axis=0)\n",
    "    print(preprocessed_df.shape)\n",
    "    print(preprocessed_df.nbytes / (1024 * 1024))  # Memory size in RAM\n",
    "    gc.collect()\n",
    "    return preprocessed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"def fixed_text_to_square_image(values):\\n    return text_to_square_image(values, image_size=96, cut_length=None)\";\n",
       "                var nbb_formatted_code = \"def fixed_text_to_square_image(values):\\n    return text_to_square_image(values, image_size=96, cut_length=None)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fixed_text_to_square_image(values):\n",
    "    return text_to_square_image(values, image_size=96, cut_length=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 96, 96, 3)\n",
      "26.3671875\n",
      "CPU times: user 638 ms, sys: 277 ms, total: 915 ms\n",
      "Wall time: 2.22 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"%%time\\npreproc_ex = preprocess_data(X[:1000], fixed_text_to_square_image)\\ndel preproc_ex\\ngc.collect()\";\n",
       "                var nbb_formatted_code = \"%%time\\npreproc_ex = preprocess_data(X[:1000], fixed_text_to_square_image)\\ndel preproc_ex\\ngc.collect()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "preproc_ex = preprocess_data(X[:1000], fixed_text_to_square_image)\n",
    "del preproc_ex\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"from tensorflow.keras.utils import to_categorical\\nfrom concurrent.futures import ProcessPoolExecutor as PoolExecutor\\n\\n\\nclass TabularToImagesDataset:\\n    def __init__(self, values, target, func, prefetch=1024):\\n        self.values = values\\n        self.target = to_categorical(target.reshape(-1))\\n        assert target.shape[0] == self.values.shape[0]\\n        self.current = -1\\n        self.max_prefetch = -1\\n        self.prefetch_nb = prefetch\\n        self.func = func\\n        self.ready = None\\n\\n    def __iter__(self):\\n        # print(\\\"Calling __iter__\\\")\\n        self.current = -1\\n        self.max_prefetch = -1\\n        return self\\n\\n    def __call__(self):\\n        # print(\\\"Calling __call__\\\")\\n        self.current = -1\\n        self.max_prefetch = -1\\n        return self\\n\\n    def __next__(self):\\n        return self.next()\\n\\n    def __len__(self):\\n        return self.values.shape[0]\\n\\n    def prefetch(self):\\n        if self.ready is not None and self.ready.shape[0] == self.values.shape[0]:\\n            return\\n        # print(\\\"HERE\\\")\\n        self.max_prefetch = min(self.current + self.prefetch_nb, self.values.shape[0])\\n        # if self.current == self.max_prefetch:\\n\\n        with PoolExecutor() as executor:\\n            self.ready = np.stack(\\n                list(\\n                    executor.map(\\n                        self.func, self.values[self.current : self.max_prefetch]\\n                    )\\n                ),\\n                axis=0,\\n            )\\n        return\\n\\n    def next(self):\\n        self.current += 1\\n        # print(self.current)\\n        if self.current >= self.values.shape[0]:\\n            raise StopIteration()\\n        # self.current = 0\\n        # self.max_prefetch = -1\\n        if self.current >= self.max_prefetch:\\n            # print(\\\"Will prefetch\\\")\\n            self.prefetch()\\n            # print(self.ready.shape)\\n        return (\\n            self.ready[self.current % self.prefetch_nb],\\n            # text_to_square_image(self.values[self.current]),\\n            self.target[self.current],\\n        )\";\n",
       "                var nbb_formatted_code = \"from tensorflow.keras.utils import to_categorical\\nfrom concurrent.futures import ProcessPoolExecutor as PoolExecutor\\n\\n\\nclass TabularToImagesDataset:\\n    def __init__(self, values, target, func, prefetch=1024):\\n        self.values = values\\n        self.target = to_categorical(target.reshape(-1))\\n        assert target.shape[0] == self.values.shape[0]\\n        self.current = -1\\n        self.max_prefetch = -1\\n        self.prefetch_nb = prefetch\\n        self.func = func\\n        self.ready = None\\n\\n    def __iter__(self):\\n        # print(\\\"Calling __iter__\\\")\\n        self.current = -1\\n        self.max_prefetch = -1\\n        return self\\n\\n    def __call__(self):\\n        # print(\\\"Calling __call__\\\")\\n        self.current = -1\\n        self.max_prefetch = -1\\n        return self\\n\\n    def __next__(self):\\n        return self.next()\\n\\n    def __len__(self):\\n        return self.values.shape[0]\\n\\n    def prefetch(self):\\n        if self.ready is not None and self.ready.shape[0] == self.values.shape[0]:\\n            return\\n        # print(\\\"HERE\\\")\\n        self.max_prefetch = min(self.current + self.prefetch_nb, self.values.shape[0])\\n        # if self.current == self.max_prefetch:\\n\\n        with PoolExecutor() as executor:\\n            self.ready = np.stack(\\n                list(\\n                    executor.map(\\n                        self.func, self.values[self.current : self.max_prefetch]\\n                    )\\n                ),\\n                axis=0,\\n            )\\n        return\\n\\n    def next(self):\\n        self.current += 1\\n        # print(self.current)\\n        if self.current >= self.values.shape[0]:\\n            raise StopIteration()\\n        # self.current = 0\\n        # self.max_prefetch = -1\\n        if self.current >= self.max_prefetch:\\n            # print(\\\"Will prefetch\\\")\\n            self.prefetch()\\n            # print(self.ready.shape)\\n        return (\\n            self.ready[self.current % self.prefetch_nb],\\n            # text_to_square_image(self.values[self.current]),\\n            self.target[self.current],\\n        )\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "from concurrent.futures import ProcessPoolExecutor as PoolExecutor\n",
    "\n",
    "\n",
    "class TabularToImagesDataset:\n",
    "    def __init__(self, values, target, func, prefetch=1024):\n",
    "        self.values = values\n",
    "        self.target = to_categorical(target.reshape(-1))\n",
    "        assert target.shape[0] == self.values.shape[0]\n",
    "        self.current = -1\n",
    "        self.max_prefetch = -1\n",
    "        self.prefetch_nb = prefetch\n",
    "        self.func = func\n",
    "        self.ready = None\n",
    "\n",
    "    def __iter__(self):\n",
    "        # print(\"Calling __iter__\")\n",
    "        self.current = -1\n",
    "        self.max_prefetch = -1\n",
    "        return self\n",
    "\n",
    "    def __call__(self):\n",
    "        # print(\"Calling __call__\")\n",
    "        self.current = -1\n",
    "        self.max_prefetch = -1\n",
    "        return self\n",
    "\n",
    "    def __next__(self):\n",
    "        return self.next()\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.values.shape[0]\n",
    "\n",
    "    def prefetch(self):\n",
    "        if self.ready is not None and self.ready.shape[0] == self.values.shape[0]:\n",
    "            return\n",
    "        # print(\"HERE\")\n",
    "        self.max_prefetch = min(self.current + self.prefetch_nb, self.values.shape[0])\n",
    "        # if self.current == self.max_prefetch:\n",
    "\n",
    "        with PoolExecutor() as executor:\n",
    "            self.ready = np.stack(\n",
    "                list(\n",
    "                    executor.map(\n",
    "                        self.func, self.values[self.current : self.max_prefetch]\n",
    "                    )\n",
    "                ),\n",
    "                axis=0,\n",
    "            )\n",
    "        return\n",
    "\n",
    "    def next(self):\n",
    "        self.current += 1\n",
    "        # print(self.current)\n",
    "        if self.current >= self.values.shape[0]:\n",
    "            raise StopIteration()\n",
    "        # self.current = 0\n",
    "        # self.max_prefetch = -1\n",
    "        if self.current >= self.max_prefetch:\n",
    "            # print(\"Will prefetch\")\n",
    "            self.prefetch()\n",
    "            # print(self.ready.shape)\n",
    "        return (\n",
    "            self.ready[self.current % self.prefetch_nb],\n",
    "            # text_to_square_image(self.values[self.current]),\n",
    "            self.target[self.current],\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 34;\n",
       "                var nbb_unformatted_code = \"def build_tf_dataset(X_values, Y_values, image_size, fix_func, prefetch, batch_size):\\n    gen = TabularToImagesDataset(\\n        X_values, Y_values, fix_func, prefetch=prefetch * batch_size,\\n    )\\n    if prefetch * batch_size > X_values.shape[0]:\\n        prefetch = np.ceil(X_values.shape[0] / batch_size).astype(\\\"int\\\")\\n\\n    dataset = tf.data.Dataset.from_generator(\\n        gen,\\n        (tf.uint8, tf.uint8),\\n        (tf.TensorShape([image_size, image_size, 3]), tf.TensorShape([2])),\\n    )\\n\\n    dataset = dataset.repeat().batch(batch_size)\\n    return dataset.prefetch(prefetch)\";\n",
       "                var nbb_formatted_code = \"def build_tf_dataset(X_values, Y_values, image_size, fix_func, prefetch, batch_size):\\n    gen = TabularToImagesDataset(\\n        X_values, Y_values, fix_func, prefetch=prefetch * batch_size,\\n    )\\n    if prefetch * batch_size > X_values.shape[0]:\\n        prefetch = np.ceil(X_values.shape[0] / batch_size).astype(\\\"int\\\")\\n\\n    dataset = tf.data.Dataset.from_generator(\\n        gen,\\n        (tf.uint8, tf.uint8),\\n        (tf.TensorShape([image_size, image_size, 3]), tf.TensorShape([2])),\\n    )\\n\\n    dataset = dataset.repeat().batch(batch_size)\\n    return dataset.prefetch(prefetch)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def build_tf_dataset(X_values, Y_values, image_size, fix_func, prefetch, batch_size):\n",
    "    gen = TabularToImagesDataset(\n",
    "        X_values, Y_values, fix_func, prefetch=prefetch * batch_size,\n",
    "    )\n",
    "    if prefetch * batch_size > X_values.shape[0]:\n",
    "        prefetch = np.ceil(X_values.shape[0] / batch_size).astype(\"int\")\n",
    "\n",
    "    dataset = tf.data.Dataset.from_generator(\n",
    "        gen,\n",
    "        (tf.uint8, tf.uint8),\n",
    "        (tf.TensorShape([image_size, image_size, 3]), tf.TensorShape([2])),\n",
    "    )\n",
    "\n",
    "    dataset = dataset.repeat().batch(batch_size)\n",
    "    return dataset.prefetch(prefetch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 35;\n",
       "                var nbb_unformatted_code = \"epochs_1 = 5\\nepochs_2 = 200\\nimage_size = 96\\ncut_length = None\\nBATCH_SIZE = 64\\nPREFETCH = 10000  # 40\\n# SHUFFLE_BUFFER_SIZE = 10000\\npatience = 10\\n\\n\\ndef fixed_text_to_square_image(values):\\n    return text_to_square_image(values, image_size=image_size, cut_length=cut_length)\\n\\n\\nsteps_per_epoch = np.ceil(train_indices.shape[0] / BATCH_SIZE)\\nsteps_per_epoch_val = np.ceil(valid_indices.shape[0] / BATCH_SIZE)\\n\\ndataset = build_tf_dataset(\\n    X[train_indices],\\n    Y[train_indices],\\n    image_size,\\n    fixed_text_to_square_image,\\n    PREFETCH,\\n    BATCH_SIZE,\\n)\\n\\ndataset_valid = build_tf_dataset(\\n    X[valid_indices],\\n    Y[valid_indices],\\n    image_size,\\n    fixed_text_to_square_image,\\n    PREFETCH,\\n    BATCH_SIZE,\\n)\";\n",
       "                var nbb_formatted_code = \"epochs_1 = 5\\nepochs_2 = 200\\nimage_size = 96\\ncut_length = None\\nBATCH_SIZE = 64\\nPREFETCH = 10000  # 40\\n# SHUFFLE_BUFFER_SIZE = 10000\\npatience = 10\\n\\n\\ndef fixed_text_to_square_image(values):\\n    return text_to_square_image(values, image_size=image_size, cut_length=cut_length)\\n\\n\\nsteps_per_epoch = np.ceil(train_indices.shape[0] / BATCH_SIZE)\\nsteps_per_epoch_val = np.ceil(valid_indices.shape[0] / BATCH_SIZE)\\n\\ndataset = build_tf_dataset(\\n    X[train_indices],\\n    Y[train_indices],\\n    image_size,\\n    fixed_text_to_square_image,\\n    PREFETCH,\\n    BATCH_SIZE,\\n)\\n\\ndataset_valid = build_tf_dataset(\\n    X[valid_indices],\\n    Y[valid_indices],\\n    image_size,\\n    fixed_text_to_square_image,\\n    PREFETCH,\\n    BATCH_SIZE,\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs_1 = 5\n",
    "epochs_2 = 200\n",
    "image_size = 96\n",
    "cut_length = None\n",
    "BATCH_SIZE = 64\n",
    "PREFETCH = 10000  # 40\n",
    "# SHUFFLE_BUFFER_SIZE = 10000\n",
    "patience = 10\n",
    "\n",
    "\n",
    "def fixed_text_to_square_image(values):\n",
    "    return text_to_square_image(values, image_size=image_size, cut_length=cut_length)\n",
    "\n",
    "\n",
    "steps_per_epoch = np.ceil(train_indices.shape[0] / BATCH_SIZE)\n",
    "steps_per_epoch_val = np.ceil(valid_indices.shape[0] / BATCH_SIZE)\n",
    "\n",
    "dataset = build_tf_dataset(\n",
    "    X[train_indices],\n",
    "    Y[train_indices],\n",
    "    image_size,\n",
    "    fixed_text_to_square_image,\n",
    "    PREFETCH,\n",
    "    BATCH_SIZE,\n",
    ")\n",
    "\n",
    "dataset_valid = build_tf_dataset(\n",
    "    X[valid_indices],\n",
    "    Y[valid_indices],\n",
    "    image_size,\n",
    "    fixed_text_to_square_image,\n",
    "    PREFETCH,\n",
    "    BATCH_SIZE,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for image, label in dataset.as_numpy_iterator():\n",
    "    # print(label)\n",
    "    imshow(image[0])\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 37;\n",
       "                var nbb_unformatted_code = \"activation = mish\";\n",
       "                var nbb_formatted_code = \"activation = mish\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "activation = mish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 38;\n",
       "                var nbb_unformatted_code = \"optimizer = Lookahead(RectifiedAdam(), sync_period=6, slow_step_size=0.5)\\nes = EarlyStopping(\\n    monitor=\\\"val_AUC\\\",\\n    verbose=1,\\n    mode=\\\"max\\\",\\n    patience=patience,\\n    restore_best_weights=True,\\n)\";\n",
       "                var nbb_formatted_code = \"optimizer = Lookahead(RectifiedAdam(), sync_period=6, slow_step_size=0.5)\\nes = EarlyStopping(\\n    monitor=\\\"val_AUC\\\",\\n    verbose=1,\\n    mode=\\\"max\\\",\\n    patience=patience,\\n    restore_best_weights=True,\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimizer = Lookahead(RectifiedAdam(), sync_period=6, slow_step_size=0.5)\n",
    "es = EarlyStopping(\n",
    "    monitor=\"val_AUC\",\n",
    "    verbose=1,\n",
    "    mode=\"max\",\n",
    "    patience=patience,\n",
    "    restore_best_weights=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 39;\n",
       "                var nbb_unformatted_code = \"from tensorflow.keras.applications.inception_v3 import InceptionV3\\nfrom tensorflow.keras.preprocessing import image\\nfrom tensorflow.keras.models import Model\\nfrom tensorflow.keras.layers import (\\n    Dense,\\n    GlobalAveragePooling2D,\\n    BatchNormalization,\\n    Dropout,\\n)\\nfrom tensorflow.keras import backend as K\\n\\n# create the base pre-trained model\\nbase_model = InceptionV3(weights=\\\"imagenet\\\", include_top=False)\\n\\n# add a global spatial average pooling layer\\nx = base_model.output\\nx = GlobalAveragePooling2D()(x)\\n# let's add a fully-connected layer\\nx = Dense(1024, activation=activation, kernel_initializer=\\\"he_normal\\\")(x)\\nx = Dropout(0.5)(x)\\nx = Dense(128, activation=activation, kernel_initializer=\\\"he_normal\\\")(x)\\nx = Dropout(0.5)(x)\\n\\n# and a logistic layer -- let's say we have 200 classes\\n# predictions = Dense(200, activation='softmax')(x)\\npredictions = Dense(2, activation=\\\"softmax\\\")(x)\\n\\n# this is the model we will train\\nmodel = Model(inputs=base_model.input, outputs=predictions)\\n\\n# first: train only the top layers (which were randomly initialized)\\n# i.e. freeze all convolutional InceptionV3 layers\\nfor layer in base_model.layers:\\n    layer.trainable = False\\n\\nes.set_model(model)\\n# compile the model (should be done *after* setting layers to non-trainable)\\nmodel.compile(optimizer=optimizer, loss=\\\"binary_crossentropy\\\", metrics=[\\\"AUC\\\"])\";\n",
       "                var nbb_formatted_code = \"from tensorflow.keras.applications.inception_v3 import InceptionV3\\nfrom tensorflow.keras.preprocessing import image\\nfrom tensorflow.keras.models import Model\\nfrom tensorflow.keras.layers import (\\n    Dense,\\n    GlobalAveragePooling2D,\\n    BatchNormalization,\\n    Dropout,\\n)\\nfrom tensorflow.keras import backend as K\\n\\n# create the base pre-trained model\\nbase_model = InceptionV3(weights=\\\"imagenet\\\", include_top=False)\\n\\n# add a global spatial average pooling layer\\nx = base_model.output\\nx = GlobalAveragePooling2D()(x)\\n# let's add a fully-connected layer\\nx = Dense(1024, activation=activation, kernel_initializer=\\\"he_normal\\\")(x)\\nx = Dropout(0.5)(x)\\nx = Dense(128, activation=activation, kernel_initializer=\\\"he_normal\\\")(x)\\nx = Dropout(0.5)(x)\\n\\n# and a logistic layer -- let's say we have 200 classes\\n# predictions = Dense(200, activation='softmax')(x)\\npredictions = Dense(2, activation=\\\"softmax\\\")(x)\\n\\n# this is the model we will train\\nmodel = Model(inputs=base_model.input, outputs=predictions)\\n\\n# first: train only the top layers (which were randomly initialized)\\n# i.e. freeze all convolutional InceptionV3 layers\\nfor layer in base_model.layers:\\n    layer.trainable = False\\n\\nes.set_model(model)\\n# compile the model (should be done *after* setting layers to non-trainable)\\nmodel.compile(optimizer=optimizer, loss=\\\"binary_crossentropy\\\", metrics=[\\\"AUC\\\"])\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import (\n",
    "    Dense,\n",
    "    GlobalAveragePooling2D,\n",
    "    BatchNormalization,\n",
    "    Dropout,\n",
    ")\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "# create the base pre-trained model\n",
    "base_model = InceptionV3(weights=\"imagenet\", include_top=False)\n",
    "\n",
    "# add a global spatial average pooling layer\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "# let's add a fully-connected layer\n",
    "x = Dense(1024, activation=activation, kernel_initializer=\"he_normal\")(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(128, activation=activation, kernel_initializer=\"he_normal\")(x)\n",
    "x = Dropout(0.5)(x)\n",
    "\n",
    "# and a logistic layer -- let's say we have 200 classes\n",
    "# predictions = Dense(200, activation='softmax')(x)\n",
    "predictions = Dense(2, activation=\"softmax\")(x)\n",
    "\n",
    "# this is the model we will train\n",
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "# first: train only the top layers (which were randomly initialized)\n",
    "# i.e. freeze all convolutional InceptionV3 layers\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "es.set_model(model)\n",
    "# compile the model (should be done *after* setting layers to non-trainable)\n",
    "model.compile(optimizer=optimizer, loss=\"binary_crossentropy\", metrics=[\"AUC\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 408.0 steps, validate for 102.0 steps\n",
      "Epoch 1/5\n",
      "WARNING:tensorflow:From /work/.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "408/408 [==============================] - 48s 118ms/step - loss: 0.5488 - AUC: 0.8096 - val_loss: 3.6476 - val_AUC: 0.7621\n",
      "Epoch 2/5\n",
      "408/408 [==============================] - 21s 51ms/step - loss: 0.4886 - AUC: 0.8490 - val_loss: 3.6263 - val_AUC: 0.7621\n",
      "Epoch 3/5\n",
      "408/408 [==============================] - 21s 52ms/step - loss: 0.4765 - AUC: 0.8561 - val_loss: 3.4488 - val_AUC: 0.7623\n",
      "Epoch 4/5\n",
      "408/408 [==============================] - 21s 51ms/step - loss: 0.4625 - AUC: 0.8640 - val_loss: 3.4863 - val_AUC: 0.7621\n",
      "Epoch 5/5\n",
      "408/408 [==============================] - 21s 51ms/step - loss: 0.4551 - AUC: 0.8682 - val_loss: 3.5099 - val_AUC: 0.7621\n",
      "CPU times: user 2min 42s, sys: 15.2 s, total: 2min 57s\n",
      "Wall time: 2min 12s\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 40;\n",
       "                var nbb_unformatted_code = \"%%time\\n# train the model on the new data for a few epochs\\nhistory_1 = model.fit(\\n    dataset, \\n    callbacks=[es],\\n    epochs=epochs_1,\\n    steps_per_epoch=steps_per_epoch,\\n    validation_data=dataset_valid,\\n    validation_steps=steps_per_epoch_val\\n)\\n# at this point, the top layers are well trained and we can start fine-tuning\\n# convolutional layers from inception V3. We will freeze the bottom N layers\\n# and train the remaining top layers.\";\n",
       "                var nbb_formatted_code = \"%%time\\n# train the model on the new data for a few epochs\\nhistory_1 = model.fit(\\n    dataset, \\n    callbacks=[es],\\n    epochs=epochs_1,\\n    steps_per_epoch=steps_per_epoch,\\n    validation_data=dataset_valid,\\n    validation_steps=steps_per_epoch_val\\n)\\n# at this point, the top layers are well trained and we can start fine-tuning\\n# convolutional layers from inception V3. We will freeze the bottom N layers\\n# and train the remaining top layers.\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# train the model on the new data for a few epochs\n",
    "history_1 = model.fit(\n",
    "    dataset, \n",
    "    callbacks=[es],\n",
    "    epochs=epochs_1,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    validation_data=dataset_valid,\n",
    "    validation_steps=steps_per_epoch_val\n",
    ")\n",
    "# at this point, the top layers are well trained and we can start fine-tuning\n",
    "# convolutional layers from inception V3. We will freeze the bottom N layers\n",
    "# and train the remaining top layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 41;\n",
       "                var nbb_unformatted_code = \"def plot_metric(history, metric):\\n    # Plot training & validation loss values\\n    plt.plot(history_1.history[metric])\\n    plt.plot(history_1.history[f\\\"val_{metric}\\\"])\\n    plt.title(f\\\"Model {metric}\\\")\\n    plt.ylabel(f\\\"{metric}\\\")\\n    plt.xlabel(\\\"Epoch\\\")\\n    plt.legend([\\\"Train\\\", \\\"Test\\\"], loc=\\\"upper left\\\")\\n    plt.show()\";\n",
       "                var nbb_formatted_code = \"def plot_metric(history, metric):\\n    # Plot training & validation loss values\\n    plt.plot(history_1.history[metric])\\n    plt.plot(history_1.history[f\\\"val_{metric}\\\"])\\n    plt.title(f\\\"Model {metric}\\\")\\n    plt.ylabel(f\\\"{metric}\\\")\\n    plt.xlabel(\\\"Epoch\\\")\\n    plt.legend([\\\"Train\\\", \\\"Test\\\"], loc=\\\"upper left\\\")\\n    plt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_metric(history, metric):\n",
    "    # Plot training & validation loss values\n",
    "    plt.plot(history.history[metric])\n",
    "    plt.plot(history.history[f\"val_{metric}\"])\n",
    "    plt.title(f\"Model {metric}\")\n",
    "    plt.ylabel(f\"{metric}\")\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.legend([\"Train\", \"Test\"], loc=\"upper left\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 42;\n",
       "                var nbb_unformatted_code = \"plot_metric(history_1, \\\"loss\\\")\";\n",
       "                var nbb_formatted_code = \"plot_metric(history_1, \\\"loss\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metric(history_1, \"loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 43;\n",
       "                var nbb_unformatted_code = \"plot_metric(history_1, \\\"AUC\\\")\";\n",
       "                var nbb_formatted_code = \"plot_metric(history_1, \\\"AUC\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metric(history_1, \"AUC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, None, None,  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)              (None, None, None, 3 864         input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_94 (BatchNo (None, None, None, 3 96          conv2d_94[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, None, None, 3 0           batch_normalization_94[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_95 (Conv2D)              (None, None, None, 3 9216        activation_94[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_95 (BatchNo (None, None, None, 3 96          conv2d_95[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_95 (Activation)      (None, None, None, 3 0           batch_normalization_95[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_96 (Conv2D)              (None, None, None, 6 18432       activation_95[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_96 (BatchNo (None, None, None, 6 192         conv2d_96[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_96 (Activation)      (None, None, None, 6 0           batch_normalization_96[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, None, None, 6 0           activation_96[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_97 (Conv2D)              (None, None, None, 8 5120        max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_97 (BatchNo (None, None, None, 8 240         conv2d_97[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_97 (Activation)      (None, None, None, 8 0           batch_normalization_97[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_98 (Conv2D)              (None, None, None, 1 138240      activation_97[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_98 (BatchNo (None, None, None, 1 576         conv2d_98[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_98 (Activation)      (None, None, None, 1 0           batch_normalization_98[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)  (None, None, None, 1 0           activation_98[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_102 (Conv2D)             (None, None, None, 6 12288       max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_102 (BatchN (None, None, None, 6 192         conv2d_102[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_102 (Activation)     (None, None, None, 6 0           batch_normalization_102[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_100 (Conv2D)             (None, None, None, 4 9216        max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_103 (Conv2D)             (None, None, None, 9 55296       activation_102[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_100 (BatchN (None, None, None, 4 144         conv2d_100[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_103 (BatchN (None, None, None, 9 288         conv2d_103[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_100 (Activation)     (None, None, None, 4 0           batch_normalization_100[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_103 (Activation)     (None, None, None, 9 0           batch_normalization_103[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePoo (None, None, None, 1 0           max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_99 (Conv2D)              (None, None, None, 6 12288       max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_101 (Conv2D)             (None, None, None, 6 76800       activation_100[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_104 (Conv2D)             (None, None, None, 9 82944       activation_103[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_105 (Conv2D)             (None, None, None, 3 6144        average_pooling2d_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_99 (BatchNo (None, None, None, 6 192         conv2d_99[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_101 (BatchN (None, None, None, 6 192         conv2d_101[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_104 (BatchN (None, None, None, 9 288         conv2d_104[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_105 (BatchN (None, None, None, 3 96          conv2d_105[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_99 (Activation)      (None, None, None, 6 0           batch_normalization_99[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_101 (Activation)     (None, None, None, 6 0           batch_normalization_101[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_104 (Activation)     (None, None, None, 9 0           batch_normalization_104[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_105 (Activation)     (None, None, None, 3 0           batch_normalization_105[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, None, None, 2 0           activation_99[0][0]              \n",
      "                                                                 activation_101[0][0]             \n",
      "                                                                 activation_104[0][0]             \n",
      "                                                                 activation_105[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_109 (Conv2D)             (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_109 (BatchN (None, None, None, 6 192         conv2d_109[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_109 (Activation)     (None, None, None, 6 0           batch_normalization_109[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_107 (Conv2D)             (None, None, None, 4 12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_110 (Conv2D)             (None, None, None, 9 55296       activation_109[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_107 (BatchN (None, None, None, 4 144         conv2d_107[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_110 (BatchN (None, None, None, 9 288         conv2d_110[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_107 (Activation)     (None, None, None, 4 0           batch_normalization_107[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_110 (Activation)     (None, None, None, 9 0           batch_normalization_110[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_10 (AveragePo (None, None, None, 2 0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_106 (Conv2D)             (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_108 (Conv2D)             (None, None, None, 6 76800       activation_107[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_111 (Conv2D)             (None, None, None, 9 82944       activation_110[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_112 (Conv2D)             (None, None, None, 6 16384       average_pooling2d_10[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_106 (BatchN (None, None, None, 6 192         conv2d_106[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_108 (BatchN (None, None, None, 6 192         conv2d_108[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_111 (BatchN (None, None, None, 9 288         conv2d_111[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_112 (BatchN (None, None, None, 6 192         conv2d_112[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_106 (Activation)     (None, None, None, 6 0           batch_normalization_106[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_108 (Activation)     (None, None, None, 6 0           batch_normalization_108[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_111 (Activation)     (None, None, None, 9 0           batch_normalization_111[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_112 (Activation)     (None, None, None, 6 0           batch_normalization_112[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, None, None, 2 0           activation_106[0][0]             \n",
      "                                                                 activation_108[0][0]             \n",
      "                                                                 activation_111[0][0]             \n",
      "                                                                 activation_112[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_116 (Conv2D)             (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_116 (BatchN (None, None, None, 6 192         conv2d_116[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_116 (Activation)     (None, None, None, 6 0           batch_normalization_116[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_114 (Conv2D)             (None, None, None, 4 13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_117 (Conv2D)             (None, None, None, 9 55296       activation_116[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_114 (BatchN (None, None, None, 4 144         conv2d_114[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_117 (BatchN (None, None, None, 9 288         conv2d_117[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_114 (Activation)     (None, None, None, 4 0           batch_normalization_114[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_117 (Activation)     (None, None, None, 9 0           batch_normalization_117[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_11 (AveragePo (None, None, None, 2 0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_113 (Conv2D)             (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_115 (Conv2D)             (None, None, None, 6 76800       activation_114[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_118 (Conv2D)             (None, None, None, 9 82944       activation_117[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_119 (Conv2D)             (None, None, None, 6 18432       average_pooling2d_11[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_113 (BatchN (None, None, None, 6 192         conv2d_113[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_115 (BatchN (None, None, None, 6 192         conv2d_115[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_118 (BatchN (None, None, None, 9 288         conv2d_118[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_119 (BatchN (None, None, None, 6 192         conv2d_119[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_113 (Activation)     (None, None, None, 6 0           batch_normalization_113[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_115 (Activation)     (None, None, None, 6 0           batch_normalization_115[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_118 (Activation)     (None, None, None, 9 0           batch_normalization_118[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_119 (Activation)     (None, None, None, 6 0           batch_normalization_119[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, None, None, 2 0           activation_113[0][0]             \n",
      "                                                                 activation_115[0][0]             \n",
      "                                                                 activation_118[0][0]             \n",
      "                                                                 activation_119[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_121 (Conv2D)             (None, None, None, 6 18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_121 (BatchN (None, None, None, 6 192         conv2d_121[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_121 (Activation)     (None, None, None, 6 0           batch_normalization_121[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_122 (Conv2D)             (None, None, None, 9 55296       activation_121[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_122 (BatchN (None, None, None, 9 288         conv2d_122[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_122 (Activation)     (None, None, None, 9 0           batch_normalization_122[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_120 (Conv2D)             (None, None, None, 3 995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_123 (Conv2D)             (None, None, None, 9 82944       activation_122[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_120 (BatchN (None, None, None, 3 1152        conv2d_120[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_123 (BatchN (None, None, None, 9 288         conv2d_123[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_120 (Activation)     (None, None, None, 3 0           batch_normalization_120[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_123 (Activation)     (None, None, None, 9 0           batch_normalization_123[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2D)  (None, None, None, 2 0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, None, None, 7 0           activation_120[0][0]             \n",
      "                                                                 activation_123[0][0]             \n",
      "                                                                 max_pooling2d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_128 (Conv2D)             (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_128 (BatchN (None, None, None, 1 384         conv2d_128[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_128 (Activation)     (None, None, None, 1 0           batch_normalization_128[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_129 (Conv2D)             (None, None, None, 1 114688      activation_128[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_129 (BatchN (None, None, None, 1 384         conv2d_129[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_129 (Activation)     (None, None, None, 1 0           batch_normalization_129[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_125 (Conv2D)             (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_130 (Conv2D)             (None, None, None, 1 114688      activation_129[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_125 (BatchN (None, None, None, 1 384         conv2d_125[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_130 (BatchN (None, None, None, 1 384         conv2d_130[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_125 (Activation)     (None, None, None, 1 0           batch_normalization_125[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_130 (Activation)     (None, None, None, 1 0           batch_normalization_130[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_126 (Conv2D)             (None, None, None, 1 114688      activation_125[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_131 (Conv2D)             (None, None, None, 1 114688      activation_130[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_126 (BatchN (None, None, None, 1 384         conv2d_126[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_131 (BatchN (None, None, None, 1 384         conv2d_131[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_126 (Activation)     (None, None, None, 1 0           batch_normalization_126[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_131 (Activation)     (None, None, None, 1 0           batch_normalization_131[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_12 (AveragePo (None, None, None, 7 0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_124 (Conv2D)             (None, None, None, 1 147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_127 (Conv2D)             (None, None, None, 1 172032      activation_126[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_132 (Conv2D)             (None, None, None, 1 172032      activation_131[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_133 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_12[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_124 (BatchN (None, None, None, 1 576         conv2d_124[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_127 (BatchN (None, None, None, 1 576         conv2d_127[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_132 (BatchN (None, None, None, 1 576         conv2d_132[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_133 (BatchN (None, None, None, 1 576         conv2d_133[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_124 (Activation)     (None, None, None, 1 0           batch_normalization_124[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_127 (Activation)     (None, None, None, 1 0           batch_normalization_127[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_132 (Activation)     (None, None, None, 1 0           batch_normalization_132[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_133 (Activation)     (None, None, None, 1 0           batch_normalization_133[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, None, None, 7 0           activation_124[0][0]             \n",
      "                                                                 activation_127[0][0]             \n",
      "                                                                 activation_132[0][0]             \n",
      "                                                                 activation_133[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_138 (Conv2D)             (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_138 (BatchN (None, None, None, 1 480         conv2d_138[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_138 (Activation)     (None, None, None, 1 0           batch_normalization_138[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_139 (Conv2D)             (None, None, None, 1 179200      activation_138[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_139 (BatchN (None, None, None, 1 480         conv2d_139[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_139 (Activation)     (None, None, None, 1 0           batch_normalization_139[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_135 (Conv2D)             (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_140 (Conv2D)             (None, None, None, 1 179200      activation_139[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_135 (BatchN (None, None, None, 1 480         conv2d_135[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_140 (BatchN (None, None, None, 1 480         conv2d_140[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_135 (Activation)     (None, None, None, 1 0           batch_normalization_135[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_140 (Activation)     (None, None, None, 1 0           batch_normalization_140[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_136 (Conv2D)             (None, None, None, 1 179200      activation_135[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_141 (Conv2D)             (None, None, None, 1 179200      activation_140[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_136 (BatchN (None, None, None, 1 480         conv2d_136[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_141 (BatchN (None, None, None, 1 480         conv2d_141[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_136 (Activation)     (None, None, None, 1 0           batch_normalization_136[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_141 (Activation)     (None, None, None, 1 0           batch_normalization_141[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_13 (AveragePo (None, None, None, 7 0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_134 (Conv2D)             (None, None, None, 1 147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_137 (Conv2D)             (None, None, None, 1 215040      activation_136[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_142 (Conv2D)             (None, None, None, 1 215040      activation_141[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_143 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_13[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_134 (BatchN (None, None, None, 1 576         conv2d_134[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_137 (BatchN (None, None, None, 1 576         conv2d_137[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_142 (BatchN (None, None, None, 1 576         conv2d_142[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_143 (BatchN (None, None, None, 1 576         conv2d_143[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_134 (Activation)     (None, None, None, 1 0           batch_normalization_134[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_137 (Activation)     (None, None, None, 1 0           batch_normalization_137[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_142 (Activation)     (None, None, None, 1 0           batch_normalization_142[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_143 (Activation)     (None, None, None, 1 0           batch_normalization_143[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, None, None, 7 0           activation_134[0][0]             \n",
      "                                                                 activation_137[0][0]             \n",
      "                                                                 activation_142[0][0]             \n",
      "                                                                 activation_143[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_148 (Conv2D)             (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_148 (BatchN (None, None, None, 1 480         conv2d_148[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_148 (Activation)     (None, None, None, 1 0           batch_normalization_148[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_149 (Conv2D)             (None, None, None, 1 179200      activation_148[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_149 (BatchN (None, None, None, 1 480         conv2d_149[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_149 (Activation)     (None, None, None, 1 0           batch_normalization_149[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_145 (Conv2D)             (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_150 (Conv2D)             (None, None, None, 1 179200      activation_149[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_145 (BatchN (None, None, None, 1 480         conv2d_145[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_150 (BatchN (None, None, None, 1 480         conv2d_150[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_145 (Activation)     (None, None, None, 1 0           batch_normalization_145[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_150 (Activation)     (None, None, None, 1 0           batch_normalization_150[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_146 (Conv2D)             (None, None, None, 1 179200      activation_145[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_151 (Conv2D)             (None, None, None, 1 179200      activation_150[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_146 (BatchN (None, None, None, 1 480         conv2d_146[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_151 (BatchN (None, None, None, 1 480         conv2d_151[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_146 (Activation)     (None, None, None, 1 0           batch_normalization_146[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_151 (Activation)     (None, None, None, 1 0           batch_normalization_151[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_14 (AveragePo (None, None, None, 7 0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_144 (Conv2D)             (None, None, None, 1 147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_147 (Conv2D)             (None, None, None, 1 215040      activation_146[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_152 (Conv2D)             (None, None, None, 1 215040      activation_151[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_153 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_14[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_144 (BatchN (None, None, None, 1 576         conv2d_144[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_147 (BatchN (None, None, None, 1 576         conv2d_147[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_152 (BatchN (None, None, None, 1 576         conv2d_152[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_153 (BatchN (None, None, None, 1 576         conv2d_153[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_144 (Activation)     (None, None, None, 1 0           batch_normalization_144[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_147 (Activation)     (None, None, None, 1 0           batch_normalization_147[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_152 (Activation)     (None, None, None, 1 0           batch_normalization_152[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_153 (Activation)     (None, None, None, 1 0           batch_normalization_153[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, None, None, 7 0           activation_144[0][0]             \n",
      "                                                                 activation_147[0][0]             \n",
      "                                                                 activation_152[0][0]             \n",
      "                                                                 activation_153[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_158 (Conv2D)             (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_158 (BatchN (None, None, None, 1 576         conv2d_158[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_158 (Activation)     (None, None, None, 1 0           batch_normalization_158[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_159 (Conv2D)             (None, None, None, 1 258048      activation_158[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_159 (BatchN (None, None, None, 1 576         conv2d_159[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_159 (Activation)     (None, None, None, 1 0           batch_normalization_159[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_155 (Conv2D)             (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_160 (Conv2D)             (None, None, None, 1 258048      activation_159[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_155 (BatchN (None, None, None, 1 576         conv2d_155[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_160 (BatchN (None, None, None, 1 576         conv2d_160[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_155 (Activation)     (None, None, None, 1 0           batch_normalization_155[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_160 (Activation)     (None, None, None, 1 0           batch_normalization_160[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_156 (Conv2D)             (None, None, None, 1 258048      activation_155[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_161 (Conv2D)             (None, None, None, 1 258048      activation_160[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_156 (BatchN (None, None, None, 1 576         conv2d_156[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_161 (BatchN (None, None, None, 1 576         conv2d_161[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_156 (Activation)     (None, None, None, 1 0           batch_normalization_156[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_161 (Activation)     (None, None, None, 1 0           batch_normalization_161[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_15 (AveragePo (None, None, None, 7 0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_154 (Conv2D)             (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_157 (Conv2D)             (None, None, None, 1 258048      activation_156[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_162 (Conv2D)             (None, None, None, 1 258048      activation_161[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_163 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_15[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_154 (BatchN (None, None, None, 1 576         conv2d_154[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_157 (BatchN (None, None, None, 1 576         conv2d_157[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_162 (BatchN (None, None, None, 1 576         conv2d_162[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_163 (BatchN (None, None, None, 1 576         conv2d_163[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_154 (Activation)     (None, None, None, 1 0           batch_normalization_154[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_157 (Activation)     (None, None, None, 1 0           batch_normalization_157[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_162 (Activation)     (None, None, None, 1 0           batch_normalization_162[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_163 (Activation)     (None, None, None, 1 0           batch_normalization_163[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, None, None, 7 0           activation_154[0][0]             \n",
      "                                                                 activation_157[0][0]             \n",
      "                                                                 activation_162[0][0]             \n",
      "                                                                 activation_163[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_166 (Conv2D)             (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_166 (BatchN (None, None, None, 1 576         conv2d_166[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_166 (Activation)     (None, None, None, 1 0           batch_normalization_166[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_167 (Conv2D)             (None, None, None, 1 258048      activation_166[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_167 (BatchN (None, None, None, 1 576         conv2d_167[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_167 (Activation)     (None, None, None, 1 0           batch_normalization_167[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_164 (Conv2D)             (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_168 (Conv2D)             (None, None, None, 1 258048      activation_167[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_164 (BatchN (None, None, None, 1 576         conv2d_164[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_168 (BatchN (None, None, None, 1 576         conv2d_168[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_164 (Activation)     (None, None, None, 1 0           batch_normalization_164[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_168 (Activation)     (None, None, None, 1 0           batch_normalization_168[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_165 (Conv2D)             (None, None, None, 3 552960      activation_164[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_169 (Conv2D)             (None, None, None, 1 331776      activation_168[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_165 (BatchN (None, None, None, 3 960         conv2d_165[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_169 (BatchN (None, None, None, 1 576         conv2d_169[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_165 (Activation)     (None, None, None, 3 0           batch_normalization_165[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_169 (Activation)     (None, None, None, 1 0           batch_normalization_169[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2D)  (None, None, None, 7 0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, None, None, 1 0           activation_165[0][0]             \n",
      "                                                                 activation_169[0][0]             \n",
      "                                                                 max_pooling2d_7[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_174 (Conv2D)             (None, None, None, 4 573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_174 (BatchN (None, None, None, 4 1344        conv2d_174[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_174 (Activation)     (None, None, None, 4 0           batch_normalization_174[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_171 (Conv2D)             (None, None, None, 3 491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_175 (Conv2D)             (None, None, None, 3 1548288     activation_174[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_171 (BatchN (None, None, None, 3 1152        conv2d_171[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_175 (BatchN (None, None, None, 3 1152        conv2d_175[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_171 (Activation)     (None, None, None, 3 0           batch_normalization_171[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_175 (Activation)     (None, None, None, 3 0           batch_normalization_175[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_172 (Conv2D)             (None, None, None, 3 442368      activation_171[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_173 (Conv2D)             (None, None, None, 3 442368      activation_171[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_176 (Conv2D)             (None, None, None, 3 442368      activation_175[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_177 (Conv2D)             (None, None, None, 3 442368      activation_175[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_16 (AveragePo (None, None, None, 1 0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_170 (Conv2D)             (None, None, None, 3 409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_172 (BatchN (None, None, None, 3 1152        conv2d_172[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_173 (BatchN (None, None, None, 3 1152        conv2d_173[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_176 (BatchN (None, None, None, 3 1152        conv2d_176[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_177 (BatchN (None, None, None, 3 1152        conv2d_177[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_178 (Conv2D)             (None, None, None, 1 245760      average_pooling2d_16[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_170 (BatchN (None, None, None, 3 960         conv2d_170[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_172 (Activation)     (None, None, None, 3 0           batch_normalization_172[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_173 (Activation)     (None, None, None, 3 0           batch_normalization_173[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_176 (Activation)     (None, None, None, 3 0           batch_normalization_176[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_177 (Activation)     (None, None, None, 3 0           batch_normalization_177[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_178 (BatchN (None, None, None, 1 576         conv2d_178[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_170 (Activation)     (None, None, None, 3 0           batch_normalization_170[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_172[0][0]             \n",
      "                                                                 activation_173[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, None, None, 7 0           activation_176[0][0]             \n",
      "                                                                 activation_177[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_178 (Activation)     (None, None, None, 1 0           batch_normalization_178[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, None, None, 2 0           activation_170[0][0]             \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate_2[0][0]              \n",
      "                                                                 activation_178[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_183 (Conv2D)             (None, None, None, 4 917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_183 (BatchN (None, None, None, 4 1344        conv2d_183[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_183 (Activation)     (None, None, None, 4 0           batch_normalization_183[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_180 (Conv2D)             (None, None, None, 3 786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_184 (Conv2D)             (None, None, None, 3 1548288     activation_183[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_180 (BatchN (None, None, None, 3 1152        conv2d_180[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_184 (BatchN (None, None, None, 3 1152        conv2d_184[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_180 (Activation)     (None, None, None, 3 0           batch_normalization_180[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_184 (Activation)     (None, None, None, 3 0           batch_normalization_184[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_181 (Conv2D)             (None, None, None, 3 442368      activation_180[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_182 (Conv2D)             (None, None, None, 3 442368      activation_180[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_185 (Conv2D)             (None, None, None, 3 442368      activation_184[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_186 (Conv2D)             (None, None, None, 3 442368      activation_184[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_17 (AveragePo (None, None, None, 2 0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_179 (Conv2D)             (None, None, None, 3 655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_181 (BatchN (None, None, None, 3 1152        conv2d_181[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_182 (BatchN (None, None, None, 3 1152        conv2d_182[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_185 (BatchN (None, None, None, 3 1152        conv2d_185[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_186 (BatchN (None, None, None, 3 1152        conv2d_186[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_187 (Conv2D)             (None, None, None, 1 393216      average_pooling2d_17[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_179 (BatchN (None, None, None, 3 960         conv2d_179[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_181 (Activation)     (None, None, None, 3 0           batch_normalization_181[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_182 (Activation)     (None, None, None, 3 0           batch_normalization_182[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_185 (Activation)     (None, None, None, 3 0           batch_normalization_185[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_186 (Activation)     (None, None, None, 3 0           batch_normalization_186[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_187 (BatchN (None, None, None, 1 576         conv2d_187[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_179 (Activation)     (None, None, None, 3 0           batch_normalization_179[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_181[0][0]             \n",
      "                                                                 activation_182[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, None, None, 7 0           activation_185[0][0]             \n",
      "                                                                 activation_186[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_187 (Activation)     (None, None, None, 1 0           batch_normalization_187[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, None, None, 2 0           activation_179[0][0]             \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_3[0][0]              \n",
      "                                                                 activation_187[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 1024)         2098176     global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 1024)         0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 128)          131200      dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 128)          0           dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 2)            258         dropout_3[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 24,032,418\n",
      "Trainable params: 2,229,634\n",
      "Non-trainable params: 21,802,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 44;\n",
       "                var nbb_unformatted_code = \"# let's visualize layer names and layer indices to see how many layers\\n# we should freeze:\\nmodel.summary()\\n#for i, layer in enumerate(model.layers):\\n#    print(i, layer.name)\";\n",
       "                var nbb_formatted_code = \"# let's visualize layer names and layer indices to see how many layers\\n# we should freeze:\\nmodel.summary()\\n# for i, layer in enumerate(model.layers):\\n#    print(i, layer.name)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's visualize layer names and layer indices to see how many layers\n",
    "# we should freeze:\n",
    "model.summary()\n",
    "# for i, layer in enumerate(model.layers):\n",
    "#    print(i, layer.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 45;\n",
       "                var nbb_unformatted_code = \"# Let's unfreeze the whole model\\nfor layer in model.layers:\\n    layer.trainable = True\\n# Let's build an optimizer\\noptimizer = Lookahead(RectifiedAdam(), sync_period=6, slow_step_size=0.5)\\nes = EarlyStopping(\\n    monitor=\\\"val_AUC\\\",\\n    verbose=1,\\n    mode=\\\"max\\\",\\n    patience=patience,\\n    restore_best_weights=True,\\n)\\n# We need to recompile the model for these modifications to take effect\\nes.set_model(model)\\nmodel.compile(optimizer=optimizer, loss=\\\"binary_crossentropy\\\", metrics=[\\\"AUC\\\"])\";\n",
       "                var nbb_formatted_code = \"# Let's unfreeze the whole model\\nfor layer in model.layers:\\n    layer.trainable = True\\n# Let's build an optimizer\\noptimizer = Lookahead(RectifiedAdam(), sync_period=6, slow_step_size=0.5)\\nes = EarlyStopping(\\n    monitor=\\\"val_AUC\\\",\\n    verbose=1,\\n    mode=\\\"max\\\",\\n    patience=patience,\\n    restore_best_weights=True,\\n)\\n# We need to recompile the model for these modifications to take effect\\nes.set_model(model)\\nmodel.compile(optimizer=optimizer, loss=\\\"binary_crossentropy\\\", metrics=[\\\"AUC\\\"])\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's unfreeze the whole model\n",
    "for layer in model.layers:\n",
    "    layer.trainable = True\n",
    "# Let's build an optimizer\n",
    "optimizer = Lookahead(RectifiedAdam(), sync_period=6, slow_step_size=0.5)\n",
    "es = EarlyStopping(\n",
    "    monitor=\"val_AUC\",\n",
    "    verbose=1,\n",
    "    mode=\"max\",\n",
    "    patience=patience,\n",
    "    restore_best_weights=True,\n",
    ")\n",
    "# We need to recompile the model for these modifications to take effect\n",
    "es.set_model(model)\n",
    "model.compile(optimizer=optimizer, loss=\"binary_crossentropy\", metrics=[\"AUC\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 408.0 steps, validate for 102.0 steps\n",
      "Epoch 1/200\n",
      "408/408 [==============================] - 115s 282ms/step - loss: 0.3754 - AUC: 0.9130 - val_loss: 0.3292 - val_AUC: 0.9322\n",
      "Epoch 2/200\n",
      "408/408 [==============================] - 66s 162ms/step - loss: 0.3223 - AUC: 0.9363 - val_loss: 0.3038 - val_AUC: 0.9442\n",
      "Epoch 3/200\n",
      "408/408 [==============================] - 67s 165ms/step - loss: 0.3282 - AUC: 0.9350 - val_loss: 0.5594 - val_AUC: 0.8704\n",
      "Epoch 4/200\n",
      "408/408 [==============================] - 67s 164ms/step - loss: 0.3294 - AUC: 0.9349 - val_loss: 0.2997 - val_AUC: 0.9452\n",
      "Epoch 5/200\n",
      "408/408 [==============================] - 67s 164ms/step - loss: 0.4264 - AUC: 0.9114 - val_loss: 0.7540 - val_AUC: 0.8656\n",
      "Epoch 6/200\n",
      "408/408 [==============================] - 66s 162ms/step - loss: 0.3525 - AUC: 0.9242 - val_loss: 0.3276 - val_AUC: 0.9372\n",
      "Epoch 7/200\n",
      "408/408 [==============================] - 67s 163ms/step - loss: 0.3222 - AUC: 0.9356 - val_loss: 0.3053 - val_AUC: 0.9415\n",
      "Epoch 8/200\n",
      "408/408 [==============================] - 66s 162ms/step - loss: 0.3147 - AUC: 0.9385 - val_loss: 0.3112 - val_AUC: 0.9408\n",
      "Epoch 9/200\n",
      "408/408 [==============================] - 66s 163ms/step - loss: 0.3181 - AUC: 0.9385 - val_loss: 0.4305 - val_AUC: 0.8905\n",
      "Epoch 10/200\n",
      "408/408 [==============================] - 66s 162ms/step - loss: 0.3151 - AUC: 0.9388 - val_loss: 2.9793 - val_AUC: 0.7432\n",
      "Epoch 11/200\n",
      "408/408 [==============================] - 66s 161ms/step - loss: 0.3144 - AUC: 0.9396 - val_loss: 2.9428 - val_AUC: 0.7944\n",
      "Epoch 12/200\n",
      "408/408 [==============================] - 66s 162ms/step - loss: 0.3029 - AUC: 0.9440 - val_loss: 0.2900 - val_AUC: 0.9484\n",
      "Epoch 13/200\n",
      "408/408 [==============================] - 66s 161ms/step - loss: 0.3000 - AUC: 0.9450 - val_loss: 0.4743 - val_AUC: 0.9151\n",
      "Epoch 14/200\n",
      "408/408 [==============================] - 67s 164ms/step - loss: 0.2988 - AUC: 0.9459 - val_loss: 0.3088 - val_AUC: 0.9455\n",
      "Epoch 15/200\n",
      "408/408 [==============================] - 67s 164ms/step - loss: 0.2990 - AUC: 0.9456 - val_loss: 0.3151 - val_AUC: 0.9423\n",
      "Epoch 16/200\n",
      "408/408 [==============================] - 67s 163ms/step - loss: 0.2962 - AUC: 0.9458 - val_loss: 0.2817 - val_AUC: 0.9504\n",
      "Epoch 17/200\n",
      "408/408 [==============================] - 67s 163ms/step - loss: 0.2866 - AUC: 0.9497 - val_loss: 0.2886 - val_AUC: 0.9495\n",
      "Epoch 18/200\n",
      "408/408 [==============================] - 66s 162ms/step - loss: 0.2994 - AUC: 0.9479 - val_loss: 0.2947 - val_AUC: 0.9494\n",
      "Epoch 19/200\n",
      "408/408 [==============================] - 66s 162ms/step - loss: 0.2876 - AUC: 0.9499 - val_loss: 0.2821 - val_AUC: 0.9506\n",
      "Epoch 20/200\n",
      "408/408 [==============================] - 67s 165ms/step - loss: 0.2939 - AUC: 0.9484 - val_loss: 0.2912 - val_AUC: 0.9483\n",
      "Epoch 21/200\n",
      "408/408 [==============================] - 68s 166ms/step - loss: 0.2883 - AUC: 0.9494 - val_loss: 0.4435 - val_AUC: 0.8714\n",
      "Epoch 22/200\n",
      "408/408 [==============================] - 67s 164ms/step - loss: 0.3122 - AUC: 0.9408 - val_loss: 0.3492 - val_AUC: 0.9335\n",
      "Epoch 23/200\n",
      "408/408 [==============================] - 66s 162ms/step - loss: 0.3112 - AUC: 0.9437 - val_loss: 0.3407 - val_AUC: 0.9286\n",
      "Epoch 24/200\n",
      "408/408 [==============================] - 65s 160ms/step - loss: 0.3089 - AUC: 0.9440 - val_loss: 0.2901 - val_AUC: 0.9489\n",
      "Epoch 25/200\n",
      "408/408 [==============================] - 65s 160ms/step - loss: 0.2953 - AUC: 0.9465 - val_loss: 0.2898 - val_AUC: 0.9492\n",
      "Epoch 26/200\n",
      "408/408 [==============================] - 65s 160ms/step - loss: 0.2876 - AUC: 0.9493 - val_loss: 0.2824 - val_AUC: 0.9503\n",
      "Epoch 27/200\n",
      "408/408 [==============================] - 65s 160ms/step - loss: 0.2927 - AUC: 0.9477 - val_loss: 0.2850 - val_AUC: 0.9495\n",
      "Epoch 28/200\n",
      "408/408 [==============================] - 66s 161ms/step - loss: 0.2899 - AUC: 0.9497 - val_loss: 0.4112 - val_AUC: 0.9403\n",
      "Epoch 29/200\n",
      "407/408 [============================>.] - ETA: 0s - loss: 0.3028 - AUC: 0.9471Restoring model weights from the end of the best epoch.\n",
      "408/408 [==============================] - 66s 161ms/step - loss: 0.3033 - AUC: 0.9469 - val_loss: 0.4482 - val_AUC: 0.8702\n",
      "Epoch 00029: early stopping\n",
      "CPU times: user 35min 11s, sys: 3min, total: 38min 11s\n",
      "Wall time: 32min 50s\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 46;\n",
       "                var nbb_unformatted_code = \"%%time\\n# we train our model again (this time fine-tuning the top 2 inception blocks\\n# alongside the top Dense layers\\nhistory_2 = model.fit(\\n    dataset, \\n    callbacks=[es],\\n    epochs=epochs_2,\\n    steps_per_epoch=steps_per_epoch,\\n    validation_data=dataset_valid,\\n    validation_steps=steps_per_epoch_val\\n)\";\n",
       "                var nbb_formatted_code = \"%%time\\n# we train our model again (this time fine-tuning the top 2 inception blocks\\n# alongside the top Dense layers\\nhistory_2 = model.fit(\\n    dataset, \\n    callbacks=[es],\\n    epochs=epochs_2,\\n    steps_per_epoch=steps_per_epoch,\\n    validation_data=dataset_valid,\\n    validation_steps=steps_per_epoch_val\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# we train our model again (this time fine-tuning the top 2 inception blocks\n",
    "# alongside the top Dense layers\n",
    "history_2 = model.fit(\n",
    "    dataset, \n",
    "    callbacks=[es],\n",
    "    epochs=epochs_2,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    validation_data=dataset_valid,\n",
    "    validation_steps=steps_per_epoch_val\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 47;\n",
       "                var nbb_unformatted_code = \"plot_metric(history_2, \\\"loss\\\")\";\n",
       "                var nbb_formatted_code = \"plot_metric(history_2, \\\"loss\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metric(history_2, \"loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deXhV5bn38e9NRiADQ0IChEkFBJzQCGjrqUNtrdahp7bFGevQ03PU2tbX2tb2WFvP6+npqPXUigN1RF5t61Ct1VarbREI4sAgiIAQhhBAM4AJJLnfP/ZK2OwsMkj2Xhl+n+vKxdprPWvvO5vs9dvredZg7o6IiEiiflEXICIi3ZMCQkREQikgREQklAJCRERCKSBERCSUAkJEREIpIEQ+IjMba2ZuZukdaDvLzP6eirpEuooCQvoEM1tnZrvNrCBh/pJgIz82msr2qSXHzGrN7NmQZW5mhyTMu8nMHox7nGdmvzCz9cHzvBs8Lkh8PpGOUEBIX7IWOK/5gZkdDgyIrpxWPg/UA6eaWXFnVjSzTOAvwBTgNCAPOA7YDkzr4jqlj1BASF/yAHBx3ONLgPvjG5hZvpndb2aVZvaemd1oZv2CZWlm9hMz22Zma4AzQta9x8w2m9lGM/uRmaV1or5LgDuBN4ELO/m7XQyMBj7n7svdvcndt7r7D939mU4+lwiggJC+5VUgz8wmBRvumcCDCW1uB/KBg4BPENvwXhosuwL4LDAVKAXOTVh3DtAAHBK0+RRweUcKM7MxwInAQ8HPxW2u0NongT+5e20n1xPZLwWE9DXNexGnAiuAjc0L4kLj2+5e4+7rgJ8CFwVNvgj8wt03uPsO4P/GrVsEnA5c6+473X0r8PPg+TriIuBNd18OzAWmmNnUTvxeQ4HNnWgv0q52j74Q6WUeAF4GxpHQvQQUABnAe3Hz3gNGBtMjgA0Jy5qNCdbdbGbN8/oltG/LxcBsAHffaGZ/I9bltCRY3hg8f7wMYE8wvR0Y3sHXEukQ7UFIn+Lu7xEbrD4d+F3C4m3ENrhj4uaNZu9exmZgVMKyZhuIDTAXuPug4CfP3ae0V5OZHQ+MB75tZlvMbAswHTg/7hDa9cDYhFXHsTekXgA+bWYD23s9kY5SQEhfdBlwsrvvjJ/p7o3APOAWM8sNxgW+wd5xinnANWZWYmaDgRvi1t0M/Bn4aXC4aT8zO9jMPtGBei4BngcmA0cFP4cB/YHPBG0eBW4MXrufmX0SOBN4LFj+ALGQetzMDg3aDDWz75jZ6Z16d0QCCgjpc9z9XXcv28/iq4GdwBrg78DDwL3BstnAc8AbwGu03gO5GMgElgPvE9t4t9ntY2bZxMY2bnf3LXE/a4lt9C8Jmt4M/DOo6X3gx8AF7r40+J3qiQ1Uv00sbKqBhcS6zRa0VYPI/phuGCQiImG0ByEiIqEUECIiEkoBISIioRQQIiISqtecKFdQUOBjx46NugwRkR5l8eLF29y9MGxZrwmIsWPHUla2vyMXRUQkjJm9t79l6mISEZFQCggREQmlgBARkVC9ZgwizJ49eygvL6euri7qUpIuOzubkpISMjISL/gpIvLR9OqAKC8vJzc3l7FjxxJ3CeZex93Zvn075eXljBs3LupyRKSX6NVdTHV1dQwdOrRXhwOAmTF06NA+sackIqnTqwMC6PXh0Kyv/J4ikjq9uotJRKS32bW7gS1VdVRU11NRXUdFdR252RmcP310+yt3kgIiibZv384pp5wCwJYtW0hLS6OwMHbC4sKFC8nMzNzvumVlZdx///3cdtttKalVRKK1p7GJrTXBRr8qtuGvqKmPTdfUsaWqjq3V9dTUN7Rad+roQQqInmbo0KG8/vrrANx0003k5ORw3XXXtSxvaGggPT38v6C0tJTS0tKU1CkiydPU5OzYtZuK6tgGfkvwrT/2Ux/b8NfUsa12d6t1M9KMYbnZFOVlMaEolxPGF1KUF3tcnJfNsLxsivOzyclKzqZcAZFis2bNIjs7myVLlvCxj32MmTNn8rWvfY26ujr69+/Pfffdx8SJE3nppZf4yU9+wtNPP81NN93E+vXrWbNmDevXr+faa6/lmmuuifpXEenzaur2UFFdz9bqumDDXx+38Y893lpTx57G1jdmK8jJpCjYwB85ahBFeVmxx3nZDAsCYPCATPr1i258sc8ExA+eWsbyTdVd+pyTR+Txn2e2e0/6VsrLy/nnP/9JWloa1dXVvPLKK6Snp/PCCy/wne98h8cff7zVOm+//TYvvvgiNTU1TJw4ka9+9as650EkSeobGtkabNy3VLXe6DdP79zd2Grd3Kx0ivJj3/KnjxsSm87Nojg/9o2/KC+bwpwsMtO7/zFCfSYgupMvfOELpKWlAVBVVcUll1zCO++8g5mxZ8+e0HXOOOMMsrKyyMrKYtiwYVRUVFBSUpLKskV6vKYmZ9vO+lhXT9C3X1Gd0M9fU8+Ona27ezLT+lGUn0VRbjaThudx4sRhsa6e/OyWbqCivGwGJqm7Jwq95zdpx0f5pp8sAwcObJn+3ve+x0knncTvf/971q1bx4knnhi6TlZWVst0WloaDQ2tB6pE+ip3p7quYb9dPVuCbqDKmnoamvbt7jGDwpzYxr1kcH+OGTN4n66e5ulBAzL63OHkfSYguquqqipGjhwJwJw5c6ItRqQbqtsT6+5p/oa/zwBvdR1bg+kP97Tu7snvn9HyzX78sIJ9BnebN/wFOZmkp3X/7p4oKCAidv3113PJJZfwox/9iDPOOCPqckQiUbenkVUVNSzbVM2yTVWs3/FhS7fPB7tad7tmpfejOD+botxsDi8ZxCdzYyGwT39/bjb9M9Mi+G16D3NvPbreE5WWlnriDYNWrFjBpEmTIqoo9fra7ys9U03dHpZvqg7CIBYIq7fWtnT95Ganc1DBwOBwzr19+81H/BTlZpPXP73Pdfcki5ktdvfQY+q1ByEiSbOttr4lBJZtjP27bvuuluUFOVkcNjKPUyYN47AR+UwZkc+oIf218e8mFBAicsDcnY0ffMjSjdUs31TFsk3VLN1URUV1fUubUUP6M2V4PuceU8KUEflMGZHHsLzsCKuW9iggRKRTGpuctdtqW7qIlm6MBULVh7Gxgn4GhwzL4fiDC5gyIo/JI/KYMjyf/AE6b6enSWpAmNlpwC+BNOBud781Yflo4LfAoKDNDe7+TLDsCOA3QB7QBBzr7rqetUgK1Tc08k5FbUsILNtUxYrNNS1HDGWm9+PQ4lxOP7y4Za/g0OI8DQ73EkkLCDNLA+4ATgXKgUVm9qS7L49rdiMwz91/bWaTgWeAsWaWDjwIXOTub5jZUCD8DDIR6RK19Q2s2FzNso1VLA32Dt6pqGkZPM7JSmfyiDxmThvFlBH5HDYyj4MLc8jQIaK9VjL3IKYBq919DYCZzQXOBuIDwontIQDkA5uC6U8Bb7r7GwDuvj2JdYr0OTt27mbZpiqWBgPHyzdVs3b7TpoPaizIyWTyiHxOnFgYDB7nMXrIgEivCySpl8yAGAlsiHtcDkxPaHMT8GczuxoYCHwymD8BcDN7DigE5rr7j5NYa1IcyOW+AV566SUyMzM5/vjjk16r9E7uzqaqOpbFdREt21TN5qq9vbUjB/XnsJF5nDN1JFNG5HHYyHyG5WbpSCKJfJD6PGCOu//UzI4DHjCzw4K6Pg4cC+wC/hIcq/uX+JXN7ErgSoDRo7v+WugHqr3LfbfnpZdeIicnRwEhHdLU5KzdvjMWBHGB8H5wopkZHFyYw7RxQ2JBMCKfySPyGDSg7S8q0nclMyA2AqPiHpcE8+JdBpwG4O7zzSwbKCC2t/Gyu28DMLNngKOBfQLC3e8C7oLYiXJJ+B263OLFi/nGN75BbW0tBQUFzJkzh+HDh3Pbbbdx5513kp6ezuTJk7n11lu58847SUtL48EHH+T222/nhBNOiLp86SZ2NzSxqqImOOEsNmawYnM1u4Kri2am9WNCcQ6fnlIcHEmUz6ThuQzIjPo7ofQkyfxrWQSMN7NxxIJhJnB+Qpv1wCnAHDObBGQDlcBzwPVmNgDYDXwC+PkBVfPsDbDlrQN6ilaKD4fP3Np+u4C7c/XVV/PEE09QWFjIo48+yne/+13uvfdebr31VtauXUtWVhYffPABgwYN4t/+7d86vdchvc/O+gbe3lLdMl6wbFM1qypqWu4xMDAzjckj8vhi6SgmB3sGhwzL6RGXk5buLWkB4e4NZnYVsY19GnCvuy8zs5uBMnd/EvgmMNvMvk5swHqWx6798b6Z/YxYyDjwjLv/MVm1pkp9fT1Lly7l1FNPBaCxsZHhw4cDcMQRR3DBBRdwzjnncM4550RZpkTo/Z27W7qGlgb/rt22d/B4yMBMpozI47KPH8SUEXlMGZHH2KEDNXgsSZHU/c3gnIZnEuZ9P256OfCx/az7ILFDXbtGJ77pJ4u7M2XKFObPn99q2R//+EdefvllnnrqKW655RbeequL93akW3F3tlTXsWxj7IzjZZuqWb6pmo0ffNjSZuSg/kwekcdZR46IHUk0Mo/ivGwNHkvKqEMyhbKysqisrGT+/Pkcd9xx7Nmzh1WrVjFp0iQ2bNjASSedxMc//nHmzp1LbW0tubm5VFd37V3wJPWampx1zYPHcUcSNd+UxgzGFQzk6DGDufi4MS0nnA0eqMFjiZYCIoX69evHY489xjXXXENVVRUNDQ1ce+21TJgwgQsvvJCqqircnWuuuYZBgwZx5plncu655/LEE09okLqH2FZbz8otNby9pYZVW2p4u6KGdypqWgaPM9KMCUW5fHLSsJYgmDQ8r1fdhUx6D13uuxfpa79vlHbWN7CqomZvGATT2+NuVTlkYCYTi3KZWJzL5OGxaxJNKMrV4LF0K7rct8hHtKexiTWVO1lZUcPKLdWs3FLDyooaNuzYO1bQPyONCcW5nDJpGBOL81pCoSAnU+MF0qMpIESIjRNs/ODDlgBYuSX2s2ZbbcvhpGn9jIMKBnJkySC+eMwoJhbHgmDUYF2CQnqnXh8Q7t4nvsX1lq7CVNheW98SAqsq9o4X7Ny9957GIwf1Z2JxLidPGtayR3BQ4UCy0nWVUuk7enVAZGdns337doYOHdqrQ8Ld2b59O9nZuvlKvF27G1hVURsbLI4Lg221e29iM3hABhOLczn3mJJY91BxDhOKcsnN1r0LRHp1QJSUlFBeXk5lZWXUpSRddnY2JSUlUZcRiT2NTazbtnOfEFi5pYYN7+9qOcEsO6MfE4pyOWliYUvX0MSiXAp1UTqR/erVAZGRkcG4ceOiLkO6SPNtLeO7hd7eUsOayp3sbmwCYuMEY4cO4PCRsVtbTijK5dDiXEYNGUCaxglEOqVXB4T0XO/v3L3PHsGqilgg1NQ3tLQZkZ/NxOJcPjGxkEOLc5lQlMvBhTlkZ2icQKQrKCAkUh/ubuSdrXv3CJoHj7fW7B0nyO8fGyf43NEjW/YIxhflkt9f4wQiyaSAkJRoaGxi3fZdweGj1S1B8N6OveMEWen9GF+Uwwnjgz2C4lgY6OY1ItFQQEiXcnc2V9Xtcy7Byi01rK6sZXdDbJygn8HYgoFMGh67i1lz99CYoQM1TiDSjSgg5COr2rWHt7dU73Pk0MqKGmrq9o4TFOfFxglOGF/AhOB8gkOGaZxApCdQQEin1NTt4Xt/WMr8NdupqN47TpCXnc6hxXmcfdSIvZebKMolf4DGCUR6KgWEdFjVrj1cfN9Clm6s4qwjR3Bo8/kExbm6T4FIL6SAkA7ZXlvPRfcsZPXWWn59wdF8akpx1CWJSJIpIKRdW6vruODuBazfsYu7Lj6GEycOi7okEUkBBYS0adMHH3LB3QuoqK7jvkuP5fiDC6IuSURSRAEh+7Vhxy7Om/0qVbv28MBl0zhmzJCoSxKRFFJASKg1lbWcP3sBH+5p5KErpnNEyaCoSxKRFFNASCsrt9Rwwd0LcHfmXjmDScPzoi5JRCKggJB9LN1YxUX3LCAzvR8PXX4chwzLibokEYmIAkJavLb+fS65dyF52Rk8fMV0xgwdGHVJIhIhBYQAsGDNdr48ZxEFuVk8dPl0SgYPiLokEYmYAkJ45Z1Krri/jJGD+vPwFTMoytOtS0VEAdHnvbC8gn9/6DUOKhzIg5dPpyAnK+qSRKSbUED0Yc+8tZlrHlnC5BF53P/laQwakBl1SSLSjSgg+qjfLynnm/PeYOrowdx36bHkZeuqqyKyr37JfHIzO83MVprZajO7IWT5aDN70cyWmNmbZnZ6yPJaM7sumXX2NXMXrucb895g+rih3P/laQoHEQmVtIAwszTgDuAzwGTgPDObnNDsRmCeu08FZgL/m7D8Z8CzyaqxL5rzj7Xc8Lu3+Jfxhdx36bEMzNJOpIiES+bWYRqw2t3XAJjZXOBsYHlcGweaT9PNBzY1LzCzc4C1wM4k1tin3Pm3d7n12bc5dXIRvzp/KlnpuqubiOxfMruYRgIb4h6XB/Pi3QRcaGblwDPA1QBmlgN8C/hBWy9gZleaWZmZlVVWVnZV3b2Ou/OLF1Zx67Nv89kjhvO/FxytcBCRdiV1DKIDzgPmuHsJcDrwgJn1IxYcP3f32rZWdve73L3U3UsLCwuTX20P5O78959W8osX3uHcY0r45cypZKRF/d8uIj1BMruYNgKj4h6XBPPiXQacBuDu880sGygApgPnmtmPgUFAk5nVufuvklhvr9PU5Nz89HLm/HMdF84Yzc1nHUa/frotqIh0TDIDYhEw3szGEQuGmcD5CW3WA6cAc8xsEpANVLr7Cc0NzOwmoFbh0DlNTc53//AWjyzcwGUfH8eNZ0zSPaNFpFOSFhDu3mBmVwHPAWnAve6+zMxuBsrc/Ungm8BsM/s6sQHrWe7uyaqpr2hobOL6x97kd0s28h8nHcx1n5qocBCRTrPesj0uLS31srKyqMuI3O6GJq59dAnPvLWF6z41gatOHh91SSLSjZnZYncvDVumg+B7kbo9jVz18Gu8sGIrN54xictPOCjqkkSkB1NA9BIf7m7kygfKeOWdbfzwnMO4aMaYqEsSkR5OAdEL1NY3cNmcRSxct4Mfn3sEXywd1f5KIiLtUED0cFUf7mHWfQt5s7yKX3zpKM4+KvFcRBGRj0YB0YPt2Lmbi+5ZwKqKGu44/2hOO6w46pJEpBdRQPRQlTX1XHj3AtZu38ldF5Vy0qHDoi5JRHoZBUQPtLnqQy6YvYDNVXXcN+tYPnZIQdQliUgvpIDoYTbs2MX5d7/K+zv3cP9l0zh27JCoSxKRXkoB0YOs3baT82e/yq7djTx0+XSOHDUo6pJEpBdTQPQQ71TUcP7dC2hsch65YgaTR+S1v5KIyAFQQPQAyzZVcdE9C0nrZzx65QzGF+VGXZKI9AG6MUA39/qGDzjvrlfJTu/HvK8cp3AQkZTRHkQ3tmjdDi69bxFDBmby0OXTGTVkQNQliUgfooDopv6xehuX/7aM4YOyefjyGRTnZ0ddkoj0Mepi6oZefHsrl85ZxJihA3j0yuMUDiISCe1BdDN/WrqZqx9ZwsTiXB748nQGD8yMuiQR6aO0B9GNPPH6Rv7j4SUcPjKfhy6foXAQkUhpD6KbmLdoA9/63ZtMGzuEe2YdS06W/mtEJFraCnUD989fx/efWMYJ4wu466JS+memRV2SiIgCImqzX17DLc+s4JOTirjjgqlkpSscRKR7UEBE6Pa/vMNPn1/FGYcP5xczjyIjTUNCItJ9KCAi4O785M8ruePFd/nXqSP58blHkK5wEJFuRgGRYu7OD59ewb3/WMt500ZzyzmH0a+fRV2WiEgrCogUampybnxiKQ8vWM+s48fyn2dOxkzhICLdkwIiRRqbnOsfe5PHXyvnqycezPWfnqhwEJFuTQGRAnsam/j6o6/z9Jub+capE7j65EMUDiLS7Skgkqy+oZGrHl7C88sr+PZnDuUrnzg46pJERDpEAZFEH+5u5CsPLublVZX84KwpXHL82KhLEhHpsKQeW2lmp5nZSjNbbWY3hCwfbWYvmtkSM3vTzE4P5p9qZovN7K3g35OTWWcy7Kxv4NI5C3nlnUr++/OHKxxEpMdJ2h6EmaUBdwCnAuXAIjN70t2XxzW7EZjn7r82s8nAM8BYYBtwprtvMrPDgOeAkcmqtatV1+3h0vsW8fqGD/j5F4/inKk9pnQRkRbJ7GKaBqx29zUAZjYXOBuIDwgH8oLpfGATgLsviWuzDOhvZlnuXp/EervE+zt3c/G9C1mxuZpfnTeVzxw+POqSREQ+kmQGxEhgQ9zjcmB6QpubgD+b2dXAQOCTIc/zeeC1sHAwsyuBKwFGjx7dBSUfmG219Vx49wLWbNvJXRcfw8mHFkVdkojIR7bfMQgz+7SZnRsy/1wzO7WLXv88YI67lwCnAw+YWUtNZjYF+G/gK2Eru/td7l7q7qWFhYVdVNJHU1Fdx5d+M59123dy7yXHKhxEpMdra5D6+8DfQua/BNzcgefeCIyKe1wSzIt3GTAPwN3nA9lAAYCZlQC/By5293c78HqRKX9/F1/8zXy2VNVx/5en8/HxBVGXJCJywNoKiCx3r0yc6e7biHUHtWcRMN7MxplZJjATeDKhzXrgFAAzm0QsICrNbBDwR+AGd/9HB14rMuu27eRLv3mV93fu5sHLpzNt3JCoSxIR6RJtBUSembUaozCzDKB/e0/s7g3AVcSOQFpB7GilZWZ2s5mdFTT7JnCFmb0BPALMcncP1jsE+L6ZvR78DOvUb5YCq7fW8MXfzGfX7gYevmIGU0cPjrokEZEuY7HtccgCs1uBIuAqd98ZzMsBfglsc/dvpazKDigtLfWysrKUvd7yTdVcdM8CzIyHr5jOhKLclL22iEhXMbPF7l4atqytPYgbgQrgveBktdeAtUBlsKzPerP8A86b/SqZ6f2Y95UZCgcR6ZX2e5hr0EV0g5n9gFh3D8TOa/gwJZV1U2XrdnDpfYvIH5DBI1fMYNSQAVGXJCKSFPsNCDP714RZDgwys9fdvSa5ZXVP/3x3G5f/tozivGweumI6w/PbHYoREemx2jpR7syQeUOAI8zsMnf/a5Jq6pZeWrmVrzywmDFDB/Dg5dMZlpsddUkiIknVVhfTpWHzzWwMsXMXEs+K7rWeW7aFqx5+jQlFuTxw2XSGDMyMuiQRkaTr9NVc3f09ICMJtXRLT72xiX9/6DWmjMjn4ctnKBxEpM/o9LWYzOxQoNtfNK8rPLa4nOsfe4PSsUO4d9ax5GTp9hki0ne0NUj9FLGB6XhDgOHAhcksqjt48NX3uPEPSzlhfAF3XVRK/8y0qEsSEUmptr4S/yThsQM7iIXEhcD8ZBUVtXv+vpYfPr2cUw4dxh0XHE12hsJBRPqetgapWy7UZ2ZTgfOBLxA7We7x5JcWjTteXM3/PLeSzxxWzC9nTiUzPak33RMR6bba6mKaQOxy3OcRu8Pbo8QuzXFSimpLKXfnZ8+v4va/ruaco0bwky8cSXqawkFE+q62upjeBl4BPuvuqwHM7OspqSrF3J3/emYFs19Zy8xjR3HL5w4nrZ9FXZaISKTa+or8r8Bm4EUzm21mpwC9bqvZ1OR8/4llzH5lLZccN4b/UjiIiABtBIS7/8HdZwKHAi8C1wLDzOzXZvapVBWYbOu27+Tx18r5yr8cxE1nTaGfwkFEBGjjct+hjc0GExuo/pK7n5K0qj6CA7nc94YduygZ3B8zhYOI9C0f9XLfrbj7+8F9oLtVOByoUUMGKBxERBLoMB0REQmlgBARkVAKCBERCaWAEBGRUAoIEREJpYAQEZFQCggREQmlgBARkVAKCBERCaWAEBGRUAoIEREJpYAQEZFQSQ0IMzvNzFaa2WozuyFk+Wgze9HMlpjZm2Z2etyybwfrrTSzTyezThERaa2tO8odEDNLA+4ATgXKgUVm9qS7L49rdiMwz91/bWaTgWeAscH0TGAKMAJ4wcwmuHtjsuoVEZF9JXMPYhqw2t3XuPtuYC5wdkIbB/KC6XxgUzB9NjDX3evdfS2wOng+ERFJkWQGxEhgQ9zj8mBevJuAC82snNjew9WdWBczu9LMysysrLKysqvqFhERoh+kPg+Y4+4lwOnAA2bW4ZqCmxeVuntpYWFh0ooUEemLkjYGAWwERsU9LgnmxbsMOA3A3eebWTZQ0MF1RUQkiZK5B7EIGG9m48wsk9ig85MJbdYDpwCY2SQgG6gM2s00sywzGweMBxYmsVYREUmQtD0Id28ws6uA54A04F53X2ZmNwNl7v4k8E1gtpl9ndiA9Sx3d2CZmc0DlgMNwH/oCCYRkdSy2Pa45ystLfWysrKoyxAR6VHMbLG7l4Yti3qQWkREuikFhIiIhFJAiIhIKAWEiIiEUkCIiEgoBYSIiIRSQIiISCgFhIiIhFJAiIhIKAWEiIiEUkCIiEgoBYSIiIRSQIiISCgFhIiIhFJAiIhIKAWEiIiEUkCIiEgoBYSIiIRSQIiISCgFhIiIhFJAiIhIKAWEiIiEUkCIiEgoBYSIiIRSQIiISCgFhIiIhFJAiIhIKAWEiIiESmpAmNlpZrbSzFab2Q0hy39uZq8HP6vM7IO4ZT82s2VmtsLMbjMzS2atIiKyr/RkPbGZpQF3AKcC5cAiM3vS3Zc3t3H3r8e1vxqYGkwfD3wMOCJY/HfgE8BLyapXRET2lcw9iGnAandf4+67gbnA2W20Pw94JJh2IBvIBLKADKAiibWKiEiCZAbESGBD3OPyYF4rZjYGGAf8FcDd5wMvApuDn+fcfUUSaxURkQTdZZB6JvCYuzcCmNkhwCSghFionGxmJySuZGZXmlmZmZVVVlamtGARkd4umQGxERgV97gkmBdmJnu7lwA+B7zq7rXuXgs8CxyXuJK73+Xupe5eWlhY2EVli4gIJDcgFgHjzWycmWUSC4EnExuZ2aHAYGB+3Oz1wCfMLN3MMogNUKuLSUQkhZIWEO7eAFwFPEds4z7P3ZeZ2c1mdlZc05nAXHf3uHmPAe8CbwFvAG+4+1PJqlVERFqzfbfLPVdpaamXlZVFXYaISI9iZovdvTRsWXcZpBYRkW5GASEiIqEUECIiEkoBIaU/6TMAAAiXSURBVCIioRQQIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBISIioRQQIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBISIioRQQIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBISIioRQQIiISSgEhIiKhFBAiIhJKASEiIqHSoy5ABPfYDwn/elPCvKaQ5ex/+X7XaV5OO8vbmteJOgley/oBFvvXLO6xJSxrq23Yum09V9hja3t54jKzpPy3S/engNi1A/53RtRV9CxtboQJmdfORlR6gI8YPh81qFpJ+DvxxL+bkL+j9tp0yXO0WiGaOoYfCRf8v9brHSAFRFoGTPxM1FX0QCHfXEPnkbCxCFsnYXn8N+OOrtO8Uen0Om3U2e46Hakz+BfYJxhbAjMxSEOWhT6mnedKXNaZ5w6CvsNt23vuxGXtPXdT3HsW/yeXOM/aWd6RNlE9R2dfo502g8aEtD9wSQ0IMzsN+CWQBtzt7rcmLP85cFLwcAAwzN0HBctGA3cDo4j9SZ3u7uu6vMisXDjzl13+tCIiPV3SAsLM0oA7gFOBcmCRmT3p7sub27j71+PaXw1MjXuK+4Fb3P15M8sBmpJVq4iItJbMo5imAavdfY277wbmAme30f484BEAM5sMpLv78wDuXuvuu5JYq4iIJEhmQIwENsQ9Lg/mtWJmY4BxwF+DWROAD8zsd2a2xMz+J9gjSVzvSjMrM7OyysrKLi5fRKRv6y7nQcwEHnP3xuBxOnACcB1wLHAQMCtxJXe/y91L3b20sLAwVbWKiPQJyQyIjcQGmJuVBPPCzCToXgqUA68H3VMNwB+Ao5NSpYiIhEpmQCwCxpvZODPLJBYCTyY2MrNDgcHA/IR1B5lZ827BycDyxHVFRCR5khYQwTf/q4DngBXAPHdfZmY3m9lZcU1nAnPd954JEnQ1XQf8xczeInYA8Oxk1SoiIq2ZtzpDr2cqLS31srKyqMsQEelRzGyxu5eGLustAWFmlcB7B/AUBcC2LiqnK6muzlFdnaO6Oqc31jXG3UOP8uk1AXGgzKxsfykaJdXVOaqrc1RX5/S1urrLYa4iItLNKCBERCSUAmKvu6IuYD9UV+eors5RXZ3Tp+rSGISIiITSHoSIiIRSQIiISKg+FRBmdpqZrTSz1WZ2Q8jyLDN7NFi+wMzGdpO6ZplZpZm9HvxcnqK67jWzrWa2dD/LzcxuC+p+08xScr2sDtR1oplVxb1f309RXaPM7EUzW25my8zsayFtUv6edbCulL9nZpZtZgvN7I2grh+EtEn5Z7KDdUXymQxeOy24yvXTIcu69v1y9z7xQ+yudu8SuzJsJvAGMDmhzb8DdwbTM4FHu0lds4BfRfCe/QuxiyQu3c/y04FniV0KZQawoJvUdSLwdATv13Dg6GA6F1gV8n+Z8vesg3Wl/D0L3oOcYDoDWADMSGgTxWeyI3VF8pkMXvsbwMNh/19d/X71pT2IjtzA6Gzgt8H0Y8ApZqE3h011XZFw95eBHW00ORu432NeJXaBxeHdoK5IuPtmd38tmK4hdg2yxHugpPw962BdKRe8B7XBw4zgJ/GomZR/JjtYVyTMrAQ4g9jtmMN06fvVlwKiIzcwamnjsYsNVgFDu0FdAJ8PuiQeM7NRIcuj0OGbQkXguKCL4Fkzm5LqFw927acS+/YZL9L3rI26IIL3LOgueR3YCjzv7vt9v1L4mexIXRDNZ/IXwPXs/xbMXfp+9aWA6MmeAsa6+xHA8+z9hiDhXiN2fZkjgduJ3U8kZSx2D/XHgWvdvTqVr92WduqK5D1z90Z3P4rY/WKmmdlhqXjd9nSgrpR/Js3ss8BWd1+c7Ndq1pcCoiM3MGppY2bpQD6wPeq63H27u9cHD+8GjklyTR3VmZtCpYy7Vzd3Ebj7M0CGmRWk4rXNLIPYRvghd/9dSJNI3rP26oryPQte8wPgReC0hEVRfCbbrSuiz+THgLPMbB2xruiTzezBhDZd+n71pYDoyA2MngQuCabPBf7qwWhPlHUl9FGfRawPuTt4Erg4ODJnBlDl7pujLsrMipv7Xc1sGrG/86RvVILXvAdY4e4/20+zlL9nHakrivfMzArNbFAw3R84FXg7oVnKP5MdqSuKz6S7f9vdS9x9LLHtxF/d/cKEZl36fqV/1BV7GndvMLPmGxilAfd6cAMjoMzdnyT2IXrAzFYTGwSd2U3qusZiN1lqCOqaley6AMzsEWJHtxSYWTnwn8QG7HD3O4FniB2VsxrYBVzaTeo6F/iqmTUAHwIzUxD0EPuGdxHwVtB/DfAdYHRcbVG8Zx2pK4r3bDjwWzNLIxZI89z96ag/kx2sK5LPZJhkvl+61IaIiITqS11MIiLSCQoIEREJpYAQEZFQCggREQmlgBARkVAKCJFOMLPGuCt4vm4hV989gOcea/u5Qq1IFPrMeRAiXeTD4BIMIr2e9iBEuoCZrTOzH5vZW8G9BA4J5o81s78GF3X7i5mNDuYXmdnvg4vjvWFmxwdPlWZmsy12H4I/B2fyikRCASHSOf0Tupi+FLesyt0PB35F7KqbELvw3W+Di7o9BNwWzL8N+FtwcbyjgWXB/PHAHe4+BfgA+HySfx+R/dKZ1CKdYGa17p4TMn8dcLK7rwkujLfF3Yea2TZguLvvCeZvdvcCM6sESuIu+NZ8Ke7n3X188PhbQIa7/yj5v5lIa9qDEOk6vp/pzqiPm25E44QSIQWESNf5Uty/84Ppf7L3gmkXAK8E038BvgotN6fJT1WRIh2lbycindM/7oqoAH9y9+ZDXQeb2ZvE9gLOC+ZdDdxnZv8HqGTv1Vu/BtxlZpcR21P4KhD5pdJF4mkMQqQLBGMQpe6+LepaRLqKuphERCSU9iBERCSU9iBERCSUAkJEREIpIEREJJQCQkREQikgREQk1P8HvtIl4Vlk9NIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 48;\n",
       "                var nbb_unformatted_code = \"plot_metric(history_2, \\\"AUC\\\")\";\n",
       "                var nbb_formatted_code = \"plot_metric(history_2, \\\"AUC\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metric(history_2, \"AUC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6489, 96, 96, 3)\n",
      "171.0966796875\n",
      "CPU times: user 3.74 s, sys: 2.54 s, total: 6.28 s\n",
      "Wall time: 14.1 s\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 49;\n",
       "                var nbb_unformatted_code = \"%%time\\nvalid_data = preprocess_data(X[valid_indices], fixed_text_to_square_image)\";\n",
       "                var nbb_formatted_code = \"%%time\\nvalid_data = preprocess_data(X[valid_indices], fixed_text_to_square_image)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "valid_data = preprocess_data(X[valid_indices], fixed_text_to_square_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9255589685024382\n",
      "CPU times: user 7.95 s, sys: 335 ms, total: 8.28 s\n",
      "Wall time: 7.69 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "685"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 50;\n",
       "                var nbb_unformatted_code = \"%%time\\npreds = model.predict(valid_data)\\nprint(roc_auc_score(Y[valid_indices], preds[:, 1]))\\ndel valid_data\\ngc.collect()\";\n",
       "                var nbb_formatted_code = \"%%time\\npreds = model.predict(valid_data)\\nprint(roc_auc_score(Y[valid_indices], preds[:, 1]))\\ndel valid_data\\ngc.collect()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "preds = model.predict(valid_data)\n",
    "print(roc_auc_score(Y[valid_indices], preds[:, 1]))\n",
    "del valid_data\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16280, 96, 96, 3)\n",
      "429.2578125\n",
      "CPU times: user 7.99 s, sys: 3.36 s, total: 11.3 s\n",
      "Wall time: 32.8 s\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 51;\n",
       "                var nbb_unformatted_code = \"%%time\\ntest_data = preprocess_data(X_test, fixed_text_to_square_image)\";\n",
       "                var nbb_formatted_code = \"%%time\\ntest_data = preprocess_data(X_test, fixed_text_to_square_image)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "test_data = preprocess_data(X_test, fixed_text_to_square_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9256573093871158\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1302"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 53;\n",
       "                var nbb_unformatted_code = \"preds = model.predict(test_data)\\nprint(roc_auc_score(Y_test, preds[:, 1]))\\ndel test_data\\ngc.collect()\";\n",
       "                var nbb_formatted_code = \"preds = model.predict(test_data)\\nprint(roc_auc_score(Y_test, preds[:, 1]))\\ndel test_data\\ngc.collect()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = model.predict(test_data)\n",
    "print(roc_auc_score(Y_test, preds[:, 1]))\n",
    "del test_data\n",
    "gc.collect()"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}