Character_recog_Training.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Character recog Training",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "qP9fxZ2hxjlz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "db04fdb7-a40b-407d-d2f1-c75524d3c6c9"
      },
      "source": [
        "#Extract all the training data and test data\n",
        "!cp '/content/drive/My Drive/Projects/Nishikita Competition/data.zip' /content/\n",
        "!unzip /content/data.zip\n",
        "!unzip /content/train.zip\n",
        "!unzip /content/test.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8T1IPGUkOsT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "5ba4f4d7-b139-4736-93c5-e39c063aaeb0"
      },
      "source": [
        "!pip install -U --pre efficientnet\n",
        "!pip install tf2cv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting efficientnet\n",
            "  Downloading https://files.pythonhosted.org/packages/28/91/67848a143b54c331605bfba5fd31cf4e9db13d2e429d103fe807acc3bcf4/efficientnet-1.1.0-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: keras-applications<=1.0.8,>=1.0.7 in /usr/local/lib/python3.6/dist-packages (from efficientnet) (1.0.8)\n",
            "Requirement already satisfied, skipping upgrade: scikit-image in /usr/local/lib/python3.6/dist-packages (from efficientnet) (0.16.2)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (2.10.0)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (3.2.2)\n",
            "Requirement already satisfied, skipping upgrade: networkx>=2.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (2.4)\n",
            "Requirement already satisfied, skipping upgrade: pillow>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (7.0.0)\n",
            "Requirement already satisfied, skipping upgrade: PyWavelets>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (1.1.1)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: imageio>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (2.4.1)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from h5py->keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (1.2.0)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.0->scikit-image->efficientnet) (4.4.2)\n",
            "Installing collected packages: efficientnet\n",
            "Successfully installed efficientnet-1.1.0\n",
            "Collecting tf2cv\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a2/c8/0274bdbcd9504287406cf2fe3fd465a3b2e80fb53335f7512374bae92f3e/tf2cv-0.0.11-py2.py3-none-any.whl (340kB)\n",
            "\u001b[K     |████████████████████████████████| 348kB 13.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from tf2cv) (1.18.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from tf2cv) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->tf2cv) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->tf2cv) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->tf2cv) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->tf2cv) (2.9)\n",
            "Installing collected packages: tf2cv\n",
            "Successfully installed tf2cv-0.0.11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgGGitcXy4pE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import tensorflow \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D,GlobalMaxPooling2D,Dense,concatenate,Input\n",
        "from tensorflow.keras.models import Model,Sequential,load_model\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.applications.resnet import ResNet101,ResNet50,ResNet152\n",
        "from tensorflow.keras.utils import Sequence \n",
        "from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.callbacks import *\n",
        "from tensorflow import keras\n",
        "import efficientnet.tfkeras as efn\n",
        "import math\n",
        "import tf2cv\n",
        "from tf2cv.model_provider import get_model as tf2cv_get_model\n",
        "from albumentations import (\n",
        "    Compose, HorizontalFlip, CLAHE, HueSaturationValue,\n",
        "    RandomBrightness, RandomContrast, RandomGamma,\n",
        "    ToFloat, ShiftScaleRotate,Cutout\n",
        ")\n",
        "#@title Imports\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_datasets as tfds\n",
        "import time\n",
        "from PIL import Image\n",
        "import requests\n",
        "from io import BytesIO\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pathlib\n",
        "from tf2cv.model_provider import _models as models\n",
        "import re"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pu1OXRMnPn1V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_dir='/content/'\n",
        "classes_map={\n",
        "    0: 'Male-Aristocrat', 1: 'Male-Samurai', 2: 'Male-Animal', 3: 'Male-Common People',\n",
        "4: 'Female-Aristocrat', 5: 'Female-Samurai', 6: 'Female-Animal' , 7: 'Woman-common people'\n",
        "}\n",
        "\n",
        "img_size=256\n",
        "batch_size=128\n",
        "epochs=10\n",
        "n_classes=len(classes_map.items())\n",
        "lr=1e-5\n",
        "max_lr=1e-4\n",
        "model_dir='/content/drive/My Drive/Projects/Nishikita Competition/Models/'\n",
        "SIZE=img_size"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8CvV5CdLSNZm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "models=[i for i in models if re.search(r'senet',i)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQAEOIrZzCdi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "dec38866-b71c-4829-936c-daf2f5aeb290"
      },
      "source": [
        "train_df=pd.read_csv(data_dir+'train.csv')\n",
        "train_df['image']=train_df['image'].apply(lambda x:data_dir+'train/'+x)\n",
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image</th>\n",
              "      <th>gender_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/train/n_00001.jpg</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/train/n_00002.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/train/n_00003.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/train/n_00005.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/train/n_00006.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        image  gender_status\n",
              "0  /content/train/n_00001.jpg              4\n",
              "1  /content/train/n_00002.jpg              0\n",
              "2  /content/train/n_00003.jpg              0\n",
              "3  /content/train/n_00005.jpg              0\n",
              "4  /content/train/n_00006.jpg              0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuygfgGLzSkF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a7114516-b40b-42e1-e3e1-d55d8f47deaf"
      },
      "source": [
        "sample_submission=pd.read_csv(data_dir+'sample_submission.csv')\n",
        "sample_submission.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image</th>\n",
              "      <th>gender_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>n_00000.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>n_00004.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>n_00009.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>n_00026.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>n_00030.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         image  gender_status\n",
              "0  n_00000.jpg              0\n",
              "1  n_00004.jpg              0\n",
              "2  n_00009.jpg              0\n",
              "3  n_00026.jpg              0\n",
              "4  n_00030.jpg              0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hw9Kmqzsz78o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "6bed8028-4d9e-455f-c752-ad47ab6a9477"
      },
      "source": [
        "import cv2\n",
        "img_sample=cv2.imread(train_df.iloc[0].image)\n",
        "plt.imshow(img_sample)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7bd2ae2c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Q6C2w0ec_FK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# augmix : https://github.com/google-research/augmix\n",
        "\n",
        "from PIL import Image\n",
        "from PIL import ImageOps\n",
        "import numpy as np\n",
        "\n",
        "def int_parameter(level, maxval):\n",
        "    \"\"\"Helper function to scale `val` between 0 and maxval .\n",
        "    Args:\n",
        "    level: Level of the operation that will be between [0, `PARAMETER_MAX`].\n",
        "    maxval: Maximum value that the operation can have. This will be scaled to\n",
        "      level/PARAMETER_MAX.\n",
        "    Returns:\n",
        "    An int that results from scaling `maxval` according to `level`.\n",
        "    \"\"\"\n",
        "    return int(level * maxval / 10)\n",
        "\n",
        "\n",
        "def float_parameter(level, maxval):\n",
        "    \"\"\"Helper function to scale `val` between 0 and maxval.\n",
        "    Args:\n",
        "    level: Level of the operation that will be between [0, `PARAMETER_MAX`].\n",
        "    maxval: Maximum value that the operation can have. This will be scaled to\n",
        "      level/PARAMETER_MAX.\n",
        "    Returns:\n",
        "    A float that results from scaling `maxval` according to `level`.\n",
        "    \"\"\"\n",
        "    return float(level) * maxval / 10.\n",
        "\n",
        "def sample_level(n):\n",
        "    return np.random.uniform(low=0.1, high=n)\n",
        "\n",
        "def autocontrast(pil_img, _):\n",
        "    return ImageOps.autocontrast(pil_img)\n",
        "\n",
        "def equalize(pil_img, _):\n",
        "    return ImageOps.equalize(pil_img)\n",
        "\n",
        "def posterize(pil_img, level):\n",
        "    level = int_parameter(sample_level(level), 4)\n",
        "    return ImageOps.posterize(pil_img, 4 - level)\n",
        "\n",
        "def rotate(pil_img, level):\n",
        "    degrees = int_parameter(sample_level(level), 30)\n",
        "    if np.random.uniform() > 0.5:\n",
        "        degrees = -degrees\n",
        "    return pil_img.rotate(degrees, resample=Image.BILINEAR)\n",
        "\n",
        "def solarize(pil_img, level):\n",
        "    level = int_parameter(sample_level(level), 256)\n",
        "    return ImageOps.solarize(pil_img, 256 - level)\n",
        "\n",
        "def shear_x(pil_img, level):\n",
        "    level = float_parameter(sample_level(level), 0.3)\n",
        "    if np.random.uniform() > 0.5:\n",
        "        level = -level\n",
        "    return pil_img.transform((SIZE, SIZE),\n",
        "                           Image.AFFINE, (1, level, 0, 0, 1, 0),\n",
        "                           resample=Image.BILINEAR)\n",
        "\n",
        "def shear_y(pil_img, level):\n",
        "    level = float_parameter(sample_level(level), 0.3)\n",
        "    if np.random.uniform() > 0.5:\n",
        "        level = -level\n",
        "    return pil_img.transform((SIZE, SIZE),\n",
        "                           Image.AFFINE, (1, 0, 0, level, 1, 0),\n",
        "                           resample=Image.BILINEAR)\n",
        "\n",
        "def translate_x(pil_img, level):\n",
        "    level = int_parameter(sample_level(level), SIZE / 3)\n",
        "    if np.random.random() > 0.5:\n",
        "        level = -level\n",
        "    return pil_img.transform((SIZE, SIZE),\n",
        "                           Image.AFFINE, (1, 0, level, 0, 1, 0),\n",
        "                           resample=Image.BILINEAR)\n",
        "\n",
        "\n",
        "def translate_y(pil_img, level):\n",
        "    level = int_parameter(sample_level(level), SIZE / 3)\n",
        "    if np.random.random() > 0.5:\n",
        "        level = -level\n",
        "    return pil_img.transform((SIZE, SIZE),\n",
        "                           Image.AFFINE, (1, 0, 0, 0, 1, level),\n",
        "                           resample=Image.BILINEAR)\n",
        "\n",
        "augmentations = [\n",
        "    autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y,\n",
        "    translate_x, translate_y\n",
        "]\n",
        "\n",
        "\n",
        "\n",
        "def apply_op(image, op, severity):\n",
        "    image = np.clip(image * 255., 0, 255).astype(np.uint8)\n",
        "    pil_img = Image.fromarray(image)  # Convert to PIL.Image\n",
        "    pil_img = op(pil_img, severity)\n",
        "    return np.asarray(pil_img) / 255.\n",
        "\n",
        "\n",
        "def augment_and_mix(image, severity=1, width=3, depth=1, alpha=1.):\n",
        "    \"\"\"Perform AugMix augmentations and compute mixture.\n",
        "    Args:\n",
        "    image: Raw input image as float32 np.ndarray of shape (h, w, c)\n",
        "    severity: Severity of underlying augmentation operators (between 1 to 10).\n",
        "    width: Width of augmentation chain\n",
        "    depth: Depth of augmentation chain. -1 enables stochastic depth uniformly\n",
        "      from [1, 3]\n",
        "    alpha: Probability coefficient for Beta and Dirichlet distributions.\n",
        "    Returns:\n",
        "    mixed: Augmented and mixed image.\n",
        "  \"\"\"\n",
        "    ws = np.float32(\n",
        "      np.random.dirichlet([alpha] * width))\n",
        "    m = np.float32(np.random.beta(alpha, alpha))\n",
        "\n",
        "    mix = np.zeros_like(image)\n",
        "    for i in range(width):\n",
        "        image_aug = image.copy()\n",
        "        depth = depth if depth > 0 else np.random.randint(1, 4)\n",
        "        \n",
        "        for _ in range(depth):\n",
        "            op = np.random.choice(augmentations)\n",
        "            image_aug = apply_op(image_aug, op, severity)\n",
        "        mix = np.add(mix, ws[i] * (image_aug), out=mix, \n",
        "                     casting=\"unsafe\")\n",
        "\n",
        "    mixed = (1 - m) * (image) + m * mix\n",
        "    return mixed"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMdYBWdxM9W9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "AUGMENTATIONS_TRAIN = Compose([\n",
        "    HorizontalFlip(p=0.5),\n",
        "    #RandomContrast(limit=0.2, p=0.5),\n",
        "    #RandomGamma(gamma_limit=(80, 120), p=0.5),\n",
        "    #RandomBrightness(limit=0.2, p=0.5),\n",
        "    #HueSaturationValue(hue_shift_limit=5, sat_shift_limit=20,\n",
        "                       #val_shift_limit=10, p=.9),\n",
        "    # CLAHE(p=1.0, clip_limit=2.0),\n",
        "    ShiftScaleRotate(\n",
        "        shift_limit=0.0625, scale_limit=0.1, \n",
        "        rotate_limit=15, border_mode=cv2.BORDER_REFLECT_101, p=0.8),\n",
        "    Cutout(), \n",
        "    ToFloat(max_value=255)\n",
        "])\n",
        "\n",
        "AUGMENTATIONS_TEST = Compose([\n",
        "    # CLAHE(p=1.0, clip_limit=2.0),\n",
        "    ToFloat(max_value=255)\n",
        "])\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "class AnimeData(Sequence):\n",
        "  def __init__(self,x_set,y_set,batch_size=128,augmentations=AUGMENTATIONS_TEST,train=False):\n",
        "    self.x=x_set\n",
        "    self.y=y_set\n",
        "    self.batch_size=batch_size \n",
        "    self.augmentations=augmentations\n",
        "    self.train=train\n",
        "  def __getitem__(self,step):\n",
        "    start=step*self.batch_size \n",
        "    end=(step+1)*self.batch_size \n",
        "    x_batch,y_batch=self.x[start:end],self.y[start:end]\n",
        "    x_batch,y_batch=self.data_gen(x_batch,y_batch)\n",
        "    if self.train:\n",
        "      l2=np.random.randint(0,self.y.shape[0],x_batch.shape[0])\n",
        "      x2,y2=self.x[l2],self.y[l2]\n",
        "      x2,y2=self.data_gen(x2,y2)\n",
        "      alphas=np.random.beta(0.4,0.4,x_batch.shape[0])\n",
        "      l1=np.resize(alphas,(x_batch.shape[0],1,1,1))\n",
        "      ly=np.resize(alphas,(x_batch.shape[0],1))\n",
        "      x_batch=l1*x_batch+(1-l1)*x2\n",
        "      y_batch=ly*y_batch+(1-ly)*y2\n",
        "    return x_batch,y_batch\n",
        "  \n",
        "  def data_gen(self,batch_x,batch_y):\n",
        "    x_batch=np.empty((batch_y.shape[0],img_size,img_size,3))\n",
        "    y_batch=np.empty((batch_y.shape[0],n_classes))\n",
        "    for i in range(batch_y.shape[0]):\n",
        "      x=cv2.imread(batch_x[i])\n",
        "      x=self.augmentations(image=x)[\"image\"]\n",
        "      y=batch_y[i]\n",
        "      x_batch[i]=x\n",
        "      y_batch[i]=y\n",
        "    return np.array(x_batch),np.array(y_batch)\n",
        "  \n",
        "  def __len__(self):\n",
        "    return math.ceil(len(self.x) / self.batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lP-qq9cONEFd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class CyclicLR(Callback):\n",
        "    \"\"\"This callback implements a cyclical learning rate policy (CLR).\n",
        "    The method cycles the learning rate between two boundaries with\n",
        "    some constant frequency, as detailed in this paper (https://arxiv.org/abs/1506.01186).\n",
        "    The amplitude of the cycle can be scaled on a per-iteration or \n",
        "    per-cycle basis.\n",
        "    This class has three built-in policies, as put forth in the paper.\n",
        "    \"triangular\":\n",
        "        A basic triangular cycle w/ no amplitude scaling.\n",
        "    \"triangular2\":\n",
        "        A basic triangular cycle that scales initial amplitude by half each cycle.\n",
        "    \"exp_range\":\n",
        "        A cycle that scales initial amplitude by gamma**(cycle iterations) at each \n",
        "        cycle iteration.\n",
        "    For more detail, please see paper.\n",
        "    \n",
        "    # Example\n",
        "        ```python\n",
        "            clr = CyclicLR(base_lr=0.001, max_lr=0.006,\n",
        "                                step_size=2000., mode='triangular')\n",
        "            model.fit(X_train, Y_train, callbacks=[clr])\n",
        "        ```\n",
        "    \n",
        "    Class also supports custom scaling functions:\n",
        "        ```python\n",
        "            clr_fn = lambda x: 0.5*(1+np.sin(x*np.pi/2.))\n",
        "            clr = CyclicLR(base_lr=0.001, max_lr=0.006,\n",
        "                                step_size=2000., scale_fn=clr_fn,\n",
        "                                scale_mode='cycle')\n",
        "            model.fit(X_train, Y_train, callbacks=[clr])\n",
        "        ```    \n",
        "    # Arguments\n",
        "        base_lr: initial learning rate which is the\n",
        "            lower boundary in the cycle.\n",
        "        max_lr: upper boundary in the cycle. Functionally,\n",
        "            it defines the cycle amplitude (max_lr - base_lr).\n",
        "            The lr at any cycle is the sum of base_lr\n",
        "            and some scaling of the amplitude; therefore \n",
        "            max_lr may not actually be reached depending on\n",
        "            scaling function.\n",
        "        step_size: number of training iterations per\n",
        "            half cycle. Authors suggest setting step_size\n",
        "            2-8 x training iterations in epoch.\n",
        "        mode: one of {triangular, triangular2, exp_range}.\n",
        "            Default 'triangular'.\n",
        "            Values correspond to policies detailed above.\n",
        "            If scale_fn is not None, this argument is ignored.\n",
        "        gamma: constant in 'exp_range' scaling function:\n",
        "            gamma**(cycle iterations)\n",
        "        scale_fn: Custom scaling policy defined by a single\n",
        "            argument lambda function, where \n",
        "            0 <= scale_fn(x) <= 1 for all x >= 0.\n",
        "            mode paramater is ignored \n",
        "        scale_mode: {'cycle', 'iterations'}.\n",
        "            Defines whether scale_fn is evaluated on \n",
        "            cycle number or cycle iterations (training\n",
        "            iterations since start of cycle). Default is 'cycle'.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, base_lr=0.001, max_lr=0.006, step_size=2000., mode='triangular',\n",
        "                 gamma=1., scale_fn=None, scale_mode='cycle'):\n",
        "        super(CyclicLR, self).__init__()\n",
        "\n",
        "        self.base_lr = base_lr\n",
        "        self.max_lr = max_lr\n",
        "        self.step_size = step_size\n",
        "        self.mode = mode\n",
        "        self.gamma = gamma\n",
        "        if scale_fn == None:\n",
        "            if self.mode == 'triangular':\n",
        "                self.scale_fn = lambda x: 1.\n",
        "                self.scale_mode = 'cycle'\n",
        "            elif self.mode == 'triangular2':\n",
        "                self.scale_fn = lambda x: 1/(2.**(x-1))\n",
        "                self.scale_mode = 'cycle'\n",
        "            elif self.mode == 'exp_range':\n",
        "                self.scale_fn = lambda x: gamma**(x)\n",
        "                self.scale_mode = 'iterations'\n",
        "        else:\n",
        "            self.scale_fn = scale_fn\n",
        "            self.scale_mode = scale_mode\n",
        "        self.clr_iterations = 0.\n",
        "        self.trn_iterations = 0.\n",
        "        self.history = {}\n",
        "\n",
        "        self._reset()\n",
        "\n",
        "    def _reset(self, new_base_lr=None, new_max_lr=None,\n",
        "               new_step_size=None):\n",
        "        \"\"\"Resets cycle iterations.\n",
        "        Optional boundary/step size adjustment.\n",
        "        \"\"\"\n",
        "        if new_base_lr != None:\n",
        "            self.base_lr = new_base_lr\n",
        "        if new_max_lr != None:\n",
        "            self.max_lr = new_max_lr\n",
        "        if new_step_size != None:\n",
        "            self.step_size = new_step_size\n",
        "        self.clr_iterations = 0.\n",
        "        \n",
        "    def clr(self):\n",
        "        cycle = np.floor(1+self.clr_iterations/(2*self.step_size))\n",
        "        x = np.abs(self.clr_iterations/self.step_size - 2*cycle + 1)\n",
        "        if self.scale_mode == 'cycle':\n",
        "            return self.base_lr + (self.max_lr-self.base_lr)*np.maximum(0, (1-x))*self.scale_fn(cycle)\n",
        "        else:\n",
        "            return self.base_lr + (self.max_lr-self.base_lr)*np.maximum(0, (1-x))*self.scale_fn(self.clr_iterations)\n",
        "        \n",
        "    def on_train_begin(self, logs={}):\n",
        "        logs = logs or {}\n",
        "\n",
        "        if self.clr_iterations == 0:\n",
        "            K.set_value(self.model.optimizer.lr, self.base_lr)\n",
        "        else:\n",
        "            K.set_value(self.model.optimizer.lr, self.clr())        \n",
        "            \n",
        "    def on_batch_end(self, epoch, logs=None):\n",
        "        \n",
        "        logs = logs or {}\n",
        "        self.trn_iterations += 1\n",
        "        self.clr_iterations += 1\n",
        "\n",
        "        self.history.setdefault('lr', []).append(K.get_value(self.model.optimizer.lr))\n",
        "        self.history.setdefault('iterations', []).append(self.trn_iterations)\n",
        "\n",
        "        for k, v in logs.items():\n",
        "            self.history.setdefault(k, []).append(v)\n",
        "        \n",
        "        K.set_value(self.model.optimizer.lr, self.clr())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xbd7qfQR0oqd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_efficientnet(shape=(img_size,img_size,3),weights='noisy-student',trainable_base=False,name='b7'):\n",
        "  if name=='b7':\n",
        "    base=efn.EfficientNetB7(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b6':\n",
        "    base=efn.EfficientNetB6(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b5':\n",
        "    base=efn.EfficientNetB5(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b4':\n",
        "    base=efn.EfficientNetB4(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b3':\n",
        "    base=efn.EfficientNetB3(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b2':\n",
        "    base=efn.EfficientNetB2(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b1':\n",
        "    base=efn.EfficientNetB1(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='l2':\n",
        "    base=efn.EfficientNetL2(weights=weights,input_shape=shape,include_top=False)\n",
        "  base.trainable=trainable_base\n",
        "  avg_pool=GlobalAveragePooling2D()(base.output)\n",
        "  #max_pool=GlobalMaxPooling2D()(base.output)\n",
        "  #out=concatenate([avg_pool,max_pool])\n",
        "  #avg_pool=Dense(1024,activation='relu')(avg_pool)\n",
        "  out=Dense(n_classes,activation='softmax')(avg_pool)\n",
        "  model=Model(inputs=[base.input],outputs=[out])\n",
        "  return model\n",
        "\n",
        "\n",
        "\n",
        "def get_tf2cv_model(input_shape=(img_size,img_size,3),trainable_base=False):\n",
        "  net=tf2cv_get_model('senet154',pretrained=True)\n",
        "  net.trainable=trainable_base\n",
        "  inp=Input(input_shape)\n",
        "  model_out=net.layers[-2](inp)\n",
        "  model_out=GlobalAveragePooling2D()(model_out)\n",
        "  model_out=Dense(n_classes,activation='softmax')(model_out)\n",
        "  model=Model(inputs=[inp],outputs=[model_out])\n",
        "  return model\n",
        "\n",
        "\n",
        "def get_bitresnet(input_shape=(img_size,img_size,3),trainable_base=False):\n",
        "  # Load model into KerasLayer\n",
        "  inp=Input(shape=input_shape)\n",
        "  #model_url = \"https://tfhub.dev/google/bit/m-r50x1/1\"\n",
        "  model_url=\"https://tfhub.dev/google/bit/m-r101x1/1\"\n",
        "  module = hub.KerasLayer(model_url)\n",
        "  model_out=module(inp)\n",
        "  model_out=Dense(n_classes,activation='softmax')(model_out)\n",
        "  model=Model(inputs=[inp],outputs=[model_out])\n",
        "  return model\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuHAnh2H8JXM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXRKKfbh49Q_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelBinarizer\n",
        "lb=LabelBinarizer()\n",
        "train_y=lb.fit_transform(train_df.gender_status.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHy-EkLm7YHA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import KFold \n",
        "folds=KFold(n_splits=5,shuffle=True)\n",
        "splits=folds.split(train_df.image.values,y=train_df.gender_status.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K94ArwjvdMU_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_name='bitresnet'\n",
        "variant='101'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KC134Qfq7gSJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "62848b5d-621f-4909-a903-00f64b29ece7"
      },
      "source": [
        "for i,(train_idx,val_idx) in enumerate(splits):\n",
        "  print(f'Fold {i+1}')\n",
        "  path=model_dir+model_name+variant+'_fold_'+str(i+1)+'.hdf5'\n",
        "  best_path=model_dir+model_name+variant+'_fold_best'+str(i+1)+'.hdf5'\n",
        "  #model=get_efficientnet(name='b6',weights='imagenet')\n",
        "  model=get_bitresnet()\n",
        "  #model.load_weights(path)\n",
        "  train_gen=AnimeData(train_df.image.values[train_idx],train_y[train_idx],8,augmentations=AUGMENTATIONS_TRAIN,train=True)\n",
        "  val_gen=AnimeData(train_df.image.values[val_idx],train_y[val_idx],8,augmentations=AUGMENTATIONS_TEST,train=False)\n",
        "  model.trainable=True\n",
        "  model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=1e-6),metrics=['accuracy',f1_m])\n",
        "  cycle=CyclicLR(mode='exp_range',base_lr=lr,max_lr=max_lr,step_size=4*len(train_gen))\n",
        "  checkpoint=ModelCheckpoint(path,save_best_only=False,save_weights_only=True)\n",
        "  best_checkpoint=ModelCheckpoint(best_path,save_best_only=True,save_weights_only=True)\n",
        "  model.fit(train_gen,epochs=9,validation_data=val_gen,validation_steps=len(val_gen),callbacks=[cycle,checkpoint,best_checkpoint])\n",
        "  \"\"\"for _ in range(2):\n",
        "    lr/=10\n",
        "    max_lr/=10\n",
        "    cycle=CyclicLR(mode='exp_range',base_lr=lr,max_lr=max_lr,step_size=4*len(train_gen))\n",
        "    model.fit(train_gen,epochs=3,validation_data=val_gen,validation_steps=len(val_gen),callbacks=[cycle,checkpoint,best_checkpoint])\"\"\"\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fold 1\n",
            "Epoch 1/9\n",
            "424/424 [==============================] - 168s 397ms/step - loss: 1.4477 - accuracy: 0.5923 - f1_m: 0.5234 - val_loss: 0.9722 - val_accuracy: 0.7052 - val_f1_m: 0.7031\n",
            "Epoch 2/9\n",
            "424/424 [==============================] - 167s 394ms/step - loss: 0.9529 - accuracy: 0.7413 - f1_m: 0.6564 - val_loss: 0.6803 - val_accuracy: 0.7535 - val_f1_m: 0.7538\n",
            "Epoch 3/9\n",
            "424/424 [==============================] - 167s 394ms/step - loss: 0.8426 - accuracy: 0.7932 - f1_m: 0.6913 - val_loss: 0.6394 - val_accuracy: 0.7771 - val_f1_m: 0.7763\n",
            "Epoch 4/9\n",
            "424/424 [==============================] - 167s 394ms/step - loss: 0.8199 - accuracy: 0.7991 - f1_m: 0.7034 - val_loss: 0.6245 - val_accuracy: 0.7736 - val_f1_m: 0.7744\n",
            "Epoch 5/9\n",
            "424/424 [==============================] - 167s 395ms/step - loss: 0.7440 - accuracy: 0.8336 - f1_m: 0.7417 - val_loss: 0.5340 - val_accuracy: 0.8090 - val_f1_m: 0.8062\n",
            "Epoch 6/9\n",
            "424/424 [==============================] - 165s 390ms/step - loss: 0.6722 - accuracy: 0.8617 - f1_m: 0.7663 - val_loss: 0.5485 - val_accuracy: 0.8172 - val_f1_m: 0.8238\n",
            "Epoch 7/9\n",
            " 83/424 [====>.........................] - ETA: 2:00 - loss: 0.5953 - accuracy: 0.8780 - f1_m: 0.7948"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yogvuXl06a_y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 970
        },
        "outputId": "875a359f-d978-4824-9972-f0e0b90fbb69"
      },
      "source": [
        "#Training on Full Data\n",
        "epochs=12\n",
        "path=model_dir+model_name+variant+'.hdf5'\n",
        "train_gen=AnimeData(train_df.image.values,train_y,8,augmentations=AUGMENTATIONS_TRAIN,train=True)\n",
        "model=get_bitresnet()\n",
        "model.trainable=True\n",
        "model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=1e-5),metrics=['accuracy',f1_m])\n",
        "checkpoint=ModelCheckpoint(path,save_best_only=False,save_weights_only=True)\n",
        "cycle=CyclicLR(mode='exp_range',base_lr=lr,max_lr=max_lr)\n",
        "model.fit(train_gen,epochs=epochs,callbacks=[checkpoint,cycle])\n",
        "\n",
        "for _ in range(2):\n",
        "  lr,max_lr=lr/10,max_lr/10\n",
        "  epochs-=4\n",
        "  checkpoint=ModelCheckpoint(path,save_best_only=False,save_weights_only=True)\n",
        "  cycle=CyclicLR(mode='exp_range',base_lr=lr,max_lr=max_lr)\n",
        "  model.fit(train_gen,epochs=8,callbacks=[checkpoint,cycle])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/12\n",
            "530/530 [==============================] - 135s 255ms/step - loss: 1.3481 - accuracy: 0.6008 - f1_m: 0.5050\n",
            "Epoch 2/12\n",
            "530/530 [==============================] - 136s 256ms/step - loss: 0.9232 - accuracy: 0.7478 - f1_m: 0.6482\n",
            "Epoch 3/12\n",
            "530/530 [==============================] - 136s 256ms/step - loss: 0.8230 - accuracy: 0.7902 - f1_m: 0.6969\n",
            "Epoch 4/12\n",
            "530/530 [==============================] - 135s 255ms/step - loss: 0.7725 - accuracy: 0.8152 - f1_m: 0.7165\n",
            "Epoch 5/12\n",
            "530/530 [==============================] - 136s 256ms/step - loss: 0.7118 - accuracy: 0.8358 - f1_m: 0.7491\n",
            "Epoch 6/12\n",
            "530/530 [==============================] - 136s 256ms/step - loss: 0.6595 - accuracy: 0.8695 - f1_m: 0.7726\n",
            "Epoch 7/12\n",
            "530/530 [==============================] - 135s 256ms/step - loss: 0.6345 - accuracy: 0.8841 - f1_m: 0.7873\n",
            "Epoch 8/12\n",
            "530/530 [==============================] - 135s 256ms/step - loss: 0.6121 - accuracy: 0.8879 - f1_m: 0.7946\n",
            "Epoch 9/12\n",
            "530/530 [==============================] - 135s 255ms/step - loss: 0.5981 - accuracy: 0.8905 - f1_m: 0.7997\n",
            "Epoch 10/12\n",
            "530/530 [==============================] - 135s 255ms/step - loss: 0.5735 - accuracy: 0.9035 - f1_m: 0.8088\n",
            "Epoch 11/12\n",
            "530/530 [==============================] - 135s 255ms/step - loss: 0.5694 - accuracy: 0.9087 - f1_m: 0.8101\n",
            "Epoch 12/12\n",
            "530/530 [==============================] - 136s 256ms/step - loss: 0.5698 - accuracy: 0.9113 - f1_m: 0.8154\n",
            "Epoch 1/8\n",
            "530/530 [==============================] - 136s 257ms/step - loss: 0.5260 - accuracy: 0.9304 - f1_m: 0.8263\n",
            "Epoch 2/8\n",
            "530/530 [==============================] - 136s 257ms/step - loss: 0.5194 - accuracy: 0.9283 - f1_m: 0.8303\n",
            "Epoch 3/8\n",
            "530/530 [==============================] - 136s 257ms/step - loss: 0.5241 - accuracy: 0.9212 - f1_m: 0.8271\n",
            "Epoch 4/8\n",
            "409/530 [======================>.......] - ETA: 30s - loss: 0.5317 - accuracy: 0.9275 - f1_m: 0.8212"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-5240dcb7dedd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mcheckpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mModelCheckpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msave_best_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msave_weights_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCyclicLR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'exp_range'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbase_lr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_lr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_lr\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_gen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcycle\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     13\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCyclicLR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'exp_range'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbase_lr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_lr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_lr\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_gen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcycle\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[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    846\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    847\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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    849\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\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    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\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[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1664\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}