backpropagation.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled8.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPAHS3s9xze+o7J41WjEitH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/big_pie/blob/master/backpropagation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
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        "id": "wLCItG3sTBjO",
        "colab_type": "code",
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "271bb636-3afa-4a0b-ae57-df7cb3e0501a"
      },
      "source": [
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "import seaborn as sns\n",
        "import matplotlib.animation as animation\n",
        "from scipy import stats \n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn.datasets.samples_generator import make_regression \n",
        "import csv\n",
        "import math\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "file_data   = \"mnist_test.csv\"\n",
        "handle_file = open(file_data, \"r\")\n",
        "data        = handle_file.readlines()\n",
        "handle_file.close()\n",
        "\n",
        "size_row    = 28    # height of the image\n",
        "size_col    = 28    # width of the image\n",
        "\n",
        "num_image   = len(data)\n",
        "count       = 0     # count for the number of images\n",
        "\n",
        "#\n",
        "# make a matrix each column of which represents an images in a vector form \n",
        "#\n",
        "list_image  = np.empty((size_row * size_col, num_image), dtype=float)\n",
        "list_label  = np.empty(num_image, dtype=int)\n",
        "\n",
        "for line in data:\n",
        "\n",
        "    line_data   = line.split(',')\n",
        "    label       = line_data[0]\n",
        "    im_vector   = np.asfarray(line_data[1:])\n",
        "\n",
        "    list_label[count]       = label\n",
        "    list_image[:, count]    = im_vector    \n",
        "\n",
        "    count += 1\n",
        "\n",
        "# \n",
        "# plot first 100 images out of 10,000 with their labels\n",
        "# \n",
        "f1 = plt.figure(1)\n",
        "\n",
        "for i in range(100):\n",
        "\n",
        "    label       = list_label[i]\n",
        "    im_vector   = list_image[:, i]\n",
        "    im_matrix   = im_vector.reshape((size_row, size_col))\n",
        "\n",
        "    plt.subplot(10, 10, i+1)\n",
        "    plt.title(label)\n",
        "    plt.imshow(im_matrix, cmap='Greys', interpolation='None')\n",
        "\n",
        "    frame   = plt.gca()\n",
        "    frame.axes.get_xaxis().set_visible(False)\n",
        "    frame.axes.get_yaxis().set_visible(False)\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d79545c7-45f0-484d-af75-8e413a982dec\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-d79545c7-45f0-484d-af75-8e413a982dec\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving mnist_test.csv to mnist_test (1).csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 100 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDyjCnHDqL-X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vector_list = np.arange(10*size_col*size_col).reshape(10, size_row, size_col)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsKca8ZGuE5S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for nx in range(0, 10):\n",
        "  for ny in range(0, size_row):\n",
        "    for nz in range(0, size_col):\n",
        "      vector_list[nx][ny][nz] = 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoMaSPNcrzhb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "71cf96c2-2e89-495b-e3ec-760b97b0e642"
      },
      "source": [
        "label_list = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
        "\n",
        "for i in range(100):\n",
        "  label_list[list_label[i]] = label_list[list_label[i]] + 1\n",
        "\n",
        "print(label_list)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[8, 14, 8, 11, 14, 7, 10, 15, 2, 11]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XLJZgzKpT5p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(100):\n",
        "\n",
        "    label       = list_label[i]\n",
        "    im_vector   = list_image[:, i]\n",
        "    im_matrix   = im_vector.reshape((size_row, size_col))\n",
        "    print(im_matrix)\n",
        "\n",
        "    for ny in range(0, size_row):\n",
        "      for nz in range(0, size_col):\n",
        "        vector_list[label][ny][nz] = vector_list[label][ny][nz] + im_matrix[ny][nz]\n",
        "\n",
        "print(vector_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "akf7PofUx5Ju",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#평균내기\n",
        "\n",
        "for i in range(0, 10):\n",
        "  vector_list[i] = vector_list[i] / label_list[i]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EKCoC6MHyOnL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "outputId": "698ebdd7-d525-4966-bfcc-db705cafe870"
      },
      "source": [
        "for i in range(10):\n",
        "\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.title(i)\n",
        "    plt.imshow(vector_list[i], cmap='Greys', interpolation='None')\n",
        "\n",
        "    frame   = plt.gca()\n",
        "    frame.axes.get_xaxis().set_visible(False)\n",
        "    frame.axes.get_yaxis().set_visible(False)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihTPwzYW_PsD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "theta = np.random.normal(size=(size_row*size_col))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HHzr-eJUAGDu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sigmoid(z): \n",
        "  g = 1 / (1 + np.exp(-z)) \n",
        "  \n",
        "  return g "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xVRt2bT1AiPd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1a7d8d7e-da62-46b2-d0f5-cb31944567e6"
      },
      "source": [
        "z_list = []\n",
        "for i in range(0, 10):\n",
        "  z_list.append(0.0)\n",
        "\n",
        "print(z_list)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfRYBsN_DPUZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e705817c-274a-4168-eb8f-df097757f70e"
      },
      "source": [
        "for i in range(0, 100):\n",
        "  label       = list_label[i]\n",
        "  im_vector   = list_image[:, i]\n",
        "  im_matrix   = im_vector.reshape((size_row, size_col))\n",
        "  y = 0\n",
        "  for ny in range(0, size_row):\n",
        "      for nz in range(0, size_col):\n",
        "        y = y + theta[size_col*ny + nz]*im_matrix[ny][nz]\n",
        "  # print(y)\n",
        "  z_list[label] = z_list[label] + sigmoid(y)\n",
        "  # print(sigmoid(y))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in exp\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJkLXB4lHhZn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#평균내기\n",
        "\n",
        "for i in range(0, 10):\n",
        "  z_list[i] = z_list[i] / label_list[i]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXCX9aE-EB8R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "be1fd8e8-e032-484e-8ff6-55e80d85486d"
      },
      "source": [
        "print(z_list)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.5, 0.6428571431736698, 0.3726776563182899, 1.0909090909090908, 0.35714285714285715, 0.8571428571428571, 0.3, 0.6666666666666666, 0.5, 0.7272727268669205]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}