lession_4_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "lession_4_2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNzELLv3Ov4QFWNd/pErAyz",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tlkt/fast_lesson_work/blob/master/lession_4_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1vF-5eo1s4I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai import *\n",
        "from fastai.tabular import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ruegZRjz2Kqa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "cef3b7ce-70b1-46f0-b80a-630e3974e2f7"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3zbWOJC2gTI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e1361bee-7c05-4867-df45-d6c9b4e8e750"
      },
      "source": [
        "path = untar_data(URLs.ADULT_SAMPLE)\n",
        "df = pd.read_csv(path/'adult.csv')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://files.fast.ai/data/examples/adult_sample\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjAG5P_q2sNf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "befce23b-de9e-4620-e0b5-ec6c098e7398"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "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>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education-num</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>49</td>\n",
              "      <td>Private</td>\n",
              "      <td>101320</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12.0</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>1902</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>Private</td>\n",
              "      <td>236746</td>\n",
              "      <td>Masters</td>\n",
              "      <td>14.0</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>10520</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>96185</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38</td>\n",
              "      <td>Self-emp-inc</td>\n",
              "      <td>112847</td>\n",
              "      <td>Prof-school</td>\n",
              "      <td>15.0</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Asian-Pac-Islander</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>42</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>82297</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Other-service</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age          workclass  fnlwgt  ... hours-per-week  native-country salary\n",
              "0   49            Private  101320  ...             40   United-States  >=50k\n",
              "1   44            Private  236746  ...             45   United-States  >=50k\n",
              "2   38            Private   96185  ...             32   United-States   <50k\n",
              "3   38       Self-emp-inc  112847  ...             40   United-States  >=50k\n",
              "4   42   Self-emp-not-inc   82297  ...             50   United-States   <50k\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayXm3A7G2y9A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dev_var = 'salary'\n",
        "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']\n",
        "cont_names = ['age', 'fnlwgt', 'education-num']\n",
        "procs = [FillMissing,Categorify,Normalize]\n",
        "\n",
        "test = TabularList.from_df(df.iloc[800:1000].copy(),path=path,cat_names=cat_names,cont_names=cont_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQaFR7XV3a1z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = (TabularList.from_df(df,path=path,cat_names=cat_names,cont_names=cont_names,procs=procs)\n",
        "                    .split_by_idx(list(range(800,1000)))\n",
        "                    .label_from_df(cols=dev_var)\n",
        "                    .add_test(test,label=0)\n",
        "                    .databunch())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBvDYvwy3xKa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9779de32-bf9d-4217-c7bb-3a6d1b6d0e15"
      },
      "source": [
        "data.show_batch()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>education-num_na</th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education-num</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>Self-emp-inc</td>\n",
              "      <td>Prof-school</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>False</td>\n",
              "      <td>1.8627</td>\n",
              "      <td>-0.0361</td>\n",
              "      <td>1.9245</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Private</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Craft-repair</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>False</td>\n",
              "      <td>-0.7760</td>\n",
              "      <td>0.0649</td>\n",
              "      <td>-0.4224</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Federal-gov</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>False</td>\n",
              "      <td>1.8627</td>\n",
              "      <td>-0.2635</td>\n",
              "      <td>-0.4224</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Private</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>False</td>\n",
              "      <td>1.4229</td>\n",
              "      <td>-0.4493</td>\n",
              "      <td>1.1422</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Self-emp-inc</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Craft-repair</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>False</td>\n",
              "      <td>1.3496</td>\n",
              "      <td>-1.4790</td>\n",
              "      <td>1.1422</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuoQ1oZZ4Acv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = tabular_learner(data,layers=[200,100],metrics=accuracy)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WI432ulV4MHL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "93adc5b1-c41f-4ec3-ed7b-28810cbc5960"
      },
      "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='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/1 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='92' class='' max='505', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      18.22% [92/505 00:00<00:04 1.7883]\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": "ECOSm4I_4RPa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "fac61c9b-6c6b-48f5-c165-900717475980"
      },
      "source": [
        "learn.recorder.plot()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO3dd5iU1dnH8e+9vQBL2QVh6Z0Fpa0U\nsVfwNSKxBJVYo689UV+NJsYWTUyMMfbYK3aNwWjEilgAWXpvizRhWdrC9nbeP2bQcV3YBWbmmZn9\nfa5rLmaeMs99mJ2995TnHHPOISIisjdxXgcgIiKRT8lCREQapGQhIiINUrIQEZEGKVmIiEiDErwO\nIFgyMzNd165dvQ5DRCSqzJo1a4tzLquh42ImWXTt2pW8vDyvwxARiSpmtqYxx6kZSkREGqRkISIi\nDVKyEBGRBilZiIhIg5QsRESkQUoWIiLSICULERFpUEjvszCz0cADQDzwlHPunjr7OwPPAy39x9zk\nnHvfzLoCS4Bl/kOnO+cuC2Ws0WJFwS4mL9pEWlICzVN8jxYpibRMS6JVeiKt0pJISYz3OkwRiTEh\nSxZmFg88ApwArAdmmtkk59zigMNuAV53zj1mZjnA+0BX/75VzrlBoYovGm0sKuPsJ6ezpbhyr8cd\n2rUVT51/KBmpiWGKTERiXShrFsOAlc65fAAzexUYCwQmCwe08D/PAL4LYTxRraK6hstfmk1ZZQ3/\n/fURtM9IYVd5NbvKqykqq2JHaSXbS6vYWFTGPz9fxS+fnsGLFw9XwhCRoAhlssgG1gW8Xg8Mr3PM\n7cCHZnY1kA4cH7Cvm5nNAXYCtzjnvqh7ATO7FLgUoHPnzsGLPALdPmkRc9ft4J8ThtCvvS+/tkxL\nqvfYgR1bcvnEWUoYIhI0Xndwnw0855zrCJwMvGhmccBGoLNzbjBwHfCymbWoe7Jz7gnnXK5zLjcr\nq8F5sKLWyzPW8so367jqmJ6MHtC+weOPz2nHY+cOZcnGnZz39AyKyqrCEKWIxLJQJosNQKeA1x39\n2wJdDLwO4JybBqQAmc65CufcVv/2WcAqoHcIY40YNbWOJRt38vnyQt7IW8cDH6/gtkkLOap3Ftee\n0Pj/gt0JY/HGnZzz5HQKdpaHMGoRiXWhbIaaCfQys274ksR44Jw6x6wFjgOeM7N++JJFoZllAduc\nczVm1h3oBeSHMNaIULirgitfns03q7f9aHv/Di14cPxg4uNsn97v+Jx2PHFeLldOnM24R77imQsP\npe9BP1TQamodU5cX0rFVKr3aNQ9KGUQkNplzLnRvbnYy8A98w2Kfcc7dbWZ3AnnOuUn+EVBPAs3w\ndXbf6Jz70MxOB+4EqoBa4Dbn3Lt7u1Zubq6L5inKZ6/dzuUvzaKorIrfju7LwdkZtG2eQlbzZFKT\nDmwo7MINRVz8/ExKK2p4dMIQDu3amn/N2cCTU/PJ31JCRmoib1w2kt5KGCJNjpnNcs7lNnhcKJNF\nOB1osqipdXy3o4zVW0rYUlzB8TntaJESno7hl2es5fZJi2iXkczjE3LJ6fCT7pkD9t2OMi56biYr\nNxfTMi2RLcWVHJydwTnDO3P/R8uJM+PNy0fSsVXaj86rqK4hKT4Os32r1YhIdFCyaKTNO8uZ8PQM\nvt1aSmV17ffbB3bM4MVfDT/ghPGvOet59qtv+d3J/RjRvc2P9lVU13D7pEW88s06juydxYPjB+1x\nhFMw7Cqv4sY351NeVcMlR3RnZI82mBlLN+3krH9OI7NZMm9cNpI2zZLZVlLJU1/k8/zX3zK0a2se\nPmdw2JKniISPkkUjVVbXcsXE2XTPSqd7ZjrdMtMp2FXB9a/PZUB2Bi9cNIzm+/FL0jnHI5+t5G8f\nLicpIY7qmlpuOKkv/3tkd+LijE1F5Vw+cRZz1u7giqN7cP2Jffa5TyKY8r7dxoSnZ9CrbXMO75XJ\nC19/S2lVDUf0yuLrlVvonpXO0+cfSqfWP9Q8dpVX8eGiAnq1a8YhHVt6FruI7D8liwP0wcJNXPny\nbAZ3asnzFw0jPbnxYwGqamr5wzsLeXXmOn4+OJs/nJLDLf9eyHvzN3Jc37acO6IzN765gNLKau47\ncyBjDm54OGw4fLq0gEtemEWtc5xySAeuPrYnvds15+uVW7jspVkkJcTx5Hm5ZDZL5rmvv+W1meso\nrqgGYEjnllwwqhtjBhxEYrzXI7JFpLGULILgvfkbuebVOeR2acV9Zw38SXt+fcoqa7jspVl8vryQ\nq4/tyXUn9MbMcM7xwrQ13PXeYqpqHN0y03n8l0MjrlN57rodNEtOoGfbZj/avnJzMRc9N5NNReVU\n19YSZ8b/HNKeCSO6sHBDEc9//S3fbi2lbfNkxg3J5tSBHchp30J9HSIRTskiSCbN+45rX5tLTa1j\neLfWjBuczcmHtN9j+/1Nb83ntbx1/GncwZw97Kd3lc9dt4MPFm7i8qN7RN2d1dtKKrn13wvJbpXK\nBYd1pX1G6vf7amsdn68o5MVpa/h8eSE1tY4eWemMHZTNGUM70qFl6l7eWUS8omQRROu2lfLOnA38\na84G8reUkJIYxwPjB3NS/4N+dNwHCzdx2UuzuPzoHvx2dN+QxBINtpVU8v6CjUya9x3frN5GnMEJ\nOe04b2RXDvN3qotIZFCyCAHnHPPWF3HbpEUs+W4nT56fy1G9fdOMbN5Zzkn/mEp2q1TevnwUSQlq\ntwdfop04Yy2vzVzL9tIqerZtxt2nDWB4nZFhIuKNxiYL/UbbB2bGoE4teeHCYfRo24z/fTGPGflb\nqa11/N+b8ymrquEfvxisRBGgU+s0bhrTl2k3H8ffzhxIdU0t5zw1g6e+yCdW/lARaQr0W20/ZKQl\n8uLFw8humcrFz+dx66SFTF1eyO//J+cnHcPik5IYzxlDO/Lu1YdzfL+23PXeEq56ec73o6lEJLKp\nGeoAbCoq58zHv2bdtjKO69uWp87PVXt8IzjneGJqPn/5YCld2qQzIDsDA8wgLSmBiw/vpqQrEibq\nswiTddtKefrL1Vx1bE8ymyWH/frR7OuVW/jzf5dSUlGNA2qdo3BXBdU1jl8f34tLj+yuezZEQkzJ\nQqLS5l3l3D5pEe8v2ERO+xb89YxDGJCd4XVYIjFLHdwSldo2T+HRc4fyzwlDKCyuYOwjX/HE1FXq\nDBfxmJKFRKTRA9rz8bVHcUK/dvzp/aVc9tIsdpZrxT8RryhZSMTKSEvksQlDuOV/+vHJks387KEv\nWbihSLUMEQ+EcqU8kQNmZvzqiO4M6tSSK1+ezSkPfQlAckIcKYnxtExLZES3NhzRO5PDe2aGdIp3\nkaZMHdwSNbYUV/D27PUUV9RQUVVDeVUNm3aW8/Wqrewqr8YMRnZvw0NnD6aNRqaJNEpjO7hVs5Co\nkdksmUuP7PGT7dU1tcxbX8Tnywt5YuoqJjz9Da9eMoKMtOiaqFEkkqnPQqJeQnwcQ7u04roTevPE\nL3NZtbmY8579hl3qEBcJGiULiSlH9s7ikXOHsGhDERc/l0dppaYTEQkGJQuJOSfktOP+Xwwib802\nfvV8HttLKr0OSSTqKVlITPrZwA7cd9ZA8r7dzugHpvL1qi1ehyQS1ZQsJGaNG9yRt684jPTkBM59\nagb3Tl5KVU2t12GJRCUlC4lpA7Iz+M/Vh3PW0E488tkqznlyuqZFF9kPShYS89KSEvjLGYfwwPhB\nzF67g0uez6O8qsbrsESCYkXBLlYU7Ar5dZQspMkYOyib+84cyLT8rVz18hw1SUlM+ON7S7jhzfkh\nv46ShTQppw3O5s6x/fl4SQE3vjmf2trYmMFAmq7SimrSkuJDfh3dwS1Nznkju1JUWsV9Hy0nPTme\nO04dQHycVjiU6FRaWROWOdGULKRJuurYnhRXVPP41Hw2FVXwwPhBpCfr6yDRp6yqJiw1CzVDSZNk\nZtx8cj/uOLU/ny4t4PTHvmbDjjKvwxLZZyVhaoZSspAm7fzDuvLshcPYsL2MsQ9/xZy1270OSWSf\nlFXWkJYU+lqxkoU0eUf1zuLtKw4jLSme8U9M5/0FG70OSaRRnHOUqhlKJHx6tWvOO1eOYkB2BldM\nnM3jn2vdb4l8FdW11NQ6UpUsRMKndXoSE381nFMOac+f/7uU37+zkGrdiyERrKzSd3NpuobOioRX\nSmI8D44fTJc2aTzy2Sq2Flfw2LlDidPQWolAJf4p+NVnIeKBuDjjhpP68vuT+zF5UQHPfLXa65BE\n6rW7ZqFmKBEP/eqIbpyY046/fLCUBeuLvA5H5CdKdzdDJUd5sjCz0Wa2zMxWmtlN9ezvbGafmdkc\nM5tvZicH7LvZf94yMzsplHGK1MfM+OsZh5DZLJmrX5mt2Wol4uxuhkpNjOJmKDOLBx4BxgA5wNlm\nllPnsFuA151zg4HxwKP+c3P8r/sDo4FH/e8nElYt05J4YPxg1m4r5dZ3FnodjsiP7G6Givahs8OA\nlc65fOdcJfAqMLbOMQ5o4X+eAXznfz4WeNU5V+GcWw2s9L+fSNgN69aaa47rxdtzNvDWrPVehyPy\nvVhphsoG1gW8Xu/fFuh2YIKZrQfeB67eh3Mxs0vNLM/M8goLC4MVt8hPXH1sL0Z0b81Nb8/n48UF\nXocjAkDp7maoJjAa6mzgOedcR+Bk4EUza3RMzrknnHO5zrncrKyskAUpEh9nPP7LXHLat+DyibP4\nZIkShnhvd80iLTG6axYbgE4Brzv6twW6GHgdwDk3DUgBMht5rkhYZaQm8sLFw30J46XZfLpUCUO8\n9X2yiPJmqJlALzPrZmZJ+DqsJ9U5Zi1wHICZ9cOXLAr9x403s2Qz6wb0Ar4JYawijbI7YfRt35zL\nXpzN63nrqKjWEq3ijdLKauLjjKT40DcShewKzrlq4CpgMrAE36inRWZ2p5md6j/seuASM5sHvAJc\n4HwW4atxLAY+AK50zukbKREhIzWRFy8aTk6HFtz45nxG/vlT7vrP4rCsgywSqLTSN4mgWehnGLBY\nmSwtNzfX5eXleR2GNCE1tY4vV27htZlr+WhxAVU1jvNGduHOsQO8Dk2aiJvems9nyzYz43fH7/d7\nmNks51xuQ8dpbiiR/RQfZxzVO4ujemexpbiC+z9azgvT1jCoU0t+PqSj1+FJE1ASprUswPvRUCIx\nIbNZMnec2p9h3VpzyzsLWVVY7HVI0gSUVYZnlTxQshAJmoT4OB4cP5jkhDiuenkO5VXqZpPQKqkI\nz8JHoGQhElQHZaRw31kDWbJxJ3e/t8TrcCTGlVbVhOWGPFCyEAm6Y/u245IjuvHi9DU8OTVfNQwJ\nmbLK6rAsfARKFiIhccNJfRnVsw13v7+EkX/+hHsnL2VTUbnXYUmMKamoCctaFqDRUCIhkZQQx0sX\nD2fG6m08+9VqHp2yisc/z+fQrq0Z0qUlQzq3YkjnVrRKT/I6VIliZVXh67NQshAJETNjRPc2jOje\nhnXbSnlpxhqmrdrK45/nU13ru7/p4OwMRg84iDEDDqJ7VjOPI5ZoU1pZTXqY+iyULETCoFPrNG4e\n0w/wrUEwf/0O8tZs56PFBdw7eRn3Tl5Gn3bNuXBUV84Y2pGEMEzfINGtptZRXlWrZiiRWJWaFM/w\n7m0Y3r0NVx7Tk+92lPHBwk28M3cDN729gKe/XM1NY/pybN+2YZnGQaJTWVX4Fj4CJQsRz3VomcpF\nh3fjwlFdmbxoE3/5YBkXP5/HsG6t6dw6jS3FFWwtrmRXeRU/H9KRy47qQVKCah5N3e61LHQHt0gT\nY2aMHtCeD689kj+O7c+G7WV8uWILW4oraNMsiY6t0vj7R8v5nwe/YNaabV6HKx4rrVDNQqRJS4yP\n45cju/LLkV1/su/TpQX84Z1FnP7YNCaM6MzvTu4Xtr8sJbKUhnH9bVDNQiSqHNu3HR9eeyQXjerG\nxBlrufi5PMoqddNfU1RWpWYoEdmL9OQEbv1ZDn8/ayDTV2/l0hfzdJd4E1QS5mYoJQuRKDVucEf+\nevohfLlyC5e9NEsr9jUxu5uhwjV0VslCJIqdmduJP487mCnLCrly4mx2lFZ6HZKEye5mKN2UJyKN\nMn5YZ6prHbe8s5Bhf/qEk/ofxC9yO3FYjzbExek+jVgV7mYoJQuRGDBhRBeGdmnFazPX8a85G3h3\n3nd0aZPG0+cfSs+2mkYkFpWpGUpE9ke/9i24/dT+zPjdcTwwfhC7yqu56uXZ6vyOUSW6KU9EDkRK\nYjxjB2Xz97MGsnTTLu54d5HXIUkIlFXWkJwQR3yYmhqVLERi1NF92nL50T145Zt1/HvuBq/DkSAr\nrQzf9OSgZCES064/oTe5XVrxu7cXkF9Y7HU4EkQlldVhvXtfyUIkhiXEx/Hg2YNJTIjjypfnsKu8\nyuuQJEjKVLMQkWDq0DKV+88axPKCXZz2yFes3KwaRiworawhLVk1CxEJomP6tuWli4ezo7SK0x75\nismLNnkdkhyg0spq0hJVsxCRIBvZow3/ueZwerRtxv++OIu/frCUqppar8OS/aQObhEJmfYZqbx2\n6QjGH9qJR6es4pQHv2Tmt1obIxqVqRlKREIpJTGee04/hCfPy6W4opoz/zmNG96Yx7YSzSsVTUrC\n3Ayl6T5EmqgTctoxqmcbHvhkBU9/sZoPFxdw/sgunHdYVzKbJXsdnjSgtLImbFN9gGoWIk1aWlIC\nN4/px3vXHMGwbq156LOVjLrnU255ZwFrtpZ4HZ7sgXOOssoa0pOVLEQkjPoc1Jwnz8vlo2uPYtzg\nbF6fuZ4T75/K4u92eh2a1KOyppbqWqeb8kTEGz3bNuOe0w9hyg1H0yI1kV+/OkcTEUag72ec1dBZ\nEfFSh5ap3HfmQFZsLubu95Z4HY7UUeJPFmqGEhHPHdk7i4sP78aL09fw8eICr8ORAGX+6clT1Qwl\nIpHgxtF96Ne+BTe+NZ/NO8u9Dkf8dq+/HTN3cJvZaDNbZmYrzeymevbfb2Zz/Y/lZrYjYF9NwL5J\noYxTROqXnBDPg+MHUVJRzfVvzKO21nkdkhCwpGqkNUOZWQ8zS/Y/P9rMrjGzlg2cEw88AowBcoCz\nzSwn8Bjn3LXOuUHOuUHAQ8DbAbvLdu9zzp26D2USkSDq1a45fzglhy9WbOGZr1Z7HY4AZVXhXSUP\nGl+zeAuoMbOewBNAJ+DlBs4ZBqx0zuU75yqBV4Gxezn+bOCVRsYjImF07vDOnJDTjr9+sIxF3xV5\nHU6T930zVATelFfrnKsGxgEPOeduANo3cE42sC7g9Xr/tp8wsy5AN+DTgM0pZpZnZtPN7LRGxiki\nIWBm/OX0Q2iZlsg1r8z5fuimeKO0InKTRZWZnQ2cD/zHvy0xiHGMB950zgX+BHZxzuUC5wD/MLMe\ndU8ys0v9CSWvsLAwiOGISF2t05O476yBrCos4a73FnsdTpNWWhm5zVAXAiOBu51zq82sG/BiA+ds\nwNdctVtH/7b6jKdOE5RzboP/33xgCjC47knOuSecc7nOudysrKzGlENEDsARvbK49MjuTJyxlg+1\nJoZnSqsitGbhnFvsnLvGOfeKmbUCmjvn/tLAaTOBXmbWzcyS8CWEn4xqMrO+QCtgWsC2VgEd6pnA\nKEB/yohEgP87sQ/9O7Tgt2/NZ2NRmdfhNEmlFTXEGSQnhO/uh8aOhppiZi3MrDUwG3jSzP6+t3P8\nfRxXAZOBJcDrzrlFZnanmQWObhoPvOqcCxyT1w/IM7N5wGfAPc45JQuRCJCU4FvXu6K6ll+/Mpdq\nLaAUdr6FjxIws7Bds7ENXhnOuZ1m9ivgBefcbWY2v6GTnHPvA+/X2XZrnde313Pe18DBjYxNRMKs\nR1Yz7jptANe9Po8HP1nBdSf28TqkJqWsqjqs05ND4/ssEsysPXAWP3Rwi0gT9vMhHTljaEce+mwl\nX6/c4nU4TUpJRQ3pEZos7sTXnLTKOTfTzLoDK0IXlohEgzvH9qd7Zjq/fm0uhbsqvA6nyfAtfBTe\ntesa28H9hnPuEOfc5f7X+c6500MbmohEurSkBB45dwg7y6o0HUgYlVZWh3UkFDS+g7ujmf3LzDb7\nH2+ZWcdQBycika/vQS34wyk5TF1eyIvT13gdTpPg6+COwGQBPItv2GsH/+Nd/zYREc4d3plj+mTx\n5/8uYVVhsdfhxLyyCE4WWc65Z51z1f7Hc4DughMR4IfpQFIT47nutblUaThtSJVUVpMeiX0WwFYz\nm2Bm8f7HBGBrKAMTkejStkUKd487mHnri3jks5VehxPTyiprInbo7EX4hs1uAjYCZwAXhCgmEYlS\nJx/cntMGdeChT1cyb92Ohk+Q/RKxfRbOuTXOuVOdc1nOubbOudMAjYYSkZ+4Y+wA2jZP5oY3NToq\nFGprHWVVNWGdRBAObKW864IWhYjEjIzURH53cj+WFxTzgSYbDLoyDyYRhANLFuGblEREosrJB7en\ne2Y6D3+6kh9P+yYHyouFj+DAkoV+AkSkXvFxxuVH92Dxxp18tmyz1+HEFC/WsoAGkoWZ7TKznfU8\nduG730JEpF6nDc4mu2UqD6l2EVQRWbNwzjV3zrWo59HcORfetCYiUSUxPo7Lju7BnLU7mLZKI+2D\nZXeyiNShsyIi++zMoR1p2zyZh3XfRdDsboZKT46gZigRkQORkhjPpUd25+tVW5m1ZrvX4cSE72sW\niapZiEgMOWd4Z1qlJfLgJ1rVIBjKIrHPQkTkQKUlJXDZUT34fHkhX67QIkkHqkTNUCISqy4Y1ZVO\nrVO5673F1Oiu7gNSpg5uEYlVyQnx3DymH0s37eK1meu8DieqlVT4m6HUZyEisWjMgIMY1rU1f/9o\nGbvKq7wOJ2qVVlWTlBBHQnx4f30rWYhIWJgZt5zSjy3FlTw6ZZXX4UQtLxY+AiULEQmjQzq25OeD\ns3n6y9Ws21bqdThRqaSiJuxNUKBkISJhdsPoPsQZ3PXeYk0Dsh92lleRFuaRUKBkISJh1j4jld8c\n35vJiwp4c9Z6r8OJKs455qzdQf8OLcJ+bSULEQm7S47ozojurblt0iJWbynxOpyosaqwhC3FFYzs\n3ibs11ayEJGwi48z7v/FIBLj4/jNq3Ooqqn90f7yqhoqq2v3cHbTNS3fNyHjCCULEWkq2mekcs/P\nD2be+iLu/2g5AOu3l3Lnu4sZ8sePuPb1uR5HGHmm52+lfUYKXdqkhf3ammZcRDwz5uD2/CK3E499\nvorlBcV8tmwzBnTNTOe/CzayYUcZ2S1TvQ4zIjjnmJG/lSN6ZWEW/oVKVbMQEU/d+rMcumWmM23V\nFi48rCtTbzyGZy84FAe8+s1ar8OLGCs3F7OluNKT/gpQzUJEPJaenMC7Vx3+/fPdjunTlldnruOa\n43qRGOa7lSORl/0VoJqFiESA9OSEn8yiOmFEZwp3VfDhogKPooos0/O3kt0ylU6tvWmWU7IQkYh0\nVO+2ZLdM5aXpa7wOxXO1tY7p+dsY3r21J/0VoGQhIhEqPs44Z3hnpuVvZeXmYq/D8dTyzbvYVuJd\nfwUoWYhIBPvFoZ1IjDcmzmjatYvpq7ztrwAlCxGJYJnNkhk9oD1vzVr//aI/TdG0/K10bJVKp9bh\nv79iNyULEYloE4Z3Zmd5Ne/O+87rUDxRW+uYsXqbp01QEOJkYWajzWyZma00s5vq2X+/mc31P5ab\n2Y6Afeeb2Qr/4/xQxikikWtYt9bktG/B/R8vb5KLJi3dtIsdpVWeNkFBCJOFmcUDjwBjgBzgbDPL\nCTzGOXetc26Qc24Q8BDwtv/c1sBtwHBgGHCbmbUKVawiErnMjLvHDaBgZzn3/Hep1+GE3fTd91f0\niNFkge+X/ErnXL5zrhJ4FRi7l+PPBl7xPz8J+Mg5t805tx34CBgdwlhFJIIN7tyKiw/vxsQZa5nm\n7+xtKuat30GHjBTPpz0JZbLIBgJXZl/v3/YTZtYF6AZ8ui/nmtmlZpZnZnmFhYVBCVpEItN1J/Sh\na5s0fvvWfEorq70OJ2w2FZWT3cr7+bEipYN7PPCmc26fhjs4555wzuU653KzsrJCFJqIRILUpHju\nOf0Q1m4r5b4Pl3sdTtgU7CynXYsUr8MIabLYAHQKeN3Rv60+4/mhCWpfzxWRJmJE9zZMGNGZZ75a\nzaw1270OJ+SccxTsrIj5ZDET6GVm3cwsCV9CmFT3IDPrC7QCpgVsngycaGat/B3bJ/q3iUgTd9OY\nfhzUIoXbJy2itja21/DeWV5NWVUNB8VysnDOVQNX4fslvwR43Tm3yMzuNLNTAw4dD7zqAlZud85t\nA/6IL+HMBO70bxORJq5ZcgI3nNSHBRuK+M+CjV6HE1Kbd5YD0LZFsseRhHiKcufc+8D7dbbdWuf1\n7Xs49xngmZAFJyJR67RB2Tz5xWrunbyUk/q3Izkh3uuQQmKTP1nEdM1CRCRU4uKMm8f0Zd22Ml6a\nHrsLJBXsrACI+T4LEZGQObJ3Fkf0yuShT1dQVBabd3YX7K5ZZChZiIjst9+O7ktRWRWPTVnldSgh\nUbCznIzURFISvW9mU7IQkag1IDuDcYOyeear1WzYUeZ1OEG3qaicdhHQuQ1KFiIS5a47sTcAV788\nO+YmGoyUG/JAyUJEolzHVmk88ItBzF9fxHnPfMPOGEoYkXJDHihZiEgMGHNwex4+ZwgL1hfxy6e/\niYkO75paR2FxRUQMmwUlCxGJEaMHHMSj5w5h8XdFnPf0jKhPGFuLK6ipdeqzEBEJthP7H8Rj5w5l\n8cadXP/6PAImhog6u2/IUzOUiEgIHJ/TjpvG9OPjJQW88s26hk+IUJF0Qx4oWYhIDLrwsK4c3jOT\nP/5nMfmFxV6Hs182RdANeaBkISIxKC7OuO+sgSQnxvGb1+ZSVVPrdUj7bPPOcuIMMpupz0JEJGTa\ntUjhnp8fzPz1Rfzj4+hbLGlTUTlZzZOJjzOvQwGULEQkho0e0J4zh3bk0SmrmLUmulY5KNgVOcNm\nQclCRGLcbaf256AWKdzx7uKoWiypoKictkoWIiLh0Sw5getP7MP89dG1WFLBrnLVLEREwmnc4Gz6\ntW/BvZOXUlFd43U4DSqvqmFHaVXE3JAHShYi0gTER9liSZsj7B4LULIQkSYimhZLirS7t0HJQkSa\nkN2LJT06ZaXXoexVpN2QB91U9QIAAA5qSURBVEoWItKE7F4s6dmvvo3oxZI2q2YhIuKt60/qgwF/\nem+J16Hs0aaiclIS42iRkuB1KN9TshCRJiW7ZSpXHtOT9xZsZOryQq/DqdfuG/LMIuPubVCyEJEm\n6NIju9O1TRq3TVoUkUNpI+2GPFCyEJEmKCUxnjvGDmD1lhKenJrvdTg/EWk35IGShYg0UUf1zmLM\ngIN4+LOVrNtW6nU433POsamoPKJuyAMlCxFpwv5wSg5xZtzx7mKvQ/nezrJqKqprI2okFChZiEgT\n1qFlKtcc14uPlxTw4aJNXocDROYNeaBkISJN3EWjutGvfQt+/85CdpRWeh0OBRF4Qx4oWYhIE5eU\nEMffzjyE7SWV3BkBzVHf1yyaK1mIiESU/h0yuOKYnrw9ZwMfLy7wNJbdd2+3VQe3iEjkueqYnvQ9\nqDk3/2uBp81R324tpU16EimJ8Z7FUB8lCxERdjdHDWSbh81Rzjm+XLGF4d1be3L9vYmciUdERDw2\nIDuDK4/uwYOfrmRbaSW/yO3Ecf3akZQQnr+rlxcUs2lnOUf1zgrL9faFkoWISICrju2FmfHazHVc\nPnE2bdKTOH1oR359XC/Sk0P7K3P3XFVHKlmIiES2pIQ4rj2hN9cc14upywt5beY6nvoin6WbdvHU\nebkhrWVMXVFIr7bNaJ+RGrJr7C/1WYiI1CM+zjimb1v++cuh/GncwUxdXsgNb86jttaF5HpllTXM\nWL0tIpugIMTJwsxGm9kyM1tpZjft4ZizzGyxmS0ys5cDtteY2Vz/Y1Io4xQR2Zvxwzpzw0l9+Pfc\n77jrvSU4F/yEMX31ViqrayOyCQpC2AxlZvHAI8AJwHpgpplNcs4tDjimF3AzMMo5t93M2ga8RZlz\nblCo4hMR2RdXHN2Dwl0VPPPVajKbJ3HF0T2D+v6fLyskJTGOYd0ibyQUhLbPYhiw0jmXD2BmrwJj\ngcAxaZcAjzjntgM45zaHMB4Rkf1mZtx6Sg7bSir56wfLyGqWzJm5nYL2/lNXFDK8W5uIu79it1A2\nQ2UD6wJer/dvC9Qb6G1mX5nZdDMbHbAvxczy/NtPq+8CZnap/5i8wsLIXPFKRGJHXJzxtzMHcnjP\nTG56ewFTlgXn79t120rJLyyJ2CYo8L6DOwHoBRwNnA08aWYt/fu6OOdygXOAf5hZj7onO+eecM7l\nOudys7Ii9z9ZRGJHUkIcj00YQu92zbli4mwWrC864PecusL3x26kdm5DaJPFBiCwjtbRvy3QemCS\nc67KObcaWI4veeCc2+D/Nx+YAgwOYawiIo3WPCWR5y48lFZpSVz43Des3XpgiydNXV5IdstUemSl\nBynC4AtlspgJ9DKzbmaWBIwH6o5qegdfrQIzy8TXLJVvZq3MLDlg+yh+3NchIuKpdi1SeP6iYVTX\nOs5/9hsKd1Xs1/tU1dTy1cqtHNk7CzMLcpTBE7Jk4ZyrBq4CJgNLgNedc4vM7E4zO9V/2GRgq5kt\nBj4DbnDObQX6AXlmNs+//Z7AUVQiIpGgZ9tmPH1+LpuKyjn7yen7lTDmrN1BcUU1R/XODEGEwWOh\nGC/shdzcXJeXl+d1GCLSBE3P38qFz86kY6tUXr5kBFnNGze9eFVNLZe/NJspyzYz+9YTaJGSGOJI\nf8rMZvn7h/fK6w5uEZGoN6J7G5654FDWby/jnEbWMKpravnNa3P5eEkBN5/cz5NEsS+ULEREgmBk\njx8SxrlPTaeorGqPx1bX1HLt6/N4b/5GfndyXy4+vFsYI90/ShYiIkEyskcbnj4/l9VbSvj1q3Oo\nqWceqZpax/VvzOPded9x05i+XHrkT+4KiEhKFiIiQXRYz0xuP7U/U5YVct+Hy360r7iimstemsW/\n537HjaP7cNlR0ZEoQFOUi4gE3bnDu7Bww04enbKKnA4tOOWQDqzbVsolL+SxvGAXt/8shwtGRX7T\nUyAlCxGRELjj1P4sL9jFDW/MZ1d5NfdOXkZVTS3PXTgsoqf12BM1Q4mIhMDuaUFapCZw89sLaJma\nyDtXjorKRAGqWYiIhEzb5ik8c8Gh/Gv2Bq4+thcZaZE9PHZvlCxEREKof4cM+nfI8DqMA6ZmKBER\naZCShYiINEjJQkREGqRkISIiDVKyEBGRBilZiIhIg5QsRESkQUoWIiLSoJhZKc/MCoE19ezKAIoa\n2Bb4ur7ngdsygS37EWJ9cTT2mGCUIfD5/pZhbzE25pi9xdzQ67qfRaSUob5tkfJZ7G3//n4Wkfzz\nVN82fbcb1sU51/AcJM65mH4ATzS0LfB1fc/rbMsLVhyNPSYYZahTnv0qQ7DLsS+v634WkVKGSP4s\n9rZ/fz+LSP552p/PQt/txj+aQjPUu43Y9m4Dz+t7j2DE0dhjglGGxsbQkGCWY19e67NoXCyN3b+/\nn0Uk/zzVt03f7SCJmWaocDGzPNeIxc0jmcoQOWKhHLFQBoiNcoSyDE2hZhFsT3gdQBCoDJEjFsoR\nC2WA2ChHyMqgmoWIiDRINQsREWmQkoWIiDSoySYLM3vGzDab2cL9OHeomS0ws5Vm9qCZWcC+q81s\nqZktMrO/BjfqemMJejnM7HYz22Bmc/2Pk4Mf+Y/iCMln4d9/vZk5M8sMXsR7jCUUn8UfzWy+/3P4\n0Mw6BD/yH8URijLc6/9OzDezf5lZy+BH/qM4QlGGM/3f6VozC1kn+IHEvof3O9/MVvgf5wds3+v3\npl6hGpMb6Q/gSGAIsHA/zv0GGAEY8F9gjH/7McDHQLL/ddsoLcftwP9F82fh39cJmIzvZs3MaCwH\n0CLgmGuAf0ZhGU4EEvzP/wL8JQrL0A/oA0wBciMtdn9cXetsaw3k+/9t5X/eam/l3NujydYsnHNT\ngW2B28ysh5l9YGazzOwLM+tb9zwza4/vCzzd+f7XXwBO8+++HLjHOVfhv8bm0JYiZOUIqxCW4X7g\nRiAsozhCUQ7n3M6AQ9MJcVlCVIYPnXPV/kOnAx2jsAxLnHPLQhn3gcS+BycBHznntjnntgMfAaP3\n97vfZJPFHjwBXO2cGwr8H/BoPcdkA+sDXq/3bwPoDRxhZjPM7HMzOzSk0e7ZgZYD4Cp/s8EzZtYq\ndKHu0QGVwczGAhucc/NCHWgDDvizMLO7zWwdcC5wawhj3ZNg/DztdhG+v2TDLZhlCLfGxF6fbGBd\nwOvd5dmvciY08qIxz8yaAYcBbwQ03yXv49sk4KvyjQAOBV43s+7+7B0WQSrHY8Af8f0V+0fgPnxf\n8rA40DKYWRrwO3zNH54J0meBc+73wO/N7GbgKuC2oAXZgGCVwf9evweqgYnBia7R1w1aGcJtb7Gb\n2YXAr/3begLvm1klsNo5Ny7YsShZ/CAO2OGcGxS40czigVn+l5Pw/SINrEZ3BDb4n68H3vYnh2/M\nrBbfxF6FoQy8jgMuh3OuIOC8J4H/hDLgehxoGXoA3YB5/i9YR2C2mQ1zzm0KceyBgvEzFWgi8D5h\nTBYEqQxmdgFwCnBcOP948gv25xBO9cYO4Jx7FngWwMymABc4574NOGQDcHTA6474+jY2sD/lDFVH\nTTQ8gK4EdCQBXwNn+p8bMHAP59XtHDrZv/0y4E7/8974qoAWheVoH3DMtcCr0VaGOsd8Sxg6uEP0\nWfQKOOZq4M0oLMNoYDGQFY7PIJQ/T4S4g3t/Y2fPHdyr8XVut/I/b92YctYbV7g+vEh7AK8AG4Eq\nfDWCi/H9NfoBMM//w33rHs7NBRYCq4CH+eFO+CTgJf++2cCxUVqOF4EFwHx8f3G1j7Yy1DnmW8Iz\nGioUn8Vb/u3z8U0Wlx2FZViJ7w+nuf5HqEd0haIM4/zvVQEUAJMjKXbqSRb+7Rf5//9XAhfuy/em\n7kPTfYiISIM0GkpERBqkZCEiIg1SshARkQYpWYiISIOULEREpEFKFhLTzKw4zNd7ysxygvReNeab\nbXahmb3b0GytZtbSzK4IxrVF6tLQWYlpZlbsnGsWxPdLcD9MihdSgbGb2fPAcufc3Xs5vivwH+fc\ngHDEJ02LahbS5JhZlpm9ZWYz/Y9R/u3DzGyamc0xs6/NrI9/+wVmNsnMPgU+MbOjzWyKmb1pvnUa\nJu5eD8C/Pdf/vNg/CeA8M5tuZu3823v4Xy8ws7saWfuZxg+TJDYzs0/MbLb/Pcb6j7kH6OGvjdzr\nP/YGfxnnm9kdQfxvlCZGyUKaogeA+51zhwKnA0/5ty8FjnDODcY3u+ufAs4ZApzhnDvK/3ow8Bsg\nB+gOjKrnOunAdOfcQGAqcEnA9R9wzh3Mj2f/rJd/DqPj8N1ND1AOjHPODcG3hsp9/mR1E7DKOTfI\nOXeDmZ0I9AKGAYOAoWZ2ZEPXE6mPJhKUpuh4ICdgFs8W/tk9M4DnzawXvhl3EwPO+cg5F7jOwDfO\nufUAZjYX33w+X9a5TiU/TMI4CzjB/3wkP6wf8DLwtz3Emep/72xgCb71CMA3n8+f/L/4a/3729Vz\n/on+xxz/62b4ksfUPVxPZI+ULKQpigNGOOfKAzea2cPAZ865cf72/ykBu0vqvEdFwPMa6v8uVbkf\nOgX3dMzelDnnBvmnXJ8MXAk8iG9diyxgqHOuysy+BVLqOd+APzvnHt/H64r8hJqhpCn6EN8MrgCY\n2e7pnzP4YarmC0J4/en4mr8Axjd0sHOuFN+SqtebWQK+ODf7E8UxQBf/obuA5gGnTgYu8teaMLNs\nM2sbpDJIE6NkIbEuzczWBzyuw/eLN9ff6bsY39TyAH8F/mxmcwhtrfs3wHVmNh/fojVFDZ3gnJuD\nb+bZs/Gta5FrZguA8/D1teCc2wp85R9qe69z7kN8zVzT/Me+yY+TiUijaeisSJj5m5XKnHPOzMYD\nZzvnxjZ0noiX1GchEn5DgYf9I5h2EMYla0X2l2oWIiLSIPVZiIhIg5QsRESkQUoWIiLSICULERFp\nkJKFiIg06P8B4q51GLog1tMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRtiVGFK4TWF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "e8e503ca-4d6d-4e8a-b9fc-d52469faf5f2"
      },
      "source": [
        "learn.fit_one_cycle(1,1e-2)"
      ],
      "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>0.355389</td>\n",
              "      <td>0.378155</td>\n",
              "      <td>0.815000</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAtRtWbX4Xo2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}