day4.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "day4.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-5AHyYoGJ1A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "5242099a-aea9-4793-94c9-ea2ddbb6c69f"
      },
      "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\n",
        "\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 3,
      "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": "idwejBd6GX7U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist = tf.keras.datasets.mnist"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1rkXSWoG3xa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8007cbd8-92c8-4d68-f518-fc6f46bbf5aa"
      },
      "source": [
        "mnist"
      ],
      "execution_count": 6,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gAQ2x-yG8PZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "e9f7d67a-a637-4c18-cbdb-6879e1813593"
      },
      "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": "B-SwYL3AHBAp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9cd39cb7-5ad0-4177-f110-a850c827e6a1"
      },
      "source": [
        "mdata = mnist.load_data()\n",
        "print(mdata)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "((array([[[0, 0, 0, ..., 0, 0, 0],\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
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            "\n",
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            "\n",
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            "        [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",
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            "\n",
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            "\n",
            "       ...,\n",
            "\n",
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            "\n",
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            "\n",
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            "        ...,\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": "2CD3rsAlHEAB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8445b2a1-d6cb-49ce-86cd-2bc448b55ee2"
      },
      "source": [
        ""
      ],
      "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",
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            "\n",
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            "\n",
            "       ...,\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
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            "\n",
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            "        ...,\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",
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            "\n",
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            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
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            "        ...,\n",
            "        [0, 0, 0, ..., 0, 0, 0],\n",
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            "\n",
            "       ...,\n",
            "\n",
            "       [[0, 0, 0, ..., 0, 0, 0],\n",
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            "\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": "lPYOjxsqHIyy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f93ab071-59c2-4d15-8afd-5073bf0a4f23"
      },
      "source": [
        "len(mdata) # train(학습데이터), test(테스트 데이터)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4WSr1-MIMjK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "df67ef68-1d47-4f33-8cd3-619e988fd1c5"
      },
      "source": [
        "len(mdata[0])  # x,y 데이터 2개\n",
        "len(mdata[1])  # "
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O97GNfzWIe3E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "674c5d8b-5f2f-4f2b-bc6c-7c36a99ab7e0"
      },
      "source": [
        "type(mdata[0][0])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INJ5arcJIkcJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "135b08c1-cf16-461c-9686-a354df2ebcf4"
      },
      "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": "HKZYBAsHI0JZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "06a5325f-5754-4db5-cf97-1f224a85e609"
      },
      "source": [
        "img = mdata[0][0][0]\n",
        "print(mdata[0][1][0])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-ODOVhuI6Y6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "0615ebcc-79a4-46b2-cf6e-99c88399ecf2"
      },
      "source": [
        " plt.imshow(img)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6db4b4cb70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "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": "5Tz-fcxBJcAL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "ddac9411-b431-4777-b428-b6d5ae20584e"
      },
      "source": [
        "plt.imshow(img, cmap=plt.cm.binary)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6db5665c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "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": "msjz3wNGJfq6",
        "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 = 25)\n",
        "  plt.show()"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOsTnZCHKO3M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "e0ed927d-4e67-4d0b-c39b-6e9dc88d131c"
      },
      "source": [
        "img = mdata[0][0][1]\n",
        "tag = mdata[0][1][1]\n",
        "show_num(img, tag)"
      ],
      "execution_count": 36,
      "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": "3EDrmEjHKX-y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "032e76c8-f7a2-4ef4-fc74-c1b4f59d2047"
      },
      "source": [
        "sns.countplot(mdata[0][1]) # 숫자가 몇개 있는가?"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6db4b07470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "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": "rih9_5omKyBJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "270b03d3-4b5a-4988-ff15-8ab0d5b259da"
      },
      "source": [
        "sns.countplot(mdata[1][1])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6db2ef1358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "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": "X_Re8CQtK-JK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "7cfae7ec-4bec-419f-d089-c13bbc8c4c5f"
      },
      "source": [
        "(x_train, y_train),(x_test, y_test) = mdata\n",
        "print(len(x_train))\n",
        "print(len(x_test))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60000\n",
            "10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yla5Dg6nLUhR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test = x_train/255.0, x_test/255.0"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cS0ah1_9MMTK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b3af8fc8-4e0b-4800-fb19-68ab16558986"
      },
      "source": [
        "x_train.max()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.5378700499807765e-05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YAy3QAkZMQyB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e804ef49-abdf-47cb-9e98-e509d0f8c2d3"
      },
      "source": [
        "x_train.min()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RW3uOkiMOUzQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "38739bbb-8318-441c-9902-6847da856e0b"
      },
      "source": [
        "print(x_train[0])"
      ],
      "execution_count": 54,
      "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",
            "  1.80925888e-07 1.08555533e-06 1.08555533e-06 1.08555533e-06\n",
            "  7.59888731e-06 8.20197360e-06 1.05540101e-05 1.56802436e-06\n",
            "  1.00112325e-05 1.53787005e-05 1.48962315e-05 7.65919594e-06\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",
            "  1.80925888e-06 2.17111066e-06 5.66901116e-06 9.28752893e-06\n",
            "  1.02524670e-05 1.52580832e-05 1.52580832e-05 1.52580832e-05\n",
            "  1.52580832e-05 1.52580832e-05 1.35694416e-05 1.03730843e-05\n",
            "  1.52580832e-05 1.45946883e-05 1.17601827e-05 3.85975228e-06\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 2.95512284e-06\n",
            "  1.43534538e-05 1.52580832e-05 1.52580832e-05 1.52580832e-05\n",
            "  1.52580832e-05 1.52580832e-05 1.52580832e-05 1.52580832e-05\n",
            "  1.52580832e-05 1.51374660e-05 5.60870254e-06 4.94530761e-06\n",
            "  4.94530761e-06 3.37728325e-06 2.35203655e-06 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 1.08555533e-06\n",
            "  1.32075898e-05 1.52580832e-05 1.52580832e-05 1.52580832e-05\n",
            "  1.52580832e-05 1.52580832e-05 1.19411086e-05 1.09761706e-05\n",
            "  1.48962315e-05 1.45343797e-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",
            "  4.82469035e-06 9.40814619e-06 6.45302335e-06 1.52580832e-05\n",
            "  1.52580832e-05 1.23632690e-05 6.63394924e-07 0.00000000e+00\n",
            "  2.59327106e-06 9.28752893e-06 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",
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            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
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            "  1.52580832e-05 1.52580832e-05 1.52580832e-05 1.47153056e-05\n",
            "  8.02104771e-06 6.63394924e-07 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",
            "  8.20197360e-06 1.52580832e-05 1.52580832e-05 1.52580832e-05\n",
            "  1.27854294e-05 8.14166497e-06 7.96073908e-06 9.64938071e-07\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",
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            "  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": "qh6aoM_QMa4J",
        "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": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgBgQZDvOSZx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Yi1FFdKPuSx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "d1fa46fe-c3f3-463d-da28-9288d623a2e1"
      },
      "source": [
        "model.fit(x_train, y_train,epochs=10) # epochs : 반복학습을 n번 한다\n"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7905 - accuracy: 0.7376\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7894 - accuracy: 0.7384\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7831 - accuracy: 0.7408\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7841 - accuracy: 0.7412\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7836 - accuracy: 0.7404\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7902 - accuracy: 0.7396\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7841 - accuracy: 0.7408\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7851 - accuracy: 0.7404\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7802 - accuracy: 0.7427\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7817 - accuracy: 0.7425\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f6da781c080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0gE0N5kLP25x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "b7b435b9-e072-4659-cf5a-4fd153a912a1"
      },
      "source": [
        "model.evaluate(x_test, y_test, verbose=2) # verbose : "
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.5434 - accuracy: 0.8574\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5433685779571533, 0.8574000000953674]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_b_EHh5eSdQp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i = result[0].max()"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMwkjTUlRMCh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result = model.predict(x_test)"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIdeFJRJRasQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1aa77aab-a267-4064-94e5-4e58f8e1fd1c"
      },
      "source": [
        "result[0].tolist().index(i)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "66bprGuQRpAa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "a9552bc3-68de-401a-89f2-405319614cd8"
      },
      "source": [
        "show_num(x_test[1], y_test[1])"
      ],
      "execution_count": 94,
      "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": "k9NjWF4HSSb7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 88,
      "outputs": []
    }
  ]
}