week01.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMvGh0n8HcN7Q6xH84qJgKL",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/StuPidMRE/Deeplearn/blob/main/week01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRSqIGPQZUg2",
        "outputId": "fb89fb52-4ba1-451a-af0c-4c192e559186"
      },
      "source": [
        "#what's the meaning of the '!' before pip?\n",
        "!pip install -Uqq fastbook\n",
        "import fastbook\n",
        "fastbook.setup_book()#what's this?"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 727kB 8.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.2MB 14.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 204kB 38.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 7.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 8.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 6.9MB/s \n",
            "\u001b[?25hMounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfQX_Uw6a2Rh"
      },
      "source": [
        "from fastai.vision.all import *\n",
        "from fastbook import *\n",
        "\n",
        "matplotlib.rc('image',cmap='Greys')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TEMm1xxIaGJA"
      },
      "source": [
        "path= untar_data(URLs.MNIST)#get the path"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8H8pl0kbczr"
      },
      "source": [
        "Path.BASE_PATH=path#is this necessary?"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3W17udAbnQ1",
        "outputId": "e669367c-ef4d-4fd9-c691-3c96beeebb15"
      },
      "source": [
        "path.ls()#to look at the content in the directory "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#2) [Path('testing'),Path('training')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DD3QWkq4nUzc",
        "outputId": "907d00f3-1723-407a-901a-b29c100a66af"
      },
      "source": [
        "(path/'training').ls()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#10) [Path('training/4'),Path('training/8'),Path('training/5'),Path('training/0'),Path('training/9'),Path('training/3'),Path('training/6'),Path('training/1'),Path('training/2'),Path('training/7')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TXC8Ex1nNeb",
        "outputId": "23796cd4-3754-4674-b9b1-529e28ef3ed6"
      },
      "source": [
        "#(path/'valid').ls()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#2) [Path('valid/3'),Path('valid/7')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-owlhk2b1Lh",
        "outputId": "aea53161-b663-4f3a-cc70-d615423b64b2"
      },
      "source": [
        "(path/'train#').ls()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#2) [Path('train/3'),Path('train/7')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iwd2Myhmb6aL"
      },
      "source": [
        "zeros=(path/'training'/'0').ls().sorted()\n",
        "ones=(path/'training'/'1').ls().sorted()\n",
        "twos=(path/'training'/'2').ls().sorted()\n",
        "threes=(path/'training'/'3').ls().sorted()\n",
        "fours=(path/'training'/'4').ls().sorted()\n",
        "fives=(path/'training'/'5').ls().sorted()\n",
        "sixs=(path/'training'/'6').ls().sorted()\n",
        "sevens=(path/'training'/'7').ls().sorted()\n",
        "eights=(path/'training'/'8').ls().sorted()\n",
        "nines=(path/'training'/'9').ls().sorted()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 45
        },
        "id": "VQY3LTa1cXf4",
        "outputId": "05d5f025-b113-4611-e849-11746f364cb1"
      },
      "source": [
        "im0_path=zeros[2]#the second item in the sevens\n",
        "im0=Image.open(im0_path)#open the picture\n",
        "im0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA50lEQVR4nM2RUW7CMBBEx1aPAecokbhFbNRbFHEMJO5BfjiFE5WeAlWil7Bmth9OSCD0t6r3w5aeZj07C/yDUxkt/MKaCzPTU7RsRVHsFnO2azNL1TO26XWiWD3qmMc63rs80AZdEhUA+AG+byWZTKbuw2Q2ER5uvymgERuMyt1Wpm5TdDCTAcBLga/Ou/Pp23l3XiNG5z8nbi7MrHEsE7aZe4xtw9L56xVvzjsX28q7iRDMzKtyJ2bmNI3PKAJAHPxi0lYylUfv9y5VijH2sz4ubJFuqe7DfF1dn8/XaoYGbaqfsT89PwnuyM50tpJjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x7FEBD9BF6390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXnqgG4Jcwku"
      },
      "source": [
        "#threes=(path/'train'/'3').ls().sorted()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwwbKHT1eTMj",
        "outputId": "3771e127-2044-4284-ff50-3799421cf122"
      },
      "source": [
        "zero_tensors=[tensor(Image.open(o)) for o in zeros]\n",
        "one_tensors=[tensor(Image.open(o)) for o in ones]\n",
        "two_tensors=[tensor(Image.open(o)) for o in twos]\n",
        "three_tensors=[tensor(Image.open(o)) for o in threes]\n",
        "four_tensors=[tensor(Image.open(o)) for o in fours]\n",
        "five_tensors=[tensor(Image.open(o)) for o in fives]\n",
        "six_tensors=[tensor(Image.open(o)) for o in sixs]\n",
        "seven_tensors=[tensor(Image.open(o)) for o in sevens]\n",
        "eight_tensors=[tensor(Image.open(o)) for o in eights]\n",
        "nine_tensors=[tensor(Image.open(o)) for o in nines]\n",
        "len(three_tensors),len(seven_tensors),len(one_tensors),len(two_tensors),len(three_tensors),len(four_tensors),len(five_tensors),len(six_tensors),len(eight_tensors),len(nine_tensors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6131, 6265, 6742, 5958, 6131, 5842, 5421, 5918, 5851, 5949)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "id": "S9FCap2NfECc",
        "outputId": "b1ec8a34-a5a9-46d3-c9d5-fcb98b4a3bf3"
      },
      "source": [
        "show_image(three_tensors[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f01b6929d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5IsEVLIfPvK",
        "outputId": "7265b429-e814-42f7-e084-6e079f89b1e8"
      },
      "source": [
        "stacked_sevens=torch.stack(seven_tensors).float()/255\n",
        "stacked_threes=torch.stack(three_tensors).float()/255\n",
        "stacked_zeros=torch.stack(zero_tensors).float()/255\n",
        "stacked_ones=torch.stack(one_tensors).float()/255\n",
        "stacked_twos=torch.stack(two_tensors).float()/255\n",
        "stacked_threes=torch.stack(three_tensors).float()/255\n",
        "stacked_fourns=torch.stack(four_tensors).float()/255\n",
        "stacked_fives=torch.stack(five_tensors).float()/255\n",
        "stacked_sevens=torch.stack(seven_tensors).float()/255\n",
        "stacked_sixs=torch.stack(six_tensors).float()/255\n",
        "stacked_eights=torch.stack(eight_tensors).float()/255\n",
        "stacked_nines=torch.stack(nine_tensors).float()/255\n",
        "stacked_threes.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([6131, 28, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1-jGYt_fwhg",
        "outputId": "91544ec1-5cb2-4b04-b083-816774762e02"
      },
      "source": [
        "#get the tensor's rank\n",
        "#way 1 len(stacked_sevens.shape)\n",
        "#way 2\n",
        "stacked_sevens.ndim"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehPM9rnwhBdz"
      },
      "source": [
        "a_3=stacked_threes[1]\n",
        "a_7=stacked_sevens[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "id": "oMOT8PUfgKv3",
        "outputId": "0d339345-08dd-4630-8d15-b6115c91fecc"
      },
      "source": [
        "mean3=stacked_threes.mean(0)\n",
        "show_image(mean3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f01b636e4d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TYEi96Vggoqa",
        "outputId": "2a192680-65a1-41e0-e1e0-fc63e5037c26"
      },
      "source": [
        "dist_3_abs=(a_3-mean3).abs().mean()\n",
        "dist_3_sqr=(a_3-mean3**2).mean().sqrt()\n",
        "dist_3_abs,dist_3_sqr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(0.1114), tensor(0.2804))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CemI-JpdiV23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "24ea845e-24c3-4a6f-91c8-4b6781b0b8db"
      },
      "source": [
        "mean7=stacked_sevens.mean(0)\n",
        "show_image(mean7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f01b6333910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKDpiltBh680",
        "outputId": "7f43113e-302d-4203-a0cc-d940dcdfe76b"
      },
      "source": [
        "dist_7_abs=(a_7-mean7).abs().mean()\n",
        "dist_7_sqr=(a_7-mean7**2).mean().sqrt()\n",
        "dist_7_abs,dist_7_sqr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(0.1304), tensor(0.3267))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SqWz5U8h0ad",
        "outputId": "eede1ee4-499a-4cb2-f186-e94d5dd10045"
      },
      "source": [
        "F.l1_loss(a_3.float(),mean7),F.mse_loss(a_3,mean7).sqrt()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(0.1586), tensor(0.3021))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhwRIsM5ihQB",
        "outputId": "fa806127-0c79-488d-d898-260b66741b20"
      },
      "source": [
        "data=[[1,2,3],[4,5,6]]\n",
        "arr=array(data)\n",
        "tns=tensor(data)\n",
        "arr,tns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[1, 2, 3],\n",
              "        [4, 5, 6]]), tensor([[1, 2, 3],\n",
              "         [4, 5, 6]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ocnZ1s8OizpH",
        "outputId": "e60239d1-4007-4aa5-9371-2da0654901f4"
      },
      "source": [
        "tns.type()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'torch.LongTensor'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQQzYzlejDG5",
        "outputId": "4d99607f-416d-4c5d-bf3f-4a66f02eae5c"
      },
      "source": [
        "valid_3_tens=torch.stack([tensor(Image.open(o))\n",
        "                for o in (path/'valid'/'3').ls()])\n",
        "valid_3_tens=valid_3_tens.float()/255\n",
        "valid_7_tens=torch.stack([tensor(Image.open(o))\n",
        "                for o in (path/'valid'/'7').ls()])\n",
        "valid_7_tens=valid_7_tens.float()/255\n",
        "valid_3_tens.shape,valid_7_tens.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([1010, 28, 28]), torch.Size([1028, 28, 28]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrEv--KzUQhA"
      },
      "source": [
        "valid_3_dist=mnist_distance(valid_3_tens,mean3)\n",
        "valid_7_dist=mnist_distance(valid_7_tens,mean7)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tg8KaY-Pcjvb",
        "outputId": "a6efec9e-148a-4b41-eded-f8919f94f93c"
      },
      "source": [
        "def mnist_distance(a,b): return (a-b).abs().mean((-1,-2))\n",
        "mnist_distance(a_3,mean3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.1114)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpdZ40xDdeX2"
      },
      "source": [
        "def is_3(x): return mnist_distance(x,mean3)<mnist_distance(x,mean7)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1H_5ydIdw_3",
        "outputId": "03f319c7-b2ed-4e4c-8997-f921c3f86184"
      },
      "source": [
        "is_3(a_3),is_3(a_3).float()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(True), tensor(1.))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWapypKreKXC",
        "outputId": "dfebd92d-dad9-4a4b-b8d6-0cf968e5a4e6"
      },
      "source": [
        "accuracy_3s=is_3(valid_3_tens).float().mean()\n",
        "accuracy_7s=(1-is_3(valid_7_tens).float()).mean()\n",
        "accuracy_3s,accuracy_7s,(accuracy_3s+accuracy_7s)/2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(0.9168), tensor(0.9854), tensor(0.9511))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXoCOulJnAQR"
      },
      "source": [
        "def pr_eight(x,w): return (x*w).sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k909QOYIoh6E"
      },
      "source": [
        "def f(x):return x**2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "kOoMV5OtoqeJ",
        "outputId": "b084b937-0d5f-42e0-f534-1df5fc8aa1c0"
      },
      "source": [
        "plot_function(f,'x','x**2');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/fastbook/__init__.py:73: UserWarning: Not providing a value for linspace's steps is deprecated and will throw a runtime error in a future release. This warning will appear only once per process. (Triggered internally at  /pytorch/aten/src/ATen/native/RangeFactories.cpp:23.)\n",
            "  x = torch.linspace(min,max)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "Ql2YCDKbo2Zf",
        "outputId": "241b1046-9bf9-4799-cc55-c061a7365c4e"
      },
      "source": [
        "plot_function(f,'x','x**2')\n",
        "plt.scatter(0,f(0),color='red')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f01b619e550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Va2kOmg3pKto"
      },
      "source": [
        "xt=tensor(3.).requires_grad_()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPeUt1HDpcVS",
        "outputId": "4b5c75a0-0a76-4cee-86cb-6b831b6106af"
      },
      "source": [
        "yt=f(xt)\n",
        "yt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(9., grad_fn=<PowBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCO2L7YnphLJ"
      },
      "source": [
        "yt.backward()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdVIenMUpleo",
        "outputId": "90e68211-a649-4332-921f-be91cd6437e0"
      },
      "source": [
        "xt.grad"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(6.)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtfj9tWVpsZG",
        "outputId": "6be8ffca-e352-49e4-8457-e4c89608f3ab"
      },
      "source": [
        "xt=tensor([3.,4.,10.]).requires_grad_()\n",
        "xt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 3.,  4., 10.], requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAEXeLUip4Ju",
        "outputId": "b0b2c907-68e2-4778-aa54-056f37ed91ce"
      },
      "source": [
        "def f(x):return (x**2).sum()\n",
        "yt=f(xt)\n",
        "yt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(125., grad_fn=<SumBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIsU0MscqIdY",
        "outputId": "137a3786-fedf-4900-e3f5-44652b67191d"
      },
      "source": [
        "yt.backward()\n",
        "xt.grad"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 6.,  8., 20.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0WPlmKzqLqJ",
        "outputId": "a6c85eea-8e0d-4bb6-afa5-05afedefc126"
      },
      "source": [
        "time=torch.arange(0,20).float()\n",
        "time"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "rpYSo6vYrHkD",
        "outputId": "7e1fe74e-7f5a-4855-8663-8788ec4f578d"
      },
      "source": [
        "speed=torch.randn(20)*3+0.75*(time-9.5)**2+1\n",
        "plt.scatter(time,speed)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f01b619e390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyvaM-QQrcUk"
      },
      "source": [
        "def f(t, params):\n",
        "    a,b,c=params\n",
        "    return a*(t**2)+(b*t)+c"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJW71W90rtU8"
      },
      "source": [
        "def mse(preds,targets):\n",
        "  return ((preds-targets)**2).mean().sqrt()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeIZs90Zr5KE"
      },
      "source": [
        "params=torch.randn(3).requires_grad_()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-0kWaQNsILq"
      },
      "source": [
        "orig_params=params.clone()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCZ9Pw8RsQ0w"
      },
      "source": [
        "preds=f(time,params)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GPQjVN1sd0P"
      },
      "source": [
        "def show_preds(preds,ax=None):\n",
        "      if ax is None:\n",
        "        fig,ax=plt.subplots()\n",
        "      ax.scatter(time,speed)\n",
        "      ax.scatter(time,to_np(preds),color='red')\n",
        "      ax.set_ylim(-300,100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "AdyUwoyVtG6T",
        "outputId": "6521d636-9376-4c58-b978-367e2db065fe"
      },
      "source": [
        "show_preds(preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7spEgo4moY76",
        "outputId": "4527afb3-ef43-40c6-f5e3-1a24dad99f1c"
      },
      "source": [
        "loss=mse(preds,speed)\n",
        "loss"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(113.5497, grad_fn=<SqrtBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EAbGsGT9omr6",
        "outputId": "5498946c-3b2e-4dcc-da25-58a40569e266"
      },
      "source": [
        "loss.backward()\n",
        "params.grad"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([162.9150,  10.3737,   0.6128])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dg9B7aOiox7f",
        "outputId": "ee1c63bc-d4fe-4a34-af9d-98b9d775ae8a"
      },
      "source": [
        "params*1e-5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 8.6398e-06, -1.0157e-05, -8.8875e-06], grad_fn=<MulBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jfIDprSo1-u",
        "outputId": "2da295b4-1bed-41a0-d054-5ed34f92e3be"
      },
      "source": [
        "params"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 0.8640, -1.0157, -0.8887], requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYTS9KYlo5dg"
      },
      "source": [
        "lr=1e-5\n",
        "params.data-=lr*params.grad.data\n",
        "params.grad=None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OFuZsQaxpIUJ",
        "outputId": "bb451a67-b9a8-4cca-e645-37d831e6968a"
      },
      "source": [
        "preds=f(time,params)\n",
        "mse(preds,speed)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(113.2833, grad_fn=<SqrtBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "jBYCdBEdpXMi",
        "outputId": "2a831aa7-31cf-410b-9315-620c4b7f59cf"
      },
      "source": [
        "show_preds(preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5h3ys_9LpdnV"
      },
      "source": [
        "def apply_step(params,n=1,prn=True):\n",
        "  for i in range(n):\n",
        "    preds=f(time,params)\n",
        "    loss=mse(preds,speed)\n",
        "    loss.backward()\n",
        "    params.data-=lr*params.grad.data\n",
        "    params.grad=None\n",
        "    if prn: print(loss.item())\n",
        "  return preds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSwPuMJ2qCLJ",
        "outputId": "946982fc-4a3b-41d6-c83c-d591acdf087f"
      },
      "source": [
        "for i in range(10):\n",
        "  apply_step(params)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "150.47401428222656\n",
            "150.2003631591797\n",
            "149.9267120361328\n",
            "149.6531219482422\n",
            "149.37954711914062\n",
            "149.10601806640625\n",
            "148.83250427246094\n",
            "148.5590362548828\n",
            "148.28561401367188\n",
            "148.01220703125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMCzcO8pqf0A"
      },
      "source": [
        "params=orig_params.detach().requires_grad_()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "k-DQTp91qn6e",
        "outputId": "c3ec97e3-917e-4fe2-ccf6-aedf6ed48a21"
      },
      "source": [
        "_,axs=plt.subplots(1,4,figsize=(12,3))\n",
        "for ax in axs:\n",
        "  show_preds(apply_step(params,n=100,prn=False),ax)\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9KIdQ6MFrSks"
      },
      "source": [
        "train_x=torch.cat([stacked_threes,stacked_sevens]).view(-1,28*28)\n",
        "train_y=tensor([1]*len(threes)+[0]*len(sevens)).unsqueeze(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N9LXcpE6sfhe",
        "outputId": "f19d727d-e0d2-4116-e10c-a44c34fdaf96"
      },
      "source": [
        "dset=list(zip(train_x,train_y))\n",
        "x,y=dset[0]\n",
        "x.shape,y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([784]), tensor([1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4FSwn3Us91L"
      },
      "source": [
        "valid_x=torch.cat([valid_3_tens,valid_7_tens]).view(-1,28*28)\n",
        "valid_y=tensor([1]*len(valid_3_tens)+[0]*len(valid_7_tens)).unsqueeze(1)\n",
        "valid_dset=list(zip(valid_x,valid_y))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOCUGb8dtjiE"
      },
      "source": [
        "def init_params(size,std=1.0):\n",
        "  return (torch.randn(size)*std).requires_grad_()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0Bt6wYtt4b7"
      },
      "source": [
        "weights=init_params(28*28,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lkTGvq56t_bg"
      },
      "source": [
        "bias=init_params(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tl6u7uQpuEVQ",
        "outputId": "feb3d95d-9cbf-4276-99fe-e36bbc6bb974"
      },
      "source": [
        "(train_x[0]*weights.T).sum()+bias"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([5.3017], grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YjmdAX_NuOf5",
        "outputId": "a854c29a-0f5d-4fe0-99d5-5697c2291a9a"
      },
      "source": [
        "def linear1(xb):\n",
        "  return xb@weights+bias\n",
        "preds=linear1(train_x)\n",
        "preds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 5.3017,  6.8557, 16.4948,  ..., -3.7922,  4.1024,  0.1021], grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9KsSPp3uuTl",
        "outputId": "551e5414-739f-4fcb-ba14-c8c01e630138"
      },
      "source": [
        "corrects=(preds>0.0).float()==train_y\n",
        "corrects"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ True,  True,  True,  ..., False,  True,  True],\n",
              "        [ True,  True,  True,  ..., False,  True,  True],\n",
              "        [ True,  True,  True,  ..., False,  True,  True],\n",
              "        ...,\n",
              "        [False, False, False,  ...,  True, False, False],\n",
              "        [False, False, False,  ...,  True, False, False],\n",
              "        [False, False, False,  ...,  True, False, False]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySm1u9Bqu_BH",
        "outputId": "1291447b-88d5-4c89-9ce7-3ea9474a67bc"
      },
      "source": [
        "corrects.float().mean().item()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.497275710105896"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgbUiyzfvIg2"
      },
      "source": [
        "with torch.no_grad():\n",
        "  weights[0]*=1.0001"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pK5NmT5yvS2c",
        "outputId": "345457c9-bf3b-431b-dfd0-e32bf5074ca7"
      },
      "source": [
        "preds=linear1(train_x)\n",
        "((preds>0.0).float()==train_y).float().mean().item()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.497275710105896"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDbfpxKLv1NC"
      },
      "source": [
        "trgts=tensor([1,0,1])\n",
        "prds=tensor([0.9,0.4,0.2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyQtxtZ1wAUY"
      },
      "source": [
        "def mnist_loss(predictions,targets):\n",
        "  return torch.where(targets==1,1-predictions,predictions).mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zu0Jz_NCwSNA",
        "outputId": "a41b4cbe-3110-4dc3-e9b0-ae16dd889dad"
      },
      "source": [
        "torch.where(trgts==1,1-prds,prds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.1000, 0.4000, 0.8000])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9RutVCwuwgYJ",
        "outputId": "92c967a9-c54b-4c13-e40e-97029e2a0a8f"
      },
      "source": [
        "mnist_loss(tensor([0.9,0.4,0.8]),trgts)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.2333)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07mWAe7MwpWO"
      },
      "source": [
        "def sigmoid(x): return 1/(1+torch.exp(-x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "9Cu0TIeew2CK",
        "outputId": "aa2e091e-6dda-4a4a-ad3c-f4bc01813b48"
      },
      "source": [
        "plot_function(torch.sigmoid,title='Sigmoid',min=-4,max=4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGJLR7O_xCaf"
      },
      "source": [
        "def mnist_loss(predictions,targets):\n",
        "  predictions=predictions.sigmoid()\n",
        "  return torch.where(targets==1,1-predictions,predictions).mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zqUI3TvxZqu",
        "outputId": "94d28278-2fcf-490f-b52b-916ddeca68b2"
      },
      "source": [
        "coll=range(15)\n",
        "dl=DataLoader(coll,batch_size=5,shuffle=True)\n",
        "list(dl)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[tensor([ 3, 12,  8, 10,  2]),\n",
              " tensor([ 9,  4,  7, 14,  5]),\n",
              " tensor([ 1, 13,  0,  6, 11])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNvYVnOiyAwG",
        "outputId": "6f6cfe1e-396d-4888-8da0-6b73e9c096f2"
      },
      "source": [
        "ds=L(enumerate(string.ascii_lowercase))\n",
        "ds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#26) [(0, 'a'),(1, 'b'),(2, 'c'),(3, 'd'),(4, 'e'),(5, 'f'),(6, 'g'),(7, 'h'),(8, 'i'),(9, 'j')...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkJRqRnZyOix",
        "outputId": "c76ca179-9752-4d42-ae66-17ed7c833a7f"
      },
      "source": [
        "dl=DataLoader(ds,batch_size=6,shuffle=True)\n",
        "list(dl)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor([17, 18, 10, 22,  8, 14]), ('r', 's', 'k', 'w', 'i', 'o')),\n",
              " (tensor([20, 15,  9, 13, 21, 12]), ('u', 'p', 'j', 'n', 'v', 'm')),\n",
              " (tensor([ 7, 25,  6,  5, 11, 23]), ('h', 'z', 'g', 'f', 'l', 'x')),\n",
              " (tensor([ 1,  3,  0, 24, 19, 16]), ('b', 'd', 'a', 'y', 't', 'q')),\n",
              " (tensor([2, 4]), ('c', 'e'))]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "-rkkVTkLyYq0",
        "outputId": "00cbab59-b0af-426b-8afe-6fda6e7b2add"
      },
      "source": [
        "for x,y in dl:\n",
        "  pred=model(x)\n",
        "  loss=loss_func(pred,y)\n",
        "  loss.backward()\n",
        "  parameters-=parameters.grad*lr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-142-04e3470217ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdl\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mpreds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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      3\u001b[0m   \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\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      5\u001b[0m   \u001b[0mparameters\u001b[0m\u001b[0;34m-=\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZ0_3uPqy2l0"
      },
      "source": [
        "weights=init_params((28*28,1))\n",
        "bias=init_params(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3IlS3owFzCua",
        "outputId": "794e22a4-eb79-4d98-a171-d9f8c48c814a"
      },
      "source": [
        "dl=DataLoader(dset,batch_size=256)\n",
        "xb,yb=first(dl)\n",
        "xb.shape,yb.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([256, 784]), torch.Size([256, 1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtvulcxFzQ1C"
      },
      "source": [
        "valid_dl=DataLoader(valid_dset,batch_size=256)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udC0SXbEzaNl",
        "outputId": "82da7e3d-9328-4aab-c6b2-ad10707fdc1c"
      },
      "source": [
        "batch=train_x[:4]\n",
        "batch.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([4, 784])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxX6kjW4zjT4",
        "outputId": "275e1dbb-1213-4429-bc8d-f28824098b64"
      },
      "source": [
        "loss=mnist_loss(preds,train_y[:4])\n",
        "loss"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.2580, grad_fn=<MeanBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "NA4X_zU0zreX",
        "outputId": "246e4919-5d6d-4481-acc9-0673adf58981"
      },
      "source": [
        "loss.backward()\n",
        "#weights.grad.shape,weights.grad.mean(),bias.grad"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-140-71fc9f675c01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \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      2\u001b[0m \u001b[0;31m#weights.grad.shape,weights.grad.mean(),bias.grad\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/python3.7/dist-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    243\u001b[0m                 \u001b[0mcreate_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[1;32m    244\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 245\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[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\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    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\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/python3.7/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    145\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    146\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[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Trying to backward through the graph a second time, but the saved intermediate results have already been freed. Specify retain_graph=True when calling .backward() or autograd.grad() the first time."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gegOR7re0Lv4"
      },
      "source": [
        "def calc_grad(xb,yb,model):\n",
        "  preds=model(xb)\n",
        "  loss=mnist_loss(preds,yb)\n",
        "  loss.backward()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OV-bXaq90qUY",
        "outputId": "54c6e340-8b78-4c13-9a7f-c43c6266575f"
      },
      "source": [
        "calc_grad(batch,train_y[:4],linear1)\n",
        "weights.grad.mean(),bias.grad"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(-0.0160), tensor([-0.0973]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bt4-V0541cbI",
        "outputId": "63da46d9-3e79-47bf-fdec-b21fbc86e391"
      },
      "source": [
        "calc_grad(batch,train_y[:4],linear1)\n",
        "weights.grad.mean(),bias.grad"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(-0.0213), tensor([-0.1297]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5Kdna7q1e5Y"
      },
      "source": [
        "weights.grad.zero_()\n",
        "bias.grad.zero_();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHcFkbX11ntk"
      },
      "source": [
        "def train_epoch(model,lr,params):\n",
        "  for xb,yb in dl:\n",
        "    calc_grad(xb,yb,model)\n",
        "    for p in params:\n",
        "      p.data-=p.grad*lr\n",
        "      p.grad.zero_()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noJC1F-p2Nq8",
        "outputId": "ff2304ec-b299-4af3-f49b-0fb30e7d378e"
      },
      "source": [
        "(preds>0.0).float()==train_y[:4]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ True,  True,  True,  ..., False,  True,  True],\n",
              "        [ True,  True,  True,  ..., False,  True,  True],\n",
              "        [ True,  True,  True,  ..., False,  True,  True],\n",
              "        [ True,  True,  True,  ..., False,  True,  True]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0ia6qcS2VyU"
      },
      "source": [
        "def batch_accracy(xb,yb):\n",
        "  preds=xb.sigmoid()\n",
        "  correct=(preds>0.5)==yb\n",
        "  return correct.float().mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmVTFgRD2ovL",
        "outputId": "02108383-bc2d-4c55-957f-0fe363e82ccb"
      },
      "source": [
        "batch_accracy(linear1(batch),train_y[:4])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1.)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWmJR9TD3A1R"
      },
      "source": [
        "def validate_epoch(model):\n",
        "  accs=[batch_accracy(model(xb),yb) for xb,yb in valid_dl]\n",
        "  return round(torch.stack(accs).mean().item(),4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lD4euoYG3ZrT",
        "outputId": "a4a923a0-46de-46bb-c08f-364853c31254"
      },
      "source": [
        "validate_epoch(linear1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.767"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZIY-Ml0G4FNm",
        "outputId": "62bcdef9-1dd6-4d97-fff0-3339189e2921"
      },
      "source": [
        "lr=1.\n",
        "params=weights,bias\n",
        "train_epoch(linear1,lr,params)\n",
        "validate_epoch(linear1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7695"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJ4g-GsP4RaS",
        "outputId": "3cd49065-8915-4ff0-fbc4-37a786d9ff86"
      },
      "source": [
        "for i in range(20):\n",
        "  train_epoch(linear1,lr,params)\n",
        "  print(validate_epoch(linear1),end=' ')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8788 0.9174 0.9349 0.9467 0.9535 0.9559 0.9618 0.9647 0.9686 0.9711 0.9716 0.9721 0.9731 0.9745 0.975 0.9755 0.9755 0.976 0.977 0.9775 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvgIYBNf4gxT"
      },
      "source": [
        "linear_model=nn.Linear(28*28,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DRET9UcO4qcq",
        "outputId": "d1c6dc83-e514-483b-96c2-25f9b2990436"
      },
      "source": [
        "w,b=linear_model.parameters()\n",
        "w.shape,b.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([1, 784]), torch.Size([1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eiQrfMn64xae"
      },
      "source": [
        "class BasicOptim:\n",
        "  def __init__(self,params,lr):self.params,self.lr=list(params),lr\n",
        "\n",
        "  def step(self, *args, **kwargs):\n",
        "    for p in self.params: p.data-=p.grad.data*self.lr\n",
        "\n",
        "  def zero_grad(self,*args,**kwargs):\n",
        "    for p in self.params: p.grad=None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rVEkQxj5oVn"
      },
      "source": [
        "opt=BasicOptim(linear_model.parameters(),lr)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IA0xEj7K6eeA"
      },
      "source": [
        "def train_epoch(model):\n",
        "  for xb,yb in dl:\n",
        "    calc_grad(xb,yb,model)\n",
        "    opt.step()\n",
        "    opt.zero_grad()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mbQY5o36vsj",
        "outputId": "d97403a5-ecd1-497c-fc86-6e62e6887e73"
      },
      "source": [
        "validate_epoch(linear_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.548"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtgtrXep61H5"
      },
      "source": [
        "def train_model(model,epochs):\n",
        "  for i in range(epochs):\n",
        "    train_epoch(model)\n",
        "    print(validate_epoch(model),end=' ')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5HHktEMH7Ioq",
        "outputId": "4f07cdf0-2608-435c-ed76-dd7525765d3c"
      },
      "source": [
        "train_model(linear_model,20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.4932 0.7729 0.8589 0.9165 0.937 0.9497 0.957 0.9629 0.9658 0.9673 0.9692 0.9717 0.9746 0.9751 0.9761 0.977 0.9775 0.978 0.9785 0.9785 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8heHVk6V7Rn9",
        "outputId": "72ed5467-3a9c-4202-eb45-8e2f91e26b49"
      },
      "source": [
        "linear_model=nn.Linear(28*28,1)\n",
        "opt=SGD(linear_model.parameters(),lr)\n",
        "train_model(linear_model,20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.4932 0.7393 0.8652 0.9175 0.9365 0.9497 0.9565 0.9634 0.9658 0.9673 0.9712 0.9726 0.9741 0.9751 0.9761 0.9761 0.9775 0.978 0.9785 0.9785 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34W7FT9F7jDU"
      },
      "source": [
        "dls=DataLoader(dl,valid_dl)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ddrr7ll97oxo"
      },
      "source": [
        "learn=Learner(dls,nn.Linear(28*28,1),opt_func=SGD,\n",
        "              loss_func=mnist_loss,metrics=batch_accracy)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "FL4cUSi5754n",
        "outputId": "04cb086f-493f-4561-a9ba-3f8559ed57b4"
      },
      "source": [
        "learn.fit(10,lr=lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
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              "                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",
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            "text/plain": [
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          "metadata": {
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        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-186-65d9c467984a>\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\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\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/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, n_epoch, lr, wd, cbs, reset_opt)\u001b[0m\n\u001b[1;32m    216\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_hypers\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[0mlr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_fit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelFitException\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_end_cleanup\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    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_end_cleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpred\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[0;32mNone\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[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\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--> 160\u001b[0;31m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mf\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    161\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mfinal\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/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epoch\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    208\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'epoch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelEpochException\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    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset_opt\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\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--> 160\u001b[0;31m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mf\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    161\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mfinal\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/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_epoch\u001b[0;34m(self)\u001b[0m\n\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[0m_do_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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--> 203\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch_train\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    204\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch_validate\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    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_epoch_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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--> 194\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'train'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelTrainException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/basics.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, k)\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_component_attr_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\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    387\u001b[0m             \u001b[0mattr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default\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--> 388\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\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    389\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcustom_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/basics.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, k)\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_component_attr_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\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    387\u001b[0m             \u001b[0mattr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default\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--> 388\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\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    389\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcustom_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'train'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVxoMIYq8lC7"
      },
      "source": [
        "def simple_net(xb):\n",
        "  res=xb@w1+b1\n",
        "  res=res.max(tensor(0.0))\n",
        "  res=res@w2+b2\n",
        "  return res"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZU-LFGJ85y0"
      },
      "source": [
        "w1=init_params((28*28,30))\n",
        "b1=init_params(30)\n",
        "w2=init_params((30,1))\n",
        "b2=init_params(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Pu4OyVU_9Iey",
        "outputId": "1de67b41-efa4-49c3-dd16-dd6e90a42287"
      },
      "source": [
        "plot_function(F.relu)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZrxzOgt9Oay"
      },
      "source": [
        "simple_net=nn.Sequential(\n",
        "    nn.Linear(28*28,30),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(30,1)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzqiC-Op9fdw"
      },
      "source": [
        "learn=Learner(dls,simple_net,opt_func=SGD,\n",
        "              loss_func=mnist_loss,metrics=batch_accracy)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "5g4lugVl9vAA",
        "outputId": "c215f771-d3ac-424e-f1be-272606d73934"
      },
      "source": [
        "learn.fit(40,0.1)"
      ],
      "execution_count": null,
      "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='40' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      \n",
              "    </div>\n",
              "    \n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-193-6d8302ae4668>\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\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.1\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/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, n_epoch, lr, wd, cbs, reset_opt)\u001b[0m\n\u001b[1;32m    216\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_hypers\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[0mlr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_fit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelFitException\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_end_cleanup\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    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_end_cleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpred\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[0;32mNone\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[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\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--> 160\u001b[0;31m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mf\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    161\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mfinal\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/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epoch\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    208\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'epoch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelEpochException\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    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset_opt\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\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--> 160\u001b[0;31m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mf\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    161\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mfinal\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/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_epoch\u001b[0;34m(self)\u001b[0m\n\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[0m_do_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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--> 203\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch_train\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    204\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch_validate\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    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_epoch_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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--> 194\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'train'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelTrainException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/basics.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, k)\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_component_attr_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\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    387\u001b[0m             \u001b[0mattr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default\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--> 388\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\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    389\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcustom_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/basics.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, k)\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_component_attr_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\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    387\u001b[0m             \u001b[0mattr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default\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--> 388\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\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    389\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcustom_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'train'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "wNbqYVxN94wI",
        "outputId": "8b692dec-0dbd-457b-d337-a82be4c699a7"
      },
      "source": [
        "learn.recorder.values[-1][-2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-195-1064d55d51bc>\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[0mrecorder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "2CYSEUG_-CrJ",
        "outputId": "c8774742-d418-464b-8580-72a68ab478ac"
      },
      "source": [
        "dls=ImageDataLoaders.from_folder(path)\n",
        "learn=cnn_learner(dls,resnet18,pretrained=False,\n",
        "                  loss_func=F.cross_entropy,metrics=accuracy)\n",
        "learn.fit_one_cycle(1,0.1)"
      ],
      "execution_count": null,
      "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>0.143302</td>\n",
              "      <td>0.029645</td>\n",
              "      <td>0.994603</td>\n",
              "      <td>03:49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}