ohy_day5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ohy_day5.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "pN7vq7GnFLra",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BBLiJmCzGkgb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "6590ed89-c950-409a-9aad-7e493662663f"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": null,
      "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": "kPs8TwYwGniI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist = tf.keras.datasets.mnist"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7U6C7uGHG4oA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0cffe957-cb27-488b-9da3-e758b2166b68"
      },
      "source": [
        "mnist"
      ],
      "execution_count": null,
      "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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1ZNB2QNG6e_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "137d03ab-4396-48e3-a4a7-dcf1854ea360"
      },
      "source": [
        "dir(mnist)"
      ],
      "execution_count": null,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4h5N9c9wG9oP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mdata = mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "glSnASdSHEy_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "afe1639e-d639-4b09-933c-3ab2e3d3c716"
      },
      "source": [
        "print(mdata)"
      ],
      "execution_count": null,
      "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": "On9VQeW9HJL3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "15eb6847-a822-4f20-e8dc-23fd3103e49a"
      },
      "source": [
        "type(mdata)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tuple"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4ZalOq0HVc3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "83e1e353-4202-455b-ad62-9fd039c0c4d8"
      },
      "source": [
        "len(mdata) #training (data), test (data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqLp8LDKHWxX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c362bcb0-477c-4b67-9201-d51f897cbc7f"
      },
      "source": [
        "len(mdata[0])#x,y data => 2개"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZBUuomWHZdP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d701141c-a13c-4b48-ce5b-a6495fab7f11"
      },
      "source": [
        "len(mdata[0][0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQF1j12KHb2f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d6b0d0e5-1f87-4c7e-ed30-24f84ec554da"
      },
      "source": [
        "len(mdata[0][1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPKuoi1FHgF3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1e519b23-7c08-4005-bdd0-08e8a9226a97"
      },
      "source": [
        "mdata[0][0].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stahJib0InWp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "77a333a5-c8fb-47fa-824f-2c86bfadaaf8"
      },
      "source": [
        "img = mdata[0][0][0]\n",
        "print(mdata[0][1][0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHUegySmI6Fv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "035dc82d-3c52-4e62-c579-bd8c115259d0"
      },
      "source": [
        "plt.imshow(img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f55c9ebee48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "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": "kUEc6wVdJUkn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "e3d7e77a-f550-4dbc-f58f-61d73eadb399"
      },
      "source": [
        "plt.imshow(img,cmap=plt.cm.binary)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f55c9999908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "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": "-dWF4EIwJgkP",
        "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JaU45G4pKOdo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "2f69c829-cd40-4faa-9548-30794fa45532"
      },
      "source": [
        "img = mdata[0][0][1]\n",
        "tag = mdata[0][1][1]\n",
        "show_num(img,tag)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ovevHDY-KYPI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "f24d8201-652f-44ae-d51f-7c48a52cfb3b"
      },
      "source": [
        "sns.countplot(mdata[0][1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f55c99190b8>"
            ]
          },
          "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": "XOJxTkacK0gH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "5b434b4a-1840-44c9-d042-1ff41ba09126"
      },
      "source": [
        "sns.countplot(mdata[1][1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f55c98f40f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "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": "284oNwLBK-lI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x_train, y_train),(x_test, y_test) = mdata"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzDhMWkRLVho",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "89cabc46-389f-4161-a2f3-4ca34a9a0a4f"
      },
      "source": [
        "print(len(x_train))\n",
        "print(len(x_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60000\n",
            "10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0Xyh0SNLb-4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, t_test =  x_train/255.0 , x_test/255.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qE1Y-4kPMJmA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7a861ccd-126e-4789-ddc9-74ad8caa4da7"
      },
      "source": [
        "x_train[0].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUTrIupmMPeg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9df60d7e-6dca-420e-f75d-5ae3e1a03581"
      },
      "source": [
        "x_train[0].min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2P47wqB7MSbY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "11798d38-0987-42cb-b9b1-79bd38f0513d"
      },
      "source": [
        "show_num(x_train[0], y_train[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "viTGabeoMX-v",
        "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-6prnbvOTXo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "               loss='sparse_categorical_crossentropy',\n",
        "               metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKnLCnDUPSdt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "4e187c76-d0b1-4273-b2b1-30670f554fe1"
      },
      "source": [
        "model.fit(x_train,y_train,epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2978 - accuracy: 0.9137\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1409 - accuracy: 0.9586\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1079 - accuracy: 0.9671\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0891 - accuracy: 0.9723\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0752 - accuracy: 0.9762\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f55c31a1668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8Z852AsPZOY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "8ba49b73-cac8-4492-a2e4-8c83794db265"
      },
      "source": [
        "model.evaluate(x_test,y_test,verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 13.0135 - accuracy: 0.9757\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[13.013544082641602, 0.9757000207901001]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRY8-aVRRHqC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result = model.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JMS-HxgIRZBQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4fcab4b2-331a-4344-9472-82316f28f5cf"
      },
      "source": [
        "result[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77lVSDH7RvDx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "dcb146f0-ef0e-47ee-c0ec-46ac820f8272"
      },
      "source": [
        "result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 1., 0., 0.],\n",
              "       [0., 0., 1., ..., 0., 0., 0.],\n",
              "       [0., 1., 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=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qYGxmkYKSXIS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i = result[0].max()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qCKe0lyTc8C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "02750f28-461a-4109-84c8-216c04a30a74"
      },
      "source": [
        "result[0].tolist().index(i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUma87O6Tf5_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "bfe6ff18-b371-41e9-b970-8cc2284a51c3"
      },
      "source": [
        "show_num(x_test[0],y_test[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EzMZlToTj_Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}