MLP/mnist_mlp_fastai.ipynb

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      "name": "mnist_mlp_fastai.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "ohv0ha9HPlIg"
      ]
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "metadata": {
        "id": "k8kAWGuCOyZ2",
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      "cell_type": "markdown",
      "source": [
        "# MLP\n",
        "\n",
        "In this notebook, we will go through basics of MLP using MNIST dataset. \n",
        "\n",
        "Here we will use [fastai](https://github.com/fastai/fastai/  \"fastai's Tutorial\").\n",
        "\n",
        "Everything is explained in-detail in [blog post](https://dudeperf3ct.github.io/mlp/mnist/2018/10/08/Force-of-Multi-Layer-Perceptron/). This is notebook which replicates the result of blog and runs in colab. Enjoy!\n",
        "\n",
        "\n",
        "#### Run in Colab\n",
        "\n",
        "You can run this notebook in google colab.\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dudeperf3ct/DL_notebooks/blob/master/MLP/mnist_mlp_fastai.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "ohv0ha9HPlIg",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "### Install Dependecies"
      ]
    },
    {
      "metadata": {
        "id": "sYbFG141PMxI",
        "colab_type": "code",
        "outputId": "eff2e7c9-2dc9-42ea-c9fb-69f3a8f22a41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install --upgrade torch torchvision fastai numpy"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pSNTqNYBPtJx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Fastai"
      ]
    },
    {
      "metadata": {
        "id": "hM0JdnO9OyZ_",
        "colab_type": "code",
        "outputId": "a0286926-0181-40ca-f2ad-2e4849f8368b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# load all the required libraries\n",
        "from fastai import *\n",
        "from fastai.vision import *\n",
        "\n",
        "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
        "print (device)\n",
        "\n",
        "defaults.cmap = 'gray'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7emIIhDHOyaT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## MNIST Dataset\n",
        "\n",
        "\n",
        " \n",
        "The [MNIST dataset](http://yann.lecun.com/exdb/mnist/) of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples each of size 28 x 28 pixels. The digits have been size-normalized and centered in a fixed-size image.\n",
        "\n",
        "It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. \n",
        "\n",
        "Here is one example from dataset\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "5DsOxR4OOyaY",
        "colab_type": "code",
        "outputId": "20982a03-75ce-4c0c-cfef-5503a5e437be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# batch size\n",
        "bs = 64\n",
        "\n",
        "# transform the data\n",
        "tfms = get_transforms(do_flip=False)\n",
        "\n",
        "# load mnist data\n",
        "path = untar_data(URLs.MNIST)\n",
        "path"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('/root/.fastai/data/mnist_png')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "TZlFRNZ7YvWn",
        "colab_type": "code",
        "outputId": "1b75d131-7a86-4d94-ca65-81f682ef8ade",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        }
      },
      "cell_type": "code",
      "source": [
        "(path/'training').ls(), (path/'testing').ls()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([PosixPath('/root/.fastai/data/mnist_png/training/7'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/training/1'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/training/3'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/training/2'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/training/9'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/training/5'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/training/0'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/training/8'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/training/4'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/training/6')],\n",
              " [PosixPath('/root/.fastai/data/mnist_png/testing/7'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/testing/1'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/testing/3'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/testing/2'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/testing/9'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/testing/5'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/testing/0'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/testing/8'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/testing/4'),\n",
              "  PosixPath('/root/.fastai/data/mnist_png/testing/6')])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "EJVQ3vVmYslH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# create databunch\n",
        "\n",
        "data = (ImageItemList.from_folder(path, convert_mode='L') #Where to find the data? -> in path and its subfolders\n",
        "        .random_split_by_pct()       #How to split it? Randomly with the default 20%\n",
        "        .label_from_folder()            #How to label? -> depending on the folder of the filenames\n",
        "        .add_test_folder('testing')              #Optionally add a test set (here default name is test)\n",
        "        .transform(tfms, size=28)                #Data augmentation? -> use tfms with a size of 28\n",
        "        .databunch()                   #Finally? -> use the defaults for conversion to ImageDataBunch\n",
        "        .normalize())                   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Diz9XarTZZov",
        "colab_type": "code",
        "outputId": "74bbe687-723c-4e23-dc6d-57a52824c7e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "cell_type": "code",
      "source": [
        "data.show_batch(rows=3, figsize=(5,5))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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knJ2qG7/TZNmyZUBymmw7jGxXCKSmgN27dw+xAw88EEg2ivDTBbv2MWsAMX36\n9BCzNYB+jajtGFIX8W2zdXuPPfZYiA0ePBiAVq1ahZhv2vFT/nth/vz5QOo1CcDo0aPDtT0n/6pH\nMsue13777Rdi1r7Pn/GUj60XlXGKiETK+YwzHb8UyFrH+R0itlTFv3S2HSJ+b7LPLpUt1i37+vpd\nO3aujG/RZzMD27UFqcKSP4fIsstJkyaFmJ8F+KKRZJf1iYDU7MwX6pRxiogUAQ2cIiKR8nKq7osE\nttZSbdzygz/bx3YE+Q77559/PpBsF2cFnmeeeSbErFu4b+Unucnv1LLXY7vuumuIaR2niEgRyMuM\nUwrLxIkT015LYfBLBe35HnHEESHWqFGjjN9TTSnjFBGJpIFTRCRSydbtHQIjIiIJyjhFRCJp4BQR\niaSBU0QkkgZOEZFIGjhFRCJp4BQRiaSBU0QkkgZOEZFIGjhFRCJp4BQRiaSBU0QkkgZOEZFIGjhF\nRCJp4BQRiaSBU0QkkgZOEZFIGjhFRCJp4BQRiaSBU0QkkgZOEZFIGjhFRCJp4BQRiaSBU0QkkgZO\nEZFIGjhFRCJp4BQRiaSBU0QkkgZOEZFIGjhFRCJp4BQRiVRUA+emTZu47bbb6Nq1K0uXLs327UgN\nPPvss/Tr148+ffpw7bXX8v3332f7lqSGpk2bRteuXfnss8+yfSvbVVQD59ChQ2nUqFG2b0NqqLy8\nnFtvvZX777+fqVOnsmXLFkaOHJnt25IaWL9+PSNGjKB58+bZvpUqKbqBc9iwYdm+DamhmTNn0rNn\nT9q0aUNJSQkXXXQREydOzPZtSQ3cfffd9O/fn8aNG2f7VqqkqAbOQw89NNu3ILWgpKSELVu2hP/f\nqFEjlixZksU7kpqYP38+06dP5+KLL872rVRZUQ2cUhh69epFWVkZ5eXlbN68mdGjR7Nx48Zs35ZU\nw9atW7nxxhsZPnw4O+64Y7Zvp8p2yPYNiMQqLS3lhhtu4JprrmGnnXbinHPOoUmTJtm+LamGMWPG\nUFpaSo8ePbJ9K1E0cEpeGjhwIAMHDgRg1qxZdOnSJct3JNUxZcoU3nvvPaZOnQrAypUrGTRoEHfe\neSc9e/bM8t1tmwZOyTuLFy9m2LBhjBo1ioYNG3Lvvfdy9tlnZ/u2pBp+uhrihBNO4NFHH6Vdu3ZZ\nuqOqKZqBc/ny5QwZMiT8/1/84hfUr1+fRx55hFatWmXxziRWx44d6du3LwMGDKCkpIR+/fqF7FMk\nE0q2bt26Nds3ISKST1RVFxGJpIFTRCSSBk4RkUgaOEVEImWkql5SUpKJT5OX8rE2p+e5bfn2PPUs\nt62yZ6mMU0QkkgZOEZFIGjiGh//NAAALTElEQVRFRCJp4BQRiVQ0Wy6luFiLsl122SXErEnuTjvt\nFGJr164F4Kuvvsrg3Um+U8YpIhJJA6eISKSimarvtddeAOy3334hVr9+/XA9ffp0AFavXh1iP/zw\nQ2ZuTip1wAEHANChQ4cQs0O9/Bk1u+22W7ju3bs3QOJIjc2bNwM/nnZqPvjgAwCef/75EFuxYkWt\n3bsUJmWcIiKRiibjtAzksssuC7EWLVqE65tvvhmAyZMnh5hln/m2GyTf+N0rbdu2BaBTp04hduaZ\nZwKw//77h5hlnA0bNgwxn322bNkSSD47Kxg1aNAgxObPnw8ki0Pjxo2r8HdFPGWcIiKRNHCKiEQq\nmqn6IYccAiSngHvuuWe4/vOf/wyQOF5jzpw5AKxbty4Tt1i0/NT5//7v/wA4+eSTQ8yONvFTensm\nK1euDLE333wzXNu560ceeWSIWXHJf5xmzZoByWn+zjvvDMD69eur88+RSPYKxb8asUJerlLGKSIS\nqWgyTssyfLaxww6pf37Tpk0BaNSoUYj55UpSM/a13HvvvUNs0KBBQPJrbpmmFYkA6tX78ff7F198\nEWJvvfUWkFpOBPD444+H6wEDBgBw2GGHVbiX77//Plx/8803AMybNy/Ecj3byTeWUQJ07twZSO7e\nsp89/3wXLVoUrnPxeSjjFBGJpIFTRCRSQU/V/bQ8XadrH7P1gOecc06IffrppwB8+OGHdXWLBa1J\nkybh2nb/2JpMSH2t/bTN/s6yZctC7J133gFgzJgxIWY7ffwun9atW4frHj16ANCuXbsQs+ftd4TZ\nWt3vvvsuxHJxapgv/M+UrZM+/PDDQ8xez/gibZcuXYDU+lmAe+65J1zPnTu3bm62BpRxiohEKuiM\n09ve2SqWhfgMxhcRJN4+++wTrn/+858DyYzeMsQNGzaE2Ouvvw7ApEmTQmzGjBlAapcPwKpVqyp8\nvr59+4ZrW2rmlzrZEqU1a9aE2CuvvAIklx5px1D12Y4tgFNPPRWA4cOHh5h9T9izAPjkk08AePvt\nt0OsJm3+fDFq9913B5LfBzbLsMJgdSjjFBGJpIFTRCRS0UzVzbam7LYTxTf5+PrrrzNyT4XGij2+\nhZ8Vhdq0aRNi9nrE7/gZNWoUAC+99FKILV++vEqfd9ddd61wD561k/PrBceOHQskdyBJPPvan3HG\nGSE2dOhQINkO8KOPPgLgxhtvDLH3338fgKVLl4ZYVafR/ufZ1oNaIRJS63j994OtAZ46dWqVPkc6\nyjhFRCIVZMZpO01szzGkdqf43UJ+2YkVG/xvOt/wVqrOssp99903xPyyIGNFmpEjR4aYZZzV4Qs8\nGzdurPDfLTtJ94z1rOP5LO6ss84Ckm0bS0tLgVQBDuAvf/kLADNnzgyxb7/9Fogrytmz9EveTjjh\nBACuuOKKELMi4fjx40OsNs6XUsYpIhJJA6eISKSCnKrbmi2/O8GaS/jU3hcEZs+eDSTTeO0gqR4r\nFPivf7qGKfZyfsGCBSFmr1f8n7fr9u3bh5g9R1+A8oWJjh07Vvh89prm448/DjG/nlCqxqbJBx10\nUIhdeeWVQGp6DvDMM88A8PDDD4fYrFmzgOROraryrwZsV1L37t1D7PrrrweSP+PDhg0DUgUhSJ4r\nVl3KOEVEIhVkxmlZi//tZ9mKb1j77rvvhmtbluKXvijjrB7L4mx5CKSKL35XxzHHHAMkC3b2Et+3\nFbOPd/rpp4eYnWjpzyHyz9t/bmNFQ9+Kria7R4qVFVpPOumkELNTZJ944okQGz16NJDqNQA1awru\nZxz9+/cH4NJLLw0xW/b0yCOPhJj9jPsdgbWxM0wZp4hIJA2cIiKRCnKqbnyBwaaD22o1Z3/WtxyT\n6rFp0eLFi0PMpmh2xg+kGjAcd9xxIXbooYcCyemUFfv22GOPELP/7p/X9jr22zpP37wl3XpPqZw9\nh/POOy/E7NWJL/rYa5B0X2N7bQKpoo9fd51uXe1pp50Wrm2K7puK2G4zOz8MUsXe2m7cooxTRCRS\nQWec22PNiyGV/UjNWYHN7ze3ZUN25gykP+fJMtJ0y4R8pmj7zX2xwZ9T5PetG9sL7f+Ob2knVWMZ\npG/9Z8+3T58+IWZZo+1Fh9RsxD8Dm1H47NFnrl27dgVg8ODBFWK+6GMzjn79+lX4OP77aZdddkn8\nOyCVHVe1P4UyThGRSBo4RUQiFeRU3YoAfr3ewoULgWRbM19MsGm7dpLUnE2Bpk+fHmI2NTv22GND\nzM6aad68eYhZocB3abfrdMcD+90kV199dbju1atXhfv68ssvgVRTCdBUvTpsWvvkk0+GmK2htLW5\nkNpN5AszVpD1a3fT7RZbu3ZtuLYCoN8RZOuB/XpdO9vIpvGQ/hSHVq1aAakpu//cmqqLiNQRDZwi\nIpEKcqpu0y9/rK81kujWrVvav2PTCR3UVXv8dPvVV18Fkr0ZbZrlO4RbVd1XPG265T+eNWix9YMA\nr732Wri29aB+baBN73xM4tnP13vvvRdiN998M5Dq+g6p12L+VYw9c/+Kxabt/kA1v8rFVkj4519W\nVgYkX9/YawD/cezz+a2+1oXe/zl7DeAb//gmNT+ljFNEJFJBZpyVSbcjBVKZjv9NaOvQlIVWz/a+\nbpZB+Mwlln/B77MUe3Y+u7R1nHYcrdSMnxXYM/QZp0mXAfqijmV7Bx98cIj9/ve/D9d2htCUKVNC\nzJqJ+AKwFZR8wc+yUL/DzK/9/Ok9+O8Xv270p5RxiohE0sApIhKpoKfq6Rp6+Fjr1q3DtR0jap2l\nIdUgQI0/cpffmufX+aV7Zra+14oDkhl+Sm/rJP16SZse+yKh75trP4ePPvpoiE2bNg2onW7u/uNU\n9eMp4xQRiVTQGef2+AzFjhH1DSd8diq5yWcIfkdQurZkVkjSbqHcYt3j/a4yX/R7+eWXgdQZVZB8\n1tmgjFNEJJIGThGRSEUzVd/etNuKCX7todZv5j7fGMI3dEjXrMV2oOgVTG6xo7v9ccN+B49N1f3O\noWxTxikiEqnoMs5tZRuWoaitXH7xS8p813e/N9nYUjO/d9qWM2nJWfbYGUZ+CdK2in65QhmniEgk\nDZwiIpGKbqru+c3+1qTAv4DWtD332a4SSBURIFVwsK7gkGoCcvrpp4fY5MmTAfj0009DTEcG1z0/\nLbeu8f6wtqeeeipcjx8/PnM3VkXKOEVEIhV0xrm9vep2Bg2kMk7/UlrLkXKf3yE0bty4cG3FIb8T\nzNqT3XLLLSFmWei///3vECsvL6+bm5Xw89e7d+8Q69mzZ4U/t3jx4nBdW/vRa5MyThGRSBo4RUQi\nFfRU3bP1en5q59fzdezYEUg2/li1alWG7k5qgz9/ZtKkSUBqjSCkzpDxz926fC9btizENFWvO/YK\nxReH7GfOjnyG5HlhuVikVcYpIhKpoDNO/5vKziuxzBKSmcfChQszd2NSJ/zztuLCY489VuHPXXjh\nheHazpiyM6ekbtlZQ74gZDu+7CRUyP1m08o4RUQiaeAUEYlUNFN1Kxb47t/16qV+b8yfPx9IHTEq\n+c12/yxYsCDEHnjgASA5DbRWdHPmzMng3RUvW8e5fPnyELPC3MyZM0PM//dcpIxTRCRSQWecnmUg\ntjdZis+6deuAZAu5bt26AdCgQYMQ81mq1C77OZw9e3aIWZE23UwgVynjFBGJpIFTRCRSyVZ1shAR\niaKMU0QkkgZOEZFIGjhFRCJp4BQRiaSBU0QkkgZOEZFIGjhFRCJp4BQRiaSBU0QkkgZOEZFIGjhF\nRCJp4BQRiaSBU0QkkgZOEZFIGjhFRCJp4BQRiaSBU0QkkgZOEZFIGjhFRCJp4BQRiaSBU0QkkgZO\nEZFIGjhFRCL9P4L5V8bhcCikAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff8060c7828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "YWgRM93xaJP6",
        "colab_type": "code",
        "outputId": "3f0a5329-a21e-478e-e439-c70c22f30974",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "print(data.classes)\n",
        "len(data.classes),data.c"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "OWQnpltOPojd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class mnist_model(nn.Module):\n",
        "  \n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.n_classes =  data.c\n",
        "    self.hidden = 150\n",
        "    self.linear1 = nn.Linear(784, self.hidden, bias=True)\n",
        "    self.linear2 = nn.Linear(self.hidden, self.n_classes, bias=True)\n",
        "    \n",
        "    \n",
        "  def forward(self, x):\n",
        "    x = x.view(-1, 28*28)\n",
        "    h1 = F.relu(self.linear1(x))\n",
        "    output = self.linear2(h1)\n",
        "    return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Fz5UpTR5REfI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "db528837-e54b-414f-8631-85c5ce1f7409"
      },
      "cell_type": "code",
      "source": [
        "model = mnist_model().to(device)\n",
        "print (model)\n",
        "loss_func = nn.CrossEntropyLoss()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mnist_model(\n",
            "  (linear1): Linear(in_features=784, out_features=150, bias=True)\n",
            "  (linear2): Linear(in_features=150, out_features=10, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rJ9qsJgQOyeS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn = Learner(data, mnist_model(), loss_func=loss_func, metrics=[error_rate, accuracy])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VxQEigRVOyea",
        "colab_type": "code",
        "outputId": "5bbed7c9-dc2a-477f-e563-c31017020eaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "cell_type": "code",
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 11,
      "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 style='width:375px; margin-bottom:10px'>\n",
              "  <tr>\n",
              "    <th>epoch</th>\n",
              "    <th>train_loss</th>\n",
              "    <th>valid_loss</th>\n",
              "    <th>error_rate</th>\n",
              "    <th>accuracy</th>\n",
              "  </tr>\n",
              "</table>\n",
              "\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='0' class='progress-bar-interrupted' max='875', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      Interrupted\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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8rvcFF1yA2WzGbDaTlpZGXV0du3bt4uKLLwYgMzPzlNfF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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff8047356d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "74PJ96rQOyer",
        "colab_type": "code",
        "outputId": "ab217257-2ee2-4910-bf02-aa63503f0a85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(5, 1e-2)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 07:20 <p><table style='width:375px; margin-bottom:10px'>\n",
              "  <tr>\n",
              "    <th>epoch</th>\n",
              "    <th>train_loss</th>\n",
              "    <th>valid_loss</th>\n",
              "    <th>error_rate</th>\n",
              "    <th>accuracy</th>\n",
              "  </tr>\n",
              "  <tr>\n",
              "    <th>1</th>\n",
              "    <th>0.453305</th>\n",
              "    <th>0.277082</th>\n",
              "    <th>0.083286</th>\n",
              "    <th>0.916714</th>\n",
              "  </tr>\n",
              "  <tr>\n",
              "    <th>2</th>\n",
              "    <th>0.501641</th>\n",
              "    <th>0.288107</th>\n",
              "    <th>0.086214</th>\n",
              "    <th>0.913786</th>\n",
              "  </tr>\n",
              "  <tr>\n",
              "    <th>3</th>\n",
              "    <th>0.364740</th>\n",
              "    <th>0.194725</th>\n",
              "    <th>0.059500</th>\n",
              "    <th>0.940500</th>\n",
              "  </tr>\n",
              "  <tr>\n",
              "    <th>4</th>\n",
              "    <th>0.254852</th>\n",
              "    <th>0.121299</th>\n",
              "    <th>0.037500</th>\n",
              "    <th>0.962500</th>\n",
              "  </tr>\n",
              "  <tr>\n",
              "    <th>5</th>\n",
              "    <th>0.178972</th>\n",
              "    <th>0.097190</th>\n",
              "    <th>0.029857</th>\n",
              "    <th>0.970143</th>\n",
              "  </tr>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7ujkrOs3Oye6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "fda178b8-f6c8-4ed1-ce3d-c7fdeb0b775f"
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Dhzq7zJ3BT8kBa0RE5B4cFt7x8fFYvXq1zdcfeeQRPPLII476+OsmlUjg6yNDEWdYIyKi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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff884fdb828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uyrd1qqLXzNd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Results"
      ]
    },
    {
      "metadata": {
        "id": "xWMHBgjcOyfD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp = ClassificationInterpretation.from_learner(learn)\n",
        "\n",
        "losses,idxs = interp.top_losses()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kpPyS51fRVlT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "76335fc9-ba70-486f-c99f-520cd17c43e6"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "interp.plot_top_losses(9, figsize=(5, 5))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1VdXm2NhY2Nraykk0LzY2NggKCoKjoyN+/vlnrFmzBk5OTli9ejXs7OxQqVIlLFu2DLa2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\nESmJxk1ESvIPU1n4/c3JqI4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff8025225f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZFaXYCHAQFVO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "24ebe655-a4f6-4656-98ee-db76083e1ff3"
      },
      "cell_type": "code",
      "source": [
        "interp.plot_confusion_matrix(figsize=(5, 7))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l8/Dw0NWoUUO3fft21bJNPQeTk5P1mYDO1dVV5+HhoUtKSlIlX6fT6aZMmaJz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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff801382208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_V3EY9-AWhQg",
        "colab_type": "code",
        "outputId": "610026a7-cae5-439f-c5b4-6ee4c41d5d97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 844
        }
      },
      "cell_type": "code",
      "source": [
        "interp.most_confused(min_val=2)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('4', '9', 33),\n",
              " ('5', '3', 21),\n",
              " ('9', '3', 17),\n",
              " ('3', '5', 15),\n",
              " ('9', '4', 14),\n",
              " ('3', '2', 13),\n",
              " ('5', '8', 13),\n",
              " ('7', '9', 12),\n",
              " ('8', '1', 12),\n",
              " ('9', '7', 12),\n",
              " ('1', '2', 11),\n",
              " ('7', '2', 11),\n",
              " ('3', '8', 10),\n",
              " ('5', '6', 10),\n",
              " ('6', '8', 10),\n",
              " ('0', '8', 9),\n",
              " ('2', '8', 9),\n",
              " ('8', '9', 9),\n",
              " ('0', '6', 8),\n",
              " ('2', '7', 8),\n",
              " ('3', '9', 8),\n",
              " ('8', '5', 8),\n",
              " ('8', '6', 7),\n",
              " ('8', '7', 7),\n",
              " ('1', '3', 6),\n",
              " ('1', '8', 6),\n",
              " ('2', '1', 6),\n",
              " ('9', '5', 6),\n",
              " ('9', '8', 6),\n",
              " ('2', '3', 5),\n",
              " ('3', '7', 5),\n",
              " ('4', '6', 5),\n",
              " ('6', '0', 5),\n",
              " ('8', '2', 5),\n",
              " ('1', '4', 4),\n",
              " ('3', '1', 4),\n",
              " ('5', '0', 4),\n",
              " ('7', '1', 4),\n",
              " ('7', '4', 4),\n",
              " ('2', '4', 3),\n",
              " ('2', '9', 3),\n",
              " ('5', '2', 3),\n",
              " ('5', '7', 3),\n",
              " ('5', '9', 3),\n",
              " ('8', '0', 3)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "Wznuti18nNTG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}