DeepLeaningZero.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DeepLeaningZero.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tkota0726/Jupyter/blob/master/DeepLeaningZero.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "ZI3k73sVS1_r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_YGb0GN-TGP4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## パーセプトロンの実装\n"
      ]
    },
    {
      "metadata": {
        "id": "Wj0NvN6iTIet",
        "colab_type": "code",
        "outputId": "59514931-d9c6-457f-90a3-051ba9af0ad7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "cell_type": "code",
      "source": [
        "def AND(x1, x2):\n",
        "  w1, w2, theta = .5, .5, .7\n",
        "  tmp = x1*w1 + x2*w2\n",
        "  if tmp <= theta:\n",
        "    return 0\n",
        "  elif tmp > theta:\n",
        "    return 1\n",
        "  \n",
        "print(AND(0, 0))   #0\n",
        "print(AND(1, 0))   #0\n",
        "print(AND(0, 1))   #0\n",
        "print(AND(1, 1))   #1\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "0\n",
            "0\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OSiNK4vfX-73",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## シグモイド関数"
      ]
    },
    {
      "metadata": {
        "id": "mAv8ME4iYHB3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "$$ h(x) =  \\frac{\\mathrm{1} }{\\mathrm{1} + exp(-x) }  $$"
      ]
    },
    {
      "metadata": {
        "id": "UnHRjlOVYGI9",
        "colab_type": "code",
        "outputId": "005bd6ad-64e9-41a5-d394-46b0efa160c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "def step_function(x):\n",
        "  if x < 0:\n",
        "    return 1\n",
        "  else:\n",
        "    return 0\n",
        "  \n",
        "step_function(0)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "bI6tI02ranAP",
        "colab_type": "code",
        "outputId": "77e47451-4099-40d8-ebd8-def69c1cd317",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "def sigmoid(x):\n",
        "  return 1 / (1 + np.exp(-x))\n",
        "\n",
        "x = np.arange(-5, 5, 0.1)\n",
        "y = sigmoid(x)\n",
        "plt.plot(x, y)\n",
        "plt.ylim(-0.1, 1.1)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P3DIJWo1SdCQiSmAsYaIztKvRjldPvBb4l4smwaBXi45ERAmOJUx0BvYd7sYf\n36qDQi7DL26eiKE5GaIjEVESYAkTDaDpqBPL39gLQMKDCydgZEGW6EhElCRYwkQ/oqWjD//7+m4E\nQxH8y/XjMb4kR3QkIkoiLGGi02i3u/G76l3w+UP46fyxmDzaJDoSESWZM1plftmyZdi9ezdkMhkW\nL16M8vLy/tu2bt2Kp59+GmlpaSgpKcHSpUuRlsZup8R2rMuN3676Gi5vEHddNYbnBSaimBiwLbdt\n24aWlhZUV1dj6dKlWLp06Um3/+d//if+8Ic/YPXq1XC73fjss89iFpZoMLTb3Xjyb1/D6Q7gttmj\nMXNSgehIRJSkBtwSrq2tRWVlJQCgtLQUTqcTLpcLOp0OAFBTU9N/2Wg0wuFwxDAuUWwdtbvx27/t\nRK8niNtmj8aVFxSKjkRESWzALWG73Q6D4bszwxiNRthstv7r3xaw1WrFli1bMHPmzBjEJIq9ozYX\nnjpRwLfPYQETUeyd9ZnHJUn6wce6urpw//33Y8mSJScV9qkYDFooUnCZP5NJLzpCUojVODa2OvDU\nql3o8wTx84XluPrS5D4hAx+P0cFxjI5UHscBS9hsNsNut/dft1qtMJm+O0rU5XLhZz/7GR5++GFM\nnz59wG/ocHjOMWriMpn0sNn6RMdIeLEaxwNHHHhm7R74A2H809VlmDpqSFL/vvh4jA6OY3Skyjie\n7h+NAaejKyoqsHHjRgBAfX09zGZz/xQ0ADzxxBO46667MGPGjChFJRo8u5vsePrE64Dvv2E8ZkzM\nFx2JiFLIgFvCU6ZMgcViQVVVFWQyGZYsWYKamhro9XpMnz4db731FlpaWrB27VoAwPz587Fo0aKY\nByc6X1v3dWDFu/shT5PhX28qx4QRXIiDiAbXGe0Tfuyxx066XlZW1n+5rq4uuomIYkySJGzc1orX\nNzchXS3HL26aiNFF2aJjEVEKOusDs4gSWSQiYdWHjfhoZxuydSo8fPNEDMtN3YNCiEgsljClDH8w\njD+9XY+vG+0oMGXgkZsnwpipER2LiFIYS5hSgtPlx/KavTjU3ouxxQY8sGACtBo+/IlILD4LUdL7\n5lgvnq3ZC0efH5dY8nD3vDIo5FzfnIjEYwlTUvtyXydeXr8foVAEN88qxVUXDYNMJhMdi4gIAEuY\nklQkIuHNzw7hvdoWaFRy/PymckwcOUR0LCKik7CEKen0egJ48e161B92wJydjoduKkfBkAzRsYiI\nfoAlTEmlqc2J59fVwdHnx8TSHNw7fxx06UrRsYiIToklTElBkiR8sKMNazY3ISJJWDhzBK6+uBhp\n3P9LRHGMJUwJr9cdwMvr92M5NVZPAAALVUlEQVRPcxcyM1S4/zoLyop//GxeRETxgCVMCW3voS6s\neG8/et0BWIYbcO/8ccjWqUXHIiI6IyxhSkiBYBhrP2nGh1+1QZ4mw6IrRmL2tCJOPxNRQmEJU8Jp\nanNixfr96Oz2YGiOFvdda0FxHtd/JqLEwxKmhOELhLD6o0Z8sL0VAFA5tRALZ5ZCrZQLTkZEdG5Y\nwpQQGlocWPnBl2i3u5FrSMfd88by9INElPBYwhTXnO4AXv+4EbX1nZDJgLkXFuGGy0Zw65eIkgJL\nmOJSJCLh77uO4o2/H4LHH0Jxnh6/qJqMbJ75iIiSCJ/RKO40tDiw+qNGHLG6kK6W47bZo3H55ALk\n5mbCZusTHY+IKGpYwhQ3rA4PXt/cjJ0HbQCASyy5uPnykXzdLxElLZYwCdfrCWB9bQs+3tmGUFjC\nyIIsVF05CiPyM0VHIyKKKZYwCePxhbBx2xFs2tEKfyCMnEwNbr68FNPKzDznLxGlBJYwDTqvP4SP\nd7Zhw5dH4PaFkJmhwk0zSzFjYj6UijTR8YiIBg1LmAaNyxvEhzta8eGONnj8IWjVCiycOQKVFxRB\nreJLjogo9bCEKea6nD58+FUrPtnVDn8gDF26EjfOGIErphRCy5ccEVEK4zMgxcyh9l5s2n4EOxps\niEgSsnQqLJhegpmTCrjlS0QEljBFWTAUxvYGKzZ/fRTNR3sBAIUmHeZeWIQLx+Zyny8R0fewhCkq\nOrs9+GTXUXy+5xjcvhBkAMpLczB3WhHKig082pmI6BRYwnTOvP4QtjdY8fneY2hqcwIA9Fol5l1c\njJmT8mHKTheckIgovrGE6ayEwhHUfdONbfs6sfOgDYFQBDIA44YbML18KC4YbeaUMxHRGWIJ04BC\n4QgOHOnBtv2d+OqADR5/CABgNqSjYsJQXGrJQ06WRnBKIqLEwxKmU/IHwqj7pgs7D9qwu6mrv3iz\ndSpMLz9+kFXJUD339RIRnQeWMPXrdHiwt7kLew91o+GIA8FQBABg0KtxiSUPU8tMGFWUjTQWLxFR\nVLCEU5jbF0RDiwP7WhzY9003Oh3e/tsKTBmYPGoIJo8yYXget3iJiGKBJZxCXN4gGlt7cLCtBw1H\nenCkow/SidvUKjkmjxqCCaU5KB+RA2Mm9/ESEcUaSzhJRSQJx+xuHGrvRXN7L5qPOnHU7u6/XZ4m\nw6iibIwrNmDscANKhmZCIedRzUREg4klnAQkSYK1x4uWjj4c7ug78b4XXn+4/z4qZRrGFhswpigb\no4qyMSI/E2oll44kIhKJJZxg3L4g2u1utNncaLO60Gpzoc3qgi8QPul+uUYtJo/KRGl+JkbkZ6HA\nlMEtXSKiOMMSjkMRSUJ3rw8d3R50dnvR0eVBe5cb7V1uOF2Bk+6bJpMhL0eLIrMOxbl6DM/TY1iu\nnmcnIiJKAHymFsTrD8Hu9MHu9MLu9MHm8MLa44Wtxwtbjw+hcOQHn5OTqcGEETnIH6JF/pAMDDPr\nkT9EC6WC08pERImIJRxlkiTB7Quhx+U//tYXQEBqR9uxXnT3+dHd60N3r79/8Yt/lKFRoNCUgVyj\nFnlGLXKN6cg7cVmj4q+LiCiZ8Fl9AJIkwR8Mw+UJos8bhNsbRJ8niF5P4Lv37gB6PQE43QH0ugMI\nhaXTfj2NSg5jpgalBVkYkqXBkCwNcrI0MGWnw2xIR4ZGOYg/HRERiXRGJbxs2TLs3r0bMpkMixcv\nRnl5ef9tX3zxBZ5++mnI5XLMmDEDDzzwQMzCnotQOAJfIAxfIASfPwxfIAxvIASv/9u3MDz+ELy+\nENz+IDy+EDy+ENy+INy+ENzeIMKR05fqtxTyNGRlqFBk1iMrQ4VsvRoGnQrZOjWKC7Mhj0Rg0Gu4\nr5aIiPoN2Ajbtm1DS0sLqqur0dzcjMWLF6O6urr/9t/85jdYsWIFcnNzcfvtt2Pu3LkYOXJkTEN/\ny+kO4I1PmtHrCSAQDMMfDMMfjMAfCMEXOH79x7ZKTydNJoNWo0BGuhKmLA0y0pXQ/cObXqtCZoYS\nmVoV9Fol0tWK064qZTLpYbP1ne+PS0RESWbAEq6trUVlZSUAoLS0FE6nEy6XCzqdDq2trcjKysLQ\noUMBADNnzkRtbe2glXBntwdb6o5BOtGzSkUa1Eo51Eo5snVqqFXHL2tUcmhUCmjUxy9r1QqkqxVI\nP/ExrVqBDI0SWs3xj2tUci7TSEREMTdgCdvtdlgslv7rRqMRNpsNOp0ONpsNRqPxpNtaW1tjk/QU\nRhdl49mHZ0AmA1QKOdLSWJxERJQ4znoHpSSd/fTu9xkMWihS8CU1JpNedISkwHGMDo5jdHAcoyOV\nx3HAEjabzbDb7f3XrVYrTCbTKW/r7OyE2Wz+0a/ncHjONWvC4j7h6OA4RgfHMTo4jtGRKuN4un80\nBlzHsKKiAhs3bgQA1NfXw2w2Q6fTAQAKCwvhcrnQ1taGUCiEzZs3o6KiIoqxiYiIkteAW8JTpkyB\nxWJBVVUVZDIZlixZgpqaGuj1esyePRu/+tWv8OijjwIA5s2bh5KSkpiHJiIiSgYy6Xx38p6lVJh2\n+EepMt0SaxzH6OA4RgfHMTpSZRzPeTqaiIiIYoMlTEREJAhLmIiISBCWMBERkSAsYSIiIkFYwkRE\nRIKwhImIiARhCRMREQnCEiYiIhKEJUxERCQIS5iIiEgQljAREZEgLGEiIiJBWMJERESCsISJiIgE\nYQkTEREJwhImIiIShCVMREQkCEuYiIhIEJYwERGRIDJJkiTRIYiIiFIRt4SJiIgEYQkTEREJwhIm\nIiIShCVMREQkCEuYiIhIEJYwERGRICzhQWS32zFt2jR8+eWXoqMkpFAohH/7t3/DrbfeiltuuQU7\nduwQHSnhLFu2DIsWLUJVVRX27NkjOk7Ceuqpp7Bo0SIsXLgQmzZtEh0nofl8PlRWVqKmpkZ0FCEU\nogOkkqeeegpFRUWiYySsdevWIT09HatWrUJjYyP+/d//HWvXrhUdK2Fs27YNLS0tqK6uRnNzMxYv\nXozq6mrRsRLO1q1b0djYiOrqajgcDixYsABz5swRHSthPf/888jKyhIdQxiW8CCpra1FRkYGRo8e\nLTpKwrruuuswf/58AIDRaERPT4/gRImltrYWlZWVAIDS0lI4nU64XC7odDrByRLLtGnTUF5eDgDI\nzMyE1+tFOByGXC4XnCzxNDc3o6mpCbNmzRIdRRhORw+CQCCA5557Do888ojoKAlNqVRCrVYDAF59\n9dX+QqYzY7fbYTAY+q8bjUbYbDaBiRKTXC6HVqsFAKxduxYzZsxgAZ+jJ598Eo8//rjoGEJxSzjK\n1qxZgzVr1pz0sRkzZuDmm29GZmamoFSJ51Tj+NBDD+Gyyy7Da6+9hvr6evzf//2foHTJgSvWnp8P\nP/wQa9euxcsvvyw6SkJ66623MGnSpJTfRce1owdBVVUVIpEIAODIkSMwGo145plnMGrUKMHJEs+a\nNWuwYcMG/PGPf+zfKqYzs3z5cphMJlRVVQEArrzySqxbt47T0efgs88+wzPPPIOXXnoJ2dnZouMk\npIcffhitra2Qy+Xo6OiASqXCr3/9a1x66aWiow0qbgkPgtWrV/dffvzxx7FgwQIW8DlobW3F6tWr\nsXLlShbwOaioqMDy5ctRVVWF+vp6mM1mFvA56Ovrw1NPPYVXXnmFBXwefv/73/dfXr58OQoKClKu\ngAGWMCWQNWvWoKenB/fdd1//x1asWAGVSiUwVeKYMmUKLBYLqqqqIJPJsGTJEtGREtL69evhcDjw\n8MMP93/sySefRH5+vsBUlKg4HU1ERCQIj44mIiIShCVMREQkCEuYiIhIEJYwERGRICxhIiIiQVjC\nREREgrCEiYiIBGEJExERCfL/AQx15UcbUbBxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GG6yY8gWbI5q",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## ステップ関数\n"
      ]
    },
    {
      "metadata": {
        "id": "GPLqoRVkY7JV",
        "colab_type": "code",
        "outputId": "a0fe3324-6616-4437-f74a-5dd4d6d8894b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "## numpy での実装\n",
        "def step_function(x):\n",
        "  y = x > 0\n",
        "  return y.astype(np.int)\n",
        "\n",
        "step_function(np.array([1, 2]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "LdS9larZbR4n",
        "colab_type": "code",
        "outputId": "db356267-5ac6-4c46-b5e8-360468509348",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "import matplotlib.pylab as plt\n",
        "\n",
        "def ste_function(x):\n",
        "  return np.array(x>0, dtype=np.int)\n",
        "\n",
        "x = np.arange(-5, 5, 0.1)\n",
        "y = step_function(x)\n",
        "plt.plot(x, y)\n",
        "plt.ylim(-0.1, 1.1)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeEAAAFKCAYAAAAqkecjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDACAEUIYAAAjhDAAAEb+H1zEO/enJ/C7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GPB9aompblzq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "D3b1DCE8w6Gt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 3層ニューラルネットワーック"
      ]
    },
    {
      "metadata": {
        "id": "Rl4OOLF2w9FE",
        "colab_type": "code",
        "outputId": "6abe25cc-fa02-47ad-a92e-57da18db27b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "def identity_function(x):\n",
        "  return x\n",
        "\n",
        "def init_network():\n",
        "  network = {}\n",
        "  network['W1'] = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])\n",
        "  network['b1'] = np.array([0.1, 0.2, 0.3])\n",
        "  network['W2'] = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])\n",
        "  network['b2'] = np.array([0.1, 0.2])\n",
        "  network['W3'] = np.array([[0.1, 0.3], [0.2, 0.4]])\n",
        "  network['b3'] = np.array([0.1, 0.2])\n",
        "  \n",
        "  return network\n",
        "\n",
        "\n",
        "def forward(network, x):\n",
        "  W1, W2, W3 = network['W1'], network['W2'], network['W3']\n",
        "  b1 ,  b2,  b3  = network['b1'],   network['b2'],    network['b3']\n",
        "  \n",
        "  a1 = np.dot(x, W1) + b1\n",
        "  z1 = sigmoid(a1)\n",
        "  print(z1)\n",
        "  a2 = np.dot(z1, W2) + b2\n",
        "  z2 = sigmoid(a2)\n",
        "  a3 = np.dot(z2, W3) + b3\n",
        "  y = identity_function(a3)\n",
        "  \n",
        "  return y\n",
        "\n",
        "\n",
        "network = init_network()\n",
        "x = np.array([1, 0.5])\n",
        "y = forward(network, x)\n",
        "print(y)\n",
        "  \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.57444252 0.66818777 0.75026011]\n",
            "[0.31682708 0.69627909]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gCa-ml9w2Mcy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "a3dbe43d-203f-4118-e3d4-890b068fd3f7"
      },
      "cell_type": "code",
      "source": [
        "# Import tensorflow's MNIST data handle\n",
        "from keras.datasets import mnist\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "(x_train, t_train), (x_test, t_test) = mnist.load_data()\n",
        "\n",
        "x_train = np.reshape(x_train, (-1, 784)).astype('float32')\n",
        "x_test = np.reshape(x_test, (-1, 784)).astype('float32')\n",
        "\n",
        "print(x_train.shape)\n",
        "print(x_train[:5])\n",
        "print(t_train.shape)\n",
        "print(t_train[:5])\n",
        "print(x_test.shape)\n",
        "print(x_test[:5])\n",
        "print(t_test.shape)\n",
        "print(t_test[:5])"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 784)\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "(60000,)\n",
            "[5 0 4 1 9]\n",
            "(10000, 784)\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "(10000,)\n",
            "[7 2 1 0 4]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jWbe-X3L5krb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "23001762-9b7b-464d-e9ac-a8e4082146a5"
      },
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "from matplotlib.pyplot import imshow\n",
        "%matplotlib inline \n",
        "\n",
        "def img_show(img):\n",
        "  pil_img = Image.fromarray(np.uint8(img))\n",
        "  imshow(pil_img)\n",
        "  \n",
        "  \n",
        "img = x_train[0]\n",
        "label = t_train[0]\n",
        "print(label)\n",
        "print(img.shape)\n",
        "\n",
        "#画像表示のためにreshapeする必要あり\n",
        "img_reshape = img.reshape(28, 28)\n",
        "print(img_reshape.shape)\n",
        "\n",
        "img_show(img_reshape)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n",
            "(784,)\n",
            "(28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5zfdb5OB5G+7048PR1KCb2TXAd0tTMP8L8H/AlGb2UMUb7j671U0AmNloYA3l018tA9a5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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "nRrufCB0CLaW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}