code_file.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "code_file.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NF90ljL40atT",
        "colab_type": "text"
      },
      "source": [
        "**Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onLnF3PJMiD1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from bs4 import BeautifulSoup as bs\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ijNQYP7Mpjh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import urllib.request as u\n",
        "response = u.urlopen(\"https://patents.google.com/patent/US9033315B2/en\")\n",
        "page_source = response.read()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZF7kGuV9tdVB",
        "colab_type": "code",
        "outputId": "1dc76586-d8a2-41e9-95ba-ecdc3627ee66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import csv\n",
        "import pandas as pd\n",
        "from pypatent import Patent\n",
        "file1 = open('/content/data.csv', 'r')\n",
        "csv_reader = csv.reader(file1)\n",
        "df = pd.DataFrame()\n",
        "list3 = []\n",
        "list2 = []\n",
        "c = 0\n",
        "corrupt = 0\n",
        "for row in csv_reader:\n",
        "        try:\n",
        "            for i in range(4):\n",
        "                data=Patent(title=str(i),url='https://patents.google.com/patent/' + str(row[i]) +'/en')\n",
        "                label=\"\"\n",
        "                if(i == 0):\n",
        "                    label = \"Non-Alcohol\"\n",
        "                elif (i == 1):\n",
        "                    label = \"Alcohol\"\n",
        "                elif( i == 2):\n",
        "                    label = \"Non Autonomous\"\n",
        "                elif( i== 3):\n",
        "                    label = \"Autonomous\"\n",
        "                data.fetch_details()\n",
        "                list2.append(label)\n",
        "                if(data.abstract != None):\n",
        "                    if type(data.abstract)!=str:\n",
        "                        list3.append(data.abstract.join())\n",
        "                else:\n",
        "                        if type(data.description)!=str:\n",
        "                          list3.append(''.join(data.description))\n",
        "                print(str(c)+' '+str(i))\n",
        "        except Exception as e:\n",
        "                print(e)\n",
        "        c+=1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data.csv  \u001b[0m\u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QnaVVwRo0tHm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rows_list=[]\n",
        "for i in range(len(list2)):\n",
        "  _dict={}\n",
        "  _dict['label']=list2[i]\n",
        "  _dict['text']=list3[i]\n",
        "  rows_list.append(_dict)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9g0_g5OfyCb",
        "colab_type": "code",
        "outputId": "02209fe0-fc8e-45d8-c95a-b2850861aec1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline\n",
        "from fastai.text import *\n",
        "df = pd.read_csv('data_new_abstract.csv',index_col=None)\n",
        "df.head()\n"
      ],
      "execution_count": 1,
      "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>Unnamed: 0</th>\n",
              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Non-Alcohol</td>\n",
              "      <td>Methods and compositions for targeted delivery...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Alcohol</td>\n",
              "      <td>The present invention provides a process for t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Non Autonomous</td>\n",
              "      <td>A battery charger is disposed above an exhaust...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Autonomous</td>\n",
              "      <td>Self-driving vehicles have unlimited potential...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Non-Alcohol</td>\n",
              "      <td>The present invention provides stabilized oil-...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ...                                               text\n",
              "0           0  ...  Methods and compositions for targeted delivery...\n",
              "1           1  ...  The present invention provides a process for t...\n",
              "2           2  ...  A battery charger is disposed above an exhaust...\n",
              "3           3  ...  Self-driving vehicles have unlimited potential...\n",
              "4           4  ...  The present invention provides stabilized oil-...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OSASox0hinq",
        "colab_type": "code",
        "outputId": "eb6b3e50-05e5-461a-9d74-cd42fa1dbfad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 2,
      "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>Unnamed: 0</th>\n",
              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Non-Alcohol</td>\n",
              "      <td>Methods and compositions for targeted delivery...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Alcohol</td>\n",
              "      <td>The present invention provides a process for t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Non Autonomous</td>\n",
              "      <td>A battery charger is disposed above an exhaust...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Autonomous</td>\n",
              "      <td>Self-driving vehicles have unlimited potential...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Non-Alcohol</td>\n",
              "      <td>The present invention provides stabilized oil-...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1050</th>\n",
              "      <td>1050</td>\n",
              "      <td>Alcohol</td>\n",
              "      <td>Embodiments of the present disclosure are dire...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1051</th>\n",
              "      <td>1051</td>\n",
              "      <td>Non Autonomous</td>\n",
              "      <td>An externally chargeable vehicle which is char...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052</th>\n",
              "      <td>1052</td>\n",
              "      <td>Non-Alcohol</td>\n",
              "      <td>Disclosed herein is a nutritional composition ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1053</th>\n",
              "      <td>1053</td>\n",
              "      <td>Alcohol</td>\n",
              "      <td>The present invention relates to a plumbing co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1054</th>\n",
              "      <td>1054</td>\n",
              "      <td>Non Autonomous</td>\n",
              "      <td>A resettable circuit protection system for a v...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1055 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0  ...                                               text\n",
              "0              0  ...  Methods and compositions for targeted delivery...\n",
              "1              1  ...  The present invention provides a process for t...\n",
              "2              2  ...  A battery charger is disposed above an exhaust...\n",
              "3              3  ...  Self-driving vehicles have unlimited potential...\n",
              "4              4  ...  The present invention provides stabilized oil-...\n",
              "...          ...  ...                                                ...\n",
              "1050        1050  ...  Embodiments of the present disclosure are dire...\n",
              "1051        1051  ...  An externally chargeable vehicle which is char...\n",
              "1052        1052  ...  Disclosed herein is a nutritional composition ...\n",
              "1053        1053  ...  The present invention relates to a plumbing co...\n",
              "1054        1054  ...  A resettable circuit protection system for a v...\n",
              "\n",
              "[1055 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lyyfv-yxULq8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(1055):\n",
        "  f= open(str(df['label'].iloc[i])+'/'+str(i)+\".txt\",\"w+\")\n",
        "  f.write(df['text'].iloc[i])\n",
        "  f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32mQCdwsWEUU",
        "colab_type": "code",
        "outputId": "52016943-e28f-4930-e69b-0f87bcb50385",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "bs=48\n",
        "data_lm = (TextList.from_folder('/content/')\n",
        "           #Inputs: all the text files in path\n",
        "            .filter_by_folder(include=['Alcohol', 'Non-Alcohol', 'Autonomous','Non Autonomous']) \n",
        "           #We may have other temp folders that contain text files so we only keep what's in train and test\n",
        "            .split_by_rand_pct(0.2)\n",
        "           #We randomly split and keep 10% (10,000 reviews) for validation\n",
        "            .label_for_lm()           \n",
        "           #We want to do a language model so we label accordingly\n",
        "            .databunch(bs=bs))\n",
        "data_lm.save('data_lm.pkl')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrz9lhWtVe4A",
        "colab_type": "code",
        "outputId": "e3c8f6ca-5228-427d-aa77-f48cb4c89f1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "data_lm.show_batch()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>idx</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>the valid spaces and providing a xxunk xxunk of the valid spaces to the user , wherein the identifying , selecting , ranking and providing steps are performed by a computer system comprising a memory and at least one processor coupled to the memory . xxbos xxmaj the invention provides the automatic opening control system of a kind of vehicle trunk , vehicle and method , wherein system comprises :</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>the body structure of this xxmaj operation xxmaj van easily can carry out xxmaj fuel xxmaj tank or storage battery etc . relative to the xxunk being formed in the accommodation space taking pedal rear side and xxunk this xxmaj fuel xxmaj tank or storage battery etc . xxmaj the cover of xxunk can be xxunk relative to the lateral outer ends portion of taking pedal : cover the longitudinal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>to achieve an improved xxunk of the suitable timing of the outputting of the information it is provided that the time between xxunk of the event and outputting of the information is set as a function of the orientation of the driver 's head . xxbos a method includes receiving and storing sensor data including a first plurality of data points indicative of a plurality of respective states of the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>( 18 ) has a first communication module ( 22 ) , and a second communication module ( 16 ) is provided for the use module ( 4 ) and for the storage location ( 6 , 8) . xxmaj the recognition interfaces make contact when the use module ( 4 ) is at the storage location ( 6 , 8) . xxmaj if a storage location ( 28 ,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>motor vehicle lamp ( 10 ) comprises multiple organic light emitting diodes ( 1 ) and / or multiple groups of organic light emitting diodes ( 1 ) . xxmaj the light emitting diodes ( 1 ) can be electrically controlled separately from one another and are xxunk arranged . xxmaj the light emitting diodes ( 1 ) emit radiation of the same xxunk composition . xxmaj in addition ,</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0An21Ezhp94",
        "colab_type": "code",
        "outputId": "905d2c84-ad93-40ad-9574-94299bcbb1b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "data_lm = (TextList.from_csv('/content/', 'data_new_abstract.csv', cols='text')\n",
        "                   .split_by_rand_pct(0.2)\n",
        "                   .label_for_lm()\n",
        "                   .databunch())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6hXdAPIKIag",
        "colab_type": "code",
        "outputId": "c32c768f-2d9f-432a-8407-45720130e6e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "data_lm.valid_ds"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelList (211 items)\n",
              "x: LMTextList\n",
              "xxbos xxmaj in order to provide a work vehicle display device , a display method for the same , and a work vehicle that can xxunk to an operation switch a function of xxunk output of a warning even for a display device having a small number of operation switches , provided is a work vehicle display device which includes a display screen of a display unit 31 that xxunk the operation status of the work vehicle and an operation unit 32 that switches the display of the display screen , wherein the work vehicle display device comprises : an xxunk detection unit 33 that detects an xxunk in the work vehicle ; a warning output processing unit 34 that causes a warning to be output in accordance with an xxunk detection state detected by the xxunk detection unit 33 ; and a termination function xxunk unit 35 that xxunk to the operation unit 32 a function for xxunk output of the warning . xxmaj the warning output processing unit 34 performs processing to xxunk output of the warning when the operation unit 32 has been operated in a state where the operation unit 32 has been xxunk with the function for xxunk output of the warning .,xxbos xxmaj the invention relates to an improved process for separating lignin and xxunk sugars from a liquor comprising lignin and xxunk sugars in a solvent mixture of water and at least one organic solvent , which employs membrane filtration techniques such as nanofiltration and selective water removal , preferably by xxunk through a membrane which is selective for water xxunk . xxmaj the invention further relates to a modular system for executing the process according to the invention . xxmaj the process and system according to the invention are particularly suitable to be xxunk with pre - treatment of lignocellulosic biomass , in particular by xxunk fractionation or xxunk .,xxbos xxmaj methods , devices and systems for identifying or xxunk an xxunk bottle of wine are provided .,xxbos xxmaj an xxup sdv on - board computer on an xxup sdv receives an xxup sdv recognition signal from a xxunk signal transceiver xxunk by a xxunk . xxmaj an xxup sdv signal transceiver on the xxup sdv transmits a xxunk xxunk message to the signal transceiver xxunk by the xxunk . xxmaj the xxup sdv on - board computer receives a xxunk movement signal from a set of xxunk sensors that monitor movement of the xxunk . xxmaj the xxup sdv on - board computer receives an xxup sdv movement signal from a set of xxup sdv sensors on the xxup sdv that track movement of the xxup sdv . xxmaj the xxup sdv on - board computer , based on the xxup sdv movement signal and the xxunk movement signal , xxunk an xxup sdv control processor on the xxup sdv to modify the movement of the xxup sdv in order to provide the xxunk with time and space required to avoid the xxunk being xxunk by the xxup sdv , and xxunk the xxunk that this xxup sdv movement xxunk will occur .,xxbos a method of producing a lactic acid bacteria ( xxup xxunk ) composition , or xxunk xxup xxunk composition , with increased xxup xxunk xxunk active biomass using xxunk or a fraction thereof is described . xxmaj the xxunk may be xxunk xxunk , such as a xxunk , or an xxunk xxunk selected from the group consisting of a red xxunk and a green xxunk , or any fraction thereof , and in particular is selected from the group consisting of xxunk , xxunk and a fraction thereof .\n",
              "y: LMLabelList\n",
              ",,,,\n",
              "Path: /content"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTdcnxGd1AKE",
        "colab_type": "text"
      },
      "source": [
        "**Language Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0LxYXO9IvA2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = language_model_learner(data_lm,AWD_LSTM, drop_mult=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuJxVXwQN0-b",
        "colab_type": "code",
        "outputId": "4e647614-e763-40b6-f400-d3db4ba201f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        }
      },
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": 12,
      "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='3' class='' max='4', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      75.00% [3/4 00:09<00:03]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>4.763059</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.747448</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>4.459398</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\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='21' class='' max='26', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      80.77% [21/26 00:02<00:00 8.6738]\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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fpjHVaJaWYb",
        "colab_type": "code",
        "outputId": "bb999b2a-d0bc-451d-81b0-29cf36c54d81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "learn.recorder.plot(skip_end=25)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEGCAYAAAB2EqL0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKDBERCQsCgwREQmLAkNERMLy/wEZm6aZo5FSPQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fhkus2luN6uk",
        "colab_type": "code",
        "outputId": "61b6d35e-a06c-498e-8701-1ce8d96e27d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "learn.fit_one_cycle(1, 1e-3, moms=(0.8,0.7))"
      ],
      "execution_count": 0,
      "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>4.635917</td>\n",
              "      <td>4.357613</td>\n",
              "      <td>0.268654</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yDwHaFbOVA0",
        "colab_type": "code",
        "outputId": "5e79f824-d2c3-4d25-80a7-2f7af3724fbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "learn.fit_one_cycle(6, 1e-2, moms=(0.8,0.7))"
      ],
      "execution_count": 14,
      "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>4.551450</td>\n",
              "      <td>3.932312</td>\n",
              "      <td>0.287946</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.086808</td>\n",
              "      <td>3.631257</td>\n",
              "      <td>0.310459</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.635947</td>\n",
              "      <td>3.554216</td>\n",
              "      <td>0.315402</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.259309</td>\n",
              "      <td>3.532988</td>\n",
              "      <td>0.319994</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.964633</td>\n",
              "      <td>3.527498</td>\n",
              "      <td>0.320727</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.754041</td>\n",
              "      <td>3.524242</td>\n",
              "      <td>0.321524</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDI1SMluOOhW",
        "colab_type": "code",
        "outputId": "53bcd18e-8eb3-4c4a-f633-d6b3f9d82748",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "learn.fit_one_cycle(2, 1e-2, moms=(0.8,0.7))"
      ],
      "execution_count": 0,
      "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>2.511849</td>\n",
              "      <td>3.611738</td>\n",
              "      <td>0.324043</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.402221</td>\n",
              "      <td>3.616168</td>\n",
              "      <td>0.322577</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQxmpKRgOWjy",
        "colab_type": "code",
        "outputId": "ea9de099-9ff6-4102-aee2-2342d0e9067e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn.save('fit_head')\n",
        "learn.load('fit_head')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LanguageLearner(data=TextLMDataBunch;\n",
              "\n",
              "Train: LabelList (844 items)\n",
              "x: LMTextList\n",
              "xxbos xxmaj methods and compositions for xxunk delivery of xxunk are provided . xxmaj the compositions comprise bile - sensitive xxmaj st. thermophilus bacteria modified to release a biotherapeutic agent following bile exposure . xxmaj biotherapeutic agents released by the xxmaj st. thermophilus bacteria disclosed herein include xxup xxunk and xxup xxunk rich peptides . xxmaj methods of the invention comprise administering to a subject a xxmaj st. thermophilus bacterium modified to release a biotherapeutic agent following bile exposure . xxmaj administration of the xxmaj st. thermophilus bacterium xxunk a desired therapeutic response . xxmaj the bacterium may be modified to express and release xxup xxunk or xxup xxunk rich peptides which subsequently inhibit xxunk xxunk or reduce xxunk damage . xxmaj thus , methods of the invention xxunk use in treating or preventing a variety of gastrointestinal disorders including xxup c. xxunk infection and xxunk - associated diarrhea .,xxbos xxmaj the present invention provides a process for the production of aldehydes and / or alcohols , which process comprises the steps of : ( a ) reacting an xxunk and / or olefinic feed in a reactor in the presence of a molecular sieve catalyst to form an effluent comprising xxunk , comprising xxunk ; ( b ) separating the effluent comprising xxunk as obtained in step ( a ) into at least a first olefinic product fraction comprising xxunk and a second olefinic product fraction ; ( c ) subjecting at least part of the first olefinic product fraction as obtained in step ( b ) to a xxunk process to form aldehydes ; ( d ) separating at least part of the aldehydes as obtained in step ( c ) into at least a first product fraction of aldehydes and a second product fraction of aldehydes ; and ( e ) xxunk at least part of the aldehydes in the first and / or second product fraction of aldehydes as obtained in step ( d ) to form a first product fraction of alcohols and / or a second product fraction of alcohols ; ( f ) xxunk at least part of the first and / or second product fraction of alcohols obtained in step ( e ) to step ( a ) .,xxbos a battery charger is disposed above an xxunk pipe , and a xxunk box is disposed above the charger . xxmaj the charger includes a cooler having a forced cooling function .,xxbos xxmaj self - driving vehicles have xxunk potential to xxunk and predict human behavior and perform actions accordingly . xxmaj several embodiments described herein enable a self - driving vehicle to monitor human activity and predict when and where the human will be located and whether the human needs a ride from the self - driving vehicle . xxmaj self - driving vehicles will be able to perform such tasks with xxunk efficacy and accuracy that will allow self - driving vehicles to xxunk at a much xxunk rate than would otherwise be the case .,xxbos xxmaj the present invention provides xxunk oil - in - water xxunk with an extended range of chemical , thermal and / or mechanical xxunk , and xxunk ) for their preparation . xxmaj such xxunk provide an xxunk - protective xxunk component xxunk improved xxunk to the dispersed phase , reducing or eliminating xxunk therefrom under such conditions , for use in the context of a range of food , pharmaceutical , personal care , health care , cosmetic and other end - use applications .\n",
              "y: LMLabelList\n",
              ",,,,\n",
              "Path: /content;\n",
              "\n",
              "Valid: LabelList (211 items)\n",
              "x: LMTextList\n",
              "xxbos xxmaj in order to provide a work vehicle display device , a display method for the same , and a work vehicle that can xxunk to an operation switch a function of xxunk output of a warning even for a display device having a small number of operation switches , provided is a work vehicle display device which includes a display screen of a display unit 31 that xxunk the operation status of the work vehicle and an operation unit 32 that switches the display of the display screen , wherein the work vehicle display device comprises : an xxunk detection unit 33 that detects an xxunk in the work vehicle ; a warning output processing unit 34 that causes a warning to be output in accordance with an xxunk detection state detected by the xxunk detection unit 33 ; and a termination function xxunk unit 35 that xxunk to the operation unit 32 a function for xxunk output of the warning . xxmaj the warning output processing unit 34 performs processing to xxunk output of the warning when the operation unit 32 has been operated in a state where the operation unit 32 has been xxunk with the function for xxunk output of the warning .,xxbos xxmaj the invention relates to an improved process for separating lignin and xxunk sugars from a liquor comprising lignin and xxunk sugars in a solvent mixture of water and at least one organic solvent , which employs membrane filtration techniques such as nanofiltration and selective water removal , preferably by xxunk through a membrane which is selective for water xxunk . xxmaj the invention further relates to a modular system for executing the process according to the invention . xxmaj the process and system according to the invention are particularly suitable to be xxunk with pre - treatment of lignocellulosic biomass , in particular by xxunk fractionation or xxunk .,xxbos xxmaj methods , devices and systems for identifying or xxunk an xxunk bottle of wine are provided .,xxbos xxmaj an xxup sdv on - board computer on an xxup sdv receives an xxup sdv recognition signal from a xxunk signal transceiver xxunk by a xxunk . xxmaj an xxup sdv signal transceiver on the xxup sdv transmits a xxunk xxunk message to the signal transceiver xxunk by the xxunk . xxmaj the xxup sdv on - board computer receives a xxunk movement signal from a set of xxunk sensors that monitor movement of the xxunk . xxmaj the xxup sdv on - board computer receives an xxup sdv movement signal from a set of xxup sdv sensors on the xxup sdv that track movement of the xxup sdv . xxmaj the xxup sdv on - board computer , based on the xxup sdv movement signal and the xxunk movement signal , xxunk an xxup sdv control processor on the xxup sdv to modify the movement of the xxup sdv in order to provide the xxunk with time and space required to avoid the xxunk being xxunk by the xxup sdv , and xxunk the xxunk that this xxup sdv movement xxunk will occur .,xxbos a method of producing a lactic acid bacteria ( xxup xxunk ) composition , or xxunk xxup xxunk composition , with increased xxup xxunk xxunk active biomass using xxunk or a fraction thereof is described . xxmaj the xxunk may be xxunk xxunk , such as a xxunk , or an xxunk xxunk selected from the group consisting of a red xxunk and a green xxunk , or any fraction thereof , and in particular is selected from the group consisting of xxunk , xxunk and a fraction thereof .\n",
              "y: LMLabelList\n",
              ",,,,\n",
              "Path: /content;\n",
              "\n",
              "Test: None, model=SequentialRNN(\n",
              "  (0): AWD_LSTM(\n",
              "    (encoder): Embedding(3392, 400, padding_idx=1)\n",
              "    (encoder_dp): EmbeddingDropout(\n",
              "      (emb): Embedding(3392, 400, padding_idx=1)\n",
              "    )\n",
              "    (rnns): ModuleList(\n",
              "      (0): WeightDropout(\n",
              "        (module): LSTM(400, 1152, batch_first=True)\n",
              "      )\n",
              "      (1): WeightDropout(\n",
              "        (module): LSTM(1152, 1152, batch_first=True)\n",
              "      )\n",
              "      (2): WeightDropout(\n",
              "        (module): LSTM(1152, 400, batch_first=True)\n",
              "      )\n",
              "    )\n",
              "    (input_dp): RNNDropout()\n",
              "    (hidden_dps): ModuleList(\n",
              "      (0): RNNDropout()\n",
              "      (1): RNNDropout()\n",
              "      (2): RNNDropout()\n",
              "    )\n",
              "  )\n",
              "  (1): LinearDecoder(\n",
              "    (decoder): Linear(in_features=400, out_features=3392, bias=True)\n",
              "    (output_dp): RNNDropout()\n",
              "  )\n",
              "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of CrossEntropyLoss(), metrics=[<function accuracy at 0x7f52584be158>], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('/content'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[RNNTrainer\n",
              "learn: LanguageLearner(data=TextLMDataBunch;\n",
              "\n",
              "Train: LabelList (844 items)\n",
              "x: LMTextList\n",
              "xxbos xxmaj methods and compositions for xxunk delivery of xxunk are provided . xxmaj the compositions comprise bile - sensitive xxmaj st. thermophilus bacteria modified to release a biotherapeutic agent following bile exposure . xxmaj biotherapeutic agents released by the xxmaj st. thermophilus bacteria disclosed herein include xxup xxunk and xxup xxunk rich peptides . xxmaj methods of the invention comprise administering to a subject a xxmaj st. thermophilus bacterium modified to release a biotherapeutic agent following bile exposure . xxmaj administration of the xxmaj st. thermophilus bacterium xxunk a desired therapeutic response . xxmaj the bacterium may be modified to express and release xxup xxunk or xxup xxunk rich peptides which subsequently inhibit xxunk xxunk or reduce xxunk damage . xxmaj thus , methods of the invention xxunk use in treating or preventing a variety of gastrointestinal disorders including xxup c. xxunk infection and xxunk - associated diarrhea .,xxbos xxmaj the present invention provides a process for the production of aldehydes and / or alcohols , which process comprises the steps of : ( a ) reacting an xxunk and / or olefinic feed in a reactor in the presence of a molecular sieve catalyst to form an effluent comprising xxunk , comprising xxunk ; ( b ) separating the effluent comprising xxunk as obtained in step ( a ) into at least a first olefinic product fraction comprising xxunk and a second olefinic product fraction ; ( c ) subjecting at least part of the first olefinic product fraction as obtained in step ( b ) to a xxunk process to form aldehydes ; ( d ) separating at least part of the aldehydes as obtained in step ( c ) into at least a first product fraction of aldehydes and a second product fraction of aldehydes ; and ( e ) xxunk at least part of the aldehydes in the first and / or second product fraction of aldehydes as obtained in step ( d ) to form a first product fraction of alcohols and / or a second product fraction of alcohols ; ( f ) xxunk at least part of the first and / or second product fraction of alcohols obtained in step ( e ) to step ( a ) .,xxbos a battery charger is disposed above an xxunk pipe , and a xxunk box is disposed above the charger . xxmaj the charger includes a cooler having a forced cooling function .,xxbos xxmaj self - driving vehicles have xxunk potential to xxunk and predict human behavior and perform actions accordingly . xxmaj several embodiments described herein enable a self - driving vehicle to monitor human activity and predict when and where the human will be located and whether the human needs a ride from the self - driving vehicle . xxmaj self - driving vehicles will be able to perform such tasks with xxunk efficacy and accuracy that will allow self - driving vehicles to xxunk at a much xxunk rate than would otherwise be the case .,xxbos xxmaj the present invention provides xxunk oil - in - water xxunk with an extended range of chemical , thermal and / or mechanical xxunk , and xxunk ) for their preparation . xxmaj such xxunk provide an xxunk - protective xxunk component xxunk improved xxunk to the dispersed phase , reducing or eliminating xxunk therefrom under such conditions , for use in the context of a range of food , pharmaceutical , personal care , health care , cosmetic and other end - use applications .\n",
              "y: LMLabelList\n",
              ",,,,\n",
              "Path: /content;\n",
              "\n",
              "Valid: LabelList (211 items)\n",
              "x: LMTextList\n",
              "xxbos xxmaj in order to provide a work vehicle display device , a display method for the same , and a work vehicle that can xxunk to an operation switch a function of xxunk output of a warning even for a display device having a small number of operation switches , provided is a work vehicle display device which includes a display screen of a display unit 31 that xxunk the operation status of the work vehicle and an operation unit 32 that switches the display of the display screen , wherein the work vehicle display device comprises : an xxunk detection unit 33 that detects an xxunk in the work vehicle ; a warning output processing unit 34 that causes a warning to be output in accordance with an xxunk detection state detected by the xxunk detection unit 33 ; and a termination function xxunk unit 35 that xxunk to the operation unit 32 a function for xxunk output of the warning . xxmaj the warning output processing unit 34 performs processing to xxunk output of the warning when the operation unit 32 has been operated in a state where the operation unit 32 has been xxunk with the function for xxunk output of the warning .,xxbos xxmaj the invention relates to an improved process for separating lignin and xxunk sugars from a liquor comprising lignin and xxunk sugars in a solvent mixture of water and at least one organic solvent , which employs membrane filtration techniques such as nanofiltration and selective water removal , preferably by xxunk through a membrane which is selective for water xxunk . xxmaj the invention further relates to a modular system for executing the process according to the invention . xxmaj the process and system according to the invention are particularly suitable to be xxunk with pre - treatment of lignocellulosic biomass , in particular by xxunk fractionation or xxunk .,xxbos xxmaj methods , devices and systems for identifying or xxunk an xxunk bottle of wine are provided .,xxbos xxmaj an xxup sdv on - board computer on an xxup sdv receives an xxup sdv recognition signal from a xxunk signal transceiver xxunk by a xxunk . xxmaj an xxup sdv signal transceiver on the xxup sdv transmits a xxunk xxunk message to the signal transceiver xxunk by the xxunk . xxmaj the xxup sdv on - board computer receives a xxunk movement signal from a set of xxunk sensors that monitor movement of the xxunk . xxmaj the xxup sdv on - board computer receives an xxup sdv movement signal from a set of xxup sdv sensors on the xxup sdv that track movement of the xxup sdv . xxmaj the xxup sdv on - board computer , based on the xxup sdv movement signal and the xxunk movement signal , xxunk an xxup sdv control processor on the xxup sdv to modify the movement of the xxup sdv in order to provide the xxunk with time and space required to avoid the xxunk being xxunk by the xxup sdv , and xxunk the xxunk that this xxup sdv movement xxunk will occur .,xxbos a method of producing a lactic acid bacteria ( xxup xxunk ) composition , or xxunk xxup xxunk composition , with increased xxup xxunk xxunk active biomass using xxunk or a fraction thereof is described . xxmaj the xxunk may be xxunk xxunk , such as a xxunk , or an xxunk xxunk selected from the group consisting of a red xxunk and a green xxunk , or any fraction thereof , and in particular is selected from the group consisting of xxunk , xxunk and a fraction thereof .\n",
              "y: LMLabelList\n",
              ",,,,\n",
              "Path: /content;\n",
              "\n",
              "Test: None, model=SequentialRNN(\n",
              "  (0): AWD_LSTM(\n",
              "    (encoder): Embedding(3392, 400, padding_idx=1)\n",
              "    (encoder_dp): EmbeddingDropout(\n",
              "      (emb): Embedding(3392, 400, padding_idx=1)\n",
              "    )\n",
              "    (rnns): ModuleList(\n",
              "      (0): WeightDropout(\n",
              "        (module): LSTM(400, 1152, batch_first=True)\n",
              "      )\n",
              "      (1): WeightDropout(\n",
              "        (module): LSTM(1152, 1152, batch_first=True)\n",
              "      )\n",
              "      (2): WeightDropout(\n",
              "        (module): LSTM(1152, 400, batch_first=True)\n",
              "      )\n",
              "    )\n",
              "    (input_dp): RNNDropout()\n",
              "    (hidden_dps): ModuleList(\n",
              "      (0): RNNDropout()\n",
              "      (1): RNNDropout()\n",
              "      (2): RNNDropout()\n",
              "    )\n",
              "  )\n",
              "  (1): LinearDecoder(\n",
              "    (decoder): Linear(in_features=400, out_features=3392, bias=True)\n",
              "    (output_dp): RNNDropout()\n",
              "  )\n",
              "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of CrossEntropyLoss(), metrics=[<function accuracy at 0x7f52584be158>], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('/content'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[...], layer_groups=[Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(400, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 400, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): Embedding(3392, 400, padding_idx=1)\n",
              "  (1): EmbeddingDropout(\n",
              "    (emb): Embedding(3392, 400, padding_idx=1)\n",
              "  )\n",
              "  (2): LinearDecoder(\n",
              "    (decoder): Linear(in_features=400, out_features=3392, bias=True)\n",
              "    (output_dp): RNNDropout()\n",
              "  )\n",
              ")], add_time=True, silent=False)\n",
              "alpha: 2.0\n",
              "beta: 1.0], layer_groups=[Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(400, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 400, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): Embedding(3392, 400, padding_idx=1)\n",
              "  (1): EmbeddingDropout(\n",
              "    (emb): Embedding(3392, 400, padding_idx=1)\n",
              "  )\n",
              "  (2): LinearDecoder(\n",
              "    (decoder): Linear(in_features=400, out_features=3392, bias=True)\n",
              "    (output_dp): RNNDropout()\n",
              "  )\n",
              ")], add_time=True, silent=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TkEWJZkxT1a6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('fine_tuned_enc')\n",
        "learn.load('fine_tuned_enc')\n",
        "learn.save_encoder('fine_tuned_enc')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NXZjgjBO1Hgf",
        "colab_type": "text"
      },
      "source": [
        "**Text Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPHmqXbWbkqQ",
        "colab_type": "code",
        "outputId": "ba39b05d-0b0f-49fc-865e-c988276d49b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "data = TextClasDataBunch.from_csv('/content/', 'data_new_abstract.csv')\n",
        "data.show_batch()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/fastai/data_block.py:537: UserWarning: You are labelling your items with CategoryList.\n",
            "Your valid set contained the following unknown labels, the corresponding items have been discarded.\n",
            "772, 1005, 808, 437, 970...\n",
            "  if getattr(ds, 'warn', False): warn(ds.warn)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj non - xxmaj alcohol</td>\n",
              "      <td>460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj non - xxmaj alcohol</td>\n",
              "      <td>817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj non - xxmaj alcohol</td>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj non - xxmaj alcohol</td>\n",
              "      <td>228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj non - xxmaj alcohol</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EF7WC7f3T2uT",
        "colab_type": "code",
        "outputId": "6e877ffc-4c70-49a4-a02c-c570f6353c6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "data_clas = (TextList.from_folder('/content/', vocab=data_lm.vocab)\n",
        "             #grab all the text files in path\n",
        "             .split_by_rand_pct()\n",
        "             #split by train and valid folder (that only keeps 'train' and 'test' so no need to filter)\n",
        "             .label_from_folder(classes=['Alcohol','Autonomous','Non Autonomous','Non-Alcohol'])\n",
        "             #label them all with their folders\n",
        "             .databunch(bs=bs))\n",
        "\n",
        "data_clas.save('data_clas.pkl')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGeMMTfKbi8F",
        "colab_type": "code",
        "outputId": "1665e946-a99e-4c8d-8425-d65a75824148",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "data_clas = load_data('/content/', 'data_clas.pkl', bs=bs)\n",
        "data_clas.show_batch()\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj the invention relates to an electrical ribbon conductor ( 40 , 60 , 80 , 100 ) for motor vehicles , comprising a ribbon conductor core ( 42 , 62 , 82 , 102 ) which is composed of electrically conductive ribbon conductor material , and comprising an insulation which xxunk the ribbon conductor core ( 42 , 62 , 82 , 102 ) and is composed of</td>\n",
              "      <td>Non Autonomous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj the invention relates to a facility which has a xxunk facility area and an adjacent clean facility area , xxunk for receiving the xxunk xxunk , and a wall separating the xxunk area from the clean area , the xxunk area including an xxunk door for receiving the xxunk xxunk , pre - processing stations with xxunk for the pre - washing and / or xxunk by a</td>\n",
              "      <td>Autonomous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj an engine support device ( 100 ) , which is provided for supporting an engine arranged in a front - end vehicle structure of a motor vehicle , comprises the following : a first half - shell ( 120 ) comprising a first end section ( 122 ) , a second end section ( 126 ) opposite same and a first lateral cheek section ( 128 ) ,</td>\n",
              "      <td>Non Autonomous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj the invention relates to a method for the controlled application of a liquid to a material xxunk ( 1 ) , in particular for the application of a xxunk liquor to a material xxunk ( 1 ) , wherein the material xxunk ( 1 ) is moved along a running path ( 2 ) , and the direction of the material xxunk ( 1 ) is changed multiple</td>\n",
              "      <td>Alcohol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj the invention discloses a gastrodia elata - ganoderma lucidum wine and a preparation method thereof . xxmaj the invention belongs to health tonic wines for human drinking and especially relates to a health tonic wine prepared by using gastrodia elata and ganoderma lucidum extract xxunk as xxunk functional raw materials and a preparation thereof . xxmaj active ingredients of the health tonic wine are prepared from gastrodia elata</td>\n",
              "      <td>Alcohol</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiQDKOr07qUK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "3f7c9626-0cc2-4eef-ed96-a86845dc5507"
      },
      "source": [
        "data_clas.valid_ds"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelList (211 items)\n",
              "x: TextList\n",
              "xxbos xxmaj an electrical apparatus for automatically xxunk a beverage , which comprises an air processing system takes in xxunk air and xxunk oxygen content to reach as much as 90 % or higher by volume . xxmaj the apparatus further comprises a controller for automatically controlling parameters of the oxygen xxunk process including a time period for delivery of oxygen xxunk air to a beverage , and the time period is predetermined in accordance with one or more characteristics or xxunk of the beverage .,xxbos xxmaj the invention relates to the development of microorganisms capable of producing fermentation products via an engineered xxunk in the microorganisms . xxmaj the invention also relates to microorganisms with improved cell xxunk and methods to improve cell xxunk and cell xxunk of a microorganism .,xxbos a wine barrel rack system having first and second ground level side rails oriented generally parallel to one another , each of the first and second ground level side rails xxunk from sheet metal panels , and having an upper bend forming a flange and a lower bend forming a flange , and having a plurality of upper arcuate cut xxunk shaped and sized for xxunk a wine barrel . xxmaj connecting members xxunk the rails to form the rack , which may have ground level configuration and an upper , xxunk configuration , the latter including either xxunk rails with xxunk for mounting on lower barrels and support upper barrels , or discrete arcuate barrel xxunk for mounting between two lower barrels and support a single barrel .,xxbos xxmaj an automated driving system can determine , based on input from one or more sensors disposed on an autonomous vehicle , a required fuel level to complete a planned vehicle path and a current fuel level for the autonomous vehicle . xxmaj if the current fuel level is below the required fuel level , the automated driving system can identify one or more refueling stations and send an indication to a driver of the autonomous vehicle requesting selection of one of the identified refueling stations . xxmaj if a refueling station selection is received , the automated driving system can xxunk the planned vehicle path to include a stop at the selected refueling station . xxmaj if a refueling station selection is not received , the automated driving system can determine a xxunk fuel level . xxmaj if the current fuel level falls below the xxunk fuel level , the automated driving system can drive the autonomous vehicle to a proximate refueling station .,xxbos a work vehicle comprising , a power unit , an electric motor activated by electric power from the power unit , a xxunk transmission system for receiving drive power from the electric motor , a cabin including a roof portion the driver xxunk , an air conditioner for adjusting air temperature in the interior of the cabin . the power unit and the air conditioner are provided in the roof portion of the cabin .\n",
              "y: CategoryList\n",
              "Non-Alcohol,Alcohol,Alcohol,Autonomous,Non Autonomous\n",
              "Path: /content"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KiowlcQKbsX",
        "colab_type": "code",
        "outputId": "785a9bab-3a43-4736-c5d8-e59f7ea151bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn = text_classifier_learner(data_clas, AWD_LSTM, drop_mult=0.1)\n",
        "learn.load_encoder('fine_tuned_enc')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RNNLearner(data=TextClasDataBunch;\n",
              "\n",
              "Train: LabelList (844 items)\n",
              "x: TextList\n",
              "xxbos xxmaj systems and methods for providing a user with information on xxunk in which to park a vehicle are provided . a method for providing a user with information on xxunk in which to park a vehicle , comprises identifying one or more xxunk in which to park the vehicle , selecting valid xxunk from the identified one or more xxunk based on a xxunk xxunk , and xxunk the valid xxunk and providing a xxunk xxunk of the valid xxunk to the user , wherein the identifying , selecting , xxunk and providing steps are performed by a computer system comprising a memory and at least one processor coupled to the memory .,xxbos xxmaj the invention provides the automatic opening control system of a kind of vehicle trunk , vehicle and method , wherein system comprises : xxunk enter system , xxup can network system , receiving wire , inductive probe , and boot control system ; xxmaj the described system that xxunk enters carries out xxunk process to the information that described xxup can network system , receiving wire , inductive probe pass over , and then sends instruction to described boot control system , controls the unlatching of boot . xxmaj the solution of the present invention , can by xxunk that the foot action of operator controls the unlatching of vehicle trunk , and convenient operation person is to placing xxunk in boot .,xxbos xxmaj an in - vehicle information system includes a camera and a controller that xxunk xxunk input . a controller receives frames of video data and generates trajectory data for a movement of a hand in the video data . xxmaj the controller uses a first xxunk xxmaj xxunk model ( xxup xxunk ) to xxunk a sequence of xxunk from the trajectory data , removes a starting and xxunk stroke to form an edited stroke sequence , and re - xxunk the xxunk in the edited stroke sequence . xxmaj the controller uses a second xxup xxunk corresponding to a predetermined set of xxunk to identify a xxunk corresponding to the re - xxunk edited stroke sequence .,xxbos xxmaj an apparatus for controlling at least one function of a motor vehicle has a touch - sensitive input panel . xxmaj the touch - sensitive input panel includes a transparent material .,xxbos a display apparatus for vehicle includes a xxunk that xxunk speed of a vehicle , an eco - drive level determination unit that determines an eco - drive level at driving the vehicle based on a xxunk xxunk amount and an actual xxunk amount , a determination display portion located inside a peripheral visual field region of the driver , a xxunk display portion located outside of the peripheral visual field region , and a display control unit that controls display in the determination display portion and the xxunk display portion . xxmaj the display control unit performs the following controls of ( a ) setting the display in the determination display portion to ambient display in which contrast of the outline region is lower than contrast inside the outline region , and ( b ) changing the ambient display according to the change in the eco - drive level .\n",
              "y: CategoryList\n",
              "Non Autonomous,Non Autonomous,Non Autonomous,Non Autonomous,Non Autonomous\n",
              "Path: /content;\n",
              "\n",
              "Valid: LabelList (211 items)\n",
              "x: TextList\n",
              "xxbos xxmaj an electrical apparatus for automatically xxunk a beverage , which comprises an air processing system takes in xxunk air and xxunk oxygen content to reach as much as 90 % or higher by volume . xxmaj the apparatus further comprises a controller for automatically controlling parameters of the oxygen xxunk process including a time period for delivery of oxygen xxunk air to a beverage , and the time period is predetermined in accordance with one or more characteristics or xxunk of the beverage .,xxbos xxmaj the invention relates to the development of microorganisms capable of producing fermentation products via an engineered xxunk in the microorganisms . xxmaj the invention also relates to microorganisms with improved cell xxunk and methods to improve cell xxunk and cell xxunk of a microorganism .,xxbos a wine barrel rack system having first and second ground level side rails oriented generally parallel to one another , each of the first and second ground level side rails xxunk from sheet metal panels , and having an upper bend forming a flange and a lower bend forming a flange , and having a plurality of upper arcuate cut xxunk shaped and sized for xxunk a wine barrel . xxmaj connecting members xxunk the rails to form the rack , which may have ground level configuration and an upper , xxunk configuration , the latter including either xxunk rails with xxunk for mounting on lower barrels and support upper barrels , or discrete arcuate barrel xxunk for mounting between two lower barrels and support a single barrel .,xxbos xxmaj an automated driving system can determine , based on input from one or more sensors disposed on an autonomous vehicle , a required fuel level to complete a planned vehicle path and a current fuel level for the autonomous vehicle . xxmaj if the current fuel level is below the required fuel level , the automated driving system can identify one or more refueling stations and send an indication to a driver of the autonomous vehicle requesting selection of one of the identified refueling stations . xxmaj if a refueling station selection is received , the automated driving system can xxunk the planned vehicle path to include a stop at the selected refueling station . xxmaj if a refueling station selection is not received , the automated driving system can determine a xxunk fuel level . xxmaj if the current fuel level falls below the xxunk fuel level , the automated driving system can drive the autonomous vehicle to a proximate refueling station .,xxbos a work vehicle comprising , a power unit , an electric motor activated by electric power from the power unit , a xxunk transmission system for receiving drive power from the electric motor , a cabin including a roof portion the driver xxunk , an air conditioner for adjusting air temperature in the interior of the cabin . the power unit and the air conditioner are provided in the roof portion of the cabin .\n",
              "y: CategoryList\n",
              "Non-Alcohol,Alcohol,Alcohol,Autonomous,Non Autonomous\n",
              "Path: /content;\n",
              "\n",
              "Test: None, model=SequentialRNN(\n",
              "  (0): MultiBatchEncoder(\n",
              "    (module): AWD_LSTM(\n",
              "      (encoder): Embedding(3392, 400, padding_idx=1)\n",
              "      (encoder_dp): EmbeddingDropout(\n",
              "        (emb): Embedding(3392, 400, padding_idx=1)\n",
              "      )\n",
              "      (rnns): ModuleList(\n",
              "        (0): WeightDropout(\n",
              "          (module): LSTM(400, 1152, batch_first=True)\n",
              "        )\n",
              "        (1): WeightDropout(\n",
              "          (module): LSTM(1152, 1152, batch_first=True)\n",
              "        )\n",
              "        (2): WeightDropout(\n",
              "          (module): LSTM(1152, 400, batch_first=True)\n",
              "        )\n",
              "      )\n",
              "      (input_dp): RNNDropout()\n",
              "      (hidden_dps): ModuleList(\n",
              "        (0): RNNDropout()\n",
              "        (1): RNNDropout()\n",
              "        (2): RNNDropout()\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (1): PoolingLinearClassifier(\n",
              "    (layers): Sequential(\n",
              "      (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Dropout(p=0.04000000000000001, inplace=False)\n",
              "      (2): Linear(in_features=1200, out_features=50, bias=True)\n",
              "      (3): ReLU(inplace=True)\n",
              "      (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): Dropout(p=0.1, inplace=False)\n",
              "      (6): Linear(in_features=50, out_features=4, bias=True)\n",
              "    )\n",
              "  )\n",
              "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of CrossEntropyLoss(), metrics=[<function accuracy at 0x7f52584be158>], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('/content'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[RNNTrainer\n",
              "learn: RNNLearner(data=TextClasDataBunch;\n",
              "\n",
              "Train: LabelList (844 items)\n",
              "x: TextList\n",
              "xxbos xxmaj systems and methods for providing a user with information on xxunk in which to park a vehicle are provided . a method for providing a user with information on xxunk in which to park a vehicle , comprises identifying one or more xxunk in which to park the vehicle , selecting valid xxunk from the identified one or more xxunk based on a xxunk xxunk , and xxunk the valid xxunk and providing a xxunk xxunk of the valid xxunk to the user , wherein the identifying , selecting , xxunk and providing steps are performed by a computer system comprising a memory and at least one processor coupled to the memory .,xxbos xxmaj the invention provides the automatic opening control system of a kind of vehicle trunk , vehicle and method , wherein system comprises : xxunk enter system , xxup can network system , receiving wire , inductive probe , and boot control system ; xxmaj the described system that xxunk enters carries out xxunk process to the information that described xxup can network system , receiving wire , inductive probe pass over , and then sends instruction to described boot control system , controls the unlatching of boot . xxmaj the solution of the present invention , can by xxunk that the foot action of operator controls the unlatching of vehicle trunk , and convenient operation person is to placing xxunk in boot .,xxbos xxmaj an in - vehicle information system includes a camera and a controller that xxunk xxunk input . a controller receives frames of video data and generates trajectory data for a movement of a hand in the video data . xxmaj the controller uses a first xxunk xxmaj xxunk model ( xxup xxunk ) to xxunk a sequence of xxunk from the trajectory data , removes a starting and xxunk stroke to form an edited stroke sequence , and re - xxunk the xxunk in the edited stroke sequence . xxmaj the controller uses a second xxup xxunk corresponding to a predetermined set of xxunk to identify a xxunk corresponding to the re - xxunk edited stroke sequence .,xxbos xxmaj an apparatus for controlling at least one function of a motor vehicle has a touch - sensitive input panel . xxmaj the touch - sensitive input panel includes a transparent material .,xxbos a display apparatus for vehicle includes a xxunk that xxunk speed of a vehicle , an eco - drive level determination unit that determines an eco - drive level at driving the vehicle based on a xxunk xxunk amount and an actual xxunk amount , a determination display portion located inside a peripheral visual field region of the driver , a xxunk display portion located outside of the peripheral visual field region , and a display control unit that controls display in the determination display portion and the xxunk display portion . xxmaj the display control unit performs the following controls of ( a ) setting the display in the determination display portion to ambient display in which contrast of the outline region is lower than contrast inside the outline region , and ( b ) changing the ambient display according to the change in the eco - drive level .\n",
              "y: CategoryList\n",
              "Non Autonomous,Non Autonomous,Non Autonomous,Non Autonomous,Non Autonomous\n",
              "Path: /content;\n",
              "\n",
              "Valid: LabelList (211 items)\n",
              "x: TextList\n",
              "xxbos xxmaj an electrical apparatus for automatically xxunk a beverage , which comprises an air processing system takes in xxunk air and xxunk oxygen content to reach as much as 90 % or higher by volume . xxmaj the apparatus further comprises a controller for automatically controlling parameters of the oxygen xxunk process including a time period for delivery of oxygen xxunk air to a beverage , and the time period is predetermined in accordance with one or more characteristics or xxunk of the beverage .,xxbos xxmaj the invention relates to the development of microorganisms capable of producing fermentation products via an engineered xxunk in the microorganisms . xxmaj the invention also relates to microorganisms with improved cell xxunk and methods to improve cell xxunk and cell xxunk of a microorganism .,xxbos a wine barrel rack system having first and second ground level side rails oriented generally parallel to one another , each of the first and second ground level side rails xxunk from sheet metal panels , and having an upper bend forming a flange and a lower bend forming a flange , and having a plurality of upper arcuate cut xxunk shaped and sized for xxunk a wine barrel . xxmaj connecting members xxunk the rails to form the rack , which may have ground level configuration and an upper , xxunk configuration , the latter including either xxunk rails with xxunk for mounting on lower barrels and support upper barrels , or discrete arcuate barrel xxunk for mounting between two lower barrels and support a single barrel .,xxbos xxmaj an automated driving system can determine , based on input from one or more sensors disposed on an autonomous vehicle , a required fuel level to complete a planned vehicle path and a current fuel level for the autonomous vehicle . xxmaj if the current fuel level is below the required fuel level , the automated driving system can identify one or more refueling stations and send an indication to a driver of the autonomous vehicle requesting selection of one of the identified refueling stations . xxmaj if a refueling station selection is received , the automated driving system can xxunk the planned vehicle path to include a stop at the selected refueling station . xxmaj if a refueling station selection is not received , the automated driving system can determine a xxunk fuel level . xxmaj if the current fuel level falls below the xxunk fuel level , the automated driving system can drive the autonomous vehicle to a proximate refueling station .,xxbos a work vehicle comprising , a power unit , an electric motor activated by electric power from the power unit , a xxunk transmission system for receiving drive power from the electric motor , a cabin including a roof portion the driver xxunk , an air conditioner for adjusting air temperature in the interior of the cabin . the power unit and the air conditioner are provided in the roof portion of the cabin .\n",
              "y: CategoryList\n",
              "Non-Alcohol,Alcohol,Alcohol,Autonomous,Non Autonomous\n",
              "Path: /content;\n",
              "\n",
              "Test: None, model=SequentialRNN(\n",
              "  (0): MultiBatchEncoder(\n",
              "    (module): AWD_LSTM(\n",
              "      (encoder): Embedding(3392, 400, padding_idx=1)\n",
              "      (encoder_dp): EmbeddingDropout(\n",
              "        (emb): Embedding(3392, 400, padding_idx=1)\n",
              "      )\n",
              "      (rnns): ModuleList(\n",
              "        (0): WeightDropout(\n",
              "          (module): LSTM(400, 1152, batch_first=True)\n",
              "        )\n",
              "        (1): WeightDropout(\n",
              "          (module): LSTM(1152, 1152, batch_first=True)\n",
              "        )\n",
              "        (2): WeightDropout(\n",
              "          (module): LSTM(1152, 400, batch_first=True)\n",
              "        )\n",
              "      )\n",
              "      (input_dp): RNNDropout()\n",
              "      (hidden_dps): ModuleList(\n",
              "        (0): RNNDropout()\n",
              "        (1): RNNDropout()\n",
              "        (2): RNNDropout()\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (1): PoolingLinearClassifier(\n",
              "    (layers): Sequential(\n",
              "      (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Dropout(p=0.04000000000000001, inplace=False)\n",
              "      (2): Linear(in_features=1200, out_features=50, bias=True)\n",
              "      (3): ReLU(inplace=True)\n",
              "      (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): Dropout(p=0.1, inplace=False)\n",
              "      (6): Linear(in_features=50, out_features=4, bias=True)\n",
              "    )\n",
              "  )\n",
              "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of CrossEntropyLoss(), metrics=[<function accuracy at 0x7f52584be158>], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('/content'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[...], layer_groups=[Sequential(\n",
              "  (0): Embedding(3392, 400, padding_idx=1)\n",
              "  (1): EmbeddingDropout(\n",
              "    (emb): Embedding(3392, 400, padding_idx=1)\n",
              "  )\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(400, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 400, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): PoolingLinearClassifier(\n",
              "    (layers): Sequential(\n",
              "      (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Dropout(p=0.04000000000000001, inplace=False)\n",
              "      (2): Linear(in_features=1200, out_features=50, bias=True)\n",
              "      (3): ReLU(inplace=True)\n",
              "      (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): Dropout(p=0.1, inplace=False)\n",
              "      (6): Linear(in_features=50, out_features=4, bias=True)\n",
              "    )\n",
              "  )\n",
              ")], add_time=True, silent=False)\n",
              "alpha: 2.0\n",
              "beta: 1.0], layer_groups=[Sequential(\n",
              "  (0): Embedding(3392, 400, padding_idx=1)\n",
              "  (1): EmbeddingDropout(\n",
              "    (emb): Embedding(3392, 400, padding_idx=1)\n",
              "  )\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(400, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 400, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): PoolingLinearClassifier(\n",
              "    (layers): Sequential(\n",
              "      (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Dropout(p=0.04000000000000001, inplace=False)\n",
              "      (2): Linear(in_features=1200, out_features=50, bias=True)\n",
              "      (3): ReLU(inplace=True)\n",
              "      (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): Dropout(p=0.1, inplace=False)\n",
              "      (6): Linear(in_features=50, out_features=4, bias=True)\n",
              "    )\n",
              "  )\n",
              ")], add_time=True, silent=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_J5i_OobrUbk",
        "colab_type": "code",
        "outputId": "3499d0ae-3628-48b2-d21a-693799b906bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        }
      },
      "source": [
        "learn.lr_find()"
      ],
      "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='5' class='' max='6', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      83.33% [5/6 00:21<00:04]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.574819</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.553877</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.393808</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.079054</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.953484</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\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='8' class='' max='17', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      47.06% [8/17 00:02<00:02 2.3474]\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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GF7aLRub3cAb",
        "colab_type": "code",
        "outputId": "886629ef-32e0-4ba7-c15d-4733bb3d5065",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "learn.recorder.plot()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQiAiEuH+H5r/wxGCsAsWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vb94i5d5MVGM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.freeze_to(-3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLF8cNwAPc4k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# stopped after 5 iterations with 1e-1 lr, 0.7 momentum acc = 86.3;;;;\n",
        "#stopped after 1 iteration with 1e-1 lr, 0.7 momentum acc = 87.5\n",
        "#stopped after 6 iterations with 1e-3 lr, 0.9 momentum acc = 87.5\n",
        "#stopped after 5 iterations with 1e-3 lr, 0.7 momentum acc = 87.5\n",
        "#stopped after 3 iterations with 1e-2 lr, 0.7 momentum acc = 88.1\n",
        "#stopped after 6 iterations with 1e-2 lr, 0.7 momentum acc = 87.5\n",
        "#stopped after 6 iterations with 1e-1 lr, 0.9 momentum acc = 88.6"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWYfY96CuyKF",
        "colab_type": "code",
        "outputId": "618db09d-764a-45ce-adda-879a2c0d63ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        }
      },
      "source": [
        "learn.fit_one_cycle(100, slice(1e-2/(2.6**4),1e-1), moms=(0.7,0.7))"
      ],
      "execution_count": 24,
      "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='9' class='' max='100', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      9.00% [9/100 00:43<07:18]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.744292</td>\n",
              "      <td>0.801292</td>\n",
              "      <td>0.810427</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.553595</td>\n",
              "      <td>0.494802</td>\n",
              "      <td>0.867299</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.404642</td>\n",
              "      <td>0.424435</td>\n",
              "      <td>0.838863</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.290085</td>\n",
              "      <td>0.404134</td>\n",
              "      <td>0.848341</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.225843</td>\n",
              "      <td>0.532461</td>\n",
              "      <td>0.838863</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.188216</td>\n",
              "      <td>0.638863</td>\n",
              "      <td>0.853081</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.176554</td>\n",
              "      <td>0.488600</td>\n",
              "      <td>0.848341</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.164653</td>\n",
              "      <td>0.438782</td>\n",
              "      <td>0.886256</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.129503</td>\n",
              "      <td>0.442913</td>\n",
              "      <td>0.890995</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\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='15' class='' max='17', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      88.24% [15/17 00:03<00:00 0.1215]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-24-31b8f396f88e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.6\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1e-1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.7\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\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    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\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    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopt\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;32m---> 33\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mskip_bwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_backward_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\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     34\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_bwd\u001b[0m\u001b[0;34m:\u001b[0m                     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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     35\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_backward_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mopt\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/callback.py\u001b[0m in \u001b[0;36mon_backward_begin\u001b[0;34m(self, loss)\u001b[0m\n\u001b[1;32m    288\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_backward_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mAny\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    289\u001b[0m         \u001b[0;34m\"Handle gradient calculation on `loss`.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\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[0m\u001b[1;32m    291\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'last_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'smooth_loss'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmooth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'backward_begin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcall_mets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1jhl4a5vKeg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('final')#90.5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypRgRGCNrzfG",
        "colab_type": "code",
        "outputId": "b166bce0-6652-486a-b47d-37d87fa32d49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        }
      },
      "source": [
        "learn.fit_one_cycle(100, slice(1e-2/(2.6**4),1e-2), moms=(0.9,0.9))"
      ],
      "execution_count": 0,
      "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='10' class='' max='100', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      10.00% [10/100 00:59<08:57]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.002334</td>\n",
              "      <td>0.546085</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.002262</td>\n",
              "      <td>0.560236</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.003382</td>\n",
              "      <td>0.566618</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.002809</td>\n",
              "      <td>0.594172</td>\n",
              "      <td>0.863095</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.003237</td>\n",
              "      <td>0.606978</td>\n",
              "      <td>0.869048</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.003520</td>\n",
              "      <td>0.619262</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.003368</td>\n",
              "      <td>0.636253</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.003041</td>\n",
              "      <td>0.642964</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.002963</td>\n",
              "      <td>0.617544</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.003545</td>\n",
              "      <td>0.581913</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\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='6' class='' max='14', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      42.86% [6/14 00:03<00:04 0.0031]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-66-7a52a67c1db4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.6\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.9\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\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    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\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    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_step_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m     \u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\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     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m def get_preds(model:nn.Module, dl:DataLoader, pbar:Optional[PBar]=None, cb_handler:Optional[CallbackHandler]=None,\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vo7zmDfV3ct",
        "colab_type": "code",
        "outputId": "269011aa-ced0-4ae3-d98f-cb7ee5e168bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        }
      },
      "source": [
        "learn.fit_one_cycle(100, slice(1e-2/(2.6**4),1e-2), moms=(0.9,0.9))"
      ],
      "execution_count": 0,
      "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='3' class='' max='100', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      3.00% [3/100 00:17<09:21]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.003704</td>\n",
              "      <td>0.574468</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.004257</td>\n",
              "      <td>0.599962</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.003940</td>\n",
              "      <td>0.590318</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\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='8' class='' max='14', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      57.14% [8/14 00:03<00:02 0.0031]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-68-7a52a67c1db4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.6\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.9\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\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    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\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    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_step_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m     \u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\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     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m def get_preds(model:nn.Module, dl:DataLoader, pbar:Optional[PBar]=None, cb_handler:Optional[CallbackHandler]=None,\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipWBJPgZWAno",
        "colab_type": "code",
        "outputId": "acaeb267-7196-430e-856e-ccb47f176457",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        }
      },
      "source": [
        "learn.fit_one_cycle(100, slice(1e-2/(2.6**4),1e-3), moms=(0.9,0.9))"
      ],
      "execution_count": 0,
      "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='4' class='' max='100', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      4.00% [4/100 00:24<09:40]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.000927</td>\n",
              "      <td>0.576821</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.001398</td>\n",
              "      <td>0.593341</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.001588</td>\n",
              "      <td>0.592217</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.001245</td>\n",
              "      <td>0.595715</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\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='6' class='' max='14', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      42.86% [6/14 00:02<00:03 0.0011]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-69-6884af8bd355>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.6\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1e-3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.9\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\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    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\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    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopt\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;32m---> 33\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mskip_bwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_backward_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\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     34\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_bwd\u001b[0m\u001b[0;34m:\u001b[0m                     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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     35\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_backward_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mopt\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/callback.py\u001b[0m in \u001b[0;36mon_backward_begin\u001b[0;34m(self, loss)\u001b[0m\n\u001b[1;32m    288\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_backward_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mAny\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    289\u001b[0m         \u001b[0;34m\"Handle gradient calculation on `loss`.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\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[0m\u001b[1;32m    291\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'last_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'smooth_loss'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmooth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'backward_begin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcall_mets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-Kw19qdWMO1",
        "colab_type": "code",
        "outputId": "db7cf17d-9f51-4ea6-d77b-b8adae966a95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        }
      },
      "source": [
        "learn.fit_one_cycle(100, slice(1e-2/(2.6**4),1e-3), moms=(0.7,0.7))"
      ],
      "execution_count": 0,
      "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='10' class='' max='100', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      10.00% [10/100 00:59<08:52]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.004805</td>\n",
              "      <td>0.591111</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.004421</td>\n",
              "      <td>0.583605</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.004125</td>\n",
              "      <td>0.593193</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.004169</td>\n",
              "      <td>0.612267</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.003815</td>\n",
              "      <td>0.597226</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.003366</td>\n",
              "      <td>0.608094</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.003909</td>\n",
              "      <td>0.602928</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.003657</td>\n",
              "      <td>0.655739</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.004053</td>\n",
              "      <td>0.669686</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.004604</td>\n",
              "      <td>0.642592</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\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='9' class='' max='14', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      64.29% [9/14 00:03<00:02 0.0043]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-70-ddc9d5be1534>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.6\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1e-3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.7\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\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    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\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    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_step_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m     \u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\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     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m def get_preds(model:nn.Module, dl:DataLoader, pbar:Optional[PBar]=None, cb_handler:Optional[CallbackHandler]=None,\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-KeV6XlxXnnH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.freeze_to(-3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "viieazvvWfJv",
        "colab_type": "code",
        "outputId": "569cc673-46d0-4ca1-b30b-6507176410bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        }
      },
      "source": [
        "learn.fit_one_cycle(100, slice(1e-2/(2.6**4),1e-1), moms=(0.7,0.7))"
      ],
      "execution_count": 0,
      "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='7' class='' max='100', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      7.00% [7/100 00:38<08:35]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.003347</td>\n",
              "      <td>0.575722</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.004509</td>\n",
              "      <td>0.886958</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.012392</td>\n",
              "      <td>0.629748</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.017751</td>\n",
              "      <td>0.755225</td>\n",
              "      <td>0.851190</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.023324</td>\n",
              "      <td>1.075603</td>\n",
              "      <td>0.827381</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.037769</td>\n",
              "      <td>1.329947</td>\n",
              "      <td>0.815476</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.055512</td>\n",
              "      <td>0.686790</td>\n",
              "      <td>0.869048</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\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='9' class='' max='14', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      64.29% [9/14 00:03<00:01 0.0618]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-72-31b8f396f88e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.6\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1e-1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.7\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\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    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\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    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_step_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m     \u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\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     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m def get_preds(model:nn.Module, dl:DataLoader, pbar:Optional[PBar]=None, cb_handler:Optional[CallbackHandler]=None,\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRCWN4yCX3-k",
        "colab_type": "code",
        "outputId": "c2b80222-6564-4fdb-d1cc-eba48d64f87c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        }
      },
      "source": [
        "learn.fit_one_cycle(100, slice(1e-2/(2.6**4),1e-3), moms=(0.7,0.7))"
      ],
      "execution_count": 0,
      "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='7' class='' max='100', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      7.00% [7/100 00:40<08:59]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.005098</td>\n",
              "      <td>0.544753</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.009283</td>\n",
              "      <td>0.541683</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.008107</td>\n",
              "      <td>0.542142</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.009281</td>\n",
              "      <td>0.534959</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.008670</td>\n",
              "      <td>0.541485</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.009027</td>\n",
              "      <td>0.540149</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.009377</td>\n",
              "      <td>0.537426</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\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='9' class='' max='14', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      64.29% [9/14 00:03<00:01 0.0093]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-74-ddc9d5be1534>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.6\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1e-3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.7\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\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    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\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    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopt\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;32m---> 33\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mskip_bwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_backward_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\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     34\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_bwd\u001b[0m\u001b[0;34m:\u001b[0m                     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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     35\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_backward_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mopt\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/callback.py\u001b[0m in \u001b[0;36mon_backward_begin\u001b[0;34m(self, loss)\u001b[0m\n\u001b[1;32m    288\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_backward_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mAny\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    289\u001b[0m         \u001b[0;34m\"Handle gradient calculation on `loss`.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\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[0m\u001b[1;32m    291\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'last_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'smooth_loss'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmooth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'backward_begin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcall_mets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sic_eg4bYOhU",
        "colab_type": "code",
        "outputId": "c7180959-2510-430b-e827-9182e013ed91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        }
      },
      "source": [
        "learn.fit_one_cycle(100, slice(1e-2/(2.6**4),1e-3), moms=(0.9,0.8))"
      ],
      "execution_count": 0,
      "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='9' class='' max='100', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      9.00% [9/100 00:53<09:01]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.008593</td>\n",
              "      <td>0.555375</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.006752</td>\n",
              "      <td>0.561512</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.006893</td>\n",
              "      <td>0.554934</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.006828</td>\n",
              "      <td>0.552396</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.007347</td>\n",
              "      <td>0.548102</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.006634</td>\n",
              "      <td>0.547623</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.005880</td>\n",
              "      <td>0.558236</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.005295</td>\n",
              "      <td>0.550135</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.005484</td>\n",
              "      <td>0.541520</td>\n",
              "      <td>0.904762</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\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='8' class='' max='14', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      57.14% [8/14 00:03<00:02 0.0071]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-75-5dc18c131b49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.6\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1e-3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.8\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\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    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\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    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_step_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m     \u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\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     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m def get_preds(model:nn.Module, dl:DataLoader, pbar:Optional[PBar]=None, cb_handler:Optional[CallbackHandler]=None,\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMPXA4VsYidR",
        "colab_type": "code",
        "outputId": "bf839a69-06cf-4b5a-8ce5-c8567481a803",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn.fit_one_cycle(100, slice(1e-2/(2.6**4),1e-3), moms=(0.9,0.8))"
      ],
      "execution_count": 0,
      "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='21' class='' max='100', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      21.00% [21/100 02:02<07:41]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.007088</td>\n",
              "      <td>0.535476</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.006078</td>\n",
              "      <td>0.536205</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.006075</td>\n",
              "      <td>0.541740</td>\n",
              "      <td>0.904762</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.005489</td>\n",
              "      <td>0.538552</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.004961</td>\n",
              "      <td>0.534301</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.004897</td>\n",
              "      <td>0.541230</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.005284</td>\n",
              "      <td>0.546486</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.004822</td>\n",
              "      <td>0.539596</td>\n",
              "      <td>0.904762</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.004688</td>\n",
              "      <td>0.536633</td>\n",
              "      <td>0.904762</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.004919</td>\n",
              "      <td>0.543525</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.004697</td>\n",
              "      <td>0.557905</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.004062</td>\n",
              "      <td>0.564431</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.004063</td>\n",
              "      <td>0.553944</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.004226</td>\n",
              "      <td>0.554446</td>\n",
              "      <td>0.886905</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.004284</td>\n",
              "      <td>0.551359</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.003769</td>\n",
              "      <td>0.536146</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.003231</td>\n",
              "      <td>0.559738</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.003034</td>\n",
              "      <td>0.554976</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.002791</td>\n",
              "      <td>0.562499</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.003295</td>\n",
              "      <td>0.556083</td>\n",
              "      <td>0.898810</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.003653</td>\n",
              "      <td>0.590727</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\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='7' class='' max='14', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      50.00% [7/14 00:03<00:03 0.0032]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-78-5dc18c131b49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.6\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1e-3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.8\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\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    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\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    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopt\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     33\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mskip_bwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_backward_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_bwd\u001b[0m\u001b[0;34m:\u001b[0m                     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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     35\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_backward_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mopt\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_step_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m     \u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m    193\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m         \"\"\"\n\u001b[0;32m--> 195\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\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    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\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/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m     97\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m     98\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2tJ5Bq-ZE5C",
        "colab_type": "code",
        "outputId": "1c47a473-b98c-4d1b-eedc-95a7f2f00e0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        }
      },
      "source": [
        "learn.fit_one_cycle(100, slice(1e-2/(2.6**4),1e-3), moms=(0.7,0.7))"
      ],
      "execution_count": 0,
      "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='2' class='' max='100', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      2.00% [2/100 00:11<09:29]\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",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.004196</td>\n",
              "      <td>0.596926</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.004702</td>\n",
              "      <td>0.604806</td>\n",
              "      <td>0.880952</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\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='8' class='' max='14', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      57.14% [8/14 00:03<00:02 0.0043]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-79-ddc9d5be1534>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.6\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1e-3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.7\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,\n\u001b[1;32m     22\u001b[0m                                        final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\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     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr,  moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextra_callback_fns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcallbacks\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, learn, callbacks, metrics)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\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    100\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\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    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopt\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;32m---> 33\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mskip_bwd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_backward_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\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     34\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_bwd\u001b[0m\u001b[0;34m:\u001b[0m                     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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     35\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_backward_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mopt\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/callback.py\u001b[0m in \u001b[0;36mon_backward_begin\u001b[0;34m(self, loss)\u001b[0m\n\u001b[1;32m    288\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_backward_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mAny\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    289\u001b[0m         \u001b[0;34m\"Handle gradient calculation on `loss`.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\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[0m\u001b[1;32m    291\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'last_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'smooth_loss'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmooth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'backward_begin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcall_mets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1z5lm678_vUV",
        "colab_type": "code",
        "outputId": "0c010157-37fa-43a4-f55a-a34692868459",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn.load('final')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RNNLearner(data=TextClasDataBunch;\n",
              "\n",
              "Train: LabelList (844 items)\n",
              "x: TextList\n",
              "xxbos xxmaj systems and methods for providing a user with information on xxunk in which to park a vehicle are provided . a method for providing a user with information on xxunk in which to park a vehicle , comprises identifying one or more xxunk in which to park the vehicle , selecting valid xxunk from the identified one or more xxunk based on a xxunk xxunk , and xxunk the valid xxunk and providing a xxunk xxunk of the valid xxunk to the user , wherein the identifying , selecting , xxunk and providing steps are performed by a computer system comprising a memory and at least one processor coupled to the memory .,xxbos xxmaj the invention provides the automatic opening control system of a kind of vehicle trunk , vehicle and method , wherein system comprises : xxunk enter system , xxup can network system , receiving wire , inductive probe , and boot control system ; xxmaj the described system that xxunk enters carries out xxunk process to the information that described xxup can network system , receiving wire , inductive probe pass over , and then sends instruction to described boot control system , controls the unlatching of boot . xxmaj the solution of the present invention , can by xxunk that the foot action of operator controls the unlatching of vehicle trunk , and convenient operation person is to placing xxunk in boot .,xxbos xxmaj an in - vehicle information system includes a camera and a controller that xxunk xxunk input . a controller receives frames of video data and generates trajectory data for a movement of a hand in the video data . xxmaj the controller uses a first xxunk xxmaj xxunk model ( xxup xxunk ) to xxunk a sequence of xxunk from the trajectory data , removes a starting and xxunk stroke to form an edited stroke sequence , and re - xxunk the xxunk in the edited stroke sequence . xxmaj the controller uses a second xxup xxunk corresponding to a predetermined set of xxunk to identify a xxunk corresponding to the re - xxunk edited stroke sequence .,xxbos xxmaj an apparatus for controlling at least one function of a motor vehicle has a touch - sensitive input panel . xxmaj the touch - sensitive input panel includes a transparent material .,xxbos a display apparatus for vehicle includes a xxunk that xxunk speed of a vehicle , an eco - drive level determination unit that determines an eco - drive level at driving the vehicle based on a xxunk xxunk amount and an actual xxunk amount , a determination display portion located inside a peripheral visual field region of the driver , a xxunk display portion located outside of the peripheral visual field region , and a display control unit that controls display in the determination display portion and the xxunk display portion . xxmaj the display control unit performs the following controls of ( a ) setting the display in the determination display portion to ambient display in which contrast of the outline region is lower than contrast inside the outline region , and ( b ) changing the ambient display according to the change in the eco - drive level .\n",
              "y: CategoryList\n",
              "Non Autonomous,Non Autonomous,Non Autonomous,Non Autonomous,Non Autonomous\n",
              "Path: /content;\n",
              "\n",
              "Valid: LabelList (211 items)\n",
              "x: TextList\n",
              "xxbos xxmaj an electrical apparatus for automatically xxunk a beverage , which comprises an air processing system takes in xxunk air and xxunk oxygen content to reach as much as 90 % or higher by volume . xxmaj the apparatus further comprises a controller for automatically controlling parameters of the oxygen xxunk process including a time period for delivery of oxygen xxunk air to a beverage , and the time period is predetermined in accordance with one or more characteristics or xxunk of the beverage .,xxbos xxmaj the invention relates to the development of microorganisms capable of producing fermentation products via an engineered xxunk in the microorganisms . xxmaj the invention also relates to microorganisms with improved cell xxunk and methods to improve cell xxunk and cell xxunk of a microorganism .,xxbos a wine barrel rack system having first and second ground level side rails oriented generally parallel to one another , each of the first and second ground level side rails xxunk from sheet metal panels , and having an upper bend forming a flange and a lower bend forming a flange , and having a plurality of upper arcuate cut xxunk shaped and sized for xxunk a wine barrel . xxmaj connecting members xxunk the rails to form the rack , which may have ground level configuration and an upper , xxunk configuration , the latter including either xxunk rails with xxunk for mounting on lower barrels and support upper barrels , or discrete arcuate barrel xxunk for mounting between two lower barrels and support a single barrel .,xxbos xxmaj an automated driving system can determine , based on input from one or more sensors disposed on an autonomous vehicle , a required fuel level to complete a planned vehicle path and a current fuel level for the autonomous vehicle . xxmaj if the current fuel level is below the required fuel level , the automated driving system can identify one or more refueling stations and send an indication to a driver of the autonomous vehicle requesting selection of one of the identified refueling stations . xxmaj if a refueling station selection is received , the automated driving system can xxunk the planned vehicle path to include a stop at the selected refueling station . xxmaj if a refueling station selection is not received , the automated driving system can determine a xxunk fuel level . xxmaj if the current fuel level falls below the xxunk fuel level , the automated driving system can drive the autonomous vehicle to a proximate refueling station .,xxbos a work vehicle comprising , a power unit , an electric motor activated by electric power from the power unit , a xxunk transmission system for receiving drive power from the electric motor , a cabin including a roof portion the driver xxunk , an air conditioner for adjusting air temperature in the interior of the cabin . the power unit and the air conditioner are provided in the roof portion of the cabin .\n",
              "y: CategoryList\n",
              "Non-Alcohol,Alcohol,Alcohol,Autonomous,Non Autonomous\n",
              "Path: /content;\n",
              "\n",
              "Test: None, model=SequentialRNN(\n",
              "  (0): MultiBatchEncoder(\n",
              "    (module): AWD_LSTM(\n",
              "      (encoder): Embedding(3392, 400, padding_idx=1)\n",
              "      (encoder_dp): EmbeddingDropout(\n",
              "        (emb): Embedding(3392, 400, padding_idx=1)\n",
              "      )\n",
              "      (rnns): ModuleList(\n",
              "        (0): WeightDropout(\n",
              "          (module): LSTM(400, 1152, batch_first=True)\n",
              "        )\n",
              "        (1): WeightDropout(\n",
              "          (module): LSTM(1152, 1152, batch_first=True)\n",
              "        )\n",
              "        (2): WeightDropout(\n",
              "          (module): LSTM(1152, 400, batch_first=True)\n",
              "        )\n",
              "      )\n",
              "      (input_dp): RNNDropout()\n",
              "      (hidden_dps): ModuleList(\n",
              "        (0): RNNDropout()\n",
              "        (1): RNNDropout()\n",
              "        (2): RNNDropout()\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (1): PoolingLinearClassifier(\n",
              "    (layers): Sequential(\n",
              "      (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Dropout(p=0.04000000000000001, inplace=False)\n",
              "      (2): Linear(in_features=1200, out_features=50, bias=True)\n",
              "      (3): ReLU(inplace=True)\n",
              "      (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): Dropout(p=0.1, inplace=False)\n",
              "      (6): Linear(in_features=50, out_features=4, bias=True)\n",
              "    )\n",
              "  )\n",
              "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of CrossEntropyLoss(), metrics=[<function accuracy at 0x7f52584be158>], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('/content'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[RNNTrainer\n",
              "learn: RNNLearner(data=TextClasDataBunch;\n",
              "\n",
              "Train: LabelList (844 items)\n",
              "x: TextList\n",
              "xxbos xxmaj systems and methods for providing a user with information on xxunk in which to park a vehicle are provided . a method for providing a user with information on xxunk in which to park a vehicle , comprises identifying one or more xxunk in which to park the vehicle , selecting valid xxunk from the identified one or more xxunk based on a xxunk xxunk , and xxunk the valid xxunk and providing a xxunk xxunk of the valid xxunk to the user , wherein the identifying , selecting , xxunk and providing steps are performed by a computer system comprising a memory and at least one processor coupled to the memory .,xxbos xxmaj the invention provides the automatic opening control system of a kind of vehicle trunk , vehicle and method , wherein system comprises : xxunk enter system , xxup can network system , receiving wire , inductive probe , and boot control system ; xxmaj the described system that xxunk enters carries out xxunk process to the information that described xxup can network system , receiving wire , inductive probe pass over , and then sends instruction to described boot control system , controls the unlatching of boot . xxmaj the solution of the present invention , can by xxunk that the foot action of operator controls the unlatching of vehicle trunk , and convenient operation person is to placing xxunk in boot .,xxbos xxmaj an in - vehicle information system includes a camera and a controller that xxunk xxunk input . a controller receives frames of video data and generates trajectory data for a movement of a hand in the video data . xxmaj the controller uses a first xxunk xxmaj xxunk model ( xxup xxunk ) to xxunk a sequence of xxunk from the trajectory data , removes a starting and xxunk stroke to form an edited stroke sequence , and re - xxunk the xxunk in the edited stroke sequence . xxmaj the controller uses a second xxup xxunk corresponding to a predetermined set of xxunk to identify a xxunk corresponding to the re - xxunk edited stroke sequence .,xxbos xxmaj an apparatus for controlling at least one function of a motor vehicle has a touch - sensitive input panel . xxmaj the touch - sensitive input panel includes a transparent material .,xxbos a display apparatus for vehicle includes a xxunk that xxunk speed of a vehicle , an eco - drive level determination unit that determines an eco - drive level at driving the vehicle based on a xxunk xxunk amount and an actual xxunk amount , a determination display portion located inside a peripheral visual field region of the driver , a xxunk display portion located outside of the peripheral visual field region , and a display control unit that controls display in the determination display portion and the xxunk display portion . xxmaj the display control unit performs the following controls of ( a ) setting the display in the determination display portion to ambient display in which contrast of the outline region is lower than contrast inside the outline region , and ( b ) changing the ambient display according to the change in the eco - drive level .\n",
              "y: CategoryList\n",
              "Non Autonomous,Non Autonomous,Non Autonomous,Non Autonomous,Non Autonomous\n",
              "Path: /content;\n",
              "\n",
              "Valid: LabelList (211 items)\n",
              "x: TextList\n",
              "xxbos xxmaj an electrical apparatus for automatically xxunk a beverage , which comprises an air processing system takes in xxunk air and xxunk oxygen content to reach as much as 90 % or higher by volume . xxmaj the apparatus further comprises a controller for automatically controlling parameters of the oxygen xxunk process including a time period for delivery of oxygen xxunk air to a beverage , and the time period is predetermined in accordance with one or more characteristics or xxunk of the beverage .,xxbos xxmaj the invention relates to the development of microorganisms capable of producing fermentation products via an engineered xxunk in the microorganisms . xxmaj the invention also relates to microorganisms with improved cell xxunk and methods to improve cell xxunk and cell xxunk of a microorganism .,xxbos a wine barrel rack system having first and second ground level side rails oriented generally parallel to one another , each of the first and second ground level side rails xxunk from sheet metal panels , and having an upper bend forming a flange and a lower bend forming a flange , and having a plurality of upper arcuate cut xxunk shaped and sized for xxunk a wine barrel . xxmaj connecting members xxunk the rails to form the rack , which may have ground level configuration and an upper , xxunk configuration , the latter including either xxunk rails with xxunk for mounting on lower barrels and support upper barrels , or discrete arcuate barrel xxunk for mounting between two lower barrels and support a single barrel .,xxbos xxmaj an automated driving system can determine , based on input from one or more sensors disposed on an autonomous vehicle , a required fuel level to complete a planned vehicle path and a current fuel level for the autonomous vehicle . xxmaj if the current fuel level is below the required fuel level , the automated driving system can identify one or more refueling stations and send an indication to a driver of the autonomous vehicle requesting selection of one of the identified refueling stations . xxmaj if a refueling station selection is received , the automated driving system can xxunk the planned vehicle path to include a stop at the selected refueling station . xxmaj if a refueling station selection is not received , the automated driving system can determine a xxunk fuel level . xxmaj if the current fuel level falls below the xxunk fuel level , the automated driving system can drive the autonomous vehicle to a proximate refueling station .,xxbos a work vehicle comprising , a power unit , an electric motor activated by electric power from the power unit , a xxunk transmission system for receiving drive power from the electric motor , a cabin including a roof portion the driver xxunk , an air conditioner for adjusting air temperature in the interior of the cabin . the power unit and the air conditioner are provided in the roof portion of the cabin .\n",
              "y: CategoryList\n",
              "Non-Alcohol,Alcohol,Alcohol,Autonomous,Non Autonomous\n",
              "Path: /content;\n",
              "\n",
              "Test: None, model=SequentialRNN(\n",
              "  (0): MultiBatchEncoder(\n",
              "    (module): AWD_LSTM(\n",
              "      (encoder): Embedding(3392, 400, padding_idx=1)\n",
              "      (encoder_dp): EmbeddingDropout(\n",
              "        (emb): Embedding(3392, 400, padding_idx=1)\n",
              "      )\n",
              "      (rnns): ModuleList(\n",
              "        (0): WeightDropout(\n",
              "          (module): LSTM(400, 1152, batch_first=True)\n",
              "        )\n",
              "        (1): WeightDropout(\n",
              "          (module): LSTM(1152, 1152, batch_first=True)\n",
              "        )\n",
              "        (2): WeightDropout(\n",
              "          (module): LSTM(1152, 400, batch_first=True)\n",
              "        )\n",
              "      )\n",
              "      (input_dp): RNNDropout()\n",
              "      (hidden_dps): ModuleList(\n",
              "        (0): RNNDropout()\n",
              "        (1): RNNDropout()\n",
              "        (2): RNNDropout()\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (1): PoolingLinearClassifier(\n",
              "    (layers): Sequential(\n",
              "      (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Dropout(p=0.04000000000000001, inplace=False)\n",
              "      (2): Linear(in_features=1200, out_features=50, bias=True)\n",
              "      (3): ReLU(inplace=True)\n",
              "      (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): Dropout(p=0.1, inplace=False)\n",
              "      (6): Linear(in_features=50, out_features=4, bias=True)\n",
              "    )\n",
              "  )\n",
              "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of CrossEntropyLoss(), metrics=[<function accuracy at 0x7f52584be158>], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('/content'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[...], layer_groups=[Sequential(\n",
              "  (0): Embedding(3392, 400, padding_idx=1)\n",
              "  (1): EmbeddingDropout(\n",
              "    (emb): Embedding(3392, 400, padding_idx=1)\n",
              "  )\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(400, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 400, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): PoolingLinearClassifier(\n",
              "    (layers): Sequential(\n",
              "      (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Dropout(p=0.04000000000000001, inplace=False)\n",
              "      (2): Linear(in_features=1200, out_features=50, bias=True)\n",
              "      (3): ReLU(inplace=True)\n",
              "      (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): Dropout(p=0.1, inplace=False)\n",
              "      (6): Linear(in_features=50, out_features=4, bias=True)\n",
              "    )\n",
              "  )\n",
              ")], add_time=True, silent=False)\n",
              "alpha: 2.0\n",
              "beta: 1.0], layer_groups=[Sequential(\n",
              "  (0): Embedding(3392, 400, padding_idx=1)\n",
              "  (1): EmbeddingDropout(\n",
              "    (emb): Embedding(3392, 400, padding_idx=1)\n",
              "  )\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(400, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 1152, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): WeightDropout(\n",
              "    (module): LSTM(1152, 400, batch_first=True)\n",
              "  )\n",
              "  (1): RNNDropout()\n",
              "), Sequential(\n",
              "  (0): PoolingLinearClassifier(\n",
              "    (layers): Sequential(\n",
              "      (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Dropout(p=0.04000000000000001, inplace=False)\n",
              "      (2): Linear(in_features=1200, out_features=50, bias=True)\n",
              "      (3): ReLU(inplace=True)\n",
              "      (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): Dropout(p=0.1, inplace=False)\n",
              "      (6): Linear(in_features=50, out_features=4, bias=True)\n",
              "    )\n",
              "  )\n",
              ")], add_time=True, silent=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-e0ewSogzdY4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fVEBN2mc1Sm6",
        "colab_type": "text"
      },
      "source": [
        "**Predictions on the Test Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ou1HbE-OQH5Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from bs4 import BeautifulSoup as bs\n",
        "import urllib.request as u\n",
        "response = u.urlopen(\"https://patents.google.com/patent/US9033315B2/en\")\n",
        "page_source = response.read()\n",
        "soup=bs(page_source)\n",
        "# x = (soup.find(\"meta\",{\"name\":\"description\"})['content'])\n",
        "# print(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymtC5aVEzCfv",
        "colab_type": "code",
        "outputId": "fbab97eb-40ce-4e36-f4c4-68ee4141b604",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import csv\n",
        "import pandas as pd\n",
        "file1 = open('test_file1.csv', 'r')\n",
        "csv_reader = csv.reader(file1)\n",
        "df = pd.DataFrame()\n",
        "list3 = []\n",
        "list2 = []\n",
        "dict_ = {}\n",
        "c = 0\n",
        "for row in csv_reader:\n",
        "        try:\n",
        "          print(row[1])\n",
        "          response = u.urlopen(\"https://patents.google.com/patent/\" + row[1] + \"/en\")\n",
        "          page_source = response.read()\n",
        "          soup=bs(page_source)\n",
        "          label=\"\"\n",
        "          list2.append(row[1])\n",
        "          x = (soup.find(\"meta\",{\"name\":\"description\"})['content'])\n",
        "          list3.append(x)\n",
        "          print(c)\n",
        "          c = c+1\n",
        "          dict_[row[1]]=x\n",
        "        except Exception as e:\n",
        "          print(\"exce\")\n",
        "          print(e)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "US20150093479A1\n",
            "0\n",
            "US20150086688A1\n",
            "1\n",
            "US20150086675A1\n",
            "2\n",
            "US20150086655A1\n",
            "3\n",
            "US20150080469A1\n",
            "4\n",
            "US20150079269A1\n",
            "5\n",
            "US20150079232A1\n",
            "6\n",
            "US20150079207A1\n",
            "7\n",
            "US20150079157A1\n",
            "8\n",
            "US20150073125A1\n",
            "9\n",
            "US20150072060A1\n",
            "10\n",
            "US20150072045A1\n",
            "11\n",
            "US20150071891A1\n",
            "12\n",
            "US20150068411A1\n",
            "13\n",
            "US20150065587A1\n",
            "14\n",
            "US20150064310A1\n",
            "15\n",
            "US20150064271A1\n",
            "16\n",
            "US20150064162A1\n",
            "17\n",
            "EP2859818A1\n",
            "18\n",
            "EP2843038A1\n",
            "19\n",
            "EP2839747A1\n",
            "20\n",
            "EP2839749A1\n",
            "21\n",
            "EP2832437A1\n",
            "22\n",
            "EP2832235A1\n",
            "23\n",
            "EP1915058B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2629629B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2268792B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2413708B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP1965663B2\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP1723854B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2530148B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP1937091B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2412251B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2279659B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2648546B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "WO2015030253A1\n",
            "24\n",
            "WO2015030162A1\n",
            "25\n",
            "WO2015030161A1\n",
            "26\n",
            "WO2015029605A1\n",
            "27\n",
            "WO2015028784A1\n",
            "28\n",
            "WO2015026216A1\n",
            "29\n",
            "WO2015025072A1\n",
            "30\n",
            "WO2015024593A1\n",
            "31\n",
            "WO2014107642A8\n",
            "32\n",
            "WO2015023281A1\n",
            "33\n",
            "EP2875056A1\n",
            "34\n",
            "WO2015021193A1\n",
            "35\n",
            "WO2015018944A1\n",
            "36\n",
            "WO2015007848A1\n",
            "37\n",
            "CN104655619A\n",
            "38\n",
            "CN104651878A\n",
            "39\n",
            "CN104651546A\n",
            "40\n",
            "US9040460B2\n",
            "41\n",
            "US9033918B2\n",
            "42\n",
            "US9029608B2\n",
            "43\n",
            "US9028795B2\n",
            "44\n",
            "US9027792B2\n",
            "45\n",
            "US9022257B2\n",
            "46\n",
            "US9018427B2\n",
            "47\n",
            "WO2015022978A1\n",
            "48\n",
            "WO2014202564A3\n",
            "49\n",
            "WO2015020912A1\n",
            "50\n",
            "WO2015037679A1\n",
            "51\n",
            "WO2015032620A1\n",
            "52\n",
            "US20150044328A1\n",
            "53\n",
            "US20150037855A1\n",
            "54\n",
            "US20150030721A1\n",
            "55\n",
            "US20150030710A1\n",
            "56\n",
            "US20150021794A1\n",
            "57\n",
            "US20150014272A1\n",
            "58\n",
            "US20150000530A1\n",
            "59\n",
            "EP2013327B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2496758B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2250274B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2592135B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2548815B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "US9936856B1\n",
            "60\n",
            "US9926954B2\n",
            "61\n",
            "US9926246B2\n",
            "62\n",
            "CN104651177A\n",
            "63\n",
            "US9914684B2\n",
            "64\n",
            "US9909148B2\n",
            "65\n",
            "US9909089B2\n",
            "66\n",
            "US20170245666A1\n",
            "67\n",
            "US9719210B2\n",
            "68\n",
            "US9719048B2\n",
            "69\n",
            "US9707171B2\n",
            "70\n",
            "US9706864B2\n",
            "71\n",
            "US9702083B2\n",
            "72\n",
            "US20180112159A9\n",
            "73\n",
            "US9688522B2\n",
            "74\n",
            "US20170226537A1\n",
            "75\n",
            "US9670049B2\n",
            "76\n",
            "US9669966B2\n",
            "77\n",
            "CN104651172A\n",
            "78\n",
            "CN104651171A\n",
            "79\n",
            "WO2015010015A3\n",
            "80\n",
            "WO2015032551A1\n",
            "81\n",
            "CN104651199A\n",
            "82\n",
            "CN104651198A\n",
            "83\n",
            "US20160016484A1\n",
            "84\n",
            "EP2983270A1\n",
            "85\n",
            "US20160016481A1\n",
            "86\n",
            "CN105320025A\n",
            "87\n",
            "US20160016479A1\n",
            "88\n",
            "US20160016475A1\n",
            "89\n",
            "CN105270292A\n",
            "90\n",
            "US20160016469A1\n",
            "91\n",
            "US20160016465A1\n",
            "92\n",
            "CN105313803A\n",
            "93\n",
            "US20160015137A1\n",
            "94\n",
            "US20160014956A1\n",
            "95\n",
            "US20160014954A1\n",
            "96\n",
            "CN105313774A\n",
            "97\n",
            "CN105323350A\n",
            "98\n",
            "US20160014252A1\n",
            "99\n",
            "US20160014105A1\n",
            "100\n",
            "CN105260198A\n",
            "101\n",
            "CN105322595A\n",
            "102\n",
            "US20160013587A1\n",
            "103\n",
            "EP2955795A4\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "US20160012654A1\n",
            "104\n",
            "WO2016007712A9\n",
            "105\n",
            "US20160012274A1\n",
            "106\n",
            "US20160011795A1\n",
            "107\n",
            "US20160011712A1\n",
            "108\n",
            "US20160011595A1\n",
            "109\n",
            "EP2985516A1\n",
            "110\n",
            "US20160010823A1\n",
            "111\n",
            "US20160010737A1\n",
            "112\n",
            "US20160010379A1\n",
            "113\n",
            "US20160010366A1\n",
            "114\n",
            "US20160009509A1\n",
            "115\n",
            "US20160009405A1\n",
            "116\n",
            "US20160009332A1\n",
            "117\n",
            "CN105313899A\n",
            "118\n",
            "EP2986479A1\n",
            "119\n",
            "CN105253144A\n",
            "120\n",
            "CN105270401A\n",
            "121\n",
            "EP2977283A1\n",
            "122\n",
            "US20160009272A1\n",
            "123\n",
            "US20160009269A1\n",
            "124\n",
            "US20160009255A1\n",
            "125\n",
            "US20160009252A1\n",
            "126\n",
            "US20160009251A1\n",
            "127\n",
            "US20170031446A1\n",
            "128\n",
            "US20170030725A1\n",
            "129\n",
            "US20160379494A1\n",
            "130\n",
            "CN106256643A\n",
            "131\n",
            "US20160347383A1\n",
            "132\n",
            "US20160311480A1\n",
            "133\n",
            "CN106080597A\n",
            "134\n",
            "US20160009250A1\n",
            "135\n",
            "US20160009247A1\n",
            "136\n",
            "CN105313830A\n",
            "137\n",
            "CN105253076A\n",
            "138\n",
            "US20160009231A1\n",
            "139\n",
            "US20170038773A1\n",
            "140\n",
            "US20160306030A1\n",
            "141\n",
            "US20160291150A1\n",
            "142\n",
            "US20160221681A1\n",
            "143\n",
            "US20160152416A1\n",
            "144\n",
            "US20160075418A1\n",
            "145\n",
            "CN105318884A\n",
            "146\n",
            "US20160011596A1\n",
            "147\n",
            "EP2390613B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2279467B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2892800B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP3154063A1\n",
            "148\n",
            "EP2681635A4\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "WO2016109540A1\n",
            "149\n",
            "WO2016107674A1\n",
            "150\n",
            "WO2016096304A1\n",
            "151\n",
            "WO2016096240A1\n",
            "152\n",
            "WO2016088147A1\n",
            "153\n",
            "WO2016072186A1\n",
            "154\n",
            "WO2016028360A3\n",
            "155\n",
            "WO2016037734A1\n",
            "156\n",
            "WO2016013365A1\n",
            "157\n",
            "CN106794842A\n",
            "158\n",
            "CN106627261A\n",
            "159\n",
            "CN106428007A\n",
            "160\n",
            "CN106351490A\n",
            "161\n",
            "US20160202700A1\n",
            "162\n",
            "EP3043289A1\n",
            "163\n",
            "US20160185387A1\n",
            "164\n",
            "US20160179093A1\n",
            "165\n",
            "US20160139598A1\n",
            "166\n",
            "US20160138924A1\n",
            "167\n",
            "US20160132705A1\n",
            "168\n",
            "CN105599764A\n",
            "169\n",
            "WO2016066719A1\n",
            "170\n",
            "EP3048732A1\n",
            "171\n",
            "US20160091899A1\n",
            "172\n",
            "EP2995519A1\n",
            "173\n",
            "US20160068103A1\n",
            "174\n",
            "US20160046358A1\n",
            "175\n",
            "EP2969657A4\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n",
            "EP2988991A1\n",
            "176\n",
            "EP2902864B1\n",
            "exce\n",
            "'NoneType' object is not subscriptable\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_VJQt54R_AC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = learn.predict('A system for controlling an autonomous vehicle having cameras for obtaining image data of neighboring vehicles in a proximity of the autonomous vehicle and for identifying physical characteristics of the neighboring vehicles, including makes, models, and trims of neighboring vehicles, colors of the neighboring vehicles, and exposed cargo being carried by the neighboring vehicles. The system maps the identified physical characteristics into predicted potential on-road events in the proximity of the autonomous vehicle, and makes driving decisions based on the predicted potential on-road event')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vj4hAVxrSMts",
        "colab_type": "code",
        "outputId": "b683ea0d-92a7-4d6b-e4d5-66f0e9aa6060",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "str(y[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Autonomous'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgRHuL9LRAU3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "f = open('./subm.txt','w+')\n",
        "for key, value in dict_.items():\n",
        "  y = learn.predict(value)\n",
        "  f.write(key+','+str(y[0])+'\\n')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUvEbNofSnra",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9TGhkcc9aPL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "076c4891-221a-4315-e3bc-adf3b4395f32"
      },
      "source": [
        "!pip install pypatent"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pypatent\n",
            "  Downloading https://files.pythonhosted.org/packages/7b/4c/0071e2f448cf9125bbf7f987003b7cbae319f03fe5dd11dec29ae1bedb1c/pypatent-1.2.0-py3-none-any.whl\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from pypatent) (2.21.0)\n",
            "Requirement already satisfied: bs4 in /usr/local/lib/python3.6/dist-packages (from pypatent) (0.0.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from pypatent) (0.25.3)\n",
            "Collecting selenium\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/80/d6/4294f0b4bce4de0abf13e17190289f9d0613b0a44e5dd6a7f5ca98459853/selenium-3.141.0-py2.py3-none-any.whl (904kB)\n",
            "\r\u001b[K     |▍                               | 10kB 6.6MB/s eta 0:00:01\r\u001b[K     |▊                               | 20kB 4.9MB/s eta 0:00:01\r\u001b[K     |█                               | 30kB 4.0MB/s eta 0:00:01\r\u001b[K     |█▌                              | 40kB 2.8MB/s eta 0:00:01\r\u001b[K     |█▉                              | 51kB 3.0MB/s eta 0:00:01\r\u001b[K     |██▏                             | 61kB 3.6MB/s eta 0:00:01\r\u001b[K     |██▌                             | 71kB 3.7MB/s eta 0:00:01\r\u001b[K     |███                             | 81kB 3.4MB/s eta 0:00:01\r\u001b[K     |███▎                            | 92kB 3.8MB/s eta 0:00:01\r\u001b[K     |███▋                            | 102kB 3.7MB/s eta 0:00:01\r\u001b[K     |████                            | 112kB 3.7MB/s eta 0:00:01\r\u001b[K     |████▍                           | 122kB 3.7MB/s eta 0:00:01\r\u001b[K     |████▊                           | 133kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████                           | 143kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████▍                          | 153kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████▉                          | 163kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████▏                         | 174kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████▌                         | 184kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████▉                         | 194kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████▎                        | 204kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 215kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████                        | 225kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 235kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████▊                       | 245kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████                       | 256kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████▍                      | 266kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████▉                      | 276kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████▏                     | 286kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████▌                     | 296kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████▉                     | 307kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████▎                    | 317kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████▋                    | 327kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████                    | 337kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████▎                   | 348kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 358kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 368kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████▍                  | 378kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████▊                  | 389kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████▏                 | 399kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████▌                 | 409kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████▉                 | 419kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████▏                | 430kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 440kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████                | 450kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████▎               | 460kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████▋               | 471kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 481kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████▍              | 491kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████▊              | 501kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████▏             | 512kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████▌             | 522kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████▉             | 532kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████████▏            | 542kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 552kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 563kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████████▎           | 573kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████████▋           | 583kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 593kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████▍          | 604kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████▊          | 614kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 624kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████▌         | 634kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████▉         | 645kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 655kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 665kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 675kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████▎       | 686kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████▋       | 696kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 706kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▍      | 716kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▊      | 727kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 737kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▍     | 747kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▉     | 757kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▏    | 768kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▌    | 778kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 788kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▎   | 798kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▋   | 808kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 819kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 829kB 3.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▊  | 839kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 849kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▍ | 860kB 3.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▉ | 870kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 880kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▌| 890kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 901kB 3.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 911kB 3.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->pypatent) (2019.11.28)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->pypatent) (2.8)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->pypatent) (1.24.3)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->pypatent) (3.0.4)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.6/dist-packages (from bs4->pypatent) (4.6.3)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->pypatent) (2.6.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas->pypatent) (1.17.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->pypatent) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->pypatent) (1.12.0)\n",
            "Installing collected packages: selenium, pypatent\n",
            "Successfully installed pypatent-1.2.0 selenium-3.141.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-EPVRmg5SsZl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "aad80def-7bf7-4983-f687-1ed00c82aa48"
      },
      "source": [
        "import csv\n",
        "import pandas as pd\n",
        "from pypatent import Patent\n",
        "file1 = open('/content/validation_set.csv', 'r')\n",
        "csv_reader = csv.reader(file1)\n",
        "df = pd.DataFrame()\n",
        "list3 = []\n",
        "list2 = []\n",
        "list1 = []\n",
        "c = 0\n",
        "corrupt = 0\n",
        "for row in csv_reader:\n",
        "        try:\n",
        "            for i in range(4):\n",
        "                data=Patent(title=str(i),url='https://patents.google.com/patent/' + str(row[i]) +'/en')\n",
        "                label=\"\"\n",
        "                if(i == 0):\n",
        "                    label = \"Non-Alcohol\"\n",
        "                elif (i == 1):\n",
        "                    label = \"Alcohol\"\n",
        "                elif( i == 2):\n",
        "                    label = \"Non Autonomous\"\n",
        "                elif( i== 3):\n",
        "                    label = \"Autonomous\"\n",
        "                data.fetch_details()\n",
        "                list2.append(label)\n",
        "                list1.append(row[i])\n",
        "                if(data.abstract != None):\n",
        "                    if type(data.abstract)!=str:\n",
        "                        list3.append(''.join(data.abstract))\n",
        "                    else:\n",
        "                        list3.append(data.abstract)\n",
        "                else:\n",
        "                    if(data.description != None):\n",
        "                        if type(data.description)!=str:\n",
        "                          list3.append(''.join(data.description))\n",
        "                        else:\n",
        "                          list3.append(data.description)\n",
        "                print(str(c)+' '+str(i))\n",
        "        except Exception as e:\n",
        "                print(e)\n",
        "        c+=1"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0\n",
            "0 1\n",
            "0 2\n",
            "0 3\n",
            "1 0\n",
            "1 1\n",
            "1 2\n",
            "1 3\n",
            "2 0\n",
            "2 1\n",
            "2 2\n",
            "2 3\n",
            "3 0\n",
            "3 1\n",
            "3 2\n",
            "3 3\n",
            "4 0\n",
            "4 1\n",
            "4 2\n",
            "4 3\n",
            "5 0\n",
            "5 1\n",
            "5 2\n",
            "5 3\n",
            "6 0\n",
            "6 1\n",
            "6 2\n",
            "6 3\n",
            "7 0\n",
            "7 1\n",
            "7 2\n",
            "7 3\n",
            "8 0\n",
            "8 1\n",
            "8 2\n",
            "8 3\n",
            "9 0\n",
            "9 1\n",
            "9 2\n",
            "9 3\n",
            "10 0\n",
            "10 1\n",
            "10 2\n",
            "10 3\n",
            "11 0\n",
            "11 1\n",
            "11 2\n",
            "11 3\n",
            "12 0\n",
            "12 1\n",
            "12 2\n",
            "12 3\n",
            "13 0\n",
            "13 1\n",
            "13 2\n",
            "13 3\n",
            "14 0\n",
            "14 1\n",
            "14 2\n",
            "14 3\n",
            "15 0\n",
            "15 1\n",
            "15 2\n",
            "15 3\n",
            "16 0\n",
            "16 1\n",
            "16 2\n",
            "16 3\n",
            "17 0\n",
            "17 1\n",
            "17 2\n",
            "17 3\n",
            "18 0\n",
            "18 1\n",
            "18 2\n",
            "18 3\n",
            "19 0\n",
            "19 1\n",
            "19 2\n",
            "19 3\n",
            "20 0\n",
            "20 1\n",
            "20 2\n",
            "20 3\n",
            "21 0\n",
            "21 1\n",
            "21 2\n",
            "21 3\n",
            "22 0\n",
            "22 1\n",
            "22 2\n",
            "22 3\n",
            "23 0\n",
            "23 1\n",
            "23 2\n",
            "23 3\n",
            "24 0\n",
            "24 1\n",
            "24 2\n",
            "24 3\n",
            "25 0\n",
            "25 1\n",
            "25 2\n",
            "25 3\n",
            "26 0\n",
            "26 1\n",
            "26 2\n",
            "26 3\n",
            "27 0\n",
            "27 1\n",
            "27 2\n",
            "27 3\n",
            "28 0\n",
            "28 1\n",
            "28 2\n",
            "28 3\n",
            "29 0\n",
            "29 1\n",
            "29 2\n",
            "29 3\n",
            "30 0\n",
            "30 1\n",
            "30 2\n",
            "30 3\n",
            "31 0\n",
            "31 1\n",
            "31 2\n",
            "31 3\n",
            "32 0\n",
            "32 1\n",
            "32 2\n",
            "32 3\n",
            "33 0\n",
            "33 1\n",
            "33 2\n",
            "33 3\n",
            "34 0\n",
            "34 1\n",
            "34 2\n",
            "34 3\n",
            "35 0\n",
            "35 1\n",
            "35 2\n",
            "35 3\n",
            "36 0\n",
            "36 1\n",
            "36 2\n",
            "36 3\n",
            "37 0\n",
            "37 1\n",
            "37 2\n",
            "37 3\n",
            "38 0\n",
            "38 1\n",
            "38 2\n",
            "38 3\n",
            "39 0\n",
            "39 1\n",
            "39 2\n",
            "39 3\n",
            "40 0\n",
            "40 1\n",
            "40 2\n",
            "40 3\n",
            "41 0\n",
            "41 1\n",
            "41 2\n",
            "41 3\n",
            "42 0\n",
            "42 1\n",
            "42 2\n",
            "42 3\n",
            "43 0\n",
            "43 1\n",
            "43 2\n",
            "43 3\n",
            "44 0\n",
            "44 1\n",
            "44 2\n",
            "44 3\n",
            "45 0\n",
            "45 1\n",
            "45 2\n",
            "45 3\n",
            "46 0\n",
            "46 1\n",
            "46 2\n",
            "46 3\n",
            "47 0\n",
            "47 1\n",
            "47 2\n",
            "47 3\n",
            "48 0\n",
            "48 1\n",
            "48 2\n",
            "48 3\n",
            "49 0\n",
            "49 1\n",
            "49 2\n",
            "49 3\n",
            "50 0\n",
            "50 1\n",
            "50 2\n",
            "50 3\n",
            "51 0\n",
            "51 1\n",
            "51 2\n",
            "51 3\n",
            "52 0\n",
            "52 1\n",
            "52 2\n",
            "52 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6FxLDVXr_bA_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = learn.predict('Non Autonomous vehicle car truck')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eckTgz5p_lf6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f93fd589-1099-4c13-cd34-a7d241646710"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(Category Autonomous, tensor(1), tensor([1.1409e-09, 8.1145e-01, 1.5651e-05, 1.8853e-01]))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMwRAB4H9MQ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2db15d6e-3fcc-43c4-8fe9-19a68227a419"
      },
      "source": [
        "#f = open('predictions.csv','w+')\n",
        "cnt =0\n",
        "for i in range(len(list2)):\n",
        "  y = learn.predict(list3[i])\n",
        "  z = list2[i]\n",
        "  if str(y[0])==z:\n",
        "    cnt += 1\n",
        "print(cnt/212)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9339622641509434\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzDf3izCAvBD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#f = open('predictions.csv','w+')\n",
        "df = pd.DataFrame()\n",
        "cnt =0\n",
        "for i in range(len(list2)):\n",
        "  y = learn.predict(list3[i])\n",
        "  z = list2[i]\n",
        "  #f.write(list1[i]+','+list3[i]+','+str(y[0])+','+str(y[2])+'\\n')\n",
        "#f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usAYVzHGDPnp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rows_list=[]\n",
        "for i in range(len(list2)):\n",
        "  _dict={}\n",
        "  y = learn.predict(list3[i])\n",
        "  _dict['patent_number']=list1[i]\n",
        "  _dict['text']=list3[i]\n",
        "  _dict['category']=y[0]\n",
        "  _dict['Alcohol']=y[2][0]\n",
        "  _dict['Autonomous']=y[2][1]\n",
        "  _dict['Non-Autonomous']=y[2][2]\n",
        "  _dict['Non-Alcohol']=y[2][3]\n",
        "  rows_list.append(_dict)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InClaMcvDgxO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(rows_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3gsv8AsF3_q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "04a57a93-473a-475a-d10f-a6a60e5d5448"
      },
      "source": [
        "df"
      ],
      "execution_count": 64,
      "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>patent_number</th>\n",
              "      <th>text</th>\n",
              "      <th>category</th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Autonomous</th>\n",
              "      <th>Non-Autonomous</th>\n",
              "      <th>Non-Alcohol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>US9040302B2</td>\n",
              "      <td>Methods and compositions for targeted delivery...</td>\n",
              "      <td>Alcohol</td>\n",
              "      <td>tensor(0.6138)</td>\n",
              "      <td>tensor(0.0002)</td>\n",
              "      <td>tensor(0.0002)</td>\n",
              "      <td>tensor(0.3858)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>US9012694B2</td>\n",
              "      <td>The present invention provides a process for t...</td>\n",
              "      <td>Alcohol</td>\n",
              "      <td>tensor(0.9945)</td>\n",
              "      <td>tensor(0.0002)</td>\n",
              "      <td>tensor(0.0050)</td>\n",
              "      <td>tensor(0.0003)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>US9277674B2</td>\n",
              "      <td>A battery charger is disposed above an exhaust...</td>\n",
              "      <td>Non Autonomous</td>\n",
              "      <td>tensor(7.0941e-06)</td>\n",
              "      <td>tensor(1.6856e-06)</td>\n",
              "      <td>tensor(0.9994)</td>\n",
              "      <td>tensor(0.0006)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>US9646356B1</td>\n",
              "      <td>Self-driving vehicles have unlimited potential...</td>\n",
              "      <td>Autonomous</td>\n",
              "      <td>tensor(1.2797e-06)</td>\n",
              "      <td>tensor(0.9996)</td>\n",
              "      <td>tensor(3.7154e-05)</td>\n",
              "      <td>tensor(0.0003)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>US9040109B2</td>\n",
              "      <td>The present invention provides stabilized oil-...</td>\n",
              "      <td>Non-Alcohol</td>\n",
              "      <td>tensor(0.0023)</td>\n",
              "      <td>tensor(0.0008)</td>\n",
              "      <td>tensor(0.0255)</td>\n",
              "      <td>tensor(0.9713)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>WO2017002093A1</td>\n",
              "      <td>The invention concerns a method using a robot ...</td>\n",
              "      <td>Autonomous</td>\n",
              "      <td>tensor(0.0121)</td>\n",
              "      <td>tensor(0.7912)</td>\n",
              "      <td>tensor(0.0546)</td>\n",
              "      <td>tensor(0.1421)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>US8960084B2</td>\n",
              "      <td>A juice extractor with a temperature displayin...</td>\n",
              "      <td>Non-Alcohol</td>\n",
              "      <td>tensor(0.0001)</td>\n",
              "      <td>tensor(8.4402e-06)</td>\n",
              "      <td>tensor(0.0006)</td>\n",
              "      <td>tensor(0.9993)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>WO2015001243A3</td>\n",
              "      <td>The invention relates to an installation (1) f...</td>\n",
              "      <td>Alcohol</td>\n",
              "      <td>tensor(1.)</td>\n",
              "      <td>tensor(5.2205e-10)</td>\n",
              "      <td>tensor(6.9326e-09)</td>\n",
              "      <td>tensor(7.3496e-13)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>US9272625B2</td>\n",
              "      <td>An electrical power supply system for powering...</td>\n",
              "      <td>Non Autonomous</td>\n",
              "      <td>tensor(6.7832e-07)</td>\n",
              "      <td>tensor(8.7877e-06)</td>\n",
              "      <td>tensor(1.0000)</td>\n",
              "      <td>tensor(1.5938e-05)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>WO2016201670A1</td>\n",
              "      <td>A method and an apparatus for representing a m...</td>\n",
              "      <td>Autonomous</td>\n",
              "      <td>tensor(0.0004)</td>\n",
              "      <td>tensor(0.9792)</td>\n",
              "      <td>tensor(0.0057)</td>\n",
              "      <td>tensor(0.0147)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>212 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      patent_number  ...         Non-Alcohol\n",
              "0       US9040302B2  ...      tensor(0.3858)\n",
              "1       US9012694B2  ...      tensor(0.0003)\n",
              "2       US9277674B2  ...      tensor(0.0006)\n",
              "3       US9646356B1  ...      tensor(0.0003)\n",
              "4       US9040109B2  ...      tensor(0.9713)\n",
              "..              ...  ...                 ...\n",
              "207  WO2017002093A1  ...      tensor(0.1421)\n",
              "208     US8960084B2  ...      tensor(0.9993)\n",
              "209  WO2015001243A3  ...  tensor(7.3496e-13)\n",
              "210     US9272625B2  ...  tensor(1.5938e-05)\n",
              "211  WO2016201670A1  ...      tensor(0.0147)\n",
              "\n",
              "[212 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MucsfyaYDGhM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv('predictions_validation.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8K5aJS5B5l2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}