new_assignment_11_hidden_layer_three2.ipynb

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  "metadata": {
    "colab": {
      "name": "new_assignment_11.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO0VC/FGeZYDCuCrpp+cb38",
      "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/dlguswn3659/class-MachineLearning/blob/master/new_assignment_11_hidden_layer_three2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbdQEPZ_o5JX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk\n",
        "import math\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from wordcloud import WordCloud,STOPWORDS\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import word_tokenize,sent_tokenize\n",
        "from bs4 import BeautifulSoup\n",
        "import spacy\n",
        "import re,string,unicodedata\n",
        "from nltk.tokenize.toktok import ToktokTokenizer\n",
        "from nltk.stem import LancasterStemmer,WordNetLemmatizer\n",
        "from sklearn.linear_model import LogisticRegression,SGDClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.svm import SVC\n",
        "from textblob import TextBlob\n",
        "from textblob import Word\n",
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMRdc5kpo9vM",
        "colab_type": "code",
        "outputId": "f6377a9e-3c17-4441-e262-53397533ee86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "import numpy as np\n",
        "import re\n",
        "import nltk\n",
        "from sklearn.datasets import load_files\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "import pickle\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.model_selection import train_test_split\n",
        "# from google.colab import files\n",
        "\n",
        "# uploaded = files.upload()\n",
        "\n",
        "# import zipfile\n",
        "# import io\n",
        "# zf = zipfile.ZipFile(io.BytesIO(uploaded['movie_review.zip']), \"r\")\n",
        "# zf.extractall()\n",
        "\n",
        "review_data = load_files(r\"movie_review\")\n",
        "X, y = review_data.data, review_data.target\n",
        "\n",
        "documents = []\n",
        "\n",
        "stemmer = WordNetLemmatizer()\n",
        "\n",
        "for sen in range(0, len(X)):\n",
        "    # Remove all the special characters\n",
        "    document = re.sub(r'\\W', ' ', str(X[sen]))\n",
        "    \n",
        "    # remove all single characters\n",
        "    document = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', document)\n",
        "    \n",
        "    # Remove single characters from the start\n",
        "    document = re.sub(r'\\^[a-zA-Z]\\s+', ' ', document) \n",
        "    \n",
        "    # Substituting multiple spaces with single space\n",
        "    document = re.sub(r'\\s+', ' ', document, flags=re.I)\n",
        "    \n",
        "    # Removing prefixed 'b'\n",
        "    document = re.sub(r'^b\\s+', '', document)\n",
        "    \n",
        "    # Converting to Lowercase\n",
        "    document = document.lower()\n",
        "    \n",
        "    # Lemmatization\n",
        "    document = document.split()\n",
        "    document = [stemmer.lemmatize(word) for word in document]\n",
        "    document = ' '.join(document)\n",
        "    \n",
        "    documents.append(document)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_uv_iLio-76",
        "colab_type": "code",
        "outputId": "f200e4b5-bad1-433b-a025-9a1ee616bbe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 962
        }
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "vectorizer = CountVectorizer(max_features=40000, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))\n",
        "# vectorizer = TfidfVectorizer()\n",
        "X = vectorizer.fit_transform(documents).toarray()\n",
        "print(X)\n",
        "\n",
        "vector_size = 749\n",
        "\n",
        "###################################################\n",
        "\n",
        "# # tfidfconverter = TfidfTransformer()\n",
        "# # print(tfidfconverter)\n",
        "# # X = tfidfconverter.fit_transform(X).toarray()\n",
        "# # print(X)\n",
        "\n",
        "# X_train_tmp, X_test_tmp, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=False)\n",
        "\n",
        "# # print(X_train.shape)\n",
        "\n",
        "# X_pos_sum = np.zeros(12638)\n",
        "# X_neg_sum = np.zeros(12638)\n",
        "\n",
        "# X_train = np.zeros((1401, 897))\n",
        "# X_test = np.zeros((601, 897))\n",
        "\n",
        "# for i in range(0, 2002):\n",
        "#   if y[i] == 0:\n",
        "#     X_neg_sum = X_neg_sum + X[i]\n",
        "#   else:\n",
        "#     X_pos_sum = X_pos_sum + X[i]\n",
        "\n",
        "# pos_neg_voca = 0\n",
        "\n",
        "# for i in range(0, 12638):\n",
        "#   # if abs(X_neg_sum[i] - X_pos_sum[i]) >= 30:\n",
        "#   if (X_neg_sum[i]*4 <= X_pos_sum[i] and X_pos_sum[i] > 8) or (X_pos_sum[i]*4 <= X_neg_sum[i] and X_neg_sum[i] > 8):\n",
        "#     for j in range(0, 1401):\n",
        "#       X_train[j][pos_neg_voca] = X_train_tmp[j][i]\n",
        "#     for j in range(0, 601):\n",
        "#       X_test[j][pos_neg_voca] = X_test_tmp[j][i]\n",
        "#     pos_neg_voca  = pos_neg_voca + 1\n",
        "\n",
        "# # tfidfconverter = TfidfTransformer()\n",
        "# # X_train = tfidfconverter.fit_transform(X_train).toarray()\n",
        "# # X_test = tfidfconverter.fit_transform(X_test).toarray()\n",
        "\n",
        "# print(X_neg_sum)\n",
        "# print(X_pos_sum)\n",
        "# print(pos_neg_voca)\n",
        "\n",
        "#################################################\n",
        "\n",
        "list_x=X.tolist()\n",
        "negative=[]\n",
        "positive=[]\n",
        "for i in range(0,2002):\n",
        "  if y[i]==0:\n",
        "    negative.append(list_x[i])\n",
        "  else:\n",
        "    positive.append(list_x[i])\n",
        "negative=np.array(negative)\n",
        "positive=np.array(positive)\n",
        "total_sum=X.sum(axis=0)\n",
        "neg_sum=negative.sum(axis=0)\n",
        "pos_sum=positive.sum(axis=0)\n",
        "\n",
        "neg_ratio=neg_sum/total_sum\n",
        "pos_ratio=pos_sum/total_sum\n",
        "\n",
        "good=[]\n",
        "for i in range(0,len(pos_sum)):\n",
        "  if neg_ratio[i]>pos_ratio[i]*3 and neg_ratio[i] > 0.88:\n",
        "    # print(neg_ratio[i])\n",
        "    good.append(i)\n",
        "  elif pos_ratio[i]>neg_ratio[i]*3 and pos_ratio[i] > 0.88:\n",
        "    # print(pos_ratio[i])\n",
        "    good.append(i)\n",
        "print(\"good의 길이:   \" , len(good))\n",
        "new=np.ones((1,2002), dtype=float)\n",
        "for i in range(0,len(good)):\n",
        "  temp=X[:,good[i]]\n",
        "  temp=temp[np.newaxis]\n",
        "  new=np.vstack((new,temp))\n",
        "new=np.transpose(new)\n",
        "new=new[:,1:]\n",
        "# vectorizer = CountVectorizer(max_features=2, min_df=0, stop_words=stopwords.words('english'))\n",
        "# Xp = vectorizer.fit_transform(pluss).toarray()\n",
        "\n",
        "# tfidfconverter = TfidfTransformer()\n",
        "# X = tfidfconverter.fit_transform(X).toarray()\n",
        "X_train, X_test, y_train, y_test = train_test_split(new, y, test_size=0.3, shuffle=False)\n",
        "\n",
        "print(X_train)\n",
        "print(X_test)\n",
        "\n",
        "print(X_train[0])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n",
            "good의 길이:    748\n",
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlTm8tUMrWdL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#first hidden layer로 가기 위한 초기 fully connected u 생성\n",
        "w_tmp = np.ones(shape=(1401, 100, vector_size))\n",
        "v_tmp = np.ones(shape=(1401, 12, 197))\n",
        "u_tmp = np.ones(shape=(1401, 1, 12))\n",
        "\n",
        "matrix_u = np.zeros((100, vector_size))  #100줄\n",
        "matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 100):\n",
        "  for j in range(0, vector_size):\n",
        "    matrix_u[i][j] = np.random.normal()\n",
        "\n",
        "\n",
        "# matrix_u[196][0] = 0            #줄, 칸\n",
        "##############################################\n",
        "matrix_v = np.zeros((12, 100))  #12줄\n",
        "matrix_v[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 12):\n",
        "  for j in range(0, 100):\n",
        "    matrix_v[i][j] = np.random.normal()\n",
        "\n",
        "\n",
        "# matrix_v[196][0] = 0            #줄, 칸\n",
        "##############################################\n",
        "matrix_w = np.zeros((1, 12))  #10줄\n",
        "# matrix_w[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(0, 1):\n",
        "  for j in range(0, 12):\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": "DrFN9qH2rXrj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "y_ = np.ones((100, 1))\n",
        "\n",
        "x_list = np.ones(shape=(1401, vector_size, 1))\n",
        "\n",
        "y__list = np.ones(shape=(1401, 100, 1))\n",
        "z__list = np.ones(shape=(1401, 12, 1))\n",
        "h__list = np.ones(shape=(1401, 1, 1))\n",
        "\n",
        "y_list = np.ones(shape=(1401, 100, 1))\n",
        "z_list = np.ones(shape=(1401, 12, 1))\n",
        "h_list = np.ones(shape=(1401, 1, 1))\n",
        "l_list = np.zeros(shape=(1401, 1, 1))\n",
        "\n",
        "y_list_tmp = np.ones(shape=(1401, 100, 1))\n",
        "z_list_tmp = np.ones(shape=(1401, 12, 1))\n",
        "h_list_tmp = np.ones(shape=(1401, 1, 1))\n",
        "y__list_tmp = np.ones(shape=(1401, 100, 1))\n",
        "z__list_tmp = np.ones(shape=(1401, 12, 1))\n",
        "h__list_tmp = np.ones(shape=(1401, 1, 1))\n",
        "\n",
        "\n",
        "#training set 1401개에 대해서\n",
        "for i in range(0, 1401):\n",
        "  bias   = [1]       #bias 1\n",
        "  im_vector = np.concatenate((bias, X_train[i]), axis = None)\n",
        "\n",
        "  im_matrix   = im_vector.reshape((vector_size, 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((100,1)) #bias\n",
        "  for j in range (1, 100):\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((100, 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((12,1)) #bias\n",
        "  for j in range (1, 12):\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((12, 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((1,1)) #bias\n",
        "  for j in range (0, 1):\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((1, 1))\n",
        "  \n",
        "  h_list[i] = h\n",
        "\n",
        "  tmp_label = 0\n",
        "  # print(h)\n",
        "\n",
        "  if h >= 0.5:\n",
        "    tmp_label = 1\n",
        "\n",
        "  l_list[i][0] = tmp_label\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": "YB-qa4x8rZD7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "y_2 = np.ones((100, 1))\n",
        "\n",
        "x_list2 = np.ones(shape=(601, vector_size, 1))\n",
        "\n",
        "y__list2 = np.ones(shape=(601, 100, 1))\n",
        "z__list2 = np.ones(shape=(601, 12, 1))\n",
        "h__list2 = np.ones(shape=(601, 1, 1))\n",
        "\n",
        "y_list2 = np.ones(shape=(601, 100, 1))\n",
        "z_list2 = np.ones(shape=(601, 12, 1))\n",
        "h_list2 = np.ones(shape=(601, 1, 1))\n",
        "l_list2 = np.zeros(shape=(601, 1, 1))\n",
        "\n",
        "y_list_tmp2 = np.ones(shape=(601, 100, 1))\n",
        "z_list_tmp2 = np.ones(shape=(601, 12, 1))\n",
        "h_list_tmp2 = np.ones(shape=(601, 1, 1))\n",
        "y__list_tmp2 = np.ones(shape=(601, 100, 1))\n",
        "z__list_tmp2 = np.ones(shape=(601, 12, 1))\n",
        "h__list_tmp2 = np.ones(shape=(601, 1, 1))\n",
        "\n",
        "\n",
        "#training set 1000개에 대해서\n",
        "for i in range(0, 601):\n",
        "  bias2   = [1]       #bias 1\n",
        "  im_vector2 = np.concatenate((bias2, X_test[i]), axis = None)\n",
        "\n",
        "  # print(type(list_image[: , i]))\n",
        "  # print(list_image[:, i])\n",
        "  # print(im_vector)\n",
        "  im_matrix2   = im_vector2.reshape((vector_size, 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((100,1)) #bias\n",
        "  for j in range (1, 100):\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((100, 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((12,1)) #bias\n",
        "  for j in range (1, 12):\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((12, 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((1,1)) #bias\n",
        "  for j in range (0, 1):\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((1, 1))\n",
        "  \n",
        "  h_list2[i] = h2\n",
        "\n",
        "  tmp_label = 0\n",
        "\n",
        "  if h2 >= 0.5:\n",
        "    tmp_label = 1\n",
        "\n",
        "  l_list2[i][0] = tmp_label\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": "j28x7DrlraXJ",
        "colab_type": "code",
        "outputId": "95b23260-a6be-410f-84f3-fec3d76d4304",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 5267
        }
      },
      "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 = 300\n",
        "l_r = 0.05\n",
        "lambda_ = 1\n",
        "\n",
        "\n",
        "#####################################\n",
        "\n",
        "cost_history = np.zeros(iteration)    #about train\n",
        "cost_history2 = np.zeros(iteration)   #about test\n",
        "\n",
        "label_group = np.zeros(shape = (1401, 1))\n",
        "label_group2 = np.zeros(shape = (601, 1))\n",
        "\n",
        "accuracy_history = np.zeros(iteration)\n",
        "accuracy_history2 = np.zeros(iteration)\n",
        "\n",
        "for i in range(0, 1401):\n",
        "  label_group[i][0] = l_list[i][0]\n",
        "\n",
        "for i in range(0, 601):\n",
        "  label_group2[i][0] = l_list2[i][0]\n",
        "\n",
        "#############################################\n",
        "\n",
        "\n",
        "for t in range(0, iteration):\n",
        "  number_of_correct = 0\n",
        "  number_of_correct2 = 0\n",
        "  \n",
        "  print(t, end=' ')\n",
        "\n",
        "  parameter_sum = 0\n",
        "\n",
        "  for q in range(0, vector_size):\n",
        "    for w in range(1, 100):\n",
        "      parameter_sum = parameter_sum + matrix_u[w][q]**2\n",
        "\n",
        "  for q in range(0, 100):\n",
        "    for w in range(1, 12):\n",
        "      parameter_sum = parameter_sum + matrix_u[w][q]**2\n",
        "\n",
        "  for q in range(0, 12):\n",
        "    for w in range(0, 1):\n",
        "      parameter_sum = parameter_sum + matrix_u[w][q]**2\n",
        "\n",
        "  result = 0.0\n",
        "  for i in range(0, 1401):\n",
        "    for k in range(0, 1):\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])) + ((lambda_)/(2 * 91012)) * parameter_sum\n",
        "  result = result / 1401\n",
        "  cost_history[t] = result\n",
        "\n",
        "##gradient descent\n",
        "  result_w = 0\n",
        "  result_v = 0\n",
        "  result_u = 0\n",
        "\n",
        "  for i in range(0, 1401):\n",
        "    small_delta_2 = np.zeros(shape = (1, 1))\n",
        "    for j in range(0, 1):\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 / 1401 *l_r)\n",
        "  matrix_v = matrix_v - (result_v / 1401 *l_r)\n",
        "  matrix_u = matrix_u - (result_u / 1401 *l_r)\n",
        "\n",
        "\n",
        "####################################################################\n",
        "\n",
        "  for k in range(0, 1401):\n",
        "    bias   = [1]       #bias 1\n",
        "    im_vector = np.concatenate((bias, X_train[k]), axis = None)\n",
        "\n",
        "    im_matrix   = im_vector.reshape((vector_size, 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((100,1)) #bias\n",
        "    for j in range (1, 100):\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((100, 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((12,1)) #bias\n",
        "    for j in range (1, 12):\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((12, 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((1,1)) #bias\n",
        "    for j in range (0, 1):\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((1, 1))\n",
        "\n",
        "    h_list[k] = h\n",
        "\n",
        "    tmp_label = 0\n",
        "\n",
        "    if h >= 0.5:\n",
        "      # print(h)\n",
        "      tmp_label = 1\n",
        "\n",
        "    l_list[k][0] = tmp_label\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",
        "    if l_list[k][0] == y_train[k]:\n",
        "      number_of_correct = number_of_correct + 1\n",
        "\n",
        "  accuracy_history[t] = number_of_correct / 1401 * 100\n",
        "  print(accuracy_history[t], end=' ')\n",
        "\n",
        "############################\n",
        "  #test data!\n",
        "  \n",
        "  # print(t, end=' ')\n",
        "\n",
        "  result2 = 0.0\n",
        "  for i in range(0, 601):\n",
        "    for k in range(0, 1):\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])) + ((lambda_)/(2 * 91012)) * parameter_sum\n",
        "  result2 = result2 / 601\n",
        "  cost_history2[t] = result2\n",
        "\n",
        "\n",
        "  for k in range(0, 601):\n",
        "  # print(i)\n",
        "    bias2   = [1]       #bias 1\n",
        "    im_vector2 = np.concatenate((bias2, X_test[k]), axis = None)\n",
        "\n",
        "    im_matrix2   = im_vector2.reshape((vector_size, 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((100,1)) #bias\n",
        "    for j in range (1, 100):\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((100, 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((12,1)) #bias\n",
        "    for j in range (1, 12):\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((12, 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((1,1)) #bias\n",
        "    for j in range (0, 1):\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((1, 1))\n",
        "\n",
        "    h_list2[k] = h2\n",
        "\n",
        "    tmp_label2 = 0\n",
        "\n",
        "    if h2 >= 0.5:\n",
        "      # print(h)\n",
        "      tmp_label2 = 1\n",
        "\n",
        "    l_list2[k][0] = tmp_label2\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",
        "    if l_list2[k][0] == y_test[k]:\n",
        "      number_of_correct2 = number_of_correct2 + 1\n",
        "\n",
        "  accuracy_history2[t] = number_of_correct2 / 601 * 100\n",
        "  print(accuracy_history2[t])\n",
        "\n",
        "  t = t + 1"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "1_ZPJSBnroog",
        "colab_type": "code",
        "outputId": "41f23a6f-9f76-4908-c2b7-09cc3c7e4114",
        "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": 16,
      "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": "L688vFb8rqtO",
        "colab_type": "code",
        "outputId": "54ecc470-ae9f-42eb-c281-aa201983ad6c",
        "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",
        "ax.set_ylabel('accuracy')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(iteration),accuracy_history,'b.', color = 'blue')\n",
        "_=ax.plot(range(iteration),accuracy_history2,'b.', color = 'red')\n",
        "# print(ac_tmp)"
      ],
      "execution_count": 17,
      "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": "duWiLsZPR8Zc"
      },
      "source": [
        "# **3. Plot the quantitative results [6pt]**\n",
        "\n",
        "Training results\n",
        "\n",
        "- print the confusion matrix using the function confusion_matrix based on the training data [1pt]\n",
        "- print the classification report using the function classification_report based on the training data [1pt]\n",
        "- print the accuracy score using the function accuracy_score based on the training data [1pt]\n",
        "\n",
        "\n",
        "Testing results\n",
        "\n",
        "- print the confusion matrix using the function confusion_matrix based on the testing data [1pt]\n",
        "- print the classification report using the function classification_report based on the testing data [1pt]\n",
        "- print the accuracy score using the function accuracy_score based on the testing data [1pt]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbg3MT81r0Kk",
        "colab_type": "code",
        "outputId": "51314923-8e7f-4037-eb64-9fb702ee288a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "source": [
        "#Codes for Evaluation\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "l_list_tmp = l_list.reshape((1401))\n",
        "l_list2_tmp = l_list2.reshape((601))\n",
        "\n",
        "print(confusion_matrix(y_train,l_list_tmp))\n",
        "print(classification_report(y_train,l_list_tmp))\n",
        "print(accuracy_score(y_train, l_list_tmp))\n",
        "\n",
        "print(confusion_matrix(y_test,l_list2_tmp))\n",
        "print(classification_report(y_test,l_list2_tmp))\n",
        "print(accuracy_score(y_test, l_list2_tmp))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[436 263]\n",
            " [323 379]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.62      0.60       699\n",
            "           1       0.59      0.54      0.56       702\n",
            "\n",
            "    accuracy                           0.58      1401\n",
            "   macro avg       0.58      0.58      0.58      1401\n",
            "weighted avg       0.58      0.58      0.58      1401\n",
            "\n",
            "0.5817273376159886\n",
            "[[177 125]\n",
            " [129 170]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.59      0.58       302\n",
            "           1       0.58      0.57      0.57       299\n",
            "\n",
            "    accuracy                           0.58       601\n",
            "   macro avg       0.58      0.58      0.58       601\n",
            "weighted avg       0.58      0.58      0.58       601\n",
            "\n",
            "0.5773710482529119\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xN8u62m6r0qk",
        "colab_type": "code",
        "outputId": "62780594-ddc7-462f-9f41-f8f5b518535a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "class_1 = 0\n",
        "\n",
        "for i in range(0, 601):\n",
        "  if l_list2_tmp[i] == 1:\n",
        "    class_1 = class_1 + 1\n",
        "\n",
        "print(class_1)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "295\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}