new_assignment_11_hidden_layer_three.ipynb

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    "colab": {
      "name": "new_assignment_11.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyORzYCferkNTNAmtiN5H1Vi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/class-MachineLearning/blob/master/new_assignment_11_hidden_layer_three.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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "b9a767c2-ba13-4b87-a974-31b0954e6414"
      },
      "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": 22,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b1dbf6a5-0638-4507-c080-d0d9b8a0549f"
      },
      "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": 23,
      "outputs": [
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            "1.0\n",
            "1.0\n",
            "0.96\n",
            "0.8860759493670886\n",
            "1.0\n",
            "0.95\n",
            "1.0\n",
            "0.8947368421052632\n",
            "0.8947368421052632\n",
            "0.9285714285714286\n",
            "1.0\n",
            "0.9666666666666667\n",
            "0.9090909090909091\n",
            "0.9375\n",
            "0.9230769230769231\n",
            "0.9\n",
            "0.9166666666666666\n",
            "1.0\n",
            "0.9130434782608695\n",
            "0.9\n",
            "0.8888888888888888\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.9090909090909091\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "0.9473684210526315\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9166666666666666\n",
            "1.0\n",
            "0.9818181818181818\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.8846153846153846\n",
            "0.9\n",
            "0.9\n",
            "0.9166666666666666\n",
            "1.0\n",
            "1.0\n",
            "0.926829268292683\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.8918918918918919\n",
            "0.9375\n",
            "1.0\n",
            "0.9375\n",
            "0.9\n",
            "1.0\n",
            "0.9117647058823529\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "0.9090909090909091\n",
            "1.0\n",
            "0.9166666666666666\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "0.9090909090909091\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.8928571428571429\n",
            "0.9090909090909091\n",
            "1.0\n",
            "1.0\n",
            "0.9285714285714286\n",
            "0.8888888888888888\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.9285714285714286\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9285714285714286\n",
            "1.0\n",
            "0.9444444444444444\n",
            "1.0\n",
            "0.9873417721518988\n",
            "1.0\n",
            "1.0\n",
            "0.9142857142857143\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9230769230769231\n",
            "0.8823529411764706\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9333333333333333\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.9090909090909091\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9230769230769231\n",
            "0.9166666666666666\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9428571428571428\n",
            "0.9166666666666666\n",
            "0.95\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "0.8888888888888888\n",
            "0.9\n",
            "1.0\n",
            "0.9333333333333333\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "0.9\n",
            "0.9545454545454546\n",
            "0.8888888888888888\n",
            "0.9\n",
            "1.0\n",
            "0.9230769230769231\n",
            "1.0\n",
            "0.8863636363636364\n",
            "1.0\n",
            "0.8823529411764706\n",
            "0.9166666666666666\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9565217391304348\n",
            "0.9090909090909091\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.8947368421052632\n",
            "0.9315068493150684\n",
            "0.8888888888888888\n",
            "0.9090909090909091\n",
            "1.0\n",
            "0.8823529411764706\n",
            "1.0\n",
            "0.9090909090909091\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.8888888888888888\n",
            "0.9090909090909091\n",
            "1.0\n",
            "1.0\n",
            "0.9444444444444444\n",
            "0.9545454545454546\n",
            "0.9166666666666666\n",
            "1.0\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9444444444444444\n",
            "0.8936170212765957\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "0.9230769230769231\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.95\n",
            "0.9166666666666666\n",
            "0.8928571428571429\n",
            "1.0\n",
            "0.8846153846153846\n",
            "0.8888888888888888\n",
            "0.8823529411764706\n",
            "0.896551724137931\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9166666666666666\n",
            "0.8888888888888888\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9230769230769231\n",
            "0.9090909090909091\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "0.9\n",
            "0.9454545454545454\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "0.9444444444444444\n",
            "1.0\n",
            "0.9705882352941176\n",
            "1.0\n",
            "0.9375\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9\n",
            "0.9090909090909091\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "0.9444444444444444\n",
            "0.9285714285714286\n",
            "0.9090909090909091\n",
            "0.9\n",
            "1.0\n",
            "0.8888888888888888\n",
            "0.9166666666666666\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9545454545454546\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.92\n",
            "1.0\n",
            "0.9090909090909091\n",
            "0.8823529411764706\n",
            "0.92\n",
            "0.9\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "0.8888888888888888\n",
            "0.9047619047619048\n",
            "0.9\n",
            "0.9210526315789473\n",
            "1.0\n",
            "0.8888888888888888\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.8823529411764706\n",
            "1.0\n",
            "0.9333333333333333\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.9166666666666666\n",
            "1.0\n",
            "0.9032258064516129\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.8846153846153846\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9090909090909091\n",
            "1.0\n",
            "0.9411764705882353\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "0.95\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.8947368421052632\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9281045751633987\n",
            "0.8837209302325582\n",
            "0.9\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9230769230769231\n",
            "1.0\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "0.8928571428571429\n",
            "1.0\n",
            "0.9285714285714286\n",
            "0.8888888888888888\n",
            "0.8888888888888888\n",
            "0.9047619047619048\n",
            "0.9166666666666666\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9166666666666666\n",
            "0.9230769230769231\n",
            "1.0\n",
            "0.8888888888888888\n",
            "0.9230769230769231\n",
            "1.0\n",
            "1.0\n",
            "0.896551724137931\n",
            "0.8928571428571429\n",
            "1.0\n",
            "0.8888888888888888\n",
            "1.0\n",
            "0.9523809523809523\n",
            "0.9545454545454546\n",
            "1.0\n",
            "0.9545454545454546\n",
            "0.9166666666666666\n",
            "1.0\n",
            "1.0\n",
            "0.9615384615384616\n",
            "0.9285714285714286\n",
            "0.8823529411764706\n",
            "0.92\n",
            "0.9\n",
            "1.0\n",
            "1.0\n",
            "0.9166666666666666\n",
            "0.8974358974358975\n",
            "good의 길이:    748\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 2. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3efd605e-697f-4dc3-c83e-a81422b2237a"
      },
      "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 = 5000\n",
        "l_r = 0.5\n",
        "lambda_ = 100\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": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43m스트리밍 출력 내용이 길어서 마지막 5000줄이 삭제되었습니다.\u001b[0m\n",
            "1 49.892933618843685 50.249584026622294\n",
            "2 49.892933618843685 50.249584026622294\n",
            "3 49.892933618843685 50.249584026622294\n",
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            "1855 49.964311206281224 50.74875207986689\n",
            "1856 49.964311206281224 50.74875207986689\n",
            "1857 49.964311206281224 50.74875207986689\n",
            "1858 49.964311206281224 50.74875207986689\n",
            "1859 49.964311206281224 50.74875207986689\n",
            "1860 49.964311206281224 50.74875207986689\n",
            "1861 49.964311206281224 50.74875207986689\n",
            "1862 49.964311206281224 50.74875207986689\n",
            "1863 49.964311206281224 50.74875207986689\n",
            "1864 49.964311206281224 50.74875207986689\n",
            "1865 49.964311206281224 50.74875207986689\n",
            "1866 49.964311206281224 50.74875207986689\n",
            "1867 49.964311206281224 50.74875207986689\n",
            "1868 49.964311206281224 50.74875207986689\n",
            "1869 49.964311206281224 50.74875207986689\n",
            "1870 49.964311206281224 50.74875207986689\n",
            "1871 49.964311206281224 50.74875207986689\n",
            "1872 49.964311206281224 50.74875207986689\n",
            "1873 49.964311206281224 50.74875207986689\n",
            "1874 49.964311206281224 50.74875207986689\n",
            "1875 49.964311206281224 50.74875207986689\n",
            "1876 49.964311206281224 50.74875207986689\n",
            "1877 49.964311206281224 50.74875207986689\n",
            "1878 49.964311206281224 50.74875207986689\n",
            "1879 49.964311206281224 50.74875207986689\n",
            "1880 49.964311206281224 50.74875207986689\n",
            "1881 49.964311206281224 50.74875207986689\n",
            "1882 49.964311206281224 50.74875207986689\n",
            "1883 49.964311206281224 50.74875207986689\n",
            "1884 49.964311206281224 50.74875207986689\n",
            "1885 49.964311206281224 50.74875207986689\n",
            "1886 49.964311206281224 50.74875207986689\n",
            "1887 49.964311206281224 50.74875207986689\n",
            "1888 49.964311206281224 50.74875207986689\n",
            "1889 49.964311206281224 50.74875207986689\n",
            "1890 49.964311206281224 50.74875207986689\n",
            "1891 49.964311206281224 50.74875207986689\n",
            "1892 49.964311206281224 50.74875207986689\n",
            "1893 49.964311206281224 50.74875207986689\n",
            "1894 49.964311206281224 50.74875207986689\n",
            "1895 49.964311206281224 50.74875207986689\n",
            "1896 49.964311206281224 50.74875207986689\n",
            "1897 49.964311206281224 50.74875207986689\n",
            "1898 49.964311206281224 50.74875207986689\n",
            "1899 49.964311206281224 50.74875207986689\n",
            "1900 49.964311206281224 50.74875207986689\n",
            "1901 49.964311206281224 50.74875207986689\n",
            "1902 49.964311206281224 50.74875207986689\n",
            "1903 49.964311206281224 50.74875207986689\n",
            "1904 49.964311206281224 50.74875207986689\n",
            "1905 49.964311206281224 50.74875207986689\n",
            "1906 49.964311206281224 50.74875207986689\n",
            "1907 49.964311206281224 50.74875207986689\n",
            "1908 49.964311206281224 50.74875207986689\n",
            "1909 49.964311206281224 50.74875207986689\n",
            "1910 49.964311206281224 50.74875207986689\n",
            "1911 49.964311206281224 50.74875207986689\n",
            "1912 49.964311206281224 50.74875207986689\n",
            "1913 49.964311206281224 50.74875207986689\n",
            "1914 49.964311206281224 50.74875207986689\n",
            "1915 49.964311206281224 50.74875207986689\n",
            "1916 49.964311206281224 50.74875207986689\n",
            "1917 49.964311206281224 50.74875207986689\n",
            "1918 49.964311206281224 50.74875207986689\n",
            "1919 49.964311206281224 50.74875207986689\n",
            "1920 49.964311206281224 50.74875207986689\n",
            "1921 49.964311206281224 50.74875207986689\n",
            "1922 49.964311206281224 50.74875207986689\n",
            "1923 49.964311206281224 50.74875207986689\n",
            "1924 49.964311206281224 50.74875207986689\n",
            "1925 49.964311206281224 50.74875207986689\n",
            "1926 49.964311206281224 50.74875207986689\n",
            "1927 49.964311206281224 50.74875207986689\n",
            "1928 49.964311206281224 50.74875207986689\n",
            "1929 49.964311206281224 50.74875207986689\n",
            "1930 49.964311206281224 50.74875207986689\n",
            "1931 49.964311206281224 50.74875207986689\n",
            "1932 49.964311206281224 50.74875207986689\n",
            "1933 49.964311206281224 50.74875207986689\n",
            "1934 49.964311206281224 50.74875207986689\n",
            "1935 49.964311206281224 50.74875207986689\n",
            "1936 49.964311206281224 50.74875207986689\n",
            "1937 49.964311206281224 50.74875207986689\n",
            "1938 49.964311206281224 50.74875207986689\n",
            "1939 49.964311206281224 50.74875207986689\n",
            "1940 49.964311206281224 50.74875207986689\n",
            "1941 49.964311206281224 50.74875207986689\n",
            "1942 49.964311206281224 50.74875207986689\n",
            "1943 49.964311206281224 50.74875207986689\n",
            "1944 49.964311206281224 50.74875207986689\n",
            "1945 49.964311206281224 50.74875207986689\n",
            "1946 49.964311206281224 50.74875207986689\n",
            "1947 49.964311206281224 50.74875207986689\n",
            "1948 49.964311206281224 50.74875207986689\n",
            "1949 49.964311206281224 50.74875207986689\n",
            "1950 49.964311206281224 50.74875207986689\n",
            "1951 49.964311206281224 50.74875207986689\n",
            "1952 49.964311206281224 50.74875207986689\n",
            "1953 49.964311206281224 50.74875207986689\n",
            "1954 49.964311206281224 50.74875207986689\n",
            "1955 49.964311206281224 50.74875207986689\n",
            "1956 49.964311206281224 50.74875207986689\n",
            "1957 49.964311206281224 50.74875207986689\n",
            "1958 49.964311206281224 50.74875207986689\n",
            "1959 49.964311206281224 50.74875207986689\n",
            "1960 49.964311206281224 50.74875207986689\n",
            "1961 49.964311206281224 50.74875207986689\n",
            "1962 49.964311206281224 50.74875207986689\n",
            "1963 49.964311206281224 50.74875207986689\n",
            "1964 49.964311206281224 50.74875207986689\n",
            "1965 49.964311206281224 50.74875207986689\n",
            "1966 49.964311206281224 50.74875207986689\n",
            "1967 49.964311206281224 50.74875207986689\n",
            "1968 49.964311206281224 50.74875207986689\n",
            "1969 49.964311206281224 50.74875207986689\n",
            "1970 49.964311206281224 50.74875207986689\n",
            "1971 49.964311206281224 50.74875207986689\n",
            "1972 49.964311206281224 50.74875207986689\n",
            "1973 49.964311206281224 50.74875207986689\n",
            "1974 49.964311206281224 50.74875207986689\n",
            "1975 49.964311206281224 50.74875207986689\n",
            "1976 49.964311206281224 50.74875207986689\n",
            "1977 49.964311206281224 50.74875207986689\n",
            "1978 49.964311206281224 50.74875207986689\n",
            "1979 49.964311206281224 50.74875207986689\n",
            "1980 49.964311206281224 50.74875207986689\n",
            "1981 49.964311206281224 50.74875207986689\n",
            "1982 49.964311206281224 50.74875207986689\n",
            "1983 49.964311206281224 50.74875207986689\n",
            "1984 49.964311206281224 50.74875207986689\n",
            "1985 49.964311206281224 50.74875207986689\n",
            "1986 49.964311206281224 50.74875207986689\n",
            "1987 49.964311206281224 50.74875207986689\n",
            "1988 49.964311206281224 50.74875207986689\n",
            "1989 49.964311206281224 50.74875207986689\n",
            "1990 49.964311206281224 50.74875207986689\n",
            "1991 49.964311206281224 50.74875207986689\n",
            "1992 49.964311206281224 50.74875207986689\n",
            "1993 49.964311206281224 50.74875207986689\n",
            "1994 49.964311206281224 50.74875207986689\n",
            "1995 49.964311206281224 50.74875207986689\n",
            "1996 49.964311206281224 50.74875207986689\n",
            "1997 49.964311206281224 50.74875207986689\n",
            "1998 49.964311206281224 50.74875207986689\n",
            "1999 49.964311206281224 50.74875207986689\n",
            "2000 49.964311206281224 50.74875207986689\n",
            "2001 49.964311206281224 50.74875207986689\n",
            "2002 49.964311206281224 50.74875207986689\n",
            "2003 49.964311206281224 50.74875207986689\n",
            "2004 49.964311206281224 50.74875207986689\n",
            "2005 49.964311206281224 50.74875207986689\n",
            "2006 49.964311206281224 50.74875207986689\n",
            "2007 49.964311206281224 50.74875207986689\n",
            "2008 49.964311206281224 50.74875207986689\n",
            "2009 49.964311206281224 50.74875207986689\n",
            "2010 49.964311206281224 50.74875207986689\n",
            "2011 49.964311206281224 50.74875207986689\n",
            "2012 49.964311206281224 50.74875207986689\n",
            "2013 49.964311206281224 50.74875207986689\n",
            "2014 49.964311206281224 50.74875207986689\n",
            "2015 49.964311206281224 50.74875207986689\n",
            "2016 49.964311206281224 50.74875207986689\n",
            "2017 49.964311206281224 50.74875207986689\n",
            "2018 49.964311206281224 50.74875207986689\n",
            "2019 49.964311206281224 50.74875207986689\n",
            "2020 49.964311206281224 50.74875207986689\n",
            "2021 49.964311206281224 50.74875207986689\n",
            "2022 49.964311206281224 50.74875207986689\n",
            "2023 49.964311206281224 50.74875207986689\n",
            "2024 49.964311206281224 50.74875207986689\n",
            "2025 49.964311206281224 50.74875207986689\n",
            "2026 49.964311206281224 50.74875207986689\n",
            "2027 49.964311206281224 50.74875207986689\n",
            "2028 49.964311206281224 50.74875207986689\n",
            "2029 49.964311206281224 50.74875207986689\n",
            "2030 49.964311206281224 50.74875207986689\n",
            "2031 49.964311206281224 50.74875207986689\n",
            "2032 49.964311206281224 50.74875207986689\n",
            "2033 49.964311206281224 50.74875207986689\n",
            "2034 49.964311206281224 50.74875207986689\n",
            "2035 49.964311206281224 50.74875207986689\n",
            "2036 49.964311206281224 50.74875207986689\n",
            "2037 49.964311206281224 50.74875207986689\n",
            "2038 49.964311206281224 50.74875207986689\n",
            "2039 49.964311206281224 50.74875207986689\n",
            "2040 49.964311206281224 50.74875207986689\n",
            "2041 49.964311206281224 50.74875207986689\n",
            "2042 49.964311206281224 50.74875207986689\n",
            "2043 49.964311206281224 50.74875207986689\n",
            "2044 49.964311206281224 50.74875207986689\n",
            "2045 49.964311206281224 50.74875207986689\n",
            "2046 49.964311206281224 50.74875207986689\n",
            "2047 49.964311206281224 50.74875207986689\n",
            "2048 49.964311206281224 50.74875207986689\n",
            "2049 49.964311206281224 50.74875207986689\n",
            "2050 49.964311206281224 50.74875207986689\n",
            "2051 49.964311206281224 50.74875207986689\n",
            "2052 49.964311206281224 50.74875207986689\n",
            "2053 49.964311206281224 50.74875207986689\n",
            "2054 49.964311206281224 50.74875207986689\n",
            "2055 49.964311206281224 50.74875207986689\n",
            "2056 49.964311206281224 50.74875207986689\n",
            "2057 49.964311206281224 50.74875207986689\n",
            "2058 49.964311206281224 50.74875207986689\n",
            "2059 49.964311206281224 50.74875207986689\n",
            "2060 49.964311206281224 50.74875207986689\n",
            "2061 49.964311206281224 50.74875207986689\n",
            "2062 49.964311206281224 50.74875207986689\n",
            "2063 49.964311206281224 50.74875207986689\n",
            "2064 49.964311206281224 50.74875207986689\n",
            "2065 49.964311206281224 50.74875207986689\n",
            "2066 49.964311206281224 50.74875207986689\n",
            "2067 49.964311206281224 50.74875207986689\n",
            "2068 49.964311206281224 50.74875207986689\n",
            "2069 49.964311206281224 50.74875207986689\n",
            "2070 49.964311206281224 50.74875207986689\n",
            "2071 49.964311206281224 50.74875207986689\n",
            "2072 49.964311206281224 50.74875207986689\n",
            "2073 49.964311206281224 50.74875207986689\n",
            "2074 49.964311206281224 50.74875207986689\n",
            "2075 49.964311206281224 50.74875207986689\n",
            "2076 49.964311206281224 50.74875207986689\n",
            "2077 49.964311206281224 50.74875207986689\n",
            "2078 49.964311206281224 50.74875207986689\n",
            "2079 49.964311206281224 50.74875207986689\n",
            "2080 49.964311206281224 50.74875207986689\n",
            "2081 49.964311206281224 50.74875207986689\n",
            "2082 49.964311206281224 50.74875207986689\n",
            "2083 49.964311206281224 50.74875207986689\n",
            "2084 49.964311206281224 50.74875207986689\n",
            "2085 49.964311206281224 50.74875207986689\n",
            "2086 49.964311206281224 50.74875207986689\n",
            "2087 49.964311206281224 50.74875207986689\n",
            "2088 49.964311206281224 50.74875207986689\n",
            "2089 49.964311206281224 50.74875207986689\n",
            "2090 49.964311206281224 50.74875207986689\n",
            "2091 49.964311206281224 50.74875207986689\n",
            "2092 49.964311206281224 50.74875207986689\n",
            "2093 49.964311206281224 50.74875207986689\n",
            "2094 49.964311206281224 50.74875207986689\n",
            "2095 49.964311206281224 50.74875207986689\n",
            "2096 49.964311206281224 50.74875207986689\n",
            "2097 49.964311206281224 50.74875207986689\n",
            "2098 49.964311206281224 50.74875207986689\n",
            "2099 49.964311206281224 50.74875207986689\n",
            "2100 49.964311206281224 50.74875207986689\n",
            "2101 49.964311206281224 50.74875207986689\n",
            "2102 49.964311206281224 50.74875207986689\n",
            "2103 49.964311206281224 50.74875207986689\n",
            "2104 49.964311206281224 50.74875207986689\n",
            "2105 49.964311206281224 50.74875207986689\n",
            "2106 49.964311206281224 50.74875207986689\n",
            "2107 49.964311206281224 50.74875207986689\n",
            "2108 49.964311206281224 50.74875207986689\n",
            "2109 49.964311206281224 50.74875207986689\n",
            "2110 49.964311206281224 50.74875207986689\n",
            "2111 49.964311206281224 50.74875207986689\n",
            "2112 49.964311206281224 50.74875207986689\n",
            "2113 49.964311206281224 50.74875207986689\n",
            "2114 49.964311206281224 50.74875207986689\n",
            "2115 49.964311206281224 50.74875207986689\n",
            "2116 49.964311206281224 50.74875207986689\n",
            "2117 49.964311206281224 50.74875207986689\n",
            "2118 49.964311206281224 50.74875207986689\n",
            "2119 49.964311206281224 50.74875207986689\n",
            "2120 49.964311206281224 50.74875207986689\n",
            "2121 49.964311206281224 50.74875207986689\n",
            "2122 49.964311206281224 50.74875207986689\n",
            "2123 49.964311206281224 50.74875207986689\n",
            "2124 49.964311206281224 50.74875207986689\n",
            "2125 49.964311206281224 50.74875207986689\n",
            "2126 49.964311206281224 50.74875207986689\n",
            "2127 49.964311206281224 50.74875207986689\n",
            "2128 49.964311206281224 50.74875207986689\n",
            "2129 49.964311206281224 50.74875207986689\n",
            "2130 49.964311206281224 50.74875207986689\n",
            "2131 49.964311206281224 50.74875207986689\n",
            "2132 49.964311206281224 50.74875207986689\n",
            "2133 49.964311206281224 50.74875207986689\n",
            "2134 49.964311206281224 50.74875207986689\n",
            "2135 49.964311206281224 50.74875207986689\n",
            "2136 49.964311206281224 50.74875207986689\n",
            "2137 49.964311206281224 50.74875207986689\n",
            "2138 49.964311206281224 50.74875207986689\n",
            "2139 49.964311206281224 50.74875207986689\n",
            "2140 49.964311206281224 50.74875207986689\n",
            "2141 49.964311206281224 50.74875207986689\n",
            "2142 49.964311206281224 50.74875207986689\n",
            "2143 49.964311206281224 50.74875207986689\n",
            "2144 49.964311206281224 50.74875207986689\n",
            "2145 49.964311206281224 50.74875207986689\n",
            "2146 49.964311206281224 50.74875207986689\n",
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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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "c6b016b8-6423-43c6-98d7-d49ceb1f8018"
      },
      "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": 28,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "5eed6470-ebcf-4c8e-b7e4-e1fb743801ca"
      },
      "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": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KcuHsxgIAgMWz1hc03je9z/VvJtlTVQ8lOTS7sQAAYPGs9QWNL53u/uuqemeSpya5fmZTAQDAAlrrmevP6e7fn8UgAACw6NZ6zTUAAHAM4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADCIuAYAgEHENQAADCKuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOJ6vS69NKlanK8nPznZvXve/9QAADYlcb0el16a3HbbvKc4Pp/6VPLqVwtsAIAZENfr8d73znuCE3fNNfOeAABg0xHX6/GsZ817ghP3spfNewIAgE1HXK/Hrbcml1wy7ymOzxlnJG98Y7Jr17wnAQDYdLbMe4CFd+ut854AAIANwplrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADDITOO6qg5W1Z1VdUdV7ZvWPa2q9lTVPdPtOatsd3FV7a2qD1bV+6vqm2c5JwAAjHAyzlw/v7sv7u6lafmqJDd19wVJbpqWj/QXSb6tu788yYuS/HRVnX0SZgUAgBM2j8tCXpzk6un+1UlecuQTuvt/dPc90/0/SfJgknNP2oQAAHACZh3XneTGqtpfVbumdVu7+4Hp/keSbH28HVTVJUmemOR/rvLYrqraV1X7Pvaxj42cGwAAjtuWGe//ud19f1Wdl2RPVX1o5YPd3VXVR9u4qp6R5FeSXNndjx75eHfvTrI7SZaWlo66HwAAOBlmeua6u++fbh9Mcm2SS5J8dIrmw/H84GrbVtUXJPnvSf5Vd79nlnMCAMAIM4vrqnpKVZ11+H6SK5J8IMl1Sa6cnnZlknessu0Tsxzjb+rut81qRgAAGGmWZ663JvnDqvqjJLcl+e/dfX2SNyT52qq6J8nXTMupqqWq+oVp25cn+TtJvn16G787quriGc4KAADrVt2b41LlpaWl3rdv37zHAABgk6uq/SveZvoxfEIjAAAMIq43gd27kzPOSKqS009PvvVb5z0RAMCpadZvxceM7d6dvPrVf7X86KPJm9+8fP9Xf3U+MwEAnKqcuV5w11yz+vrf+Z2TOwcAAOJ64b3sZauv/7qvO7lzAAAgrhferl3JG9+YPOlJy8unnZa84hUuCQEAmAfXXG8Cu3YtfwEAMF/OXAMAwCDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADCIuAYAgEHENQAADCKuN4EdO5LTT0++4AuSqsd+bd8+7+kAAE4d4nrB7diR3HVX8uijySc/+fmPHzoksAEAThZxveDuvvvYz7n33tnPAQCAuF54F1547Ods2zb7OQAAENcL78CB5KKLktNOS8466/MfP//85ODBkz4WAMApacu8B2D9DhyY9wQAACTOXAMAwDDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGEdcAADCIuAYAgEHENQAADCKuAQBgEHG9Ae3dm5xzTlK19q/t29e/jyO/zjlneT8AAKyNuN5g9u5NLrssefjh49vu0KG/CuwT3ceRHn44ec5zBDYAwFqJ6w3m5ptPfNt7713/Po7UPXZ/AACbmbjeYC6//MS33bZt/fs4UtXY/QEAbGbieoPZuTO55Zbk7LOPb7vzz08OHlzfPo509tnJu9+9vD8AAI5ty7wH4PPt3Jk89ND89wEAwPFx5hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIPMNK6r6mBV3VlVd1TVvmnd06pqT1XdM92ec5Rtr5yec09VXTnLOQEAYISTceb6+d19cXcvTctXJbmpuy9IctO0/BhV9bQkr09yaZJLkrz+aBEOAAAbxTwuC3lxkqun+1cneckqz3lhkj3d/fHufijJniQvOknzAQDACZl1XHeSG6tqf1XtmtZt7e4HpvsfSbJ1le2+KMkfr1i+b1oHAAAb1qw//vy53X1/VZ2XZE9VfWjlg93dVdUnuvMp2HclybZt29Y3KQAArNNMz1x39/3T7YNJrs3y9dMfrapnJMl0++Aqm96f5ItXLD9zWnfk/nd391J3L5177rmjxwcAgOMys7iuqqdU1VmH7ye5IskHklyX5PC7f1yZ5B2rbH5Dkiuq6pzphYxXTOsAAGDDmuVlIVuTXFtVh7/PW7r7+qq6Pclbq+qVSQ4leXmSVNVSktd096u6++NV9aNJbp/29SPd/fEZzgoAAOtW3Sd8yfOGsrS01Pv27Zv3GAAAbHJVtX/F20w/hk9oBACAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAILP++HMex+7dyfd+b/LpT6/++EUXJQcOnNyZDtuxI7nrrqM/vmVL8v3fn/zYj619n3v3JldckTzyyPrnAwA488zkxhuTnTvnPclfceZ6TnbvTl796qOHdbIctzt2nLyZDjtWWCfJZz6T/PiPJ6973dr2uXdvctllwhoAGOeRR5b7Yu/eeU/yV8T1nFxzzdqed/fds51jvd/z7W9f2/NuvvmERgEAOKaN1Bniek5e9rK1Pe/CC2c7x3q/5zd909qed/nlJzQKAMAxbaTOENdzsmtX8sY3Jk960tGfM69rrg8cWP7ej2fLluQHf3Dt11zv3JnccsvytVEAACOceeZyX2yka66ru+c9wxBLS0u9b9++eY8BAMAmV1X7u3tptcecuQYAgEHENQAADCKuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOJ6nS69NKla39ell877pzg+L3zh+n7eHTvm/RMAAMyGuF6HSy9Nbrtt/fu57bbFCewXvjC58cb17eOuuwQ2ALA5iet1eO97N+a+Zuld7xqzn7vvHrMfAICNRFyvw7OetTH3NUvPe96Y/Vx44Zj9AABsJOJ6HW69NbnkkvXv55JLlve1CG64IbniivXt46KLkgMHxswDALCRbJn3AItuUaJ4pBtumPcEAAAbkzPXAAAwiLgGAIBBxDUAAAwirgEAYBBxDQAAg4hrAAAYRFwDAMAg4hoAAAYR1wAAMIi4BgCAQcQ1AAAMIq4BAGAQcQ0AAIOIawAAGERcAwDAIOIaAAAGqe6e9wxDVNXHkhya07d/epI/ndP35uRxnDc/x/jU4DifGhznU8O8jvP53X3uag9smriep6ra191L856D2XKcNz/H+NTgOJ8aHOdTw0Y8zi4LAQCAQcQ1AAAMIq7H2D3vATgpHOfNzzE+NTjOpwbH+dSw4Y6za64BAGAQZ64BAGAQcb0OVfWiqrq7qj5cVVfNex6OT1X9UlU9WFUfWLHuaVW1p6rumW7PmdZXVf3sdKzfX1XPWrHNldPz76mqK+fxs3B0VfXFVfXOqjpQVR+sqtdO6x3rTaKqzqiq26rqj6Zj/G+m9V9SVbdOx/I3quqJ0/onTcsfnh7fvmJfPzStv7uqXjifn4jHU1WnV9X7quq3pmXHeZOpqoNVdWdV3VFV+6Z1C/M7W1yfoKo6Pcl/SvJ1SXYk+UdVtWO+U3Gc/kuSFx2x7qokN3X3BUlumpaT5eN8wfS1K8nPJ8v/sid5fZJLk1yS5PWH/4Vnw/hMkn/Z3TuSPDvJd0//rjrWm8enk7ygu78iycVJXlRVz07yY0l+qru/NMlDSV45Pf+VSR6a1v/U9LxMfy6+JcmXZ/l3w89Nv+vZWF6b5K4Vy47z5vT87r54xdvsLczvbHF94i5J8uHu/l/d/ZdJfj3Ji+c8E8ehu/8gycePWP3iJFdP969O8pIV69/Uy96T5OyqekaSFybZ090f7+6HkuzJ5wc7c9TdD3T3e6f7n8zyf5S/KI71pjEdq0emxSdMX53kBUneNq0/8hgfPvZvS/L3qqqm9b/e3Z/u7v+d5MNZ/l3PBlFVz0zyDUl+YVquOM6nioX5nS2uT9wXJfnjFcv3TetYbFu7+4Hp/keSbJ3uH+14+3OwQKa/Fv7KJLfGsd5UpksF7kjyYJb/I/o/kzzc3Z+ZnrLyeH3uWE6PfyLJF8YxXgQ/neQHkzw6LX9hHOfNqJPcWFX7q2rXtG5hfmdvORnfBBZRd3dVeTudTaKqzkxyTZJ/0d3/Z/kE1jLHevF192eTXFxVZye5NsmXzXkkBquqb0zyYHfvr6rL5z0PM/Xc7r6/qs5LsqeqPrTywY3+O9uZ6xN3f5IvXrH8zGkdi+2j018nZbp9cFp/tOPtz8ECqKonZDms39zdb59WO9abUHc/nOSdSXZm+a+HD59EWnm8Pncsp8efmuTP4hhvdM9J8g+q6mCWL8V8QZKfieO86XT3/dPtg1n+n+VLskC/s8X1ibs9yQXTq5SfmOUXR1w355lYv+uSHH5F8ZVJ3rFi/bdNr0p+dpJPTH89dUOSK6rqnOmFEldM69ggpmssfzHJXd39kysecqw3iao6dzpjnap6cpKvzfK19e9M8g+npx15jA8f+3+Y5Pd6+UMfrkvyLdO7THxJll8gddvJ+Sk4lu7+oe5+Zndvz/J/c3+vu18Rx3lTqaqnVNVZh+9n+XftB7JAv7NdFnKCuvszVfU9WT5Qpyf5pe7+4JzH4jhU1a8luTzJ06vqviy/qvgNSd5aVa9McijJy6en/3aSr8/yC1/+Isl3JEl3f7yqfjTL/7OVJD/S3Ue+SJL5ek6Sf5Lkzuma3CT54TjWm8kzklw9vePDaUne2t2/VVUHkvx6Vf3bJO/L8v9kZbr9lar6cJZf1PwtSdLdH6yqtyY5kOV3mfnu6XITNrbXxXHeTLYmuXa6dG9Lkrd09/VVdXsW5He2T2gEAIBBXBYCAACDiGsAABhEXAMAwCDiGgAABhHXAAAwiLgG2OCq6pHpdntV/ePB+/7hI5ZvGbl/gFONuAZYHNuTHFdcr/jkuqN5TFx392XHORMAK4hrgMXxhiTPq6o7qur7qur0qvqJqrq9qt5fVa9Okqq6vKreVVXXZfmDMlJVv1lV+6vqg1W1a1r3hiRPnvb35mnd4bPkNe37A1V1Z1V984p931xVb6uqD1XVm6dPwUxVvaGqDkyz/PuT/k8HYAPwCY0Ai+OqJD/Q3d+YJFMkf6K7v7qqnpTk3VV14/TcZyX5W939v6flfzp9YtmTk9xeVdd091VV9T3dffEq3+ubklyc5CuSPH3a5g+mx74yyZcn+ZMk707ynKq6K8lLk3xZd/fhjyMHONU4cw2wuK5I8m3Tx7rfmuQLk1wwPXbbirBOku+tqj9K8p4kX7zieUfz3CS/1t2f7e6PJvn9JF+9Yt/3dfejSe7I8uUqn0jyqSS/WFXflOWPIQY45YhrgMVVSf55d188fX1Jdx8+c/3nn3tS1eVJvibJzu7+iiTvS3LGOr7vp1fc/2ySLd39mSSXJHlbkm9Mcv069g+wsMQ1wOL4ZJKzVizfkOQ7q+oJSVJVf7OqnrLKdk9N8lB3/0VVfVmSZ6947P8d3v4I70ryzdN13ecm+TtJbjvaYFV1ZpKndvdvJ/m+LF9OAnDKcc01wOJ4f5LPTpd3/JckP5PlSzLeO72o8GNJXrLKdtcnec10XfTdWb405LDdSd5fVe/t7lesWH9tkp1J/ihJJ/nB7v7IFOerOSvJO6rqjCyfUf/+E/sRARZbdfe8ZwAAgE3BZSEAADCIuAYAgEHENQAADCKuAQBgEHENAACDiGsAABhEXAMAwCDiGgAABvn/EKKJwzGeWBkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbg3MT81r0Kk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "4e9710e3-284f-48bc-8a63-b4206df69612"
      },
      "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": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[695   4]\n",
            " [697   5]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.99      0.66       699\n",
            "           1       0.56      0.01      0.01       702\n",
            "\n",
            "    accuracy                           0.50      1401\n",
            "   macro avg       0.53      0.50      0.34      1401\n",
            "weighted avg       0.53      0.50      0.34      1401\n",
            "\n",
            "0.49964311206281226\n",
            "[[302   0]\n",
            " [295   4]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.51      1.00      0.67       302\n",
            "           1       1.00      0.01      0.03       299\n",
            "\n",
            "    accuracy                           0.51       601\n",
            "   macro avg       0.75      0.51      0.35       601\n",
            "weighted avg       0.75      0.51      0.35       601\n",
            "\n",
            "0.5091514143094842\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xN8u62m6r0qk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "08150401-9847-4409-c479-4f151d3a1540"
      },
      "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": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UueZmZy1r4ad",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "fbe46741-f07e-4931-a3e2-24e6fa2b30e5"
      },
      "source": [
        "print(y_train)\n",
        "print(l_list_tmp)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 1 ... 1 0 1]\n",
            "[0. 0. 0. ... 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}