day4_0831 (1).ipynb

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  "metadata": {
    "colab": {
      "name": "day4-0831.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bb32eM-lFHr0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eY56KBZXGa37",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas  as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Ad7it0lGkmF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "f957fed0-6ca8-4b52-8ba0-d957386ac2cc"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ArWko41GXn9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist = tf.keras.datasets.mnist"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbgPSsqrG5tf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e1b5817f-7407-486a-bed3-7e8180dcab40"
      },
      "source": [
        "mnist\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'tensorflow.keras.datasets.mnist' from '/usr/local/lib/python3.6/dist-packages/tensorflow/keras/datasets/mnist/__init__.py'>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOy2WXCVG7KG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "58a21404-02b2-4550-e26e-a95b7219065f"
      },
      "source": [
        "dir(mnist)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__builtins__',\n",
              " '__cached__',\n",
              " '__doc__',\n",
              " '__file__',\n",
              " '__loader__',\n",
              " '__name__',\n",
              " '__package__',\n",
              " '__path__',\n",
              " '__spec__',\n",
              " '_sys',\n",
              " 'load_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4nNkE0RuG_dQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mdata = mnist.load_data()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftFK4b44HHmv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b6466592-d140-429a-d39b-124ab5a99247"
      },
      "source": [
        "print(mdata)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "((array([[[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]],\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]],\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]],\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]],\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8), array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)), (array([[[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]],\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]],\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]],\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]],\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8), array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWBbuuvVHKkw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7e18eab7-60ca-46f1-d1ac-c64c1ebfd27d"
      },
      "source": [
        "len(mdata) # train 학습데이터 ,  test 테스트"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9iNgSJLIRCx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a374096e-8ac7-433f-f3b0-fcb927655a88"
      },
      "source": [
        "len(mdata[0]) # x,y  데이터 2개"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99TXTIlyIcgB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "595bdde4-5d79-4337-af9e-68f6c21ca9e3"
      },
      "source": [
        "len(mdata[0][1])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzSkIdT8IlYF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ed0c8488-97aa-499e-ffa8-37acd566843e"
      },
      "source": [
        "mdata[0][0].shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkkIXmMeHFlQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "35f0abda-2bc2-4fa3-d518-884c0b16853b"
      },
      "source": [
        "img = mdata[0][1][0]\n",
        "print(mdata[0][1][0])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSwyhiwvJbos",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1v-4LrYyI9vf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "2cdc98cf-45fa-4553-e5b8-04dfcfc01a7b"
      },
      "source": [
        "plt.imshow(img)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-23af6c37b3d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m   2649\u001b[0m         \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimlim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         resample=resample, url=url, **({\"data\": data} if data is not\n\u001b[0;32m-> 2651\u001b[0;31m         None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2652\u001b[0m     \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2653\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1563\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1564\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\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-> 1565\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    356\u001b[0m                 \u001b[0;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 358\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    356\u001b[0m                 \u001b[0;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 358\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5624\u001b[0m                               resample=resample, **kwargs)\n\u001b[1;32m   5625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5626\u001b[0;31m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\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   5627\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5628\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    697\u001b[0m                 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[1;32m    698\u001b[0m             raise TypeError(\"Invalid shape {} for image data\"\n\u001b[0;32m--> 699\u001b[0;31m                             .format(self._A.shape))\n\u001b[0m\u001b[1;32m    700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Invalid shape () for image data"
          ]
        },
        {
          "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": "9GMfbGkAJcYf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "7060b9e4-c601-41f0-fdf5-f27ed5d37b3b"
      },
      "source": [
        "plt.imshow(img, cmap=plt.cm.binary)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-37-e5fa7bf08319>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m   2649\u001b[0m         \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimlim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         resample=resample, url=url, **({\"data\": data} if data is not\n\u001b[0;32m-> 2651\u001b[0;31m         None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2652\u001b[0m     \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2653\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1563\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1564\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\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-> 1565\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    356\u001b[0m                 \u001b[0;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 358\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    356\u001b[0m                 \u001b[0;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 358\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5624\u001b[0m                               resample=resample, **kwargs)\n\u001b[1;32m   5625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5626\u001b[0;31m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\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   5627\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5628\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    697\u001b[0m                 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[1;32m    698\u001b[0m             raise TypeError(\"Invalid shape {} for image data\"\n\u001b[0;32m--> 699\u001b[0;31m                             .format(self._A.shape))\n\u001b[0m\u001b[1;32m    700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Invalid shape () for image data"
          ]
        },
        {
          "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": "emPGnx2JJnkH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_num(img, tag):\n",
        "  plt.imshow(img, cmap=plt.cm.binary)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.xlabel('tag : ' +str(tag), size = 40)\n",
        "  plt.show()"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KN5PtVhBKPMj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "6c9ef8e6-175d-4b18-a67d-7b94ac0bb9ac"
      },
      "source": [
        "img = mdata[0][0][1]\n",
        "tag = mdata[0][1][1]\n",
        "show_num(img,tag)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fD89GsaVKz2b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "863a567c-3d04-41c9-9dd0-b7c1a55fa7f9"
      },
      "source": [
        "sns.countplot(mdata[0][1])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc8d9fa1278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "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": "HrClt_2eK584",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "36d998eb-2862-4b06-e804-4a5326c71ec8"
      },
      "source": [
        "sns.countplot(mdata[1][1])"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc8d9e9b2b0>"
            ]
          },
          "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": "SS6bb5PELGOX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = mdata"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDdd5Ws3LUsY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "ea0e7d65-fa17-4260-ae5c-f7007ebf2a03"
      },
      "source": [
        "print(len(x_train))\n",
        "print(len(x_test))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60000\n",
            "10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkPMprOzLcyk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "06db276b-139a-4ed9-fc05-47f0c91900b8"
      },
      "source": [
        "x_train[0].max()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "255"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTOkIZorLmcY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f40afe70-9b4f-4e57-d997-039b06a44cb3"
      },
      "source": [
        "x_train[0].min()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g65hgEBZLuCh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test =  x_train/255.0, x_test/255.0"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4Hh_UYoMMyo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "45c2a428-dc11-409c-9919-0b62c00f4de2"
      },
      "source": [
        "x_train[0].max()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00392156862745098"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGDgQZPxMRxI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "d68edacc-937c-498b-817f-54181838da6b"
      },
      "source": [
        "show_num(x_train[0], y_train[0])"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Noql2gxvQ5eq6zvj3Wg0E1cfMalRprfUZdV1rneq9316jIaHKmFnriHPufufclzO0m6iw1FEe1CcIamNjZq0jpU89OFnS8wqfXLhF4ftt3lc4RnmywkEa89T1vNcPJE333u/o1wGjXxHWOlIW1jw+lDTfe7+2CkNCHWE3uL7kPRezqHDoH0FtAsysdaR0pk2rwmlfrQprueMkDVc4EWGPwpruJoXvekl+/hAaGGEFjGA3GDCCsAJGEFbACMIKGEFYASP+DzloLzj+EswlAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKe4WcicOSTb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "384865b1-a56f-4bb0-bb5f-6b9cfae6c77d"
      },
      "source": [
        "print(x_train[0])"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  4.61361015e-05 2.76816609e-04 2.76816609e-04 2.76816609e-04\n",
            "  1.93771626e-03 2.09150327e-03 2.69127259e-03 3.99846213e-04\n",
            "  2.55286428e-03 3.92156863e-03 3.79853902e-03 1.95309496e-03\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  4.61361015e-04 5.53633218e-04 1.44559785e-03 2.36831988e-03\n",
            "  2.61437908e-03 3.89081123e-03 3.89081123e-03 3.89081123e-03\n",
            "  3.89081123e-03 3.89081123e-03 3.46020761e-03 2.64513649e-03\n",
            "  3.89081123e-03 3.72164552e-03 2.99884660e-03 9.84236832e-04\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 7.53556324e-04\n",
            "  3.66013072e-03 3.89081123e-03 3.89081123e-03 3.89081123e-03\n",
            "  3.89081123e-03 3.89081123e-03 3.89081123e-03 3.89081123e-03\n",
            "  3.89081123e-03 3.86005383e-03 1.43021915e-03 1.26105344e-03\n",
            "  1.26105344e-03 8.61207228e-04 5.99769319e-04 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.76816609e-04\n",
            "  3.36793541e-03 3.89081123e-03 3.89081123e-03 3.89081123e-03\n",
            "  3.89081123e-03 3.89081123e-03 3.04498270e-03 2.79892349e-03\n",
            "  3.79853902e-03 3.70626682e-03 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  1.23029604e-03 2.39907728e-03 1.64552095e-03 3.89081123e-03\n",
            "  3.89081123e-03 3.15263360e-03 1.69165705e-04 0.00000000e+00\n",
            "  6.61284121e-04 2.36831988e-03 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 2.15301807e-04 1.53787005e-05 2.36831988e-03\n",
            "  3.89081123e-03 1.38408304e-03 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.13763937e-03\n",
            "  3.89081123e-03 2.92195309e-03 3.07574010e-05 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 1.69165705e-04\n",
            "  2.92195309e-03 3.89081123e-03 1.07650903e-03 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  5.38254517e-04 3.70626682e-03 3.46020761e-03 2.46059208e-03\n",
            "  1.66089965e-03 1.53787005e-05 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 1.24567474e-03 3.69088812e-03 3.89081123e-03\n",
            "  3.89081123e-03 1.83006536e-03 3.84467512e-04 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 6.92041522e-04 2.86043829e-03\n",
            "  3.89081123e-03 3.89081123e-03 2.30680507e-03 4.15224913e-04\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.46059208e-04\n",
            "  1.43021915e-03 3.87543253e-03 3.89081123e-03 2.87581699e-03\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 3.82929642e-03 3.89081123e-03 3.82929642e-03\n",
            "  9.84236832e-04 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 7.07420223e-04 1.99923106e-03\n",
            "  2.81430219e-03 3.89081123e-03 3.89081123e-03 3.18339100e-03\n",
            "  3.07574010e-05 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  5.99769319e-04 2.27604767e-03 3.52172241e-03 3.89081123e-03\n",
            "  3.89081123e-03 3.89081123e-03 3.84467512e-03 2.79892349e-03\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 3.69088812e-04 1.75317186e-03\n",
            "  3.39869281e-03 3.89081123e-03 3.89081123e-03 3.89081123e-03\n",
            "  3.89081123e-03 3.09111880e-03 1.19953864e-03 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  3.53710111e-04 1.01499423e-03 3.27566321e-03 3.89081123e-03\n",
            "  3.89081123e-03 3.89081123e-03 3.89081123e-03 3.04498270e-03\n",
            "  1.24567474e-03 3.07574010e-05 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 2.76816609e-04 2.62975779e-03\n",
            "  3.36793541e-03 3.89081123e-03 3.89081123e-03 3.89081123e-03\n",
            "  3.89081123e-03 2.99884660e-03 1.23029604e-03 1.38408304e-04\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  8.45828527e-04 2.64513649e-03 3.47558631e-03 3.89081123e-03\n",
            "  3.89081123e-03 3.89081123e-03 3.89081123e-03 3.75240292e-03\n",
            "  2.04536717e-03 1.69165705e-04 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  2.09150327e-03 3.89081123e-03 3.89081123e-03 3.89081123e-03\n",
            "  3.26028451e-03 2.07612457e-03 2.02998847e-03 2.46059208e-04\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNZCZ49KMZ_n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(10, activation='softmax')\n",
        "])"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WY4qarDINwf7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsBMw2vxLm3Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fdb65715-2a4d-4e14-8b07-fd93232b66c9"
      },
      "source": [
        "model.fit(x_train, y_train,epochs=8, verbose=0)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc8d053b160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mcz-fUzYLm73",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "1290bc80-dfab-48cd-8e46-6754a052bd51"
      },
      "source": [
        "model.evaluate(x_test,y_test, verbose=2)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.2963 - accuracy: 0.9161\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.2962853014469147, 0.916100025177002]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1bTsOJjJRkEt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJjM36TGLm-k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result = model.predict(x_test)"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKlsaf3hLnBn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i = result[0].max()"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGVLwIlESGpM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6d3528cb-91af-447a-a352-d244fecee03d"
      },
      "source": [
        "result[0].tolist().index(i)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vCT_DZQFLReX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "2d659c9d-14b4-4f64-e13a-069030db999a"
      },
      "source": [
        "show_num(x_test[0],y_test[0])"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}