Untitled1010.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled1010.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOZ50BAxGQX9ETtOGdWC5OP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/class-MachineLearning/blob/master/Untitled1010.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sOoEaBxtNXA",
        "colab_type": "code",
        "outputId": "fee8ba02-e657-43a5-c093-b014b6ae695f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "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",
        "import operator\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.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",
        "# normalize the values of the input data to be [0, 1]\n",
        "#\n",
        "def normalize(data):\n",
        "\n",
        "    data_normalized = (data - min(data)) / (max(data) - min(data))\n",
        "\n",
        "    return(data_normalized)\n",
        "\n",
        "#\n",
        "# example of distance function between two vectors x and y\n",
        "#\n",
        "def distance(x, y):\n",
        "\n",
        "    d = (x - y) ** 2\n",
        "    s = np.sum(d)\n",
        "    # r = np.sqrt(s)\n",
        "\n",
        "    return(s)\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",
        "    im_vector   = normalize(im_vector)\n",
        "\n",
        "    list_label[count]       = label\n",
        "    list_image[:, count]    = im_vector\n",
        "\n",
        "    count += 1\n",
        "\n",
        "#\n",
        "# plot first 150 images out of 10,000 with their labels\n",
        "#\n",
        "f1 = plt.figure(1)\n",
        "\n",
        "for i in range(150):\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, 15, 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",
        "\n",
        "#plt.show()\n",
        "\n",
        "#\n",
        "# plot the average image of all the images for each digit\n",
        "#\n",
        "f2 = plt.figure(2)\n",
        "\n",
        "im_average  = np.zeros((size_row * size_col, 10), dtype=float)\n",
        "im_count    = np.zeros(10, dtype=int)\n",
        "\n",
        "for i in range(num_image):\n",
        "\n",
        "    im_average[:, list_label[i]] += list_image[:, i]\n",
        "    im_count[list_label[i]] += 1\n",
        "\n",
        "for i in range(10):\n",
        "\n",
        "    im_average[:, i] /= im_count[i]\n",
        "\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.title(i)\n",
        "    plt.imshow(im_average[:,i].reshape((size_row, size_col)), 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",
        "\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 150 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "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": "b_YcDHFWIRFP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "theta = np.random.normal(size=(size_row*size_col))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DemnhlAQP0XJ",
        "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": "DRugdPmoP2G5",
        "colab_type": "code",
        "outputId": "947ff5b9-758d-4e71-d804-23ead6f2a9fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "z_list = []\n",
        "for i in range(0, 10):\n",
        "  z_list.append(0.0)\n",
        "\n",
        "print(z_list)"
      ],
      "execution_count": 28,
      "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": "xtbUZT3IP28F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(0, num_image):\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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWQYa_g6P4H2",
        "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": "WUWOjwQQTDdR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#first hidden layer로 가기 위한 초기 fully connected u 생성\n",
        "w_tmp = np.ones(shape=(1000, 197, 785))\n",
        "v_tmp = np.ones(shape=(1000, 50, 197))\n",
        "u_tmp = np.ones(shape=(1000, 10, 50))\n",
        "\n",
        "matrix_u = np.zeros((197, 785))  #197줄\n",
        "matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 197):\n",
        "  for j in range(0, 785):\n",
        "    matrix_u[i][j] = np.random.normal()\n",
        "\n",
        "\n",
        "# matrix_u[196][0] = 0            #줄, 칸\n",
        "##############################################\n",
        "matrix_v = np.zeros((50, 197))  #50줄\n",
        "matrix_v[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 50):\n",
        "  for j in range(0, 197):\n",
        "    matrix_v[i][j] = np.random.normal()\n",
        "\n",
        "\n",
        "# matrix_v[196][0] = 0            #줄, 칸\n",
        "##############################################\n",
        "matrix_w = np.zeros((10, 50))  #10줄\n",
        "# matrix_w[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(0, 10):\n",
        "  for j in range(0, 50):\n",
        "    matrix_w[i][j] = np.random.normal()\n",
        "\n",
        "w_tmp = matrix_w\n",
        "v_tmp = matrix_v\n",
        "u_tmp = matrix_u\n",
        "\n",
        "# matrix_w[196][0] = 0            #줄, 칸\n",
        "\n",
        "# print(matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idHIKSRGY0p9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "y_ = np.ones((197, 1))\n",
        "\n",
        "x_list = np.ones(shape=(1000, 785, 1))\n",
        "\n",
        "y__list = np.ones(shape=(1000, 197, 1))\n",
        "z__list = np.ones(shape=(1000, 50, 1))\n",
        "h__list = np.ones(shape=(1000, 10, 1))\n",
        "\n",
        "y_list = np.ones(shape=(1000, 197, 1))\n",
        "z_list = np.ones(shape=(1000, 50, 1))\n",
        "h_list = np.ones(shape=(1000, 10, 1))\n",
        "l_list = np.zeros(shape=(1000, 10, 1))\n",
        "\n",
        "y_list_tmp = np.ones(shape=(1000, 197, 1))\n",
        "z_list_tmp = np.ones(shape=(1000, 50, 1))\n",
        "h_list_tmp = np.ones(shape=(1000, 10, 1))\n",
        "y__list_tmp = np.ones(shape=(1000, 197, 1))\n",
        "z__list_tmp = np.ones(shape=(1000, 50, 1))\n",
        "h__list_tmp = np.ones(shape=(1000, 10, 1))\n",
        "\n",
        "\n",
        "\n",
        "# w_tmp = np.ones(shape=(1000, 197, 785))\n",
        "# v_tmp = np.ones(shape=(1000, 50, 197))\n",
        "# u_tmp = np.ones(shape=(1000, 10, 50))\n",
        "\n",
        "# matrix_u = np.zeros((197, 785))  #197줄\n",
        "# matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "\n",
        "#training set 1000개에 대해서\n",
        "for i in range(0, 1000):\n",
        "  # print(i)\n",
        "  label       = list_label[i]\n",
        "  bias   = [1]       #bias 1\n",
        "  im_vector = np.concatenate((bias, list_image[:, i]), axis = None)\n",
        "\n",
        "  # print(type(list_image[: , i]))\n",
        "  # print(list_image[:, i])\n",
        "  # print(im_vector)\n",
        "  im_matrix   = im_vector.reshape((size_row*size_col + 1, 1))\n",
        "  \n",
        "  x_list[i] = im_matrix\n",
        "\n",
        "  y_ = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "  y__list[i] = y_\n",
        "  \n",
        "  y_vector = np.ones((197,1)) #bias\n",
        "  for j in range (1, 197):\n",
        "    y_vector[j][0] = 1 / (1 + math.exp(-y_[j][0]))\n",
        "    # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  y   = y_vector.reshape((197, 1))\n",
        "  y_list[i] = y\n",
        "\n",
        "  z_  = np.dot(matrix_v, y)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "  z__list[i] = z_\n",
        "  z_vector = np.ones((50,1)) #bias\n",
        "  for j in range (1, 50):\n",
        "    z_vector[j][0] = 1 / (1 + math.exp(-z_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  z   = z_vector.reshape((50, 1))\n",
        "  z_list[i] = z\n",
        "  \n",
        "  # print(z)\n",
        "\n",
        "  h_  = np.dot(matrix_w, z)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "  h__list[i] = h_\n",
        "  \n",
        "  h_vector = np.ones((10,1)) #bias\n",
        "  for j in range (0, 10):\n",
        "    h_vector[j][0] = 1 / (1 + math.exp(-h_[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "  h   = h_vector.reshape((10, 1))\n",
        "  # tmp = h.ravel()\n",
        "  # max_value = max(tmp)\n",
        "  # print(max_value)\n",
        "  # max_index = tmp.index(max_value)\n",
        "  index, value = max(enumerate(h), key=operator.itemgetter(1))\n",
        "  h_list[i] = h\n",
        "  l_list[i][index] = 1\n",
        "  # print(l_list[i])\n",
        "  # print(h)\n",
        "\n",
        "  y_list_tmp[i] = y_list[i]\n",
        "  z_list_tmp[i] = z_list[i]\n",
        "  h_list_tmp[i] = h_list[i]\n",
        "  y__list_tmp[i] = y__list[i]\n",
        "  z__list_tmp[i] = z__list[i]\n",
        "  h__list_tmp[i] = h__list[i]\n",
        "\n",
        "# print(l_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Or31AKaJfeIz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "y_2 = np.ones((197, 1))\n",
        "\n",
        "x_list2 = np.ones(shape=(9000, 785, 1))\n",
        "\n",
        "y__list2 = np.ones(shape=(9000, 197, 1))\n",
        "z__list2 = np.ones(shape=(9000, 50, 1))\n",
        "h__list2 = np.ones(shape=(9000, 10, 1))\n",
        "\n",
        "y_list2 = np.ones(shape=(9000, 197, 1))\n",
        "z_list2 = np.ones(shape=(9000, 50, 1))\n",
        "h_list2 = np.ones(shape=(9000, 10, 1))\n",
        "l_list2 = np.zeros(shape=(9000, 10, 1))\n",
        "\n",
        "y_list_tmp2 = np.ones(shape=(9000, 197, 1))\n",
        "z_list_tmp2 = np.ones(shape=(9000, 50, 1))\n",
        "h_list_tmp2 = np.ones(shape=(9000, 10, 1))\n",
        "y__list_tmp2 = np.ones(shape=(9000, 197, 1))\n",
        "z__list_tmp2 = np.ones(shape=(9000, 50, 1))\n",
        "h__list_tmp2 = np.ones(shape=(9000, 10, 1))\n",
        "\n",
        "# w_tmp = np.ones(shape=(9000, 197, 785))\n",
        "# v_tmp = np.ones(shape=(9000, 50, 197))\n",
        "# u_tmp = np.ones(shape=(9000, 10, 50))\n",
        "\n",
        "# matrix_u = np.zeros((197, 785))  #197줄\n",
        "# matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "\n",
        "#training set 1000개에 대해서\n",
        "for i in range(0, 9000):\n",
        "  # print(i)\n",
        "  label2       = list_label[i + 1000]\n",
        "  bias2   = [1]       #bias 1\n",
        "  im_vector2 = np.concatenate((bias2, list_image[:, i + 1000]), axis = None)\n",
        "\n",
        "  # print(type(list_image[: , i]))\n",
        "  # print(list_image[:, i])\n",
        "  # print(im_vector)\n",
        "  im_matrix2   = im_vector2.reshape((size_row*size_col + 1, 1))\n",
        "  \n",
        "  x_list2[i] = im_matrix2\n",
        "\n",
        "  y_2 = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "  y__list2[i] = y_2\n",
        "  \n",
        "  y_vector2 = np.ones((197,1)) #bias\n",
        "  for j in range (1, 197):\n",
        "    y_vector2[j][0] = 1 / (1 + math.exp(-y_2[j][0]))\n",
        "    # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  y2   = y_vector2.reshape((197, 1))\n",
        "  y_list2[i] = y2\n",
        "\n",
        "  z_2  = np.dot(matrix_v, y2)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "  z__list2[i] = z_2\n",
        "  z_vector2 = np.ones((50,1)) #bias\n",
        "  for j in range (1, 50):\n",
        "    z_vector2[j][0] = 1 / (1 + math.exp(-z_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  z2   = z_vector2.reshape((50, 1))\n",
        "  z_list2[i] = z2\n",
        "  \n",
        "  # print(z)\n",
        "\n",
        "  h_2  = np.dot(matrix_w, z2)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "  h__list2[i] = h_2\n",
        "  \n",
        "  h_vector2 = np.ones((10,1)) #bias\n",
        "  for j in range (0, 10):\n",
        "    h_vector2[j][0] = 1 / (1 + math.exp(-h_2[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "  h2   = h_vector2.reshape((10, 1))\n",
        "  # tmp = h.ravel()\n",
        "  # max_value = max(tmp)\n",
        "  # print(max_value)\n",
        "  # max_index = tmp.index(max_value)\n",
        "  index2, value2 = max(enumerate(h2), key=operator.itemgetter(1))\n",
        "  h_list2[i] = h2\n",
        "  l_list2[i][index2] = 1\n",
        "  # print(l_list[i])\n",
        "  # print(h)\n",
        "\n",
        "  y_list_tmp2[i] = y_list2[i]\n",
        "  z_list_tmp2[i] = z_list2[i]\n",
        "  h_list_tmp2[i] = h_list2[i]\n",
        "  y__list_tmp2[i] = y__list2[i]\n",
        "  z__list_tmp2[i] = z__list2[i]\n",
        "  h__list_tmp2[i] = h__list2[i]\n",
        "\n",
        "# print(l_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1AbB1lMv1yY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def objective_function(size, label_List, h_List):\n",
        "  result = 0.0\n",
        "  for i in range(0, size):\n",
        "    for k in range(0, 10):\n",
        "      result = result + ((-1)*label_List[i][k]*math.log(h_List[i][k][0]) - (1-label_List[i][k])*math.log(1-h_List[i][k][0]))\n",
        "  result = result / size\n",
        "\n",
        "  return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7uMF29V6ZAh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def w_gredient_descent(learning_rate, k_value, n_value, size, label_List):\n",
        "  result = 0.0\n",
        "  for i in range(0, size):\n",
        "    result = result + (z_list_tmp[i][n_value][0]*(sigmoid(h_list_tmp[i][k_value][0]) - label_List[i][k_value][0]))\n",
        "  result = result / size\n",
        "  result = result * learning_rate\n",
        "  final = w_tmp[k_value][n_value] - result\n",
        "\n",
        "  return final"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQMnniqxLxzl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def v_gredient_descent(learning_rate, k_value, n_value, size, label_List):\n",
        "  result = 0.0\n",
        "  for i in range(0, size):\n",
        "    result = result + (y_list_tmp[i][n_value][0]*w_tmp[0][k_value]*(sigmoid(h_list_tmp[i][0][0]) - label_List[i][0][0])*(sigmoid(z__list_tmp[i][k_value][0])*(1 - sigmoid(z__list_tmp[i][k_value][0]))))\n",
        "  result = result / size\n",
        "  result = result * learning_rate\n",
        "  final = v_tmp[k_value][n_value] - result\n",
        "\n",
        "  return final"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UabSYhhIO6JU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def u_gredient_descent(learning_rate, k_value, n_value, size, label_List):\n",
        "  result = 0.0\n",
        "  for i in range(0, size):\n",
        "    result = result + (x_list[i][n_value][0]*w_tmp[0][1]*v_tmp[1][k_value]*(sigmoid(h_list_tmp[i][0][0]) - label_List[i][0][0])*(sigmoid(z__list_tmp[i][1][0])*(1 - sigmoid(z__list_tmp[i][1][0])))*(sigmoid(y__list_tmp[i][k_value][0])*(1 - sigmoid(y__list_tmp[i][k_value][0]))))\n",
        "  result = result / size\n",
        "  result = result * learning_rate\n",
        "  final = u_tmp[k_value][n_value] - result\n",
        "\n",
        "  return final"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0vn3FkYXStur",
        "colab_type": "code",
        "outputId": "8b6e96f3-ef88-4c69-dd24-d05e36ad0fc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "#이제 여기에다가\n",
        "\n",
        "#iteration에 따라\n",
        "#cost function 값을 배열에 저장하고\n",
        "#세타값을 업데이트 시키고(위의 세개 함수 호출)\n",
        "#업데이트 시킨값을 다시 cost함수에 넣고\n",
        "#업데이트 시키고\n",
        "#이것을 반복하는 함수를 짠다.\n",
        "\n",
        "#아직 해야하는 것 : h값에 따라 가장 큰값의 index로 L값정하는거 위에 추가해야함.\n",
        "\n",
        "iteration = 100\n",
        "l_r = 0.5\n",
        "\n",
        "cost_history = np.zeros(iteration)\n",
        "\n",
        "total_number_of_predictions = 1000\n",
        "accuracy_history = np.zeros(iteration)\n",
        "\n",
        "# small_delta = np.ones(3)  #image_size, layer 2~4(3개)\n",
        "# greece_delta_w = np.ones(shape = (10, 50))\n",
        "# greece_delta_v = np.ones(shape = (50, 197))\n",
        "# greece_delta_u = np.ones(shape = (197, 785))\n",
        "\n",
        "label_group = np.zeros(shape = (1000, 10))\n",
        "\n",
        "for i in range(0, 1000):\n",
        "  label       = list_label[i]\n",
        "  im_vector   = list_image[:, i]\n",
        "\n",
        "  # X[i] = im_vector\n",
        "  # print(i)\n",
        "  # print(X[i])\n",
        "  label_group[i][label] = 1\n",
        "\n",
        "#####################################\n",
        "\n",
        "cost_history2 = np.zeros(iteration)\n",
        "\n",
        "total_number_of_predictions2 = 9000\n",
        "accuracy_history2 = np.zeros(iteration)\n",
        "\n",
        "# small_delta = np.ones(3)  #image_size, layer 2~4(3개)\n",
        "# greece_delta_w = np.ones(shape = (10, 50))\n",
        "# greece_delta_v = np.ones(shape = (50, 197))\n",
        "# greece_delta_u = np.ones(shape = (197, 785))\n",
        "\n",
        "label_group2 = np.zeros(shape = (9000, 10))\n",
        "\n",
        "for i in range(1000, 10000):\n",
        "  label2       = list_label[i]\n",
        "  im_vector2   = list_image[:, i]\n",
        "\n",
        "  # X[i] = im_vector\n",
        "  # print(i)\n",
        "  # print(X[i])\n",
        "  label_group2[i - 1000][label2] = 1\n",
        "\n",
        "#############################################\n",
        "\n",
        "\n",
        "for t in range(0, iteration):\n",
        "  number_of_correct_predictions = 0\n",
        "  \n",
        "  print(t, end=' ')\n",
        "\n",
        "  # cost_history[t] = objective_function(1000, l_list, h_list_tmp)\n",
        "  result = 0.0\n",
        "  for i in range(0, 1000):\n",
        "    for k in range(0, 10):\n",
        "      # if h_list_tmp[i][k][0] <= 0:\n",
        "      # print(h_list_tmp[i][k][0])\n",
        "      result = result + ((-1)*label_group[i][k]*math.log(h_list[i][k][0]) - (1-label_group[i][k])*math.log(1-h_list[i][k][0]))\n",
        "  result = result / 1000\n",
        "  cost_history[t] = result\n",
        "\n",
        "# (1 / (1 + math.exp(-y__list_tmp[i][m][0])))\n",
        "#############################################\n",
        "  # for m in range(0, 10):\n",
        "  #   # print(m)\n",
        "  #   for n in range(0, 50):\n",
        "  #     # matrix_w[m][n] = w_gredient_descent(l_r, m, n, 1000, l_list)\n",
        "  #     result = 0.0\n",
        "  #     for i in range(0,1000):\n",
        "  #       result = result + (z_list_tmp[i][n][0]*((1 / (1 + math.exp(-h_list_tmp[i][m][0]))) - l_list[i][m][0]))\n",
        "  #     result = result / 1000\n",
        "  #     result = result * l_r\n",
        "  #     final = w_tmp[m][n] - result\n",
        "  #     matrix_w[m][n] = final\n",
        "\n",
        "  # for m in range(0, 50):\n",
        "  #   # print(m)\n",
        "  #   for n in range(0, 197):\n",
        "  #     # matrix_v[m][n] = v_gredient_descent(l_r, m, n, 1000, l_list)\n",
        "  #     result = 0.0\n",
        "  #     for i in range(0, 1000):\n",
        "  #       result = result + (y_list_tmp[i][n][0]*w_tmp[0][m]*((1 / (1 + math.exp(-h_list_tmp[i][0][0]))) - l_list[i][0][0])*((1 / (1 + math.exp(-z__list_tmp[i][m][0])))*(1 - (1 / (1 + math.exp(-z__list_tmp[i][m][0]))))))\n",
        "  #     result = result / 1000\n",
        "  #     result = result * l_r\n",
        "  #     final = v_tmp[m][n] - result\n",
        "  #     matrix_v[m][n] = final\n",
        "\n",
        "  # for m in range(0, 197):\n",
        "  #   # print(m)\n",
        "  #   for n in range(0, 785):\n",
        "  #     # matrix_u[m][n] = u_gredient_descent(l_r, m, n, 1000, l_list)\n",
        "  #     result = 0.0\n",
        "  #     for i in range(0, 1000):\n",
        "  #       result = result + (x_list[i][n][0]*w_tmp[0][1]*v_tmp[1][m]*((1 / (1 + math.exp(-h_list_tmp[i][0][0]))) - l_list[i][0][0])*((1 / (1 + math.exp(-z__list_tmp[i][1][0])))*(1 - (1 / (1 + math.exp(-z__list_tmp[i][1][0])))))*((1 / (1 + math.exp(-y__list_tmp[i][m][0])))*(1 - (1 / (1 + math.exp(-y__list_tmp[i][m][0]))))))\n",
        "  #     result = result / 1000\n",
        "  #     result = result * l_r\n",
        "  #     final = u_tmp[m][n] - result\n",
        "  #     matrix_u[m][n] = final\n",
        "#######################################################################\n",
        "##gradient descent\n",
        "  result_w = 0\n",
        "  result_v = 0\n",
        "  result_u = 0\n",
        "\n",
        "  for i in range(0, 1000):\n",
        "    small_delta_2 = np.zeros(shape = (10, 1))\n",
        "    for j in range(0, 10):\n",
        "      small_delta_2[j][0] = h_list[i][j] - label_group[i][j]\n",
        "    # print(h_list[i])\n",
        "    # print(label_group[i])\n",
        "    # small_delta_2 = h_list[i] - label_group[i] #여기 label_group 다시 봐야함.\n",
        "    small_delta_1 = np.dot(np.transpose(matrix_w), small_delta_2)\n",
        "    small_delta_0 = np.dot(np.transpose(matrix_v), small_delta_1)*(y_list_tmp[i]*(1 - y_list_tmp[i]))\n",
        "\n",
        "    # print(small_delta_2)\n",
        "    result_w = result_w + np.dot(small_delta_2, np.transpose(z_list[i]))\n",
        "    result_v = result_v + np.dot(small_delta_1, np.transpose(y_list[i]))\n",
        "    result_u = result_u + np.dot(small_delta_0, np.transpose(x_list[i]))\n",
        "\n",
        "  matrix_w = matrix_w - (result_w / 1000 *l_r)\n",
        "  matrix_v = matrix_v - (result_v / 1000 *l_r)\n",
        "  matrix_u = matrix_u - (result_u / 1000 *l_r)\n",
        "\n",
        "\n",
        "####################################################################\n",
        "\n",
        "  for k in range(0, 1000):\n",
        "  # print(i)\n",
        "    label       = list_label[k]\n",
        "    bias   = [1]       #bias 1\n",
        "    im_vector = np.concatenate((bias, list_image[:, k]), axis = None)\n",
        "\n",
        "    # print(type(list_image[: , k]))\n",
        "    # print(list_image[:, k])\n",
        "    # print(im_vector)\n",
        "    im_matrix   = im_vector.reshape((size_row*size_col + 1, 1))\n",
        "\n",
        "    y_ = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "    y__list[k] = y_\n",
        "    \n",
        "    y_vector = np.ones((197,1)) #bias\n",
        "    for j in range (1, 197):\n",
        "      y_vector[j][0] = 1 / (1 + math.exp(-y_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "    \n",
        "    # print(y_vector) #시그모이드 값 확인\n",
        "    y   = y_vector.reshape((197, 1))\n",
        "    y_list[k] = y\n",
        "\n",
        "    z_  = np.dot(matrix_v, y)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "    z__list[k] = z_\n",
        "    z_vector = np.ones((50,1)) #bias\n",
        "    for j in range (1, 50):\n",
        "      z_vector[j][0] = 1 / (1 + math.exp(-z_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "    z   = z_vector.reshape((50, 1))\n",
        "    z_list[k] = z\n",
        "    \n",
        "    # print(z)\n",
        "\n",
        "    h_  = np.dot(matrix_w, z)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "    h__list[k] = h_\n",
        "    \n",
        "    h_vector = np.ones((10,1)) #bias\n",
        "    for j in range (0, 10):\n",
        "      h_vector[j][0] = 1 / (1 + math.exp(-h_[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "    h   = h_vector.reshape((10, 1))\n",
        "    # print(h)\n",
        "    # tmp = h.ravel()\n",
        "    # max_value = max(tmp)\n",
        "    # print(max_value)\n",
        "    # max_index = tmp.index(max_value)\n",
        "    index, value = max(enumerate(h), key=operator.itemgetter(1))\n",
        "    h_list[k] = h\n",
        "    l_list[k][index] = 1\n",
        "    # print(l_list[i])\n",
        "    # print(h)\n",
        "    if index == label:\n",
        "      number_of_correct_predictions = number_of_correct_predictions + 1\n",
        "\n",
        "    y_list_tmp[k] = y_list[k]\n",
        "    z_list_tmp[k] = z_list[k]\n",
        "    h_list_tmp[k] = h_list[k]\n",
        "    y__list_tmp[k] = y__list[k]\n",
        "    z__list_tmp[k] = z__list[k]\n",
        "    h__list_tmp[k] = h__list[k]\n",
        "\n",
        "\n",
        "  accuracy_history[t] = number_of_correct_predictions/total_number_of_predictions*100\n",
        "  # print(accuracy_history[t])\n",
        "\n",
        "############################\n",
        "  #test data!\n",
        "\n",
        "  number_of_correct_predictions2 = 0\n",
        "  \n",
        "  print(t, end=' ')\n",
        "\n",
        "  result2 = 0.0\n",
        "  for i in range(0, 9000):\n",
        "    for k in range(0, 10):\n",
        "      # if h_list_tmp[i][k][0] <= 0:\n",
        "      # print(h_list_tmp[i][k][0])\n",
        "      result2 = result2 + ((-1)*label_group2[i][k]*math.log(h_list2[i][k][0]) - (1-label_group2[i][k])*math.log(1-h_list2[i][k][0]))\n",
        "  result2 = result2 / 9000\n",
        "  cost_history2[t] = result2\n",
        "\n",
        "\n",
        "  for k in range(0, 9000):\n",
        "  # print(i)\n",
        "    label2       = list_label[k + 1000]\n",
        "    bias2   = [1]       #bias 1\n",
        "    im_vector2 = np.concatenate((bias2, list_image[:, k + 1000]), axis = None)\n",
        "\n",
        "    # print(type(list_image[: , k]))\n",
        "    # print(list_image[:, k])\n",
        "    # print(im_vector)\n",
        "    im_matrix2   = im_vector2.reshape((size_row*size_col + 1, 1))\n",
        "\n",
        "    y_2 = np.dot(matrix_u, im_matrix2)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "    y__list2[k] = y_2\n",
        "    \n",
        "    y_vector2 = np.ones((197,1)) #bias\n",
        "    for j in range (1, 197):\n",
        "      y_vector2[j][0] = 1 / (1 + math.exp(-y_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "    \n",
        "    # print(y_vector) #시그모이드 값 확인\n",
        "    y2   = y_vector2.reshape((197, 1))\n",
        "    y_list2[k] = y2\n",
        "\n",
        "    z_2  = np.dot(matrix_v, y2)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "    z__list2[k] = z_2\n",
        "    z_vector2 = np.ones((50,1)) #bias\n",
        "    for j in range (1, 50):\n",
        "      z_vector2[j][0] = 1 / (1 + math.exp(-z_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "    z2   = z_vector2.reshape((50, 1))\n",
        "    z_list2[k] = z2\n",
        "    \n",
        "    # print(z)\n",
        "\n",
        "    h_2  = np.dot(matrix_w, z2)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "    h__list2[k] = h_2\n",
        "    \n",
        "    h_vector2 = np.ones((10,1)) #bias\n",
        "    for j in range (0, 10):\n",
        "      h_vector2[j][0] = 1 / (1 + math.exp(-h_2[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "    h2   = h_vector2.reshape((10, 1))\n",
        "    # print(h)\n",
        "    # tmp = h.ravel()\n",
        "    # max_value = max(tmp)\n",
        "    # print(max_value)\n",
        "    # max_index = tmp.index(max_value)\n",
        "    index2, value2 = max(enumerate(h2), key=operator.itemgetter(1))\n",
        "    h_list2[k] = h2\n",
        "    l_list2[k][index2] = 1\n",
        "    # print(l_list[i])\n",
        "    # print(h)\n",
        "    if index2 == label2:\n",
        "      number_of_correct_predictions2 = number_of_correct_predictions2 + 1\n",
        "\n",
        "    y_list_tmp2[k] = y_list2[k]\n",
        "    z_list_tmp2[k] = z_list2[k]\n",
        "    h_list_tmp2[k] = h_list2[k]\n",
        "    y__list_tmp2[k] = y__list2[k]\n",
        "    z__list_tmp2[k] = z__list2[k]\n",
        "    h__list_tmp2[k] = h__list2[k]\n",
        "\n",
        "\n",
        "  accuracy_history2[t] = number_of_correct_predictions2/total_number_of_predictions2*100\n",
        "  t = t + 1"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 59 59 60 60 61 61 62 62 63 63 64 64 65 65 66 66 67 67 68 68 69 69 70 70 71 71 72 72 73 73 74 74 75 75 76 76 77 77 78 78 79 79 80 80 81 81 82 82 83 83 84 84 85 85 86 86 87 87 88 88 89 89 90 90 91 91 92 92 93 93 94 94 95 95 96 96 97 97 98 98 99 99 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFfy7dv5_YR8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import operator\n",
        "\n",
        "# y_2 = np.ones((197, 1))\n",
        "\n",
        "# x_list2 = np.ones(shape=(9000, 785, 1))\n",
        "\n",
        "# y__list2 = np.ones(shape=(9000, 197, 1))\n",
        "# z__list2 = np.ones(shape=(9000, 50, 1))\n",
        "# h__list2 = np.ones(shape=(9000, 10, 1))\n",
        "\n",
        "# y_list2 = np.ones(shape=(9000, 197, 1))\n",
        "# z_list2 = np.ones(shape=(9000, 50, 1))\n",
        "# h_list2 = np.ones(shape=(9000, 10, 1))\n",
        "# l_list2 = np.zeros(shape=(9000, 10, 1))\n",
        "\n",
        "# y_list_tmp2 = np.ones(shape=(9000, 197, 1))\n",
        "# z_list_tmp2 = np.ones(shape=(9000, 50, 1))\n",
        "# h_list_tmp2 = np.ones(shape=(9000, 10, 1))\n",
        "# y__list_tmp2 = np.ones(shape=(9000, 197, 1))\n",
        "# z__list_tmp2 = np.ones(shape=(9000, 50, 1))\n",
        "# h__list_tmp2 = np.ones(shape=(9000, 10, 1))\n",
        "\n",
        "# # w_tmp = np.ones(shape=(9000, 197, 785))\n",
        "# # v_tmp = np.ones(shape=(9000, 50, 197))\n",
        "# # u_tmp = np.ones(shape=(9000, 10, 50))\n",
        "\n",
        "# # matrix_u = np.zeros((197, 785))  #197줄\n",
        "# # matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "\n",
        "# #training set 1000개에 대해서\n",
        "# for i in range(0, 9000):\n",
        "#   # print(i)\n",
        "#   label2       = list_label[i + 1000]\n",
        "#   bias2   = [1]       #bias 1\n",
        "#   im_vector2 = np.concatenate((bias2, list_image[:, i + 1000]), axis = None)\n",
        "\n",
        "#   # print(type(list_image[: , i]))\n",
        "#   # print(list_image[:, i])\n",
        "#   # print(im_vector)\n",
        "#   im_matrix2   = im_vector2.reshape((size_row*size_col + 1, 1))\n",
        "  \n",
        "#   x_list2[i] = im_matrix2\n",
        "\n",
        "#   y_2 = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "#   y__list2[i] = y_2\n",
        "  \n",
        "#   y_vector2 = np.ones((197,1)) #bias\n",
        "#   for j in range (1, 197):\n",
        "#     y_vector2[j][0] = 1 / (1 + math.exp(-y_2[j][0]))\n",
        "#     # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "#   y2   = y_vector2.reshape((197, 1))\n",
        "#   y_list2[i] = y2\n",
        "\n",
        "#   z_2  = np.dot(matrix_v, y2)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "#   z__list2[i] = z_2\n",
        "#   z_vector2 = np.ones((50,1)) #bias\n",
        "#   for j in range (1, 50):\n",
        "#     z_vector2[j][0] = 1 / (1 + math.exp(-z_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "#   z2   = z_vector2.reshape((50, 1))\n",
        "#   z_list2[i] = z2\n",
        "  \n",
        "#   # print(z)\n",
        "\n",
        "#   h_2  = np.dot(matrix_w, z2)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "#   h__list2[i] = h_2\n",
        "  \n",
        "#   h_vector2 = np.ones((10,1)) #bias\n",
        "#   for j in range (0, 10):\n",
        "#     h_vector2[j][0] = 1 / (1 + math.exp(-h_2[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "#   h2   = h_vector2.reshape((10, 1))\n",
        "#   # tmp = h.ravel()\n",
        "#   # max_value = max(tmp)\n",
        "#   # print(max_value)\n",
        "#   # max_index = tmp.index(max_value)\n",
        "#   index2, value2 = max(enumerate(h2), key=operator.itemgetter(1))\n",
        "#   h_list2[i] = h2\n",
        "#   l_list2[i][index2] = 1\n",
        "#   # print(l_list[i])\n",
        "#   # print(h)\n",
        "\n",
        "#   y_list_tmp2[i] = y_list2[i]\n",
        "#   z_list_tmp2[i] = z_list2[i]\n",
        "#   h_list_tmp2[i] = h_list2[i]\n",
        "#   y__list_tmp2[i] = y__list2[i]\n",
        "#   z__list_tmp2[i] = z__list2[i]\n",
        "#   h__list_tmp2[i] = h__list2[i]\n",
        "\n",
        "# # print(l_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nzuVCwhp-DLC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# #test data!\n",
        "\n",
        "# cost_history2 = np.zeros(iteration)\n",
        "\n",
        "# total_number_of_predictions2 = 9000\n",
        "# accuracy_history2 = np.zeros(iteration)\n",
        "\n",
        "# # small_delta = np.ones(3)  #image_size, layer 2~4(3개)\n",
        "# # greece_delta_w = np.ones(shape = (10, 50))\n",
        "# # greece_delta_v = np.ones(shape = (50, 197))\n",
        "# # greece_delta_u = np.ones(shape = (197, 785))\n",
        "\n",
        "# label_group2 = np.zeros(shape = (9000, 10))\n",
        "\n",
        "# for i in range(1000, 10000):\n",
        "#   label2       = list_label[i]\n",
        "#   im_vector2   = list_image[:, i]\n",
        "\n",
        "#   # X[i] = im_vector\n",
        "#   # print(i)\n",
        "#   # print(X[i])\n",
        "#   label_group2[i - 1000][label2] = 1\n",
        "\n",
        "# for t in range(0, iteration):\n",
        "#   number_of_correct_predictions2 = 0\n",
        "  \n",
        "#   print(t, end=' ')\n",
        "\n",
        "#   # cost_history[t] = objective_function(1000, l_list, h_list_tmp)\n",
        "#   result2 = 0.0\n",
        "#   for i in range(0, 9000):\n",
        "#     for k in range(0, 10):\n",
        "#       # if h_list_tmp[i][k][0] <= 0:\n",
        "#       # print(h_list_tmp[i][k][0])\n",
        "#       result2 = result2 + ((-1)*label_group2[i][k]*math.log(h_list2[i][k][0]) - (1-label_group2[i][k])*math.log(1-h_list2[i][k][0]))\n",
        "#   result2 = result2 / 9000\n",
        "#   cost_history2[t] = result2\n",
        "\n",
        "# # (1 / (1 + math.exp(-y__list_tmp[i][m][0])))\n",
        "# #############################################\n",
        "#   # for m in range(0, 10):\n",
        "#   #   # print(m)\n",
        "#   #   for n in range(0, 50):\n",
        "#   #     # matrix_w[m][n] = w_gredient_descent(l_r, m, n, 6000, l_list)\n",
        "#   #     result = 0.0\n",
        "#   #     for i in range(0,6000):\n",
        "#   #       result = result + (z_list_tmp[i][n][0]*((1 / (1 + math.exp(-h_list_tmp[i][m][0]))) - l_list[i][m][0]))\n",
        "#   #     result = result / 6000\n",
        "#   #     result = result * l_r\n",
        "#   #     final = w_tmp[m][n] - result\n",
        "#   #     matrix_w[m][n] = final\n",
        "\n",
        "#   # for m in range(0, 50):\n",
        "#   #   # print(m)\n",
        "#   #   for n in range(0, 197):\n",
        "#   #     # matrix_v[m][n] = v_gredient_descent(l_r, m, n, 6000, l_list)\n",
        "#   #     result = 0.0\n",
        "#   #     for i in range(0, 6000):\n",
        "#   #       result = result + (y_list_tmp[i][n][0]*w_tmp[0][m]*((1 / (1 + math.exp(-h_list_tmp[i][0][0]))) - l_list[i][0][0])*((1 / (1 + math.exp(-z__list_tmp[i][m][0])))*(1 - (1 / (1 + math.exp(-z__list_tmp[i][m][0]))))))\n",
        "#   #     result = result / 6000\n",
        "#   #     result = result * l_r\n",
        "#   #     final = v_tmp[m][n] - result\n",
        "#   #     matrix_v[m][n] = final\n",
        "\n",
        "#   # for m in range(0, 197):\n",
        "#   #   # print(m)\n",
        "#   #   for n in range(0, 785):\n",
        "#   #     # matrix_u[m][n] = u_gredient_descent(l_r, m, n, 6000, l_list)\n",
        "#   #     result = 0.0\n",
        "#   #     for i in range(0, 6000):\n",
        "#   #       result = result + (x_list[i][n][0]*w_tmp[0][1]*v_tmp[1][m]*((1 / (1 + math.exp(-h_list_tmp[i][0][0]))) - l_list[i][0][0])*((1 / (1 + math.exp(-z__list_tmp[i][1][0])))*(1 - (1 / (1 + math.exp(-z__list_tmp[i][1][0])))))*((1 / (1 + math.exp(-y__list_tmp[i][m][0])))*(1 - (1 / (1 + math.exp(-y__list_tmp[i][m][0]))))))\n",
        "#   #     result = result / 6000\n",
        "#   #     result = result * l_r\n",
        "#   #     final = u_tmp[m][n] - result\n",
        "#   #     matrix_u[m][n] = final\n",
        "# #######################################################################\n",
        "# ##gradient descent\n",
        "#   # result_w = 0\n",
        "#   # result_v = 0\n",
        "#   # result_u = 0\n",
        "\n",
        "#   # for i in range(0, 9000):\n",
        "#   #   small_delta_2 = np.zeros(shape = (10, 1))\n",
        "#   #   for j in range(0, 10):\n",
        "#   #     small_delta_2[j][0] = h_list[i][j] - label_group2[i][j]\n",
        "#   #   # print(h_list[i])\n",
        "#   #   # print(label_group[i])\n",
        "#   #   # small_delta_2 = h_list[i] - label_group[i] #여기 label_group 다시 봐야함.\n",
        "#   #   small_delta_1 = np.dot(np.transpose(matrix_w), small_delta_2)\n",
        "#   #   small_delta_0 = np.dot(np.transpose(matrix_v), small_delta_1)*(y_list_tmp[i]*(1 - y_list_tmp[i]))\n",
        "\n",
        "#   #   # print(small_delta_2)\n",
        "#   #   result_w = result_w + np.dot(small_delta_2, np.transpose(z_list[i]))\n",
        "#   #   result_v = result_v + np.dot(small_delta_1, np.transpose(y_list[i]))\n",
        "#   #   result_u = result_u + np.dot(small_delta_0, np.transpose(x_list[i]))\n",
        "\n",
        "#   # matrix_w = matrix_w - (result_w / 9000 *l_r)\n",
        "#   # matrix_v = matrix_v - (result_v / 9000 *l_r)\n",
        "#   # matrix_u = matrix_u - (result_u / 9000 *l_r)\n",
        "\n",
        "\n",
        "# ####################################################################\n",
        "\n",
        "#   for k in range(0, 9000):\n",
        "#   # print(i)\n",
        "#     label       = list_label[k + 1000]\n",
        "#     bias   = [1]       #bias 1\n",
        "#     im_vector = np.concatenate((bias, list_image[:, k + 1000]), axis = None)\n",
        "\n",
        "#     # print(type(list_image[: , k]))\n",
        "#     # print(list_image[:, k])\n",
        "#     # print(im_vector)\n",
        "#     im_matrix   = im_vector.reshape((size_row*size_col + 1, 1))\n",
        "\n",
        "#     y_ = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "#     y__list[k] = y_\n",
        "    \n",
        "#     y_vector = np.ones((197,1)) #bias\n",
        "#     for j in range (1, 197):\n",
        "#       y_vector[j][0] = 1 / (1 + math.exp(-y_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "    \n",
        "#     # print(y_vector) #시그모이드 값 확인\n",
        "#     y   = y_vector.reshape((197, 1))\n",
        "#     y_list[k] = y\n",
        "\n",
        "#     z_  = np.dot(matrix_v, y)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "#     z__list[k] = z_\n",
        "#     z_vector = np.ones((50,1)) #bias\n",
        "#     for j in range (1, 50):\n",
        "#       z_vector[j][0] = 1 / (1 + math.exp(-z_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "#     z   = z_vector.reshape((50, 1))\n",
        "#     z_list[k] = z\n",
        "    \n",
        "#     # print(z)\n",
        "\n",
        "#     h_  = np.dot(matrix_w, z)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "#     h__list[k] = h_\n",
        "    \n",
        "#     h_vector = np.ones((10,1)) #bias\n",
        "#     for j in range (0, 10):\n",
        "#       h_vector[j][0] = 1 / (1 + math.exp(-h_[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "#     h   = h_vector.reshape((10, 1))\n",
        "#     # print(h)\n",
        "#     # tmp = h.ravel()\n",
        "#     # max_value = max(tmp)\n",
        "#     # print(max_value)\n",
        "#     # max_index = tmp.index(max_value)\n",
        "#     index, value = max(enumerate(h), key=operator.itemgetter(1))\n",
        "#     h_list[k] = h\n",
        "#     l_list[k][index] = 1\n",
        "#     # print(l_list[i])\n",
        "#     # print(h)\n",
        "#     if index == label:\n",
        "#       number_of_correct_predictions2 = number_of_correct_predictions2 + 1\n",
        "\n",
        "#     y_list_tmp[k] = y_list[k]\n",
        "#     z_list_tmp[k] = z_list[k]\n",
        "#     h_list_tmp[k] = h_list[k]\n",
        "#     y__list_tmp[k] = y__list[k]\n",
        "#     z__list_tmp[k] = z__list[k]\n",
        "#     h__list_tmp[k] = h__list[k]\n",
        "\n",
        "\n",
        "#   accuracy_history2[t] = number_of_correct_predictions2/total_number_of_predictions2*100\n",
        "  # print(accuracy_history2[t])\n",
        "  # t = t + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81J4ESnphg--",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wrong_images_index = np.ones(9000)\n",
        "right_images_index = np.ones(9000)\n",
        "\n",
        "wrong_images_value = np.ones(9000)\n",
        "right_images_value = np.ones(9000)\n",
        "\n",
        "wrong_images_num = 0\n",
        "right_images_num = 0\n",
        "\n",
        "for k in range(0, 9000):\n",
        "  # print(i)\n",
        "    label       = list_label[k + 1000]\n",
        "    bias   = [1]       #bias 1\n",
        "    im_vector = np.concatenate((bias, list_image[:, k + 1000]), axis = None)\n",
        "\n",
        "    # print(type(list_image[: , k]))\n",
        "    # print(list_image[:, k])\n",
        "    # print(im_vector)\n",
        "    im_matrix   = im_vector.reshape((size_row*size_col + 1, 1))\n",
        "    # print(h_list[real_iter - 1][0])\n",
        "    index, value = max(enumerate(h_list2[k]), key=operator.itemgetter(1))\n",
        "    \n",
        "\n",
        "    # print(l_list[i])\n",
        "    # print(h)\n",
        "    if index == label:\n",
        "      right_images_index[right_images_num] = int(k + 1000)\n",
        "      right_images_value[right_images_num] = int(index)\n",
        "      right_images_num = right_images_num + 1\n",
        "    else:\n",
        "      wrong_images_index[wrong_images_num] = int(k + 1000)\n",
        "      wrong_images_value[wrong_images_num] = int(index)\n",
        "      wrong_images_num = wrong_images_num + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMcbWdbh98uY",
        "colab_type": "text"
      },
      "source": [
        "# **Machine Learning Assignment09** #\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "Name : 이현주\n",
        "\n",
        "Student # : 20184060"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CwXihsOJgZ0R"
      },
      "source": [
        "# **1. Plot the loss curve [10pt]**\n",
        "\n",
        "- plot the training loss at every iteration of gradient descent using the training data in blue color (the first 6,000 images) [5pt]\n",
        "- plot the testing loss at every iteration of gradient descent using the testing data in red color (the rest 4,000 images) [5pt]\n",
        "- the both curves should be presented in one figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTouPM3uAf8Q",
        "colab_type": "code",
        "outputId": "21961eef-5c56-4ab3-9d08-6de8379b4c46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        }
      },
      "source": [
        "#그 코드를 바탕으로 training loss plotting 하기.\n",
        "real_iter = iteration\n",
        "\n",
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "cost_tmp = np.zeros(real_iter)\n",
        "\n",
        "for i in range(0, real_iter):\n",
        "  cost_tmp[i] = cost_history[i]\n",
        "\n",
        "\n",
        "# print(cost_history[1])\n",
        "\n",
        "ax.set_ylabel('J(Theta)')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(real_iter),cost_tmp,'b.', color = 'blue')\n",
        "_=ax.plot(range(real_iter),cost_history2,'b.', color = 'red')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OiKcfzyYgaCF"
      },
      "source": [
        "# **2. Plot the accuracy curve [10pt]**\n",
        "\n",
        "- plot the training accuracy (%) at every iteration of gradient descent using the training data in blue color (the first 6,000 images) [5pt]\n",
        "- plot the testing accuracy (%) at every iteration of gradient descent using the testing data in red color (the rest 4,000 images) [5pt]\n",
        "- the both curves should be presented in one figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fF1EODxQAhri",
        "colab_type": "code",
        "outputId": "fc809174-bc82-4383-ffed-37ca5240b550",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        }
      },
      "source": [
        "#그 코드를 바탕으로 training loss plotting 하기.\n",
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "# print(cost_history[1])\n",
        "\n",
        "ac_tmp = np.zeros(real_iter)\n",
        "\n",
        "for i in range(0, real_iter):\n",
        "  ac_tmp[i] = accuracy_history[i]\n",
        "\n",
        "ax.set_ylabel('accuracy')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(real_iter),ac_tmp,'b.', color = 'blue')\n",
        "_=ax.plot(range(real_iter),accuracy_history2,'b.', color = 'red')\n",
        "# print(ac_tmp)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hf4SrnjSgaXp"
      },
      "source": [
        "# **3. Plot the accuracy value [4pt]**\n",
        "\n",
        "- print the final training accuracy (%) using the training data (the first 6,000 images) [2pt]\n",
        "- print the final testing accuracy (%) using the testing data (the rest 4,000 images) [2pt]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2tcQNdPAluv",
        "colab_type": "code",
        "outputId": "934cfc37-0d20-4619-d3ab-708a099a11cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "print(\"final training accuracy using the training data : \", end = '')\n",
        "print(accuracy_history[-1], end = '%\\n')\n",
        "\n",
        "print(\"final training accuracy using the testing data : \", end = '')\n",
        "print(accuracy_history2[-1], end = '%\\n')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "final training accuracy using the training data : 97.8%\n",
            "final training accuracy using the testing data : 81.9888888888889%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dCAltdEPhHY_"
      },
      "source": [
        "# **4. Plot the classification example [10pt]**\n",
        "\n",
        "- present 10 correctly classified testing images with their labels at the title of each sub-figure in 2x5 array [5pt]\n",
        "- present 10 misclassified testing images with their misclassified labels at the title of each sub-figure in 2x5 array [5pt]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxLgEta7hU9c",
        "colab_type": "code",
        "outputId": "14d65b2e-9560-4fc1-d2c6-37043b9e95a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "\n",
        "print(\"**correctly classified testing images**\")\n",
        "for i in range(10):\n",
        "    # print(int(right_images_index[i]))\n",
        "    # print(h_list[int(right_images_index[i])])\n",
        "    label       = list_label[int(right_images_index[i])]\n",
        "    im_vector   = list_image[:, int(right_images_index[i])]\n",
        "    im_matrix   = im_vector.reshape((size_row, size_col))\n",
        "\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.title(int(right_images_value[i]))\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",
        "print(\"\\n\\n\\n**misclassified testing images**\")\n",
        "for i in range(10):\n",
        "    # print(int(wrong_images_index[i]))\n",
        "    # print(h_list[int(right_images_index[i])])\n",
        "    label       = list_label[int(wrong_images_index[i])]\n",
        "    im_vector   = list_image[:, int(wrong_images_index[i])]\n",
        "    im_matrix   = im_vector.reshape((size_row, size_col))\n",
        "\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.title(int(wrong_images_value[i]))\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",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "**correctly classified testing images**\n"
          ],
          "name": "stdout"
        },
        {
          "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "**misclassified testing images**\n"
          ],
          "name": "stdout"
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}