fastai_v1_scene_classifier_96.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/flydragon2018/Analytics_Vidhya/blob/master/fastai_v1_scene_classifier_96.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "metadata": {
        "id": "NSxapFD09O14",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wed Nov  4 11:27:16 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.32.00    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   34C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nE_8pyU4-Fvr",
        "outputId": "f148f363-31d2-4a67-99d4-e6275d2aece5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')##,force_remount=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1D83vb5-Fz_"
      },
      "source": [
        "!unzip -q /content/drive/\"My Drive\"/Image_Classification.zip"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07te8ilQBOij"
      },
      "source": [
        "!mkdir newtrain"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFmDVvknA3tS"
      },
      "source": [
        "!unzip -q /content/drive/\"My Drive\"/intelscenetrain.zip -d newtrain"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFUeg8W8A3yP"
      },
      "source": [
        "!cp /content/drive/\"My Drive\"/all_train.csv ."
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHrGqnPMBgpi"
      },
      "source": [
        "!cp /content/drive/\"My Drive\"/mtrain.csv ."
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44EPfejkBgtp"
      },
      "source": [
        "!cp /content/drive/\"My Drive\"/mtest.csv ."
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22KDJu51Bpci"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhFn2cgsBphK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JvfR-N4F9XmJ"
      },
      "source": [
        "import numpy as np  \n",
        "import pandas as pd  \n",
        "\n",
        "from datetime import datetime\n",
        " \n",
        "import os"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_tHF9A8GMKR"
      },
      "source": [
        "!pip install fastai==1.0.61 -q"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOqDweG89XpM"
      },
      "source": [
        " \n",
        "from fastai import *\n",
        "from fastai.vision import *\n",
        "from fastai.metrics import *"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bhUe2rBU9Xsb",
        "outputId": "d43437a5-b06a-46fc-862c-edb9018227bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import fastai\n",
        "fastai.__version__"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.0.61'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zagI3i469Xu9",
        "outputId": "e1eb6aa8-ce4b-4575-bf5b-a7d41a117b1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#df=pd.read_csv(\"train.csv\")\n",
        "df=pd.read_csv(\"all_train.csv\")\n",
        "df.head()"
      ],
      "execution_count": 13,
      "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>filename</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.jpg</td>\n",
              "      <td>buildings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10006.jpg</td>\n",
              "      <td>buildings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1001.jpg</td>\n",
              "      <td>buildings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10014.jpg</td>\n",
              "      <td>buildings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10018.jpg</td>\n",
              "      <td>buildings</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    filename      label\n",
              "0      0.jpg  buildings\n",
              "1  10006.jpg  buildings\n",
              "2   1001.jpg  buildings\n",
              "3  10014.jpg  buildings\n",
              "4  10018.jpg  buildings"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSo-v48OHUNg"
      },
      "source": [
        "import warnings\n",
        "warnings.simplefilter(\"ignore\")"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XiwqId3Cthr",
        "outputId": "817cb16c-a98c-4803-f95b-1507285ff4e1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fnlist=os.listdir('newtrain/train')\n",
        "print(len(fnlist))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "17034\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qt99rfz-HURU"
      },
      "source": [
        " \n",
        "#pictures_dir=\"./train\"\n",
        "pictures_dir=\"newtrain/train\"\n",
        "data = ImageDataBunch.from_df(\n",
        "  df=df, path=pictures_dir, label_col='label', fn_col='filename',valid_pct=0.1, \n",
        "  ds_tfms=get_transforms(), size=224, bs=32).normalize(imagenet_stats)\n",
        " "
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rchH03hQRbjr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YR4Ggp2XUIIl",
        "outputId": "508f3629-2dce-4079-ebbe-3a07c6c2f760",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "983d89db4af74c1199ec79298b732b3a",
            "a11dd3d10da2445a8654d1d3e81b50cb",
            "b66c4ea6ae06459aa3e2fd9c4c5fb0bb",
            "8bafa23a7270417689e8afcbd4411e4b",
            "7106119b40f84b07b1ab41230d500286",
            "bc9f36ef525a4d8fb8ffa3df68e40d2d",
            "ec0f7ebd19e0463ca20d85ee07bc0463",
            "645bc7225015470c89a7f10e2418b7e1"
          ]
        }
      },
      "source": [
        "#learn = cnn_learner(data, models.resnet152, metrics=accuracy).mixup().to_fp16()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet152-b121ed2d.pth\" to /root/.cache/torch/hub/checkpoints/resnet152-b121ed2d.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "983d89db4af74c1199ec79298b732b3a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=241530880.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJ2fupyzUITS",
        "outputId": "3f4b394e-f1d0-42bd-cb0b-183c8d50e2c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "b6f9e13262464a8d90d65a3cc7c06e1e",
            "3185d6d664d04b34b3f926d82049a36d",
            "e4108fcbb79a46b1a14a4effbcc7671a",
            "bf8e370b9b7948e58aa142f1dc4547f1",
            "0c5fedc7fc9140da98e469aef98dc130",
            "34aab19a8451413194c468ddd41a6df7",
            "03a42f7217a3486eb0829fa145f75918",
            "82c01bf86a3742ba89b41eca2a84c877"
          ]
        }
      },
      "source": [
        "learn = cnn_learner(data, models.vgg16_bn, metrics=accuracy).mixup().to_fp16()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg16_bn-6c64b313.pth\" to /root/.cache/torch/hub/checkpoints/vgg16_bn-6c64b313.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b6f9e13262464a8d90d65a3cc7c06e1e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=553507836.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvVuIPrYT9dw"
      },
      "source": [
        "#\n",
        "#xresnext101, 0.72\n",
        "#resnet50 85\n",
        "#resnet101 88.75\n",
        "#vgg16_bn     88.75\n",
        "#vgg19_bn     87\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a7Vw0GZDBAYP",
        "outputId": "ce160dfa-199a-4ef5-d173-f46028a224f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        }
      },
      "source": [
        "learn.fit_one_cycle(30)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.177975</td>\n",
              "      <td>0.327675</td>\n",
              "      <td>0.890194</td>\n",
              "      <td>02:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.015615</td>\n",
              "      <td>0.290132</td>\n",
              "      <td>0.899589</td>\n",
              "      <td>02:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.867235</td>\n",
              "      <td>0.291996</td>\n",
              "      <td>0.904874</td>\n",
              "      <td>02:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.805815</td>\n",
              "      <td>0.296409</td>\n",
              "      <td>0.894304</td>\n",
              "      <td>02:19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.795210</td>\n",
              "      <td>0.281760</td>\n",
              "      <td>0.906048</td>\n",
              "      <td>02:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.767997</td>\n",
              "      <td>0.263131</td>\n",
              "      <td>0.913095</td>\n",
              "      <td>02:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.761271</td>\n",
              "      <td>0.233210</td>\n",
              "      <td>0.918967</td>\n",
              "      <td>02:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.751378</td>\n",
              "      <td>0.224606</td>\n",
              "      <td>0.926013</td>\n",
              "      <td>02:19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.702413</td>\n",
              "      <td>0.230622</td>\n",
              "      <td>0.928362</td>\n",
              "      <td>02:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.705797</td>\n",
              "      <td>0.220435</td>\n",
              "      <td>0.930123</td>\n",
              "      <td>02:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.701508</td>\n",
              "      <td>0.227300</td>\n",
              "      <td>0.928362</td>\n",
              "      <td>02:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.674321</td>\n",
              "      <td>0.222781</td>\n",
              "      <td>0.929536</td>\n",
              "      <td>02:23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.654401</td>\n",
              "      <td>0.208192</td>\n",
              "      <td>0.932472</td>\n",
              "      <td>02:23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.664366</td>\n",
              "      <td>0.208288</td>\n",
              "      <td>0.938344</td>\n",
              "      <td>02:23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.664949</td>\n",
              "      <td>0.205705</td>\n",
              "      <td>0.938344</td>\n",
              "      <td>02:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.651126</td>\n",
              "      <td>0.202398</td>\n",
              "      <td>0.940693</td>\n",
              "      <td>02:23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.645125</td>\n",
              "      <td>0.193817</td>\n",
              "      <td>0.939519</td>\n",
              "      <td>02:23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.650456</td>\n",
              "      <td>0.198852</td>\n",
              "      <td>0.940106</td>\n",
              "      <td>02:22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.648290</td>\n",
              "      <td>0.197698</td>\n",
              "      <td>0.939519</td>\n",
              "      <td>02:22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.621324</td>\n",
              "      <td>0.194981</td>\n",
              "      <td>0.941280</td>\n",
              "      <td>02:23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.622629</td>\n",
              "      <td>0.198844</td>\n",
              "      <td>0.941280</td>\n",
              "      <td>02:19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.650453</td>\n",
              "      <td>0.203838</td>\n",
              "      <td>0.941280</td>\n",
              "      <td>02:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.612150</td>\n",
              "      <td>0.191291</td>\n",
              "      <td>0.944216</td>\n",
              "      <td>02:19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.628169</td>\n",
              "      <td>0.194447</td>\n",
              "      <td>0.942455</td>\n",
              "      <td>02:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.629394</td>\n",
              "      <td>0.193783</td>\n",
              "      <td>0.944216</td>\n",
              "      <td>02:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.619308</td>\n",
              "      <td>0.192429</td>\n",
              "      <td>0.945978</td>\n",
              "      <td>02:19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.614456</td>\n",
              "      <td>0.191899</td>\n",
              "      <td>0.945390</td>\n",
              "      <td>02:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.611122</td>\n",
              "      <td>0.189712</td>\n",
              "      <td>0.944803</td>\n",
              "      <td>02:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.621779</td>\n",
              "      <td>0.191974</td>\n",
              "      <td>0.946565</td>\n",
              "      <td>02:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.608612</td>\n",
              "      <td>0.194555</td>\n",
              "      <td>0.944216</td>\n",
              "      <td>02:18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBrKr0ojBBJR",
        "outputId": "5087e1f3-3a10-4406-bfce-cff62633375a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot()\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/1 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='86' class='' max='479' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      17.95% [86/479 00:22<01:44 2.2107]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvNsDTAd9X7s"
      },
      "source": [
        "#learn.fine_tune(10)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhvabCwo9bW_",
        "outputId": "01f0b7ed-327a-4926-ef79-42b88db377fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        }
      },
      "source": [
        "learn.unfreeze()\n",
        "learn.fit_one_cycle(30,max_lr=slice(1e-6,1e-5))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.614635</td>\n",
              "      <td>0.194916</td>\n",
              "      <td>0.945978</td>\n",
              "      <td>02:33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.606651</td>\n",
              "      <td>0.191870</td>\n",
              "      <td>0.945978</td>\n",
              "      <td>02:34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.615704</td>\n",
              "      <td>0.195617</td>\n",
              "      <td>0.943629</td>\n",
              "      <td>02:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.602051</td>\n",
              "      <td>0.191256</td>\n",
              "      <td>0.943629</td>\n",
              "      <td>02:33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.610212</td>\n",
              "      <td>0.188630</td>\n",
              "      <td>0.948326</td>\n",
              "      <td>02:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.598740</td>\n",
              "      <td>0.188969</td>\n",
              "      <td>0.946565</td>\n",
              "      <td>02:33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.608437</td>\n",
              "      <td>0.188446</td>\n",
              "      <td>0.945978</td>\n",
              "      <td>02:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.612410</td>\n",
              "      <td>0.187232</td>\n",
              "      <td>0.945390</td>\n",
              "      <td>02:33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.578661</td>\n",
              "      <td>0.184934</td>\n",
              "      <td>0.945978</td>\n",
              "      <td>02:33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.610349</td>\n",
              "      <td>0.181900</td>\n",
              "      <td>0.947739</td>\n",
              "      <td>02:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.595442</td>\n",
              "      <td>0.183456</td>\n",
              "      <td>0.947739</td>\n",
              "      <td>02:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.581113</td>\n",
              "      <td>0.185460</td>\n",
              "      <td>0.948914</td>\n",
              "      <td>02:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.602065</td>\n",
              "      <td>0.180335</td>\n",
              "      <td>0.947152</td>\n",
              "      <td>02:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.585775</td>\n",
              "      <td>0.182852</td>\n",
              "      <td>0.947152</td>\n",
              "      <td>02:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.588772</td>\n",
              "      <td>0.180921</td>\n",
              "      <td>0.948326</td>\n",
              "      <td>02:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.581180</td>\n",
              "      <td>0.179156</td>\n",
              "      <td>0.947739</td>\n",
              "      <td>02:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.575050</td>\n",
              "      <td>0.177277</td>\n",
              "      <td>0.948326</td>\n",
              "      <td>02:41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.561368</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.948326</td>\n",
              "      <td>02:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.586820</td>\n",
              "      <td>0.176626</td>\n",
              "      <td>0.945978</td>\n",
              "      <td>02:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.562714</td>\n",
              "      <td>0.176460</td>\n",
              "      <td>0.949501</td>\n",
              "      <td>02:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.554618</td>\n",
              "      <td>0.178711</td>\n",
              "      <td>0.948326</td>\n",
              "      <td>02:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.586902</td>\n",
              "      <td>0.174612</td>\n",
              "      <td>0.949501</td>\n",
              "      <td>02:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.562370</td>\n",
              "      <td>0.177508</td>\n",
              "      <td>0.945978</td>\n",
              "      <td>02:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.569218</td>\n",
              "      <td>0.174264</td>\n",
              "      <td>0.948326</td>\n",
              "      <td>02:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.559083</td>\n",
              "      <td>0.176072</td>\n",
              "      <td>0.948914</td>\n",
              "      <td>02:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.556320</td>\n",
              "      <td>0.178404</td>\n",
              "      <td>0.948326</td>\n",
              "      <td>02:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.580696</td>\n",
              "      <td>0.178421</td>\n",
              "      <td>0.950088</td>\n",
              "      <td>02:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.567072</td>\n",
              "      <td>0.180357</td>\n",
              "      <td>0.945390</td>\n",
              "      <td>02:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.570108</td>\n",
              "      <td>0.179016</td>\n",
              "      <td>0.950088</td>\n",
              "      <td>02:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.581097</td>\n",
              "      <td>0.173864</td>\n",
              "      <td>0.948914</td>\n",
              "      <td>02:39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrr2KcaUkXSZ"
      },
      "source": [
        "learn.save('vgg16bn_95')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7uWN29YJ0KJ"
      },
      "source": [
        "#learn = cnn_learner(data, models.vgg16_bn, metrics=[error_rate, accuracy], callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', name='best_vgg16bn')]).load('vgg16bn_95')\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9O79HiQjyao"
      },
      "source": [
        "#learn.fit_one_cycle(6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHs3ka8Okp_G"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQKAOckkkqD2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "he1WnO1zkqKb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZD6j1k6-duI",
        "outputId": "b7e3c2e4-e327-4816-a1ca-092102224887",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "imgs=os.listdir(\"./test\")\n",
        "\n",
        "sub2=[]\n",
        "pred2=[]\n",
        " \n",
        "i=0\n",
        "for imgname in imgs:\n",
        "    i+=1\n",
        "    n = imgname[:-4]\n",
        "    print(n)\n",
        "    \n",
        "    img=\"./test/\"+imgname\n",
        "    p = PIL.Image.open(img).convert('RGB')\n",
        "    t = pil2tensor(p, dtype=np.float32)\n",
        "    t.div_(255)\n",
        "    im = Image(t)\n",
        "    pred2 = learn.predict(im)\n",
        "    print(pred2[0])\n",
        "\n",
        "    \n",
        "     \n",
        "    sub2.append([n,pred2[0]])\n",
        "     \n",
        "\n",
        "    #if i==10:\n",
        "    #  break\n",
        "\n",
        "    \n",
        "df2=pd.DataFrame(sub2)\n",
        " \n",
        "df2[[0]]=df2[[0]].astype(int)\n",
        " \n",
        "\n",
        "df2.sort_values(by=[0],inplace=True,ascending=True)\n",
        "\n",
        "dt=datetime.now()\n",
        "df2.to_csv(\"scene_vggbn_{}.csv\".format(dt.strftime(\"%m%d%H\")),index=None,header=None)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "forest\n",
            "438\n",
            "mountain\n",
            "3223\n",
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            "230\n",
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            "sea\n",
            "2102\n",
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            "1715\n",
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            "mountain\n",
            "3154\n",
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            "570\n",
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            "3062\n",
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            "1900\n",
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            "518\n",
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            "1936\n",
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            "1793\n",
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            "1807\n",
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            "357\n",
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            "1002\n",
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            "3184\n",
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            "3\n",
            "mountain\n",
            "3326\n",
            "forest\n",
            "124\n",
            "mountain\n",
            "2698\n",
            "forest\n",
            "2754\n",
            "mountain\n",
            "2762\n",
            "glacier\n",
            "1918\n",
            "forest\n",
            "1699\n",
            "street\n",
            "1297\n",
            "forest\n",
            "791\n",
            "glacier\n",
            "2150\n",
            "mountain\n",
            "2262\n",
            "mountain\n",
            "3061\n",
            "sea\n",
            "960\n",
            "street\n",
            "2705\n",
            "buildings\n",
            "1597\n",
            "mountain\n",
            "2613\n",
            "mountain\n",
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            "1529\n",
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            "3309\n",
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            "3006\n",
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            "1366\n",
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            "1345\n",
            "street\n",
            "901\n",
            "glacier\n",
            "1381\n",
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            "1939\n",
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            "1525\n",
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            "2870\n",
            "street\n",
            "3406\n",
            "glacier\n",
            "815\n",
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            "1778\n",
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            "2931\n",
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            "2211\n",
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            "3258\n",
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            "2618\n",
            "mountain\n",
            "114\n",
            "glacier\n",
            "2782\n",
            "street\n",
            "904\n",
            "buildings\n",
            "1910\n",
            "mountain\n",
            "802\n",
            "mountain\n",
            "538\n",
            "glacier\n",
            "2439\n",
            "sea\n",
            "596\n",
            "street\n",
            "1882\n",
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            "1781\n",
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            "1859\n",
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            "3152\n",
            "mountain\n",
            "881\n",
            "mountain\n",
            "2078\n",
            "glacier\n",
            "1107\n",
            "buildings\n",
            "1871\n",
            "forest\n",
            "633\n",
            "forest\n",
            "2492\n",
            "mountain\n",
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            "mountain\n",
            "1241\n",
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            "2535\n",
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            "1484\n",
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            "729\n",
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            "2015\n",
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            "mountain\n",
            "67\n",
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            "forest\n",
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            "6\n",
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            "buildings\n",
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            "sea\n",
            "1080\n",
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            "sea\n",
            "2813\n",
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            "2812\n",
            "buildings\n",
            "1579\n",
            "sea\n",
            "246\n",
            "mountain\n",
            "332\n",
            "forest\n",
            "1659\n",
            "glacier\n",
            "1135\n",
            "street\n",
            "851\n",
            "mountain\n",
            "1063\n",
            "mountain\n",
            "899\n",
            "sea\n",
            "162\n",
            "buildings\n",
            "1605\n",
            "forest\n",
            "3074\n",
            "forest\n",
            "447\n",
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            "2920\n",
            "forest\n",
            "259\n",
            "sea\n",
            "2403\n",
            "mountain\n",
            "2483\n",
            "buildings\n",
            "242\n",
            "forest\n",
            "884\n",
            "mountain\n",
            "1495\n",
            "glacier\n",
            "1494\n",
            "forest\n",
            "1773\n",
            "mountain\n",
            "2084\n",
            "forest\n",
            "2984\n",
            "buildings\n",
            "2929\n",
            "mountain\n",
            "2610\n",
            "street\n",
            "2448\n",
            "mountain\n",
            "2452\n",
            "buildings\n",
            "2810\n",
            "glacier\n",
            "2877\n",
            "street\n",
            "2641\n",
            "mountain\n",
            "887\n",
            "mountain\n",
            "2510\n",
            "glacier\n",
            "2712\n",
            "sea\n",
            "988\n",
            "forest\n",
            "479\n",
            "buildings\n",
            "3169\n",
            "buildings\n",
            "1802\n",
            "forest\n",
            "809\n",
            "glacier\n",
            "1831\n",
            "sea\n",
            "1199\n",
            "street\n",
            "641\n",
            "forest\n",
            "198\n",
            "forest\n",
            "1232\n",
            "forest\n",
            "687\n",
            "forest\n",
            "381\n",
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            "sea\n",
            "13\n",
            "sea\n",
            "95\n",
            "sea\n",
            "2307\n",
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            "1288\n",
            "forest\n",
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            "street\n",
            "165\n",
            "buildings\n",
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            "sea\n",
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            "552\n",
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            "1957\n",
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            "glacier\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mj8OXg2_-dzP"
      },
      "source": [
        "!cp *.csv   /content/drive/\"My Drive\"/ "
      ],
      "execution_count": 25,
      "outputs": []
    }
  ]
}