text-to-image-RAM.ipynb

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    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:18.768128Z",
     "start_time": "2020-04-17T20:18:18.644332Z"
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   "outputs": [
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 1;\n",
       "                var nbb_unformatted_code = \"%load_ext nb_black\";\n",
       "                var nbb_formatted_code = \"%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",
       "            "
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    "%load_ext nb_black"
   ]
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   "cell_type": "markdown",
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     "end_time": "2020-04-17T13:04:39.963383Z",
     "start_time": "2020-04-17T13:04:39.955914Z"
    }
   },
   "source": [
    "## Import + utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:20.780953Z",
     "start_time": "2020-04-17T20:18:18.769931Z"
    }
   },
   "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 shutil\\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\\nfrom tensorflow.keras.utils import to_categorical\\nfrom concurrent.futures import ProcessPoolExecutor as PoolExecutor\\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 shutil\\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\\nfrom tensorflow.keras.utils import to_categorical\\nfrom concurrent.futures import ProcessPoolExecutor as PoolExecutor\\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",
       "            "
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   "source": [
    "import os\n",
    "from requests import get\n",
    "from pathlib import Path\n",
    "import shutil\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",
    "from tensorflow.keras.utils import to_categorical\n",
    "from concurrent.futures import ProcessPoolExecutor as PoolExecutor\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": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:20.804655Z",
     "start_time": "2020-04-17T20:18:20.783082Z"
    }
   },
   "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",
       "            "
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   "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)"
   ]
  },
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   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:20.875415Z",
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   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"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()\";\n",
       "                var nbb_formatted_code = \"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()\";\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",
       "            "
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     "output_type": "display_data"
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   "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": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:20.937595Z",
     "start_time": "2020-04-17T20:18:20.877088Z"
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   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\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>"
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   ],
   "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": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:20.990816Z",
     "start_time": "2020-04-17T20:18:20.940052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"def word_to_square_image(text, size, cut_length=None, one_channel=False):\\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\\n    # 1 (1-bit pixels, black and white, stored with one pixel per byte)\\n    # L (8-bit pixels, black and white)\\n    # RGB (3x8-bit pixels, true color)\\n    # https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes\\n    mode = \\\"L\\\" if one_channel else \\\"RGB\\\"\\n    WHITE = 1 if one_channel else (255, 255, 255)\\n    BLACK = 0 if one_channel else (0, 0, 0)\\n\\n    image = Image.new(mode, (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 = \"def word_to_square_image(text, size, cut_length=None, one_channel=False):\\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\\n    # 1 (1-bit pixels, black and white, stored with one pixel per byte)\\n    # L (8-bit pixels, black and white)\\n    # RGB (3x8-bit pixels, true color)\\n    # https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes\\n    mode = \\\"L\\\" if one_channel else \\\"RGB\\\"\\n    WHITE = 1 if one_channel else (255, 255, 255)\\n    BLACK = 0 if one_channel else (0, 0, 0)\\n\\n    image = Image.new(mode, (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>"
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     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "def word_to_square_image(text, size, cut_length=None, one_channel=False):\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",
    "\n",
    "    # 1 (1-bit pixels, black and white, stored with one pixel per byte)\n",
    "    # L (8-bit pixels, black and white)\n",
    "    # RGB (3x8-bit pixels, true color)\n",
    "    # https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes\n",
    "    mode = \"L\" if one_channel else \"RGB\"\n",
    "    WHITE = 1 if one_channel else (255, 255, 255)\n",
    "    BLACK = 0 if one_channel else (0, 0, 0)\n",
    "\n",
    "    image = Image.new(mode, (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": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.096353Z",
     "start_time": "2020-04-17T20:18:20.992376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"def text_to_square_image(features, image_size=299, cut_length=None, one_channel=False):\\n    nb_channel = 1 if one_channel else 3\\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    if one_channel:\\n        result_image = np.zeros((image_size, image_size), dtype=\\\"uint8\\\")\\n    else:\\n        result_image = np.zeros((image_size, image_size, nb_channel), dtype=\\\"uint8\\\")\\n    results = []\\n    i_feature = 0\\n    features_str = features.astype(\\\"str\\\")\\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_str[i_feature],\\n                size=word_size,\\n                cut_length=cut_length,\\n                one_channel=one_channel,\\n            )\\n    return result_image\";\n",
       "                var nbb_formatted_code = \"def text_to_square_image(features, image_size=299, cut_length=None, one_channel=False):\\n    nb_channel = 1 if one_channel else 3\\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    if one_channel:\\n        result_image = np.zeros((image_size, image_size), dtype=\\\"uint8\\\")\\n    else:\\n        result_image = np.zeros((image_size, image_size, nb_channel), dtype=\\\"uint8\\\")\\n    results = []\\n    i_feature = 0\\n    features_str = features.astype(\\\"str\\\")\\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_str[i_feature],\\n                size=word_size,\\n                cut_length=cut_length,\\n                one_channel=one_channel,\\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, one_channel=False):\n",
    "    nb_channel = 1 if one_channel else 3\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",
    "    if one_channel:\n",
    "        result_image = np.zeros((image_size, image_size), dtype=\"uint8\")\n",
    "    else:\n",
    "        result_image = np.zeros((image_size, image_size, nb_channel), dtype=\"uint8\")\n",
    "    results = []\n",
    "    i_feature = 0\n",
    "    features_str = features.astype(\"str\")\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_str[i_feature],\n",
    "                size=word_size,\n",
    "                cut_length=cut_length,\n",
    "                one_channel=one_channel,\n",
    "            )\n",
    "    return result_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.169460Z",
     "start_time": "2020-04-17T20:18:21.099274Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"def preprocess_data(data, map_func, image_size, one_channel=False):\\n    nb_channel = 1 if one_channel else 3\\n\\n    print(f\\\"Input shape is {data.shape}\\\")\\n    print(\\n        f\\\"Output data is estimated to {(data.shape[0] * image_size * image_size * nb_channel)/ (1024 * 1024)} Mo in RAM\\\"\\n    )  # Memory size in RAM\\n    preprocessed_df = None\\n    with PoolExecutor() as executor:\\n        preprocessed_df = np.stack(list(executor.map(map_func, data)), axis=0)\\n    print(f\\\"Output shape is {preprocessed_df.shape}\\\")\\n    gc.collect()\\n    return preprocessed_df\";\n",
       "                var nbb_formatted_code = \"def preprocess_data(data, map_func, image_size, one_channel=False):\\n    nb_channel = 1 if one_channel else 3\\n\\n    print(f\\\"Input shape is {data.shape}\\\")\\n    print(\\n        f\\\"Output data is estimated to {(data.shape[0] * image_size * image_size * nb_channel)/ (1024 * 1024)} Mo in RAM\\\"\\n    )  # Memory size in RAM\\n    preprocessed_df = None\\n    with PoolExecutor() as executor:\\n        preprocessed_df = np.stack(list(executor.map(map_func, data)), axis=0)\\n    print(f\\\"Output shape is {preprocessed_df.shape}\\\")\\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, image_size, one_channel=False):\n",
    "    nb_channel = 1 if one_channel else 3\n",
    "\n",
    "    print(f\"Input shape is {data.shape}\")\n",
    "    print(\n",
    "        f\"Output data is estimated to {(data.shape[0] * image_size * image_size * nb_channel)/ (1024 * 1024)} Mo in RAM\"\n",
    "    )  # Memory size in RAM\n",
    "    preprocessed_df = None\n",
    "    with PoolExecutor() as executor:\n",
    "        preprocessed_df = np.stack(list(executor.map(map_func, data)), axis=0)\n",
    "    print(f\"Output shape is {preprocessed_df.shape}\")\n",
    "    gc.collect()\n",
    "    return preprocessed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.244161Z",
     "start_time": "2020-04-17T20:18:21.171640Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"def build_in_memory_dataset(X, Y, batch_size, prefetch, is_categorical=True):\\n    target = to_categorical(Y) if is_categorical else Y\\n    dataset = tf.data.Dataset.from_tensor_slices((X, target))\\n\\n    return dataset.repeat().batch(batch_size).prefetch(prefetch)\";\n",
       "                var nbb_formatted_code = \"def build_in_memory_dataset(X, Y, batch_size, prefetch, is_categorical=True):\\n    target = to_categorical(Y) if is_categorical else Y\\n    dataset = tf.data.Dataset.from_tensor_slices((X, target))\\n\\n    return dataset.repeat().batch(batch_size).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_in_memory_dataset(X, Y, batch_size, prefetch, is_categorical=True):\n",
    "    target = to_categorical(Y) if is_categorical else Y\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((X, target))\n",
    "\n",
    "    return dataset.repeat().batch(batch_size).prefetch(prefetch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.283703Z",
     "start_time": "2020-04-17T20:18:21.246508Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"def save_numpy_as_image(arr, name):\\n    im = Image.fromarray(arr, mode=\\\"RGB\\\")\\n    im.save(f\\\"{name}.jpeg\\\")\";\n",
       "                var nbb_formatted_code = \"def save_numpy_as_image(arr, name):\\n    im = Image.fromarray(arr, mode=\\\"RGB\\\")\\n    im.save(f\\\"{name}.jpeg\\\")\";\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 save_numpy_as_image(arr, name):\n",
    "    im = Image.fromarray(arr, mode=\"RGB\")\n",
    "    im.save(f\"{name}.jpeg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download bank-marketing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.329500Z",
     "start_time": "2020-04-17T20:18:21.286427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File already exists.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"# 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\\\"\\nfont_url = \\\"https://ff.static.1001fonts.net/r/o/roboto-condensed.regular.ttf\\\"\\n\\ndataset_name = \\\"bank-marketing\\\"\\nout = Path(os.getcwd()) / f\\\"data/{dataset_name}/train_bench.csv\\\"\\n# out_test = Path(os.getcwd()) / f\\\"data/{dataset_name}_test.csv\\\"\\nout_font = Path(os.getcwd()) / f\\\"RobotoCondensed-Regular.ttf\\\"\\n\\n# download(url, out)\\n# download(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\\\"\\n# url_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 = \\\"bank-marketing\\\"\\nout = Path(os.getcwd()) / f\\\"data/{dataset_name}/train_bench.csv\\\"\\n# out_test = Path(os.getcwd()) / f\\\"data/{dataset_name}_test.csv\\\"\\nout_font = Path(os.getcwd()) / f\\\"RobotoCondensed-Regular.ttf\\\"\\n\\n# download(url, out)\\n# download(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 = \"bank-marketing\"\n",
    "out = Path(os.getcwd()) / f\"data/{dataset_name}/train_bench.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 bank-marketing as a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.513671Z",
     "start_time": "2020-04-17T20:18:21.331256Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41188, 21)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"target = \\\"y\\\"\\ntrain = pd.read_csv(out, low_memory=False)\\nprint(train.shape)\";\n",
       "                var nbb_formatted_code = \"target = \\\"y\\\"\\ntrain = pd.read_csv(out, low_memory=False)\\nprint(train.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": [
    "target = \"y\"\n",
    "train = pd.read_csv(out, low_memory=False)\n",
    "print(train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.552720Z",
     "start_time": "2020-04-17T20:18:21.515225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"train_indices = train[train.Set == \\\"train\\\"].index.values\\nnp.random.shuffle(train_indices)\\nvalid_indices = train[train.Set == \\\"valid\\\"].index.values\\ntest_indices = train[train.Set == \\\"test\\\"].index.values\";\n",
       "                var nbb_formatted_code = \"train_indices = train[train.Set == \\\"train\\\"].index.values\\nnp.random.shuffle(train_indices)\\nvalid_indices = train[train.Set == \\\"valid\\\"].index.values\\ntest_indices = train[train.Set == \\\"test\\\"].index.values\";\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": [
    "train_indices = train[train.Set == \"train\"].index.values\n",
    "np.random.shuffle(train_indices)\n",
    "valid_indices = train[train.Set == \"valid\"].index.values\n",
    "test_indices = train[train.Set == \"test\"].index.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encode target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.699089Z",
     "start_time": "2020-04-17T20:18:21.554492Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41188, 1)\n",
      "(41188, 19)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "561"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\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)\\n# Y = train[[target]].values.reshape(-1, 1)\\n# CLASSNAMES = np.unique(Y)\\nprint(Y.shape)\\n\\nX = train.drop(columns=[target, \\\"Set\\\"]).values\\nprint(X.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)\\n# Y = train[[target]].values.reshape(-1, 1)\\n# CLASSNAMES = np.unique(Y)\\nprint(Y.shape)\\n\\nX = train.drop(columns=[target, \\\"Set\\\"]).values\\nprint(X.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",
    "# Y = train[[target]].values.reshape(-1, 1)\n",
    "# CLASSNAMES = np.unique(Y)\n",
    "print(Y.shape)\n",
    "\n",
    "X = train.drop(columns=[target, \"Set\"]).values\n",
    "print(X.shape)\n",
    "\n",
    "del train\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.709221Z",
     "start_time": "2020-04-17T20:18:21.702342Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"# CLASSNAMES\";\n",
       "                var nbb_formatted_code = \"# CLASSNAMES\";\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": [
    "# CLASSNAMES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T12:15:21.164954Z",
     "start_time": "2020-04-17T12:15:21.156761Z"
    }
   },
   "source": [
    "## Parameters for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.779938Z",
     "start_time": "2020-04-17T20:18:21.711851Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"IMAGE_SIZE = 224\\nCUT_LENGTH = None\\nBATCH_SIZE = 64\\nPREFETCH = 10\\nONE_CHANNEL = False\";\n",
       "                var nbb_formatted_code = \"IMAGE_SIZE = 224\\nCUT_LENGTH = None\\nBATCH_SIZE = 64\\nPREFETCH = 10\\nONE_CHANNEL = False\";\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": [
    "IMAGE_SIZE = 224\n",
    "CUT_LENGTH = None\n",
    "BATCH_SIZE = 64\n",
    "PREFETCH = 10\n",
    "ONE_CHANNEL = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:18:21.795378Z",
     "start_time": "2020-04-17T20:18:21.781682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"def fixed_text_to_square_image(values):\\n    return text_to_square_image(\\n        values, image_size=IMAGE_SIZE, cut_length=CUT_LENGTH, one_channel=ONE_CHANNEL\\n    )\";\n",
       "                var nbb_formatted_code = \"def fixed_text_to_square_image(values):\\n    return text_to_square_image(\\n        values, image_size=IMAGE_SIZE, cut_length=CUT_LENGTH, one_channel=ONE_CHANNEL\\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": [
    "def fixed_text_to_square_image(values):\n",
    "    return text_to_square_image(\n",
    "        values, image_size=IMAGE_SIZE, cut_length=CUT_LENGTH, one_channel=ONE_CHANNEL\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T13:09:05.896414Z",
     "start_time": "2020-04-17T13:09:05.860854Z"
    }
   },
   "source": [
    "## Preparing datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:47.871545Z",
     "start_time": "2020-04-17T20:18:21.797551Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape is (10000, 19)\n",
      "Output data is estimated to 1435.546875 Mo in RAM\n",
      "Output shape is (10000, 224, 224, 3)\n",
      "Input shape is (10000, 19)\n",
      "Output data is estimated to 1435.546875 Mo in RAM\n",
      "Output shape is (10000, 224, 224, 3)\n",
      "Input shape is (10000, 19)\n",
      "Output data is estimated to 1435.546875 Mo in RAM\n",
      "Output shape is (10000, 224, 224, 3)\n",
      "Input shape is (2950, 19)\n",
      "Output data is estimated to 423.486328125 Mo in RAM\n",
      "Output shape is (2950, 224, 224, 3)\n",
      "Input shape is (4119, 19)\n",
      "Output data is estimated to 591.3017578125 Mo in RAM\n",
      "Output shape is (4119, 224, 224, 3)\n",
      "Input shape is (4119, 19)\n",
      "Output data is estimated to 591.3017578125 Mo in RAM\n",
      "Output shape is (4119, 224, 224, 3)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"CHUNK = 10000\\nfile_list = {\\n    \\\"train\\\": [],\\n    \\\"valid\\\": [],\\n    \\\"test\\\": [],\\n}\\nfor set_label, set_indices in zip(\\n    [\\\"train\\\", \\\"valid\\\", \\\"test\\\"], [train_indices, valid_indices, test_indices]\\n):\\n    prep_data_folder = Path(os.getcwd()) / f\\\"data/{dataset_name}/prep_data/{set_label}/\\\"\\n    if prep_data_folder.exists():\\n        shutil.rmtree(prep_data_folder)\\n    prep_data_folder.mkdir(parents=True, exist_ok=True)\\n    i = 0\\n    while i * CHUNK < set_indices.shape[0]:\\n        idx = set_indices[i * CHUNK : (i + 1) * CHUNK]\\n        targets = Y[idx].reshape(-1)\\n        pred_X = preprocess_data(\\n            X[idx], fixed_text_to_square_image, IMAGE_SIZE, ONE_CHANNEL\\n        )\\n\\n        for j, (img, label) in enumerate(zip(pred_X, targets)):\\n            out_folder = prep_data_folder / str(label)\\n            out_folder.mkdir(parents=True, exist_ok=True)\\n\\n            out_file = out_folder / str(j + i * CHUNK)\\n            file_list[set_label].append(out_file.as_posix() + \\\".jpeg\\\")\\n            save_numpy_as_image(img, out_file)\\n        i += 1\";\n",
       "                var nbb_formatted_code = \"CHUNK = 10000\\nfile_list = {\\n    \\\"train\\\": [],\\n    \\\"valid\\\": [],\\n    \\\"test\\\": [],\\n}\\nfor set_label, set_indices in zip(\\n    [\\\"train\\\", \\\"valid\\\", \\\"test\\\"], [train_indices, valid_indices, test_indices]\\n):\\n    prep_data_folder = Path(os.getcwd()) / f\\\"data/{dataset_name}/prep_data/{set_label}/\\\"\\n    if prep_data_folder.exists():\\n        shutil.rmtree(prep_data_folder)\\n    prep_data_folder.mkdir(parents=True, exist_ok=True)\\n    i = 0\\n    while i * CHUNK < set_indices.shape[0]:\\n        idx = set_indices[i * CHUNK : (i + 1) * CHUNK]\\n        targets = Y[idx].reshape(-1)\\n        pred_X = preprocess_data(\\n            X[idx], fixed_text_to_square_image, IMAGE_SIZE, ONE_CHANNEL\\n        )\\n\\n        for j, (img, label) in enumerate(zip(pred_X, targets)):\\n            out_folder = prep_data_folder / str(label)\\n            out_folder.mkdir(parents=True, exist_ok=True)\\n\\n            out_file = out_folder / str(j + i * CHUNK)\\n            file_list[set_label].append(out_file.as_posix() + \\\".jpeg\\\")\\n            save_numpy_as_image(img, out_file)\\n        i += 1\";\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": [
    "CHUNK = 10000\n",
    "file_list = {\n",
    "    \"train\": [],\n",
    "    \"valid\": [],\n",
    "    \"test\": [],\n",
    "}\n",
    "for set_label, set_indices in zip(\n",
    "    [\"train\", \"valid\", \"test\"], [train_indices, valid_indices, test_indices]\n",
    "):\n",
    "    prep_data_folder = Path(os.getcwd()) / f\"data/{dataset_name}/prep_data/{set_label}/\"\n",
    "    if prep_data_folder.exists():\n",
    "        shutil.rmtree(prep_data_folder)\n",
    "    prep_data_folder.mkdir(parents=True, exist_ok=True)\n",
    "    i = 0\n",
    "    while i * CHUNK < set_indices.shape[0]:\n",
    "        idx = set_indices[i * CHUNK : (i + 1) * CHUNK]\n",
    "        targets = Y[idx].reshape(-1)\n",
    "        pred_X = preprocess_data(\n",
    "            X[idx], fixed_text_to_square_image, IMAGE_SIZE, ONE_CHANNEL\n",
    "        )\n",
    "\n",
    "        for j, (img, label) in enumerate(zip(pred_X, targets)):\n",
    "            out_folder = prep_data_folder / str(label)\n",
    "            out_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "            out_file = out_folder / str(j + i * CHUNK)\n",
    "            file_list[set_label].append(out_file.as_posix() + \".jpeg\")\n",
    "            save_numpy_as_image(img, out_file)\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### In RAM mode"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "X_train = preprocess_data(\n",
    "    X[train_indices], fixed_text_to_square_image, IMAGE_SIZE, ONE_CHANNEL\n",
    ")\n",
    "X_valid = preprocess_data(\n",
    "    X[valid_indices], fixed_text_to_square_image, IMAGE_SIZE, ONE_CHANNEL\n",
    ")\n",
    "X_test = preprocess_data(\n",
    "    X[test_indices], fixed_text_to_square_image, IMAGE_SIZE, ONE_CHANNEL\n",
    ")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Y_train = Y[train_indices]\n",
    "Y_valid = Y[valid_indices]\n",
    "Y_test = Y[test_indices]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "dataset_train = build_in_memory_dataset(\n",
    "    X_train, Y_train, BATCH_SIZE, PREFETCH, is_categorical=True\n",
    ")\n",
    "dataset_valid = build_in_memory_dataset(\n",
    "    X_train, Y_train, BATCH_SIZE, PREFETCH, is_categorical=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From file mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:47.876938Z",
     "start_time": "2020-04-17T20:20:47.873064Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"# https://www.tensorflow.org/tutorials/load_data/images\";\n",
       "                var nbb_formatted_code = \"# https://www.tensorflow.org/tutorials/load_data/images\";\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://www.tensorflow.org/tutorials/load_data/images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:47.944570Z",
     "start_time": "2020-04-17T20:20:47.878286Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"classes = np.array([\\\"0\\\", \\\"1\\\"]).astype(\\\"str\\\")\";\n",
       "                var nbb_formatted_code = \"classes = np.array([\\\"0\\\", \\\"1\\\"]).astype(\\\"str\\\")\";\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": [
    "classes = np.array([\"0\", \"1\"]).astype(\"str\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:47.965682Z",
     "start_time": "2020-04-17T20:20:47.949351Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"def get_label(file_path):\\n    # convert the path to a list of path components\\n    parts = tf.strings.split(file_path, os.path.sep)\\n    return parts[-2] == classes\";\n",
       "                var nbb_formatted_code = \"def get_label(file_path):\\n    # convert the path to a list of path components\\n    parts = tf.strings.split(file_path, os.path.sep)\\n    return parts[-2] == classes\";\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 get_label(file_path):\n",
    "    # convert the path to a list of path components\n",
    "    parts = tf.strings.split(file_path, os.path.sep)\n",
    "    return parts[-2] == classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:48.004858Z",
     "start_time": "2020-04-17T20:20:47.967930Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"def decode_img(img):\\n    # convert the compressed string to a 3D uint8 tensor\\n    img = tf.image.decode_jpeg(img, channels=3)\\n    # Use `convert_image_dtype` to convert to floats in the [0,1] range.\\n    # img = tf.image.convert_image_dtype(img, tf.float32)\\n    # resize the image to the desired size.\\n    # return tf.image.resize(img, [IMG_WIDTH, IMG_HEIGHT])\\n    return img\";\n",
       "                var nbb_formatted_code = \"def decode_img(img):\\n    # convert the compressed string to a 3D uint8 tensor\\n    img = tf.image.decode_jpeg(img, channels=3)\\n    # Use `convert_image_dtype` to convert to floats in the [0,1] range.\\n    # img = tf.image.convert_image_dtype(img, tf.float32)\\n    # resize the image to the desired size.\\n    # return tf.image.resize(img, [IMG_WIDTH, IMG_HEIGHT])\\n    return img\";\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 decode_img(img):\n",
    "    # convert the compressed string to a 3D uint8 tensor\n",
    "    img = tf.image.decode_jpeg(img, channels=3)\n",
    "    # Use `convert_image_dtype` to convert to floats in the [0,1] range.\n",
    "    # img = tf.image.convert_image_dtype(img, tf.float32)\n",
    "    # resize the image to the desired size.\n",
    "    # return tf.image.resize(img, [IMG_WIDTH, IMG_HEIGHT])\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:48.030495Z",
     "start_time": "2020-04-17T20:20:48.012813Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"def process_path(file_path):\\n    label = get_label(file_path)\\n    # load the raw data from the file as a string\\n    img = tf.io.read_file(file_path)\\n    img = decode_img(img)\\n    return img, label\";\n",
       "                var nbb_formatted_code = \"def process_path(file_path):\\n    label = get_label(file_path)\\n    # load the raw data from the file as a string\\n    img = tf.io.read_file(file_path)\\n    img = decode_img(img)\\n    return img, label\";\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 process_path(file_path):\n",
    "    label = get_label(file_path)\n",
    "    # load the raw data from the file as a string\n",
    "    img = tf.io.read_file(file_path)\n",
    "    img = decode_img(img)\n",
    "    return img, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:48.412917Z",
     "start_time": "2020-04-17T20:20:48.035237Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"list_train = tf.data.Dataset.from_tensor_slices(file_list[\\\"train\\\"]).repeat()\";\n",
       "                var nbb_formatted_code = \"list_train = tf.data.Dataset.from_tensor_slices(file_list[\\\"train\\\"]).repeat()\";\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": [
    "list_train = tf.data.Dataset.from_tensor_slices(file_list[\"train\"]).repeat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:48.426810Z",
     "start_time": "2020-04-17T20:20:48.414970Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path:  b'/work/data/bank-marketing/prep_data/train/0/0.jpeg'\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 25;\n",
       "                var nbb_unformatted_code = \"for path in list_train.take(1):\\n    print(\\\"Path: \\\", path.numpy())\";\n",
       "                var nbb_formatted_code = \"for path in list_train.take(1):\\n    print(\\\"Path: \\\", path.numpy())\";\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": [
    "for path in list_train.take(1):\n",
    "    print(\"Path: \", path.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:48.458896Z",
     "start_time": "2020-04-17T20:20:48.429069Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no', 'yes'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 26;\n",
       "                var nbb_unformatted_code = \"label_encoder.classes_\";\n",
       "                var nbb_formatted_code = \"label_encoder.classes_\";\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": [
    "label_encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:48.542872Z",
     "start_time": "2020-04-17T20:20:48.462544Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 27;\n",
       "                var nbb_unformatted_code = \"AUTOTUNE = tf.data.experimental.AUTOTUNE\\n# train_path = Path(os.getcwd()) / f\\\"data/{dataset_name}/prep_data/train/\\\"\\n# list_train = tf.data.Dataset.list_files(str(train_path / \\\"/*/*\\\"))\";\n",
       "                var nbb_formatted_code = \"AUTOTUNE = tf.data.experimental.AUTOTUNE\\n# train_path = Path(os.getcwd()) / f\\\"data/{dataset_name}/prep_data/train/\\\"\\n# list_train = tf.data.Dataset.list_files(str(train_path / \\\"/*/*\\\"))\";\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": [
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "# train_path = Path(os.getcwd()) / f\"data/{dataset_name}/prep_data/train/\"\n",
    "# list_train = tf.data.Dataset.list_files(str(train_path / \"/*/*\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:48.847499Z",
     "start_time": "2020-04-17T20:20:48.545277Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 28;\n",
       "                var nbb_unformatted_code = \"# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.\\nlabeled_train = list_train.map(process_path, num_parallel_calls=AUTOTUNE)\";\n",
       "                var nbb_formatted_code = \"# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.\\nlabeled_train = list_train.map(process_path, num_parallel_calls=AUTOTUNE)\";\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": [
    "# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.\n",
    "labeled_train = list_train.map(process_path, num_parallel_calls=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:48.885145Z",
     "start_time": "2020-04-17T20:20:48.849351Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape:  (224, 224, 3)\n",
      "Label:  [ True False]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 29;\n",
       "                var nbb_unformatted_code = \"for image, label in labeled_train.take(1):\\n    print(\\\"Image shape: \\\", image.numpy().shape)\\n    print(\\\"Label: \\\", label.numpy())\";\n",
       "                var nbb_formatted_code = \"for image, label in labeled_train.take(1):\\n    print(\\\"Image shape: \\\", image.numpy().shape)\\n    print(\\\"Label: \\\", label.numpy())\";\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": [
    "for image, label in labeled_train.take(1):\n",
    "    print(\"Image shape: \", image.numpy().shape)\n",
    "    print(\"Label: \", label.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:48.893257Z",
     "start_time": "2020-04-17T20:20:48.886837Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 30;\n",
       "                var nbb_unformatted_code = \"dataset_train = labeled_train.batch(BATCH_SIZE).prefetch(PREFETCH)\";\n",
       "                var nbb_formatted_code = \"dataset_train = labeled_train.batch(BATCH_SIZE).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": [
    "dataset_train = labeled_train.batch(BATCH_SIZE).prefetch(PREFETCH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:49.026720Z",
     "start_time": "2020-04-17T20:20:48.894826Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 31;\n",
       "                var nbb_unformatted_code = \"# valid_path = Path(os.getcwd()) / f\\\"data/{dataset_name}/prep_data/valid/\\\"\\n# list_valid = tf.data.Dataset.list_files(str(train_path / \\\"/*/*\\\"))\\nlist_valid = tf.data.Dataset.from_tensor_slices(file_list[\\\"valid\\\"]).repeat()\\n# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.\\nlabeled_valid = list_valid.map(process_path, num_parallel_calls=AUTOTUNE)\\ndataset_valid = labeled_valid.repeat().batch(BATCH_SIZE).prefetch(PREFETCH)\";\n",
       "                var nbb_formatted_code = \"# valid_path = Path(os.getcwd()) / f\\\"data/{dataset_name}/prep_data/valid/\\\"\\n# list_valid = tf.data.Dataset.list_files(str(train_path / \\\"/*/*\\\"))\\nlist_valid = tf.data.Dataset.from_tensor_slices(file_list[\\\"valid\\\"]).repeat()\\n# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.\\nlabeled_valid = list_valid.map(process_path, num_parallel_calls=AUTOTUNE)\\ndataset_valid = labeled_valid.repeat().batch(BATCH_SIZE).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": [
    "# valid_path = Path(os.getcwd()) / f\"data/{dataset_name}/prep_data/valid/\"\n",
    "# list_valid = tf.data.Dataset.list_files(str(train_path / \"/*/*\"))\n",
    "list_valid = tf.data.Dataset.from_tensor_slices(file_list[\"valid\"]).repeat()\n",
    "# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.\n",
    "labeled_valid = list_valid.map(process_path, num_parallel_calls=AUTOTUNE)\n",
    "dataset_valid = labeled_valid.repeat().batch(BATCH_SIZE).prefetch(PREFETCH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Images Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:49.051633Z",
     "start_time": "2020-04-17T20:20:49.030018Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 32;\n",
       "                var nbb_unformatted_code = \"def show_image(image, image_size, one_channel=False):\\n    if one_channel:\\n        imshow(image.reshape(IMAGE_SIZE, IMAGE_SIZE))\\n    else:\\n        imshow(image)\";\n",
       "                var nbb_formatted_code = \"def show_image(image, image_size, one_channel=False):\\n    if one_channel:\\n        imshow(image.reshape(IMAGE_SIZE, IMAGE_SIZE))\\n    else:\\n        imshow(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 show_image(image, image_size, one_channel=False):\n",
    "    if one_channel:\n",
    "        imshow(image.reshape(IMAGE_SIZE, IMAGE_SIZE))\n",
    "    else:\n",
    "        imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:49.266763Z",
     "start_time": "2020-04-17T20:20:49.053740Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:  [ True False]\n"
     ]
    },
    {
     "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 = 33;\n",
       "                var nbb_unformatted_code = \"for image, label in labeled_train.take(1):\\n    print(\\\"Label: \\\", label.numpy())\\n    show_image(image.numpy(), IMAGE_SIZE, ONE_CHANNEL)\";\n",
       "                var nbb_formatted_code = \"for image, label in labeled_train.take(1):\\n    print(\\\"Label: \\\", label.numpy())\\n    show_image(image.numpy(), IMAGE_SIZE, ONE_CHANNEL)\";\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": [
    "for image, label in labeled_train.take(1):\n",
    "    print(\"Label: \", label.numpy())\n",
    "    show_image(image.numpy(), IMAGE_SIZE, ONE_CHANNEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:49.468628Z",
     "start_time": "2020-04-17T20:20:49.268360Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:  [ True False]\n"
     ]
    },
    {
     "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 = 34;\n",
       "                var nbb_unformatted_code = \"for image, label in labeled_valid.take(1):\\n    print(\\\"Label: \\\", label.numpy())\\n    show_image(image.numpy(), IMAGE_SIZE, ONE_CHANNEL)\";\n",
       "                var nbb_formatted_code = \"for image, label in labeled_valid.take(1):\\n    print(\\\"Label: \\\", label.numpy())\\n    show_image(image.numpy(), IMAGE_SIZE, ONE_CHANNEL)\";\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": [
    "for image, label in labeled_valid.take(1):\n",
    "    print(\"Label: \", label.numpy())\n",
    "    show_image(image.numpy(), IMAGE_SIZE, ONE_CHANNEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:49.474175Z",
     "start_time": "2020-04-17T20:20:49.470214Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 35;\n",
       "                var nbb_unformatted_code = \"# show_image(X_valid[0], IMAGE_SIZE, ONE_CHANNEL)\";\n",
       "                var nbb_formatted_code = \"# show_image(X_valid[0], IMAGE_SIZE, ONE_CHANNEL)\";\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": [
    "# show_image(X_valid[0], IMAGE_SIZE, ONE_CHANNEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:49.498479Z",
     "start_time": "2020-04-17T20:20:49.475692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 36;\n",
       "                var nbb_unformatted_code = \"# show_image(X_test[0], IMAGE_SIZE, ONE_CHANNEL)\";\n",
       "                var nbb_formatted_code = \"# show_image(X_test[0], IMAGE_SIZE, ONE_CHANNEL)\";\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": [
    "# show_image(X_test[0], IMAGE_SIZE, ONE_CHANNEL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training model (transfer learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:49.531272Z",
     "start_time": "2020-04-17T20:20:49.502320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 37;\n",
       "                var nbb_unformatted_code = \"epochs_1 = 500\\nepochs_2 = 200\\npatience = 10\\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",
       "                var nbb_formatted_code = \"epochs_1 = 500\\nepochs_2 = 200\\npatience = 10\\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",
       "                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 = 500\n",
    "epochs_2 = 200\n",
    "patience = 10\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating model, using existing one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:49.553810Z",
     "start_time": "2020-04-17T20:20:49.532956Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 38;\n",
       "                var nbb_unformatted_code = \"activation = mish\\noptimizer = Lookahead(RectifiedAdam(), sync_period=6, slow_step_size=0.5)\";\n",
       "                var nbb_formatted_code = \"activation = mish\\noptimizer = Lookahead(RectifiedAdam(), sync_period=6, slow_step_size=0.5)\";\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\n",
    "optimizer = Lookahead(RectifiedAdam(), sync_period=6, slow_step_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:20:52.332338Z",
     "start_time": "2020-04-17T20:20:49.556212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 39;\n",
       "                var nbb_unformatted_code = \"from tensorflow.keras.applications import (\\n    MobileNetV2,\\n    NASNetLarge,\\n    NASNetMobile,\\n    InceptionV3,\\n)\\n\\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\\nnb_channel = 1 if ONE_CHANNEL else 3\\n# create the base pre-trained model\\n# tf.keras.applications.NASNetMobile\\n# tf.keras.applications.NASNetLarge\\n# base_model = NASNetLarge( # IMAGE_SIZE must be 224\\n#    input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\\n#    weights=\\\"imagenet\\\",\\n#    include_top=False,\\n# )\\n\\n# base_model = NASNetLarge( # IMAGE_SIZE must be 331\\n#    input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\\n#    weights=\\\"imagenet\\\",\\n#    include_top=False,\\n# )\\nbase_model = MobileNetV2(\\n    input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\\n    weights=\\\"imagenet\\\",\\n    include_top=False,\\n)\\n# base_model = InceptionV3(\\n#     input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\\n#     weights=\\\"imagenet\\\",\\n#     include_top=False,\\n# )\\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(0.3)(x)\\n# x = Dense(128, activation=activation, kernel_initializer=\\\"he_normal\\\")(x)\\n# x = Dropout(0.2)(x)\\n\\n# and a logistic layer -- let's say we have 200 classes\\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\\n# es.set_model(model)\\n# compile the model (should be done *after* setting layers to non-trainable)\\nes = EarlyStopping(\\n    monitor=\\\"val_loss\\\",\\n    verbose=1,\\n    mode=\\\"min\\\",\\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 = \"from tensorflow.keras.applications import (\\n    MobileNetV2,\\n    NASNetLarge,\\n    NASNetMobile,\\n    InceptionV3,\\n)\\n\\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\\nnb_channel = 1 if ONE_CHANNEL else 3\\n# create the base pre-trained model\\n# tf.keras.applications.NASNetMobile\\n# tf.keras.applications.NASNetLarge\\n# base_model = NASNetLarge( # IMAGE_SIZE must be 224\\n#    input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\\n#    weights=\\\"imagenet\\\",\\n#    include_top=False,\\n# )\\n\\n# base_model = NASNetLarge( # IMAGE_SIZE must be 331\\n#    input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\\n#    weights=\\\"imagenet\\\",\\n#    include_top=False,\\n# )\\nbase_model = MobileNetV2(\\n    input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\\n    weights=\\\"imagenet\\\",\\n    include_top=False,\\n)\\n# base_model = InceptionV3(\\n#     input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\\n#     weights=\\\"imagenet\\\",\\n#     include_top=False,\\n# )\\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(0.3)(x)\\n# x = Dense(128, activation=activation, kernel_initializer=\\\"he_normal\\\")(x)\\n# x = Dropout(0.2)(x)\\n\\n# and a logistic layer -- let's say we have 200 classes\\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\\n# es.set_model(model)\\n# compile the model (should be done *after* setting layers to non-trainable)\\nes = EarlyStopping(\\n    monitor=\\\"val_loss\\\",\\n    verbose=1,\\n    mode=\\\"min\\\",\\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": [
    "from tensorflow.keras.applications import (\n",
    "    MobileNetV2,\n",
    "    NASNetLarge,\n",
    "    NASNetMobile,\n",
    "    InceptionV3,\n",
    ")\n",
    "\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",
    "nb_channel = 1 if ONE_CHANNEL else 3\n",
    "# create the base pre-trained model\n",
    "# tf.keras.applications.NASNetMobile\n",
    "# tf.keras.applications.NASNetLarge\n",
    "# base_model = NASNetLarge( # IMAGE_SIZE must be 224\n",
    "#    input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\n",
    "#    weights=\"imagenet\",\n",
    "#    include_top=False,\n",
    "# )\n",
    "\n",
    "# base_model = NASNetLarge( # IMAGE_SIZE must be 331\n",
    "#    input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\n",
    "#    weights=\"imagenet\",\n",
    "#    include_top=False,\n",
    "# )\n",
    "base_model = MobileNetV2(\n",
    "    input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\n",
    "    weights=\"imagenet\",\n",
    "    include_top=False,\n",
    ")\n",
    "# base_model = InceptionV3(\n",
    "#     input_shape=(IMAGE_SIZE, IMAGE_SIZE, nb_channel),\n",
    "#     weights=\"imagenet\",\n",
    "#     include_top=False,\n",
    "# )\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(0.3)(x)\n",
    "# x = Dense(128, activation=activation, kernel_initializer=\"he_normal\")(x)\n",
    "# x = Dropout(0.2)(x)\n",
    "\n",
    "# and a logistic layer -- let's say we have 200 classes\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",
    "es = EarlyStopping(\n",
    "    monitor=\"val_loss\",\n",
    "    verbose=1,\n",
    "    mode=\"min\",\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": "markdown",
   "metadata": {},
   "source": [
    "### Fit new layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:30:00.925319Z",
     "start_time": "2020-04-17T20:20:52.333831Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 515.0 steps, validate for 65.0 steps\n",
      "Epoch 1/500\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",
      "515/515 [==============================] - 55s 106ms/step - loss: 0.3387 - AUC: 0.9267 - val_loss: 0.3678 - val_AUC: 0.8839\n",
      "Epoch 2/500\n",
      "515/515 [==============================] - 49s 96ms/step - loss: 0.2932 - AUC: 0.9422 - val_loss: 0.3768 - val_AUC: 0.8943\n",
      "Epoch 3/500\n",
      "515/515 [==============================] - 49s 96ms/step - loss: 0.2835 - AUC: 0.9452 - val_loss: 0.5512 - val_AUC: 0.8903\n",
      "Epoch 4/500\n",
      "515/515 [==============================] - 49s 96ms/step - loss: 0.2787 - AUC: 0.9474 - val_loss: 0.6426 - val_AUC: 0.7341\n",
      "Epoch 5/500\n",
      "515/515 [==============================] - 49s 96ms/step - loss: 0.2751 - AUC: 0.9487 - val_loss: 0.5652 - val_AUC: 0.8856\n",
      "Epoch 6/500\n",
      "515/515 [==============================] - 49s 96ms/step - loss: 0.2691 - AUC: 0.9507 - val_loss: 0.4959 - val_AUC: 0.8939\n",
      "Epoch 7/500\n",
      "515/515 [==============================] - 49s 96ms/step - loss: 0.2640 - AUC: 0.9526 - val_loss: 0.6021 - val_AUC: 0.8365\n",
      "Epoch 8/500\n",
      "515/515 [==============================] - 49s 96ms/step - loss: 0.2580 - AUC: 0.9548 - val_loss: 0.8392 - val_AUC: 0.1876\n",
      "Epoch 9/500\n",
      "515/515 [==============================] - 49s 96ms/step - loss: 0.2511 - AUC: 0.9571 - val_loss: 0.9972 - val_AUC: 0.1191\n",
      "Epoch 10/500\n",
      "515/515 [==============================] - 49s 96ms/step - loss: 0.2441 - AUC: 0.9600 - val_loss: 0.8942 - val_AUC: 0.1568\n",
      "Epoch 11/500\n",
      "514/515 [============================>.] - ETA: 0s - loss: 0.2384 - AUC: 0.9616Restoring model weights from the end of the best epoch.\n",
      "515/515 [==============================] - 49s 96ms/step - loss: 0.2385 - AUC: 0.9616 - val_loss: 0.7818 - val_AUC: 0.3318\n",
      "Epoch 00011: early stopping\n",
      "CPU times: user 12min 11s, sys: 55.5 s, total: 13min 7s\n",
      "Wall time: 9min 8s\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_train, \\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_train, \\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_train, \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": "markdown",
   "metadata": {},
   "source": [
    "### Unfreeze and fit more/all layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T20:30:01.026915Z",
     "start_time": "2020-04-17T20:30:00.927178Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_pad (ZeroPadding2D)       (None, 225, 225, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 112, 112, 32) 864         Conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_Conv1 (BatchNormalization)   (None, 112, 112, 32) 128         Conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_relu (ReLU)               (None, 112, 112, 32) 0           bn_Conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288         Conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128         expanded_conv_depthwise[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0           expanded_conv_depthwise_BN[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project (Conv2D)  (None, 112, 112, 16) 512         expanded_conv_depthwise_relu[0][0\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project_BN (Batch (None, 112, 112, 16) 64          expanded_conv_project[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, 112, 112, 96) 1536        expanded_conv_project_BN[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384         block_1_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_relu (ReLU)      (None, 112, 112, 96) 0           block_1_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_pad (ZeroPadding2D)     (None, 113, 113, 96) 0           block_1_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, 56, 56, 96)   864         block_1_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_BN (BatchNorm (None, 56, 56, 96)   384         block_1_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_relu (ReLU)   (None, 56, 56, 96)   0           block_1_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, 56, 56, 24)   2304        block_1_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project_BN (BatchNormal (None, 56, 56, 24)   96          block_1_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, 56, 56, 144)  3456        block_1_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_2_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_2_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_2_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_2_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_2_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, 56, 56, 24)   3456        block_2_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project_BN (BatchNormal (None, 56, 56, 24)   96          block_2_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, 56, 56, 24)   0           block_1_project_BN[0][0]         \n",
      "                                                                 block_2_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, 56, 56, 144)  3456        block_2_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_3_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_3_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         \n",
      "                                                                 block_4_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                \n",
      "                                                                 block_5_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         \n",
      "                                                                 block_7_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                \n",
      "                                                                 block_8_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                \n",
      "                                                                 block_9_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        \n",
      "                                                                 block_11_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               \n",
      "                                                                 block_12_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        \n",
      "                                                                 block_14_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               \n",
      "                                                                 block_15_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 1280)         0           out_relu[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 1024)         1311744     global_average_pooling2d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 1024)         0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 2)            2050        dropout[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 3,571,778\n",
      "Trainable params: 1,313,794\n",
      "Non-trainable params: 2,257,984\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
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   "execution_count": 42,
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     "end_time": "2020-04-17T20:30:01.046963Z",
     "start_time": "2020-04-17T20:30:01.028783Z"
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     "name": "stdout",
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     "text": [
      "0 input_1\n",
      "1 Conv1_pad\n",
      "2 Conv1\n",
      "3 bn_Conv1\n",
      "4 Conv1_relu\n",
      "5 expanded_conv_depthwise\n",
      "6 expanded_conv_depthwise_BN\n",
      "7 expanded_conv_depthwise_relu\n",
      "8 expanded_conv_project\n",
      "9 expanded_conv_project_BN\n",
      "10 block_1_expand\n",
      "11 block_1_expand_BN\n",
      "12 block_1_expand_relu\n",
      "13 block_1_pad\n",
      "14 block_1_depthwise\n",
      "15 block_1_depthwise_BN\n",
      "16 block_1_depthwise_relu\n",
      "17 block_1_project\n",
      "18 block_1_project_BN\n",
      "19 block_2_expand\n",
      "20 block_2_expand_BN\n",
      "21 block_2_expand_relu\n",
      "22 block_2_depthwise\n",
      "23 block_2_depthwise_BN\n",
      "24 block_2_depthwise_relu\n",
      "25 block_2_project\n",
      "26 block_2_project_BN\n",
      "27 block_2_add\n",
      "28 block_3_expand\n",
      "29 block_3_expand_BN\n",
      "30 block_3_expand_relu\n",
      "31 block_3_pad\n",
      "32 block_3_depthwise\n",
      "33 block_3_depthwise_BN\n",
      "34 block_3_depthwise_relu\n",
      "35 block_3_project\n",
      "36 block_3_project_BN\n",
      "37 block_4_expand\n",
      "38 block_4_expand_BN\n",
      "39 block_4_expand_relu\n",
      "40 block_4_depthwise\n",
      "41 block_4_depthwise_BN\n",
      "42 block_4_depthwise_relu\n",
      "43 block_4_project\n",
      "44 block_4_project_BN\n",
      "45 block_4_add\n",
      "46 block_5_expand\n",
      "47 block_5_expand_BN\n",
      "48 block_5_expand_relu\n",
      "49 block_5_depthwise\n",
      "50 block_5_depthwise_BN\n",
      "51 block_5_depthwise_relu\n",
      "52 block_5_project\n",
      "53 block_5_project_BN\n",
      "54 block_5_add\n",
      "55 block_6_expand\n",
      "56 block_6_expand_BN\n",
      "57 block_6_expand_relu\n",
      "58 block_6_pad\n",
      "59 block_6_depthwise\n",
      "60 block_6_depthwise_BN\n",
      "61 block_6_depthwise_relu\n",
      "62 block_6_project\n",
      "63 block_6_project_BN\n",
      "64 block_7_expand\n",
      "65 block_7_expand_BN\n",
      "66 block_7_expand_relu\n",
      "67 block_7_depthwise\n",
      "68 block_7_depthwise_BN\n",
      "69 block_7_depthwise_relu\n",
      "70 block_7_project\n",
      "71 block_7_project_BN\n",
      "72 block_7_add\n",
      "73 block_8_expand\n",
      "74 block_8_expand_BN\n",
      "75 block_8_expand_relu\n",
      "76 block_8_depthwise\n",
      "77 block_8_depthwise_BN\n",
      "78 block_8_depthwise_relu\n",
      "79 block_8_project\n",
      "80 block_8_project_BN\n",
      "81 block_8_add\n",
      "82 block_9_expand\n",
      "83 block_9_expand_BN\n",
      "84 block_9_expand_relu\n",
      "85 block_9_depthwise\n",
      "86 block_9_depthwise_BN\n",
      "87 block_9_depthwise_relu\n",
      "88 block_9_project\n",
      "89 block_9_project_BN\n",
      "90 block_9_add\n",
      "91 block_10_expand\n",
      "92 block_10_expand_BN\n",
      "93 block_10_expand_relu\n",
      "94 block_10_depthwise\n",
      "95 block_10_depthwise_BN\n",
      "96 block_10_depthwise_relu\n",
      "97 block_10_project\n",
      "98 block_10_project_BN\n",
      "99 block_11_expand\n",
      "100 block_11_expand_BN\n",
      "101 block_11_expand_relu\n",
      "102 block_11_depthwise\n",
      "103 block_11_depthwise_BN\n",
      "104 block_11_depthwise_relu\n",
      "105 block_11_project\n",
      "106 block_11_project_BN\n",
      "107 block_11_add\n",
      "108 block_12_expand\n",
      "109 block_12_expand_BN\n",
      "110 block_12_expand_relu\n",
      "111 block_12_depthwise\n",
      "112 block_12_depthwise_BN\n",
      "113 block_12_depthwise_relu\n",
      "114 block_12_project\n",
      "115 block_12_project_BN\n",
      "116 block_12_add\n",
      "117 block_13_expand\n",
      "118 block_13_expand_BN\n",
      "119 block_13_expand_relu\n",
      "120 block_13_pad\n",
      "121 block_13_depthwise\n",
      "122 block_13_depthwise_BN\n",
      "123 block_13_depthwise_relu\n",
      "124 block_13_project\n",
      "125 block_13_project_BN\n",
      "126 block_14_expand\n",
      "127 block_14_expand_BN\n",
      "128 block_14_expand_relu\n",
      "129 block_14_depthwise\n",
      "130 block_14_depthwise_BN\n",
      "131 block_14_depthwise_relu\n",
      "132 block_14_project\n",
      "133 block_14_project_BN\n",
      "134 block_14_add\n",
      "135 block_15_expand\n",
      "136 block_15_expand_BN\n",
      "137 block_15_expand_relu\n",
      "138 block_15_depthwise\n",
      "139 block_15_depthwise_BN\n",
      "140 block_15_depthwise_relu\n",
      "141 block_15_project\n",
      "142 block_15_project_BN\n",
      "143 block_15_add\n",
      "144 block_16_expand\n",
      "145 block_16_expand_BN\n",
      "146 block_16_expand_relu\n",
      "147 block_16_depthwise\n",
      "148 block_16_depthwise_BN\n",
      "149 block_16_depthwise_relu\n",
      "150 block_16_project\n",
      "151 block_16_project_BN\n",
      "152 Conv_1\n",
      "153 Conv_1_bn\n",
      "154 out_relu\n",
      "155 global_average_pooling2d\n",
      "156 dense\n",
      "157 dropout\n",
      "158 dense_1\n"
     ]
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    "# let's visualize layer names and layer indices to see how many layers\n",
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    "for i, layer in enumerate(model.layers):\n",
    "    print(i, layer.name)"
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       "            setTimeout(function() {\n",
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       "                var nbb_unformatted_code = \"# raise Exception(\\\"STOP\\\")\";\n",
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       "                var nbb_unformatted_code = \"# Let's unfreeze the whole model\\nfor layer in model.layers:  # [1019:]:  # [293:]:\\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_loss\\\",\\n    verbose=1,\\n    mode=\\\"min\\\",\\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",
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    "# Let's unfreeze the whole model\n",
    "for layer in model.layers:  # [1019:]:  # [293:]:\n",
    "    layer.trainable = True\n",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 515.0 steps, validate for 65.0 steps\n",
      "Epoch 1/200\n",
      "515/515 [==============================] - 236s 458ms/step - loss: 0.2980 - AUC: 0.9405 - val_loss: 0.3602 - val_AUC: 0.9089\n",
      "Epoch 2/200\n",
      "515/515 [==============================] - 204s 395ms/step - loss: 0.2851 - AUC: 0.9430 - val_loss: 0.3686 - val_AUC: 0.8503\n",
      "Epoch 3/200\n",
      "515/515 [==============================] - 203s 395ms/step - loss: 0.2806 - AUC: 0.9444 - val_loss: 0.3670 - val_AUC: 0.8644\n",
      "Epoch 4/200\n",
      "515/515 [==============================] - 203s 395ms/step - loss: 0.2778 - AUC: 0.9451 - val_loss: 0.3299 - val_AUC: 0.9270\n",
      "Epoch 5/200\n",
      "515/515 [==============================] - 203s 395ms/step - loss: 0.2768 - AUC: 0.9457 - val_loss: 0.3068 - val_AUC: 0.9394\n",
      "Epoch 6/200\n",
      "515/515 [==============================] - 202s 393ms/step - loss: 0.2780 - AUC: 0.9458 - val_loss: 0.2917 - val_AUC: 0.9405\n",
      "Epoch 7/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2762 - AUC: 0.9461 - val_loss: 0.3127 - val_AUC: 0.9372\n",
      "Epoch 8/200\n",
      "515/515 [==============================] - 202s 393ms/step - loss: 0.2742 - AUC: 0.9465 - val_loss: 0.2920 - val_AUC: 0.9401\n",
      "Epoch 9/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2741 - AUC: 0.9469 - val_loss: 0.3056 - val_AUC: 0.9374\n",
      "Epoch 10/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2739 - AUC: 0.9469 - val_loss: 0.2901 - val_AUC: 0.9396\n",
      "Epoch 11/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2729 - AUC: 0.9475 - val_loss: 0.2956 - val_AUC: 0.9361\n",
      "Epoch 12/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2738 - AUC: 0.9469 - val_loss: 0.2866 - val_AUC: 0.9399\n",
      "Epoch 13/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2730 - AUC: 0.9477 - val_loss: 0.3210 - val_AUC: 0.9340\n",
      "Epoch 14/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2713 - AUC: 0.9482 - val_loss: 0.3149 - val_AUC: 0.9355\n",
      "Epoch 15/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2716 - AUC: 0.9482 - val_loss: 0.3160 - val_AUC: 0.9330\n",
      "Epoch 16/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2705 - AUC: 0.9489 - val_loss: 0.3336 - val_AUC: 0.9351\n",
      "Epoch 17/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2686 - AUC: 0.9492 - val_loss: 0.2892 - val_AUC: 0.9407\n",
      "Epoch 18/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2679 - AUC: 0.9498 - val_loss: 0.3290 - val_AUC: 0.9376\n",
      "Epoch 19/200\n",
      "515/515 [==============================] - 202s 393ms/step - loss: 0.2667 - AUC: 0.9503 - val_loss: 0.2872 - val_AUC: 0.9426\n",
      "Epoch 20/200\n",
      "515/515 [==============================] - 202s 393ms/step - loss: 0.2651 - AUC: 0.9510 - val_loss: 0.2998 - val_AUC: 0.9359\n",
      "Epoch 21/200\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2638 - AUC: 0.9513 - val_loss: 0.3090 - val_AUC: 0.9362\n",
      "Epoch 22/200\n",
      "514/515 [============================>.] - ETA: 0s - loss: 0.2623 - AUC: 0.9524Restoring model weights from the end of the best epoch.\n",
      "515/515 [==============================] - 202s 392ms/step - loss: 0.2623 - AUC: 0.9524 - val_loss: 0.4027 - val_AUC: 0.8975\n",
      "Epoch 00022: early stopping\n"
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       "                var nbb_unformatted_code = \"# 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_train,\\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",
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    "# 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_train,\n",
    "    callbacks=[es],\n",
    "    epochs=epochs_2,\n",
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    {
     "data": {
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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 = 46;\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": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T21:44:45.492638Z",
     "start_time": "2020-04-17T21:44:45.326800Z"
    },
    "scrolled": true
   },
   "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, \\\"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": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T13:16:20.349217Z",
     "start_time": "2020-04-17T13:16:16.612Z"
    }
   },
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T19:35:42.421034Z",
     "start_time": "2020-04-17T19:35:42.307583Z"
    }
   },
   "source": [
    "preds_valid = model.predict(X_valid)\n",
    "print(roc_auc_score(Y_valid, preds_valid[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T21:44:55.225886Z",
     "start_time": "2020-04-17T21:44:45.494512Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape is (4119, 19)\n",
      "Output data is estimated to 591.3017578125 Mo in RAM\n",
      "Output shape is (4119, 224, 224, 3)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 48;\n",
       "                var nbb_unformatted_code = \"X_test = preprocess_data(\\n    X[test_indices], fixed_text_to_square_image, IMAGE_SIZE, ONE_CHANNEL\\n)\\nY_test = Y[test_indices]\";\n",
       "                var nbb_formatted_code = \"X_test = preprocess_data(\\n    X[test_indices], fixed_text_to_square_image, IMAGE_SIZE, ONE_CHANNEL\\n)\\nY_test = Y[test_indices]\";\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": [
    "X_test = preprocess_data(\n",
    "    X[test_indices], fixed_text_to_square_image, IMAGE_SIZE, ONE_CHANNEL\n",
    ")\n",
    "Y_test = Y[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T21:45:01.005008Z",
     "start_time": "2020-04-17T21:44:55.228101Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7809683192952084\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 49;\n",
       "                var nbb_unformatted_code = \"preds_test = model.predict(X_test)\\nprint(roc_auc_score(Y_test, preds_test[:, 1]))\";\n",
       "                var nbb_formatted_code = \"preds_test = model.predict(X_test)\\nprint(roc_auc_score(Y_test, preds_test[:, 1]))\";\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_test = model.predict(X_test)\n",
    "print(roc_auc_score(Y_test, preds_test[:, 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Explainability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T21:45:01.011654Z",
     "start_time": "2020-04-17T21:45:01.006981Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 50;\n",
       "                var nbb_unformatted_code = \"# https://medium.com/google-developer-experts/interpreting-deep-learning-models-for-computer-vision-f95683e23c1d\";\n",
       "                var nbb_formatted_code = \"# https://medium.com/google-developer-experts/interpreting-deep-learning-models-for-computer-vision-f95683e23c1d\";\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",
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       "<IPython.core.display.Javascript object>"
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     "metadata": {},
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    }
   ],
   "source": [
    "# https://medium.com/google-developer-experts/interpreting-deep-learning-models-for-computer-vision-f95683e23c1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T21:45:01.107925Z",
     "start_time": "2020-04-17T21:45:01.013792Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 input_1\n",
      "1 Conv1_pad\n",
      "2 Conv1\n",
      "3 bn_Conv1\n",
      "4 Conv1_relu\n",
      "5 expanded_conv_depthwise\n",
      "6 expanded_conv_depthwise_BN\n",
      "7 expanded_conv_depthwise_relu\n",
      "8 expanded_conv_project\n",
      "9 expanded_conv_project_BN\n",
      "10 block_1_expand\n",
      "11 block_1_expand_BN\n",
      "12 block_1_expand_relu\n",
      "13 block_1_pad\n",
      "14 block_1_depthwise\n",
      "15 block_1_depthwise_BN\n",
      "16 block_1_depthwise_relu\n",
      "17 block_1_project\n",
      "18 block_1_project_BN\n",
      "19 block_2_expand\n",
      "20 block_2_expand_BN\n",
      "21 block_2_expand_relu\n",
      "22 block_2_depthwise\n",
      "23 block_2_depthwise_BN\n",
      "24 block_2_depthwise_relu\n",
      "25 block_2_project\n",
      "26 block_2_project_BN\n",
      "27 block_2_add\n",
      "28 block_3_expand\n",
      "29 block_3_expand_BN\n",
      "30 block_3_expand_relu\n",
      "31 block_3_pad\n",
      "32 block_3_depthwise\n",
      "33 block_3_depthwise_BN\n",
      "34 block_3_depthwise_relu\n",
      "35 block_3_project\n",
      "36 block_3_project_BN\n",
      "37 block_4_expand\n",
      "38 block_4_expand_BN\n",
      "39 block_4_expand_relu\n",
      "40 block_4_depthwise\n",
      "41 block_4_depthwise_BN\n",
      "42 block_4_depthwise_relu\n",
      "43 block_4_project\n",
      "44 block_4_project_BN\n",
      "45 block_4_add\n",
      "46 block_5_expand\n",
      "47 block_5_expand_BN\n",
      "48 block_5_expand_relu\n",
      "49 block_5_depthwise\n",
      "50 block_5_depthwise_BN\n",
      "51 block_5_depthwise_relu\n",
      "52 block_5_project\n",
      "53 block_5_project_BN\n",
      "54 block_5_add\n",
      "55 block_6_expand\n",
      "56 block_6_expand_BN\n",
      "57 block_6_expand_relu\n",
      "58 block_6_pad\n",
      "59 block_6_depthwise\n",
      "60 block_6_depthwise_BN\n",
      "61 block_6_depthwise_relu\n",
      "62 block_6_project\n",
      "63 block_6_project_BN\n",
      "64 block_7_expand\n",
      "65 block_7_expand_BN\n",
      "66 block_7_expand_relu\n",
      "67 block_7_depthwise\n",
      "68 block_7_depthwise_BN\n",
      "69 block_7_depthwise_relu\n",
      "70 block_7_project\n",
      "71 block_7_project_BN\n",
      "72 block_7_add\n",
      "73 block_8_expand\n",
      "74 block_8_expand_BN\n",
      "75 block_8_expand_relu\n",
      "76 block_8_depthwise\n",
      "77 block_8_depthwise_BN\n",
      "78 block_8_depthwise_relu\n",
      "79 block_8_project\n",
      "80 block_8_project_BN\n",
      "81 block_8_add\n",
      "82 block_9_expand\n",
      "83 block_9_expand_BN\n",
      "84 block_9_expand_relu\n",
      "85 block_9_depthwise\n",
      "86 block_9_depthwise_BN\n",
      "87 block_9_depthwise_relu\n",
      "88 block_9_project\n",
      "89 block_9_project_BN\n",
      "90 block_9_add\n",
      "91 block_10_expand\n",
      "92 block_10_expand_BN\n",
      "93 block_10_expand_relu\n",
      "94 block_10_depthwise\n",
      "95 block_10_depthwise_BN\n",
      "96 block_10_depthwise_relu\n",
      "97 block_10_project\n",
      "98 block_10_project_BN\n",
      "99 block_11_expand\n",
      "100 block_11_expand_BN\n",
      "101 block_11_expand_relu\n",
      "102 block_11_depthwise\n",
      "103 block_11_depthwise_BN\n",
      "104 block_11_depthwise_relu\n",
      "105 block_11_project\n",
      "106 block_11_project_BN\n",
      "107 block_11_add\n",
      "108 block_12_expand\n",
      "109 block_12_expand_BN\n",
      "110 block_12_expand_relu\n",
      "111 block_12_depthwise\n",
      "112 block_12_depthwise_BN\n",
      "113 block_12_depthwise_relu\n",
      "114 block_12_project\n",
      "115 block_12_project_BN\n",
      "116 block_12_add\n",
      "117 block_13_expand\n",
      "118 block_13_expand_BN\n",
      "119 block_13_expand_relu\n",
      "120 block_13_pad\n",
      "121 block_13_depthwise\n",
      "122 block_13_depthwise_BN\n",
      "123 block_13_depthwise_relu\n",
      "124 block_13_project\n",
      "125 block_13_project_BN\n",
      "126 block_14_expand\n",
      "127 block_14_expand_BN\n",
      "128 block_14_expand_relu\n",
      "129 block_14_depthwise\n",
      "130 block_14_depthwise_BN\n",
      "131 block_14_depthwise_relu\n",
      "132 block_14_project\n",
      "133 block_14_project_BN\n",
      "134 block_14_add\n",
      "135 block_15_expand\n",
      "136 block_15_expand_BN\n",
      "137 block_15_expand_relu\n",
      "138 block_15_depthwise\n",
      "139 block_15_depthwise_BN\n",
      "140 block_15_depthwise_relu\n",
      "141 block_15_project\n",
      "142 block_15_project_BN\n",
      "143 block_15_add\n",
      "144 block_16_expand\n",
      "145 block_16_expand_BN\n",
      "146 block_16_expand_relu\n",
      "147 block_16_depthwise\n",
      "148 block_16_depthwise_BN\n",
      "149 block_16_depthwise_relu\n",
      "150 block_16_project\n",
      "151 block_16_project_BN\n",
      "152 Conv_1\n",
      "153 Conv_1_bn\n",
      "154 out_relu\n",
      "155 global_average_pooling2d\n",
      "156 dense\n",
      "157 dropout\n",
      "158 dense_1\n"
     ]
    },
    {
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    "for i, layer in enumerate(model.layers):\n",
    "    print(i, layer.name)"
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  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T21:45:02.771844Z",
     "start_time": "2020-04-17T21:45:01.112219Z"
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    "hidden": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: shap in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (0.35.0)\n",
      "Requirement already satisfied: tqdm>4.25.0 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from shap) (4.45.0)\n",
      "Requirement already satisfied: scikit-learn in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from shap) (0.22.2.post1)\n",
      "Requirement already satisfied: numpy in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from shap) (1.18.2)\n",
      "Requirement already satisfied: scipy in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from shap) (1.4.1)\n",
      "Requirement already satisfied: pandas in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from shap) (1.0.3)\n",
      "Requirement already satisfied: joblib>=0.11 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from scikit-learn->shap) (0.14.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from pandas->shap) (2019.3)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from pandas->shap) (2.8.1)\n",
      "Requirement already satisfied: six>=1.5 in ./.cache/poetry/thc-net-KQLMmzPP-py3.7/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas->shap) (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"
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    },
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   "metadata": {
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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       "                var nbb_unformatted_code = \"# import keras\\n# from keras.applications.vgg16 import VGG16\\n# from keras.applications.vgg16 import preprocess_input, decode_predictions\\n# from matplotlib.colors import LinearSegmentedColormap\\n# import numpy as np\\nimport shap\\n\\n# import keras.backend as K\\n# import json\\n\\nshap.initjs()\\n\\n\\n# utility function to visualize SHAP values in larger image formats\\n# this modifies the `shap.image_plot(...)` function\\ndef visualize_model_decisions(shap_values, x, labels=None, figsize=(20, 30)):\\n\\n    colors = []\\n    for l in np.linspace(1, 0, 100):\\n        colors.append((30.0 / 255, 136.0 / 255, 229.0 / 255, l))\\n    for l in np.linspace(0, 1, 100):\\n        colors.append((255.0 / 255, 13.0 / 255, 87.0 / 255, l))\\n    red_transparent_blue = LinearSegmentedColormap.from_list(\\n        \\\"red_transparent_blue\\\", colors\\n    )\\n\\n    multi_output = True\\n    if type(shap_values) != list:\\n        multi_output = False\\n        shap_values = [shap_values]\\n\\n    # make sure labels\\n    if labels is not None:\\n        assert (\\n            labels.shape[0] == shap_values[0].shape[0]\\n        ), \\\"Labels must have same row count as shap_values arrays!\\\"\\n        if multi_output:\\n            assert labels.shape[1] == len(\\n                shap_values\\n            ), \\\"Labels must have a column for each output in shap_values!\\\"\\n        else:\\n            assert (\\n                len(labels.shape) == 1\\n            ), \\\"Labels must be a vector for single output shap_values.\\\"\\n\\n    # plot our explanations\\n    fig_size = figsize\\n    fig, axes = plt.subplots(\\n        nrows=x.shape[0], ncols=len(shap_values) + 1, figsize=fig_size\\n    )\\n    if len(axes.shape) == 1:\\n        axes = axes.reshape(1, axes.size)\\n    for row in range(x.shape[0]):\\n        x_curr = x[row].copy()\\n\\n        # make sure\\n        if len(x_curr.shape) == 3 and x_curr.shape[2] == 1:\\n            x_curr = x_curr.reshape(x_curr.shape[:2])\\n        if x_curr.max() > 1:\\n            x_curr /= 255.0\\n\\n        axes[row, 0].imshow(x_curr)\\n        axes[row, 0].axis(\\\"off\\\")\\n\\n        # get a grayscale version of the image\\n        if len(x_curr.shape) == 3 and x_curr.shape[2] == 3:\\n            x_curr_gray = (\\n                0.2989 * x_curr[:, :, 0]\\n                + 0.5870 * x_curr[:, :, 1]\\n                + 0.1140 * x_curr[:, :, 2]\\n            )  # rgb to gray\\n        else:\\n            x_curr_gray = x_curr\\n\\n        if len(shap_values[0][row].shape) == 2:\\n            abs_vals = np.stack(\\n                [np.abs(shap_values[i]) for i in range(len(shap_values))], 0\\n            ).flatten()\\n        else:\\n            abs_vals = np.stack(\\n                [np.abs(shap_values[i].sum(-1)) for i in range(len(shap_values))], 0\\n            ).flatten()\\n        max_val = np.nanpercentile(abs_vals, 99.9)\\n        for i in range(len(shap_values)):\\n            if labels is not None:\\n                axes[row, i + 1].set_title(labels[row, i])\\n            sv = (\\n                shap_values[i][row]\\n                if len(shap_values[i][row].shape) == 2\\n                else shap_values[i][row].sum(-1)\\n            )\\n            axes[row, i + 1].imshow(\\n                x_curr_gray,\\n                cmap=plt.get_cmap(\\\"gray\\\"),\\n                alpha=0.15,\\n                extent=(-1, sv.shape[0], sv.shape[1], -1),\\n            )\\n            im = axes[row, i + 1].imshow(\\n                sv, cmap=red_transparent_blue, vmin=-max_val, vmax=max_val\\n            )\\n            axes[row, i + 1].axis(\\\"off\\\")\\n\\n    cb = fig.colorbar(\\n        im,\\n        ax=np.ravel(axes).tolist(),\\n        label=\\\"SHAP value\\\",\\n        orientation=\\\"horizontal\\\",\\n        aspect=fig_size[0] / 0.2,\\n    )\\n    cb.outline.set_visible(False)\";\n",
       "                var nbb_formatted_code = \"# import keras\\n# from keras.applications.vgg16 import VGG16\\n# from keras.applications.vgg16 import preprocess_input, decode_predictions\\n# from matplotlib.colors import LinearSegmentedColormap\\n# import numpy as np\\nimport shap\\n\\n# import keras.backend as K\\n# import json\\n\\nshap.initjs()\\n\\n\\n# utility function to visualize SHAP values in larger image formats\\n# this modifies the `shap.image_plot(...)` function\\ndef visualize_model_decisions(shap_values, x, labels=None, figsize=(20, 30)):\\n\\n    colors = []\\n    for l in np.linspace(1, 0, 100):\\n        colors.append((30.0 / 255, 136.0 / 255, 229.0 / 255, l))\\n    for l in np.linspace(0, 1, 100):\\n        colors.append((255.0 / 255, 13.0 / 255, 87.0 / 255, l))\\n    red_transparent_blue = LinearSegmentedColormap.from_list(\\n        \\\"red_transparent_blue\\\", colors\\n    )\\n\\n    multi_output = True\\n    if type(shap_values) != list:\\n        multi_output = False\\n        shap_values = [shap_values]\\n\\n    # make sure labels\\n    if labels is not None:\\n        assert (\\n            labels.shape[0] == shap_values[0].shape[0]\\n        ), \\\"Labels must have same row count as shap_values arrays!\\\"\\n        if multi_output:\\n            assert labels.shape[1] == len(\\n                shap_values\\n            ), \\\"Labels must have a column for each output in shap_values!\\\"\\n        else:\\n            assert (\\n                len(labels.shape) == 1\\n            ), \\\"Labels must be a vector for single output shap_values.\\\"\\n\\n    # plot our explanations\\n    fig_size = figsize\\n    fig, axes = plt.subplots(\\n        nrows=x.shape[0], ncols=len(shap_values) + 1, figsize=fig_size\\n    )\\n    if len(axes.shape) == 1:\\n        axes = axes.reshape(1, axes.size)\\n    for row in range(x.shape[0]):\\n        x_curr = x[row].copy()\\n\\n        # make sure\\n        if len(x_curr.shape) == 3 and x_curr.shape[2] == 1:\\n            x_curr = x_curr.reshape(x_curr.shape[:2])\\n        if x_curr.max() > 1:\\n            x_curr /= 255.0\\n\\n        axes[row, 0].imshow(x_curr)\\n        axes[row, 0].axis(\\\"off\\\")\\n\\n        # get a grayscale version of the image\\n        if len(x_curr.shape) == 3 and x_curr.shape[2] == 3:\\n            x_curr_gray = (\\n                0.2989 * x_curr[:, :, 0]\\n                + 0.5870 * x_curr[:, :, 1]\\n                + 0.1140 * x_curr[:, :, 2]\\n            )  # rgb to gray\\n        else:\\n            x_curr_gray = x_curr\\n\\n        if len(shap_values[0][row].shape) == 2:\\n            abs_vals = np.stack(\\n                [np.abs(shap_values[i]) for i in range(len(shap_values))], 0\\n            ).flatten()\\n        else:\\n            abs_vals = np.stack(\\n                [np.abs(shap_values[i].sum(-1)) for i in range(len(shap_values))], 0\\n            ).flatten()\\n        max_val = np.nanpercentile(abs_vals, 99.9)\\n        for i in range(len(shap_values)):\\n            if labels is not None:\\n                axes[row, i + 1].set_title(labels[row, i])\\n            sv = (\\n                shap_values[i][row]\\n                if len(shap_values[i][row].shape) == 2\\n                else shap_values[i][row].sum(-1)\\n            )\\n            axes[row, i + 1].imshow(\\n                x_curr_gray,\\n                cmap=plt.get_cmap(\\\"gray\\\"),\\n                alpha=0.15,\\n                extent=(-1, sv.shape[0], sv.shape[1], -1),\\n            )\\n            im = axes[row, i + 1].imshow(\\n                sv, cmap=red_transparent_blue, vmin=-max_val, vmax=max_val\\n            )\\n            axes[row, i + 1].axis(\\\"off\\\")\\n\\n    cb = fig.colorbar(\\n        im,\\n        ax=np.ravel(axes).tolist(),\\n        label=\\\"SHAP value\\\",\\n        orientation=\\\"horizontal\\\",\\n        aspect=fig_size[0] / 0.2,\\n    )\\n    cb.outline.set_visible(False)\";\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 keras\n",
    "# from keras.applications.vgg16 import VGG16\n",
    "# from keras.applications.vgg16 import preprocess_input, decode_predictions\n",
    "# from matplotlib.colors import LinearSegmentedColormap\n",
    "# import numpy as np\n",
    "import shap\n",
    "\n",
    "# import keras.backend as K\n",
    "# import json\n",
    "\n",
    "shap.initjs()\n",
    "\n",
    "\n",
    "# utility function to visualize SHAP values in larger image formats\n",
    "# this modifies the `shap.image_plot(...)` function\n",
    "def visualize_model_decisions(shap_values, x, labels=None, figsize=(20, 30)):\n",
    "\n",
    "    colors = []\n",
    "    for l in np.linspace(1, 0, 100):\n",
    "        colors.append((30.0 / 255, 136.0 / 255, 229.0 / 255, l))\n",
    "    for l in np.linspace(0, 1, 100):\n",
    "        colors.append((255.0 / 255, 13.0 / 255, 87.0 / 255, l))\n",
    "    red_transparent_blue = LinearSegmentedColormap.from_list(\n",
    "        \"red_transparent_blue\", colors\n",
    "    )\n",
    "\n",
    "    multi_output = True\n",
    "    if type(shap_values) != list:\n",
    "        multi_output = False\n",
    "        shap_values = [shap_values]\n",
    "\n",
    "    # make sure labels\n",
    "    if labels is not None:\n",
    "        assert (\n",
    "            labels.shape[0] == shap_values[0].shape[0]\n",
    "        ), \"Labels must have same row count as shap_values arrays!\"\n",
    "        if multi_output:\n",
    "            assert labels.shape[1] == len(\n",
    "                shap_values\n",
    "            ), \"Labels must have a column for each output in shap_values!\"\n",
    "        else:\n",
    "            assert (\n",
    "                len(labels.shape) == 1\n",
    "            ), \"Labels must be a vector for single output shap_values.\"\n",
    "\n",
    "    # plot our explanations\n",
    "    fig_size = figsize\n",
    "    fig, axes = plt.subplots(\n",
    "        nrows=x.shape[0], ncols=len(shap_values) + 1, figsize=fig_size\n",
    "    )\n",
    "    if len(axes.shape) == 1:\n",
    "        axes = axes.reshape(1, axes.size)\n",
    "    for row in range(x.shape[0]):\n",
    "        x_curr = x[row].copy()\n",
    "\n",
    "        # make sure\n",
    "        if len(x_curr.shape) == 3 and x_curr.shape[2] == 1:\n",
    "            x_curr = x_curr.reshape(x_curr.shape[:2])\n",
    "        if x_curr.max() > 1:\n",
    "            x_curr /= 255.0\n",
    "\n",
    "        axes[row, 0].imshow(x_curr)\n",
    "        axes[row, 0].axis(\"off\")\n",
    "\n",
    "        # get a grayscale version of the image\n",
    "        if len(x_curr.shape) == 3 and x_curr.shape[2] == 3:\n",
    "            x_curr_gray = (\n",
    "                0.2989 * x_curr[:, :, 0]\n",
    "                + 0.5870 * x_curr[:, :, 1]\n",
    "                + 0.1140 * x_curr[:, :, 2]\n",
    "            )  # rgb to gray\n",
    "        else:\n",
    "            x_curr_gray = x_curr\n",
    "\n",
    "        if len(shap_values[0][row].shape) == 2:\n",
    "            abs_vals = np.stack(\n",
    "                [np.abs(shap_values[i]) for i in range(len(shap_values))], 0\n",
    "            ).flatten()\n",
    "        else:\n",
    "            abs_vals = np.stack(\n",
    "                [np.abs(shap_values[i].sum(-1)) for i in range(len(shap_values))], 0\n",
    "            ).flatten()\n",
    "        max_val = np.nanpercentile(abs_vals, 99.9)\n",
    "        for i in range(len(shap_values)):\n",
    "            if labels is not None:\n",
    "                axes[row, i + 1].set_title(labels[row, i])\n",
    "            sv = (\n",
    "                shap_values[i][row]\n",
    "                if len(shap_values[i][row].shape) == 2\n",
    "                else shap_values[i][row].sum(-1)\n",
    "            )\n",
    "            axes[row, i + 1].imshow(\n",
    "                x_curr_gray,\n",
    "                cmap=plt.get_cmap(\"gray\"),\n",
    "                alpha=0.15,\n",
    "                extent=(-1, sv.shape[0], sv.shape[1], -1),\n",
    "            )\n",
    "            im = axes[row, i + 1].imshow(\n",
    "                sv, cmap=red_transparent_blue, vmin=-max_val, vmax=max_val\n",
    "            )\n",
    "            axes[row, i + 1].axis(\"off\")\n",
    "\n",
    "    cb = fig.colorbar(\n",
    "        im,\n",
    "        ax=np.ravel(axes).tolist(),\n",
    "        label=\"SHAP value\",\n",
    "        orientation=\"horizontal\",\n",
    "        aspect=fig_size[0] / 0.2,\n",
    "    )\n",
    "    cb.outline.set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T21:45:03.527732Z",
     "start_time": "2020-04-17T21:45:03.065713Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-54-12c2a91d40a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m e = shap.GradientExplainer(\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mn_layer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mmap2layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocess_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_layer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m )\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 54;\n",
       "                var nbb_unformatted_code = \"# make model predictions\\nn_layer = 311\\n\\n\\ndef map2layer(x, layer):\\n    feed_dict = dict(zip([model.layers[0].input], [x]))\\n    return K.get_session().run(model.layers[layer].input, feed_dict)\\n\\n\\ne = shap.GradientExplainer(\\n    (model.layers[n_layer].input, model.layers[-1].output),\\n    map2layer(preprocess_input(X.copy()), n_layer),\\n)\\nshap_values, indexes = e.shap_values(map2layer(to_predict, n_layer), ranked_outputs=2)\\nindex_names = np.vectorize(lambda x: class_names[str(x)][1])(indexes)\\nprint(index_names)\\n# visualize model decisions\\nvisualize_model_decisions(\\n    shap_values=shap_values, x=to_predict, labels=index_names, figsize=(20, 40)\\n)\";\n",
       "                var nbb_formatted_code = \"# make model predictions\\nn_layer = 311\\n\\n\\ndef map2layer(x, layer):\\n    feed_dict = dict(zip([model.layers[0].input], [x]))\\n    return K.get_session().run(model.layers[layer].input, feed_dict)\\n\\n\\ne = shap.GradientExplainer(\\n    (model.layers[n_layer].input, model.layers[-1].output),\\n    map2layer(preprocess_input(X.copy()), n_layer),\\n)\\nshap_values, indexes = e.shap_values(map2layer(to_predict, n_layer), ranked_outputs=2)\\nindex_names = np.vectorize(lambda x: class_names[str(x)][1])(indexes)\\nprint(index_names)\\n# visualize model decisions\\nvisualize_model_decisions(\\n    shap_values=shap_values, x=to_predict, labels=index_names, figsize=(20, 40)\\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": [
    "# make model predictions\n",
    "n_layer = 311\n",
    "\n",
    "\n",
    "def map2layer(x, layer):\n",
    "    feed_dict = dict(zip([model.layers[0].input], [x]))\n",
    "    return K.get_session().run(model.layers[layer].input, feed_dict)\n",
    "\n",
    "\n",
    "e = shap.GradientExplainer(\n",
    "    (model.layers[n_layer].input, model.layers[-1].output),\n",
    "    map2layer(preprocess_input(X.copy()), n_layer),\n",
    ")\n",
    "shap_values, indexes = e.shap_values(map2layer(to_predict, n_layer), ranked_outputs=2)\n",
    "index_names = np.vectorize(lambda x: class_names[str(x)][1])(indexes)\n",
    "print(index_names)\n",
    "# visualize model decisions\n",
    "visualize_model_decisions(\n",
    "    shap_values=shap_values, x=to_predict, labels=index_names, figsize=(20, 40)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
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