day4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "day4.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "55hhyD8uFUGb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "96f53a4b-cad8-4b56-f869-2e32af385ad1"
      },
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 5,
      "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": "Xpcqie3kGyrH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist = tf.keras.datasets.mnist"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtlLape7G55B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0313a861-c5a8-423b-8b7d-c82b7d998bc5"
      },
      "source": [
        "mnist"
      ],
      "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": "muOPvZbdG8Vg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "1df00148-3b2a-43e1-ea64-307ae9f75414"
      },
      "source": [
        "dir(mnist)"
      ],
      "execution_count": 8,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DKcVFKYG8ch",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "35370d1e-8e3e-4d89-a66a-1010de241d08"
      },
      "source": [
        "mdata = mnist.load_data()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UnwckGdG8jQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "65584da1-e766-4a4d-962d-f485e9fa2689"
      },
      "source": [
        "print(mdata)"
      ],
      "execution_count": 10,
      "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": "Hbveie2lG8pa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "721f1c6c-efa8-464b-8c3d-f48a91488c31"
      },
      "source": [
        "type(mdata)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tuple"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qqc2JcnqILZX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "len(mdata)#train(학습데이터), test(테스트 데이터)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GcJvk00HXcq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0d705086-aca5-4624-d1ed-a1a788522923"
      },
      "source": [
        "len(mdata[0])#x, y 데이터 2개"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNBBCQScIf-4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1142ce14-255d-43ff-ac13-1b9be3751f94"
      },
      "source": [
        "type(mdata[0][0])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGScCgzXHXlg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fed252ef-432f-42cc-e151-1c8a2a50004a"
      },
      "source": [
        "len(mdata[0][1])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-6OjXQfHXuo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "87a43189-9a02-41ba-b00b-aee3d8d2e5e6"
      },
      "source": [
        "mdata[0][0].shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xN9Z9_F0HX3A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d362eb8d-e449-4445-b917-8a30e14f28fb"
      },
      "source": [
        "img = mdata[0][0][0]\n",
        "print(mdata[0][1][0])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GYJIZg_I9u6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "a8e7deab-8c70-40b7-f5fb-ce725089736f"
      },
      "source": [
        "plt.imshow(img)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5fafcbba90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "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": "Uju-iZ0wJaSI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "27c5e9d5-5275-454c-d946-a1032bdacbd8"
      },
      "source": [
        "plt.imshow(img, cmap = plt.cm.binary)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5faf7464e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "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": "E7VecigaJac9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_num(img, tag): #tag는 정답을 의미\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": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LrlGbtppJamx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "17303080-d0c9-40c4-90d2-5dc4a9d5d9f5"
      },
      "source": [
        "img = mdata[0][0][1]\n",
        "tag = mdata[0][1][1]\n",
        "show_num(img, tag)"
      ],
      "execution_count": 21,
      "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": "HfCzCsxzJbH2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "7ee4e298-2271-4fc9-9625-29b33025c0c7"
      },
      "source": [
        "sns.countplot(mdata[0][1])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5faf75ad68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "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": "CwSmYTy8K4lO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "46ae1d78-b460-4410-d903-3606941ecee2"
      },
      "source": [
        "sns.countplot(mdata[1][1])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5fadc0bd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "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": "PQMxBv4XLG7S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x_train, y_train),(x_test, y_test) = mdata"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7ma5UclLVv7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "3c51e428-5929-4a04-ada8-ac858ff88d58"
      },
      "source": [
        "print(len(x_train))\n",
        "print(len(x_test))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60000\n",
            "10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gqx-BoE8Ld7b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "947ab679-ed8c-4c36-c0bc-7ea60a9e7505"
      },
      "source": [
        "x_train[0].max()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "255"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3R_o9p40Lnu7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fea3c5d8-1c67-4136-e884-2daa1afd0cf5"
      },
      "source": [
        "x_train[0].min()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ttVcQ6LNMA8j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test = x_train/255.0 , x_test/255.0"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9esjMnSYMMCz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d95e9483-7a19-46d5-82b8-44f34f38af43"
      },
      "source": [
        "x_train[0].max()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00392156862745098"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jDFj3q6MQjU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d8a3e00a-24d4-4623-dd41-f89b8916e2bb"
      },
      "source": [
        "x_train[0].min()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1hZ1NE8OSx6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "02170d66-2a91-450d-ea32-bdf5fee8b4a2"
      },
      "source": [
        "print(x_train[0])"
      ],
      "execution_count": 42,
      "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",
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            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
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            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
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            "  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",
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            "  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",
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            "  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": "ijWwaJw_MnAm",
        "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",
        "])\n"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxLUvzFiNbIu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=\"adam\",\n",
        "              loss=\"sparse_categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXCKgEB6Old1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "62a328d6-3454-4484-d285-59d7ea6a3f34"
      },
      "source": [
        "model.fit(x_train,y_train,epochs=5)#epochs는 데이터학습을몇번반복할건지"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0604 - accuracy: 0.9811\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0600 - accuracy: 0.9810\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0592 - accuracy: 0.9812\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0595 - accuracy: 0.9812\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0583 - accuracy: 0.9818\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f5fa4d1f8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-F3rVmlPrTh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "655f0f3c-6c95-407d-8949-70813dbe537f"
      },
      "source": [
        "model.evaluate(x_test,y_test, verbose=2)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.0794 - accuracy: 0.9757\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07944269478321075, 0.9757000207901001]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mI1BKFpZSEOH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i = result[0].max()"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0UlvbtMSOmw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "323d3111-dcea-4cac-f124-2627b1702d6f"
      },
      "source": [
        "result[0].tolist().index(i)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26M5EucGRnYz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "629aca09-4658-458f-fa3a-e8d3fac14f12"
      },
      "source": [
        "result = model.predict(x_test)\n",
        "result[0]"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8.8622969e-08, 1.6664383e-11, 2.0403802e-06, 1.0759572e-03,\n",
              "       1.1672213e-16, 8.2647249e-09, 5.0243715e-17, 9.9886537e-01,\n",
              "       2.0041375e-06, 5.4506399e-05], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45JniVHYRFUo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "73324c63-ae1b-41c6-ec7e-b3342428258b"
      },
      "source": [
        "model.predict(x_test)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[8.86229685e-08, 1.66643834e-11, 2.04038020e-06, ...,\n",
              "        9.98865366e-01, 2.00413751e-06, 5.45063995e-05],\n",
              "       [2.99837988e-10, 6.58761710e-05, 9.99928832e-01, ...,\n",
              "        8.60949162e-20, 9.40804270e-08, 1.04184146e-16],\n",
              "       [8.83370177e-08, 9.98213291e-01, 9.56634685e-05, ...,\n",
              "        1.21124438e-03, 4.33936657e-04, 1.62664460e-08],\n",
              "       ...,\n",
              "       [3.76804639e-14, 3.88963167e-13, 1.98712314e-12, ...,\n",
              "        1.83953694e-06, 1.09636346e-06, 1.73752109e-04],\n",
              "       [1.31134077e-13, 5.30032795e-10, 8.05112812e-13, ...,\n",
              "        1.32141222e-08, 1.68166327e-04, 1.99805493e-12],\n",
              "       [1.97242472e-10, 1.85124490e-16, 1.32409397e-07, ...,\n",
              "        1.69651832e-16, 1.07217187e-10, 4.79082750e-14]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-ISJhdeRbMm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "054dc086-77ba-4914-a234-a9455276d500"
      },
      "source": [
        "show_num(x_test[0],y_test[0])"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}