1_Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"color:navy\">1. Logistic Regression</h1>\n",
    "\n",
    "**Lojistik Regresyon**, en basit yapay sinir ağı sayılabilir. Bir sinir hücresine benzetilerek oluşturulmuştur. Lojistik regresyon; girdiler (**inputs**), değişkenler (**x**) ve değişkenlerin ağırlıklarından (**w**) oluşur. Ayrıca girdiler arasında bir bias terimi (**b**) de bulunur. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/zkcplk/Deep-Learning/main/images/1_logistic_regression.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temel amaç veri setini temsil edecek en doğru $w$ ve $b$ katsayılarını tespit etmektir. Bu amaçla forward ve backward propagation işlemleri yapılır. Forward propagation (ileri yayılım) işlemi, $w$ ve $b$ başlangıç değerleri ile  \n",
    "\n",
    "\n",
    "$$z = w_1x_1 + w_2x_2 + ... + b \\tag{1}$$ \n",
    "\n",
    "    \n",
    "formunda bir **z** fonksiyonu oluşturularak başlar. Daha sonra bu **z** fonksiyonu, aktivasyon fonksiyonu (**g**) içerisine parametre olarak verilerek (**a = g(z)**) bir a değeri bulunur. (a = $\\hat{y}$) Lojistik regresyonda aktivasyon fonksiyonu olarak **sigmoid** fonksiyonu kullanılır.  Bulunan a değeri, bilinen y değeri (veri setindeki gerçek değer) ile karşılaştırılarak **Loss** hata değeri hesaplanır. Bu şekilde veri setindeki ilk veri için forward propagation tamamlanır.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/zkcplk/Deep-Learning/main/images/2_sigmoid_function.png\" width=\"600px\">\n",
    "\n",
    "Aşağıdaki resimde veri setimizdeki bir veri için işlemler gösterilmiştir.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/zkcplk/Deep-Learning/main/images/3_lojistik_regresyon_turevleri.png\">\n",
    "\n",
    "Ardından bulunan Loss değeri ile birlikte, yapılan tüm işlemler geriye doğru **zincirleme türev alınarak** tekrarlanır. Buna da backward propagation (geri yayılım) denir. Türevlerden çıkan değerler ve $\\alpha$ (**Learning rate**) parametresi kullanılarak, yeni w ve b değerleri hesaplanmış olur. Buraya kadar yapılan tüm işlemler (forward ve backward propagation), veri setindeki her veri için tekrarlanır. Daha sonra elde edilen Loss değerlerindan faydalanılarak, Cost değeri hesaplanır. Tüm veriler için bu işlemler bir kez yapıldığında bir **Epoch** (dönem) tamamlanmış olur. Çok fazla epoch denenerek, Cost değerinin minimum olması hedeflenir. Belirli bir andan sonra, Cost değeri çok az bir miktarda azalmaya başlar. Bu süreyi uzatmadan, işlem, mümkün olan en uygun zamanda sonlandırılarak, optimum w ve b değerleri hesaplanmış olur. \n",
    "\n",
    "Kısaca tüm durumu anlattıktan sonra, durumun ayrıntılarına değinelim:\n",
    "**z** fonksiyonu, matris formunda aşağıdaki gibi yazılabilir. \n",
    "\n",
    "$$z^{(i)} = w^T x^{(i)} + b \\tag{2}$$ \n",
    "\n",
    "$x^{(i)}$ değeri, veri setindeki i. verinin x değerlerinin (yani tüm değişkenlerinin) matrisidir. $w^T$ ağırlıklar matirisidir. Matris çarpımının yapılabilmesi için transpozu alınmıştır. $z^{(i)}$ ise, i. veri için hesaplanan z değeridir. Bu vektörel yazım önemlidir. Çünkü kod kısmında döngü kullanmadan, işlemleri hızlı ve pratik olarak gerçekleştirebilmemizi sağlayacaktır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veri setindeki her bir veri (i) için **z**, **a** ve **Loss** değeri aşağıdaki gibi hesaplanır :\n",
    "\n",
    "$$z^{(i)} = w^T x^{(i)} + b \\tag{2}$$\n",
    "    \n",
    "$$\\hat{y}^{(i)} = a^{(i)} = sigmoid(z^{(i)}) \\tag{3}$$ \n",
    "    \n",
    "$$ \\mathcal{L}(a^{(i)}, y^{(i)}) =  - y^{(i)}  \\log(a^{(i)}) - (1-y^{(i)} )  \\log(1-a^{(i)}) \\tag{4}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Daha sonra Loss değerlerin toplamı ve veri miktarına (m) bölünmesi ile **Cost** değeri aşağıdaki formülle hesaplanır:\n",
    "\n",
    "$$ J (w,b) = - \\frac{1}{m} \\sum_{i=1}^m \\mathcal{L}(a^{(i)}, y^{(i)}) \\tag{5}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bulunacak her bir $a^{(i)}$ değeri de büyük bir A matrisi ile temsil edilebilir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "$$A = \\sigma(w^T X + b) = (a^{(1)}, a^{(2)}, ..., a^{(m-1)}, a^{(m)}) \\tag{6}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Backward propagation'da kullanılacak olan türev formülleri de aşağıdaki gibidir. Bu formüllerin üretilmesindeki ayrıntı,  konumuzun dışındadır. İstenirse, calculus üzerinden zincir türev kuralı incelebilir. Hesaplamalarda sürekli aynı formül kullanılacağı için her seferinde formül ispatı yapmak anlamsızdır. Aşağıdaki formüller, kodlamalarımızda direkt olarak kullanacağımız şekilde verilmiştir. (5)'te verilen **J** ifadesinin $w$ ve $b$'ye göre ayrı ayrı türevleri alınmıştır.\n",
    "\n",
    "<br>\n",
    "$$ dW = \\frac{\\partial J}{\\partial w} = \\frac{1}{m}X(A-Y)^T \\tag{7}$$\n",
    "<br>\n",
    "$$ db = dZ = \\frac{\\partial J}{\\partial b} = \\frac{1}{m} \\sum_{i=1}^m (a^{(i)}-y^{(i)}) \\tag{8}$$\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lojistik Regresyonun teorisine kısaca değindikten sonra, kodlamalarda kullanacağımız fonksiyonları oluşturalım. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy & pandas.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#====================\n",
    "# sigmoid fonksiyonu.\n",
    "def sigmoid(z):\n",
    "    return 1/(1+np.exp(-z))\n",
    "\n",
    "#====================\n",
    "# forward ve backward işlemlerini ortak olarak yapacağımız fonksiyon.\n",
    "# vektörel gösterim kullanıldığı için, döngü kullanmaya gerek kalmamıştır.\n",
    "def propagate(w, b, X, Y):\n",
    "    \"\"\"\n",
    "        Matris hesaplamalarında hata oluşmaması için matris boyutlarına dikkat edilmelidir!\n",
    "\n",
    "        w --> w.shape = (değişken sayısı, 1)\n",
    "        b --> skaler bir değerdir.\n",
    "        X --> X.shape = (değişken sayısı, veri miktarı)\n",
    "        Y --> Y.shape = (1, veri miktarı)\n",
    "        \n",
    "        dw --> w ile aynı formdadır.\n",
    "        db --> b ile aynı formdadır.\n",
    "    \"\"\"\n",
    "    \n",
    "    # veri miktarı.\n",
    "    m = X.shape[1]\n",
    "    \n",
    "    #---------------------\n",
    "    # FORWARD PROP.\n",
    "    #---------------------\n",
    "    # (6) numaralı formül.\n",
    "    A = sigmoid(np.dot(w.T,X)+b)\n",
    "\n",
    "    # (5) numaralı formül\n",
    "    cost = -np.sum((Y*np.log(A)+(1-Y)*np.log(1-A)))/m  \n",
    "    \n",
    "    #---------------------\n",
    "    # BACKWARD PROP.\n",
    "    #---------------------\n",
    "    # (7) numaralı formül.\n",
    "    dw = np.dot(X,(A-Y).T)/m\n",
    "    \n",
    "    # (8) numaralı formül.\n",
    "    db = np.sum(A-Y)/m\n",
    "    \n",
    "    # Sonuç\n",
    "    grads = {\"dw\": dw, \"db\": db}\n",
    "    cost = np.squeeze(cost)\n",
    "    \n",
    "    return grads, cost\n",
    "\n",
    "#====================\n",
    "# tüm veri için w ve b değerlerinin hesaplanacağı fonksiyon.\n",
    "# veriyi kaç dönem işleyeceğimizi epoch parametresi ile belirtiyoruz.\n",
    "def optimize(w, b, X, Y, epoch, learning_rate, print_cost = False):\n",
    "    costs = []\n",
    "    \n",
    "    for i in range(epoch):\n",
    "        # forward & backward.\n",
    "        grads, cost = propagate(w, b, X, Y)\n",
    "        \n",
    "        dw = grads[\"dw\"]\n",
    "        db = grads[\"db\"]\n",
    "        \n",
    "        # 1 epoch sonunda elde edilen dw ve db değeri, başlangıç w ve b değerlerinden çıkarılıyor.\n",
    "        # başlangıçta verilen w ve b değerlerinin hatası, yukarıdaki cost değişkeninde hesaplandı.\n",
    "        # böylece yeni w ve b değerleri elde ediliyor.\n",
    "        w = w - learning_rate * dw\n",
    "        b = b - learning_rate * db\n",
    "        \n",
    "        # 100 dönemde bir cost kaydediliyor, böylece ne kadar azaldığını grafikten izleyebileceğiz.\n",
    "        if i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "        \n",
    "        # print_cost parametresi True olursa, \n",
    "        # her 100 dönemde bir bulunan cost'un değeri ekrana bastırılır.\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print (\"Epoch: %i , Cost: %f\" %(i, cost))\n",
    "    \n",
    "    # en sonda tespit edilen w ve b değerleri.\n",
    "    params = {\"w\": w,\n",
    "              \"b\": b}\n",
    "    \n",
    "    # en son türev değerleri.\n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    # sonuç\n",
    "    return params, grads, costs\n",
    "\n",
    "#====================\n",
    "# tespit edilen w ve b değerlerini kullanarak tahminde bulunma fonksiyonu.\n",
    "def predict(w, b, X):\n",
    "    # tahmin edilecek y değerleri (y şapka).\n",
    "    Y_hat = np.zeros((1,X.shape[1]))\n",
    "    \n",
    "    # tespit edilen w değerleri bu fonksiyonun parametresine farklı matris formlarında verilirse diye,\n",
    "    # tek sütun çok satır olacak şekilde reshape işlemine alınıyor.\n",
    "    # böylece hataların önüne geçilmiş olunuyor.\n",
    "    w = w.reshape(X.shape[0], 1)\n",
    "    \n",
    "    # (6) numaralı formül.\n",
    "    A = sigmoid(np.dot(w.T,X)+b)\n",
    "    \n",
    "    # A matrisi kaç adet veriyi içeriyorsa, o kadar veri için tahmin yapılır.\n",
    "    for i in range(A.shape[1]):\n",
    "        if (A[0,i] > 0.5): \n",
    "            Y_hat[0,i] = 1.\n",
    "        else:\n",
    "            Y_hat[0,i] = 0.\n",
    "    \n",
    "    return Y_hat\n",
    "\n",
    "#====================\n",
    "# yukarıda oluşturulan tüm fonksiyonları, model fonksiyonunda bir araya getiriyoruz.\n",
    "def model(X_train, Y_train, X_test, Y_test, epoch = 2000, learning_rate = 0.5, print_cost = False):\n",
    "    # print(\"X_train shape:\", X_train.shape)\n",
    "    # print(\"Y_train shape:\", Y_train.shape)\n",
    "        \n",
    "    # başlangıç değerleri.\n",
    "    # losjistik regresyonda başlangıç değerleri sıfır alınabilir.\n",
    "    w, b = np.zeros((X_train.shape[0],1)), 0\n",
    "    \n",
    "    parameters, grads, costs = optimize(w, b, X_train, Y_train, epoch, learning_rate, print_cost)\n",
    "    \n",
    "    w = parameters[\"w\"]\n",
    "    b = parameters[\"b\"]\n",
    "    \n",
    "    # train ve test veri setleri ile tüm tahminler hesaplanıyor.\n",
    "    Y_hat_test = predict(w, b, X_test)\n",
    "    Y_hat_train = predict(w, b, X_train)\n",
    "\n",
    "    # absolute mean error - mutlak hata ortalaması.\n",
    "    # yapılan tahminler ile gerçek veriler karşılaştırılıp, doğruluk oranı hesaplanıyor.\n",
    "    print(\"Train Accuracy: {} %\".format(100 - np.mean(np.abs(Y_hat_train - Y_train)) * 100))\n",
    "    print(\"Test Accuracy: {} %\".format(100 - np.mean(np.abs(Y_hat_test - Y_test)) * 100))\n",
    "\n",
    "    # bulunan tüm sonuçlar bir sözlükte toplanıyor.\n",
    "    d = {\"costs\": costs,\n",
    "         \"Y_hat_test\": Y_hat_test, \n",
    "         \"Y_hat_train\" : Y_hat_train, \n",
    "         \"w\" : w, \n",
    "         \"b\" : b,\n",
    "         \"learning_rate\" : learning_rate,\n",
    "         \"epoch\": epoch}\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi kullanabileceğimiz fonksiyonlar hazır olduğuna göre, örnek bir veri seti için lojistik regresyonu gerçekleştirebiliriz. Aşağıdaki örneklerde amaç, sadece lojistik regresyonun nasıl çalıştığını ve yukarıda anlatılan formüllerin kullanımını göstermek olduğundan; verilerin bölünmesi, veri analizi ve veri setinin yorumlanması ve benzeri *ayrıntılara girilmemiştir*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ÖRNEK 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu örneğimizde, bir telekomünikasyon firmasının verilerini kullanarak, bu firmanın müşterilerinin başka bir firmaya gidip gitmeyeceğini (müşteri kaybı) tahmin etme amaçlı bir model oluşturacağız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 28)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>employ</th>\n",
       "      <th>equip</th>\n",
       "      <th>callcard</th>\n",
       "      <th>wireless</th>\n",
       "      <th>longmon</th>\n",
       "      <th>...</th>\n",
       "      <th>pager</th>\n",
       "      <th>internet</th>\n",
       "      <th>callwait</th>\n",
       "      <th>confer</th>\n",
       "      <th>ebill</th>\n",
       "      <th>loglong</th>\n",
       "      <th>logtoll</th>\n",
       "      <th>lninc</th>\n",
       "      <th>custcat</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.482</td>\n",
       "      <td>3.033</td>\n",
       "      <td>4.913</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.246</td>\n",
       "      <td>3.240</td>\n",
       "      <td>3.497</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.841</td>\n",
       "      <td>3.240</td>\n",
       "      <td>3.401</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.800</td>\n",
       "      <td>3.807</td>\n",
       "      <td>4.331</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.960</td>\n",
       "      <td>3.091</td>\n",
       "      <td>4.382</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure   age  address  income   ed  employ  equip  callcard  wireless  \\\n",
       "0    11.0  33.0      7.0   136.0  5.0     5.0    0.0       1.0       1.0   \n",
       "1    33.0  33.0     12.0    33.0  2.0     0.0    0.0       0.0       0.0   \n",
       "2    23.0  30.0      9.0    30.0  1.0     2.0    0.0       0.0       0.0   \n",
       "3    38.0  35.0      5.0    76.0  2.0    10.0    1.0       1.0       1.0   \n",
       "4     7.0  35.0     14.0    80.0  2.0    15.0    0.0       1.0       0.0   \n",
       "\n",
       "   longmon  ...  pager  internet  callwait  confer  ebill  loglong  logtoll  \\\n",
       "0     4.40  ...    1.0       0.0       1.0     1.0    0.0    1.482    3.033   \n",
       "1     9.45  ...    0.0       0.0       0.0     0.0    0.0    2.246    3.240   \n",
       "2     6.30  ...    0.0       0.0       0.0     1.0    0.0    1.841    3.240   \n",
       "3     6.05  ...    1.0       1.0       1.0     1.0    1.0    1.800    3.807   \n",
       "4     7.10  ...    0.0       0.0       1.0     1.0    0.0    1.960    3.091   \n",
       "\n",
       "   lninc  custcat  churn  \n",
       "0  4.913      4.0    1.0  \n",
       "1  3.497      1.0    1.0  \n",
       "2  3.401      3.0    0.0  \n",
       "3  4.331      4.0    0.0  \n",
       "4  4.382      3.0    0.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data = pd.read_csv(\"datasets/churn_data.csv\")\n",
    "df1 = churn_data.copy()\n",
    "\n",
    "print(df1.shape)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# veri setini train ve test olarak ayırıyoruz.\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = df1.drop(['churn'], axis=1)\n",
    "Y = df1['churn']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.30, random_state=30)\n",
    "\n",
    "# verilerin standardizasyonu.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Propagate fonksiyonundaki ve diğer fonksiyonlardaki matris çarpımlarında, \n",
    "# hata olmaması için transpoz ve reshape düzeltmesi.\n",
    "X_train_scaled = X_train_scaled.T\n",
    "X_test_scaled = X_test_scaled.T\n",
    "\n",
    "Y_train = Y_train.to_numpy().reshape(1,X_train_scaled.shape[1])\n",
    "Y_test = Y_test.to_numpy().reshape(1,X_test_scaled.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 , Cost: 0.693147\n",
      "Epoch: 100 , Cost: 0.511939\n",
      "Epoch: 200 , Cost: 0.466179\n",
      "Epoch: 300 , Cost: 0.440689\n",
      "Epoch: 400 , Cost: 0.424339\n",
      "Epoch: 500 , Cost: 0.412806\n",
      "Epoch: 600 , Cost: 0.404079\n",
      "Epoch: 700 , Cost: 0.397127\n",
      "Epoch: 800 , Cost: 0.391376\n",
      "Epoch: 900 , Cost: 0.386483\n",
      "Epoch: 1000 , Cost: 0.382230\n",
      "Epoch: 1100 , Cost: 0.378475\n",
      "Epoch: 1200 , Cost: 0.375116\n",
      "Epoch: 1300 , Cost: 0.372082\n",
      "Epoch: 1400 , Cost: 0.369319\n",
      "Epoch: 1500 , Cost: 0.366785\n",
      "Epoch: 1600 , Cost: 0.364450\n",
      "Epoch: 1700 , Cost: 0.362286\n",
      "Epoch: 1800 , Cost: 0.360273\n",
      "Epoch: 1900 , Cost: 0.358395\n",
      "Epoch: 2000 , Cost: 0.356636\n",
      "Epoch: 2100 , Cost: 0.354984\n",
      "Epoch: 2200 , Cost: 0.353429\n",
      "Epoch: 2300 , Cost: 0.351961\n",
      "Epoch: 2400 , Cost: 0.350574\n",
      "Train Accuracy: 85.71428571428572 %\n",
      "Test Accuracy: 73.33333333333333 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# modelimizi çalıştırıyoruz...\n",
    "d1 = model(X_train_scaled, Y_train, X_test_scaled, Y_test, epoch = 2500, learning_rate = 0.02, print_cost = True)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(d1['costs'])\n",
    "plt.xlabel('Epoch (x100)')\n",
    "plt.ylabel('Cost')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'costs': [0.6931471805599454,\n",
       "  0.5119392491394066,\n",
       "  0.46617944218510415,\n",
       "  0.44068869994791693,\n",
       "  0.4243394908704487,\n",
       "  0.4128059331716755,\n",
       "  0.40407878992971474,\n",
       "  0.39712693084695794,\n",
       "  0.3913758536898959,\n",
       "  0.386482670740984,\n",
       "  0.3822304705271357,\n",
       "  0.37847502968517505,\n",
       "  0.3751162468713882,\n",
       "  0.3720820136756008,\n",
       "  0.36931868931047207,\n",
       "  0.3667852485538532,\n",
       "  0.36444955732380435,\n",
       "  0.3622859255273105,\n",
       "  0.36027345168562064,\n",
       "  0.3583948728923332,\n",
       "  0.35663574604061515,\n",
       "  0.3549838516689328,\n",
       "  0.35342875090956455,\n",
       "  0.35196145002983975,\n",
       "  0.35057414212366145],\n",
       " 'Y_hat_test': array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
       "         0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1.,\n",
       "         0., 1., 1., 1., 0., 1., 0., 1., 0., 0., 1., 1., 0., 0., 0., 1.,\n",
       "         0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0.]]),\n",
       " 'Y_hat_train': array([[0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 0., 1., 0., 1., 0.,\n",
       "         0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 1., 0., 0., 0., 0., 0.,\n",
       "         1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1.,\n",
       "         0., 0., 1., 0., 1., 1., 0., 1., 0., 0., 0., 0., 0., 1., 0., 1.,\n",
       "         1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
       "         0., 0., 1., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 1., 0., 0.,\n",
       "         0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
       "         0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0.]]),\n",
       " 'w': array([[-0.8176932 ],\n",
       "        [-0.66169496],\n",
       "        [ 0.21444376],\n",
       "        [-0.14568026],\n",
       "        [-0.02938654],\n",
       "        [-0.77640998],\n",
       "        [ 0.42058199],\n",
       "        [-1.09556649],\n",
       "        [-0.28022775],\n",
       "        [ 0.09026921],\n",
       "        [ 0.5055507 ],\n",
       "        [ 0.09963776],\n",
       "        [ 0.40107829],\n",
       "        [ 0.02805093],\n",
       "        [-0.03979804],\n",
       "        [ 0.51929197],\n",
       "        [-0.01262086],\n",
       "        [ 0.35185433],\n",
       "        [-0.14613034],\n",
       "        [ 0.31254164],\n",
       "        [ 0.03228143],\n",
       "        [-0.40759589],\n",
       "        [ 0.339102  ],\n",
       "        [ 0.10648137],\n",
       "        [-0.04577388],\n",
       "        [ 0.25831174],\n",
       "        [ 0.23842348]]),\n",
       " 'b': -1.6241983298495406,\n",
       " 'learning_rate': 0.02,\n",
       " 'epoch': 2500}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modelin elde ettiği değerler.\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Girilen değerlere göre Tahmin:  0.0\n"
     ]
    }
   ],
   "source": [
    "# veri tablosunda bulunan 27 değişkene göre tahmin işlemi.\n",
    "# sonuç sıfır veya bir çıkacak.\n",
    "print(\"Girilen değerlere göre Tahmin: \", np.squeeze(predict(d1[\"w\"],\n",
    "                                                            d1[\"b\"],\n",
    "                                                            np.array([[9., 44., 7., 136., 5., 5., 0., 1., 1., 4.4, 10, 0., \n",
    "                                13, 35.7, 42., 50.45, 125., 1., 1., 0., 1., 1., 0., 1.482, 3.033, 4.913, 5.]]).T)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ÖRNEK 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu örneğimizde de diyabet veri setinden faydalanacağız. <br>\n",
    "8 farklı değişkene göre, kişinin diyabet olup olmadığını tahmin eden bir lojistik regresyon modeli oluşturacağız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 9)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"datasets/diabetes.csv\")\n",
    "df2 = data.copy()\n",
    "\n",
    "print(df2.shape)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 , Cost: 0.693147\n",
      "Epoch: 100 , Cost: 0.657489\n",
      "Epoch: 200 , Cost: 0.629431\n",
      "Epoch: 300 , Cost: 0.607041\n",
      "Epoch: 400 , Cost: 0.588901\n",
      "Epoch: 500 , Cost: 0.573984\n",
      "Epoch: 600 , Cost: 0.561547\n",
      "Epoch: 700 , Cost: 0.551047\n",
      "Epoch: 800 , Cost: 0.542083\n",
      "Epoch: 900 , Cost: 0.534353\n",
      "Epoch: 1000 , Cost: 0.527628\n",
      "Epoch: 1100 , Cost: 0.521732\n",
      "Epoch: 1200 , Cost: 0.516527\n",
      "Epoch: 1300 , Cost: 0.511904\n",
      "Epoch: 1400 , Cost: 0.507774\n",
      "Epoch: 1500 , Cost: 0.504068\n",
      "Epoch: 1600 , Cost: 0.500727\n",
      "Epoch: 1700 , Cost: 0.497704\n",
      "Epoch: 1800 , Cost: 0.494958\n",
      "Epoch: 1900 , Cost: 0.492456\n",
      "Epoch: 2000 , Cost: 0.490169\n",
      "Epoch: 2100 , Cost: 0.488074\n",
      "Epoch: 2200 , Cost: 0.486151\n",
      "Epoch: 2300 , Cost: 0.484379\n",
      "Epoch: 2400 , Cost: 0.482746\n",
      "Epoch: 2500 , Cost: 0.481236\n",
      "Epoch: 2600 , Cost: 0.479838\n",
      "Epoch: 2700 , Cost: 0.478542\n",
      "Epoch: 2800 , Cost: 0.477338\n",
      "Epoch: 2900 , Cost: 0.476218\n",
      "Epoch: 3000 , Cost: 0.475175\n",
      "Epoch: 3100 , Cost: 0.474203\n",
      "Epoch: 3200 , Cost: 0.473295\n",
      "Epoch: 3300 , Cost: 0.472446\n",
      "Epoch: 3400 , Cost: 0.471652\n",
      "Epoch: 3500 , Cost: 0.470908\n",
      "Epoch: 3600 , Cost: 0.470210\n",
      "Epoch: 3700 , Cost: 0.469555\n",
      "Epoch: 3800 , Cost: 0.468940\n",
      "Epoch: 3900 , Cost: 0.468362\n",
      "Train Accuracy: 77.85016286644951 %\n",
      "Test Accuracy: 75.97402597402598 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# üstte yapılan işlemlerin benzeri olduğu için,\n",
    "# ek açıklamalara gerek duymuyoruz.\n",
    "X = df2.drop(['Outcome'], axis=1)\n",
    "Y = df2['Outcome']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=20)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "X_train_scaled = X_train_scaled.T\n",
    "X_test_scaled = X_test_scaled.T\n",
    "\n",
    "Y_train = Y_train.to_numpy().reshape(1,X_train_scaled.shape[1])\n",
    "Y_test = Y_test.to_numpy().reshape(1,X_test_scaled.shape[1])\n",
    "\n",
    "d2 = model(X_train_scaled, Y_train, X_test_scaled, Y_test, epoch = 4000, learning_rate = 0.003, print_cost = True)\n",
    "\n",
    "plt.plot(d2['costs'])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Cost')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ÖRNEK 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu sefer daha farklı bir örnek yapalım. Elimizde bir takım resimler olduğunu ve bu resimlerin hangisinin kedi resmi olduğunu bulmak zorunda olduğumuzu varsayalım. Bunun için yine hazır bir veri setinden faydalanacağız. Fakat yapacağımız bazı işlemler, ÖRNEK 1 ve ÖRNEK 2'den farklı olacaktır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# h5 dosyalarını açmak için h5py kütüphanesini ekliyoruz.\n",
    "import h5py   \n",
    "\n",
    "# veri setini yükleme fonksiyonu.\n",
    "def load_dataset():\n",
    "    train_dataset = h5py.File('datasets/train_catvnoncat.h5', \"r\")\n",
    "    \n",
    "    # train\n",
    "    train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) \n",
    "    train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) \n",
    "\n",
    "    # test\n",
    "    test_dataset = h5py.File('datasets/test_catvnoncat.h5', \"r\")\n",
    "    test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) \n",
    "    test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) \n",
    "\n",
    "    # sınıflar (cat or non-cat)\n",
    "    classes = np.array(test_dataset[\"list_classes\"][:])\n",
    "    \n",
    "    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n",
    "    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n",
    "    \n",
    "    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\n",
    "\n",
    "train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = [0], Bu bir 'non-cat' resmi.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# veri setinde 'cat' veya 'non-cat' olarak etiketlenmiş bir resmi göstermek.\n",
    "import random\n",
    "index = random.randint(0,100)\n",
    "plt.imshow(train_set_x_orig[index]);\n",
    "print (\"y = \" + str(train_set_y[:, index]) + \n",
    "       \", Bu bir '\" + classes[np.squeeze(train_set_y[:, index])].decode(\"utf-8\") +  \n",
    "       \"' resmi.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train veri sayısı: m_train = 209\n",
      "Test veri sayısı: m_test = 50\n",
      "Her bir resmin en/boy değeri: num_px = 64\n",
      "Her bir resmin boyutu: (64, 64, 3)\n",
      "train_set_x boyut: (209, 64, 64, 3)\n",
      "train_set_y boyut: (1, 209)\n",
      "test_set_x boyut: (50, 64, 64, 3)\n",
      "test_set_y boyut: (1, 50)\n"
     ]
    }
   ],
   "source": [
    "# veri seti hakkında bilgiler...\n",
    "m_train = train_set_x_orig.shape[0]\n",
    "m_test = test_set_x_orig.shape[0]\n",
    "num_px = train_set_x_orig.shape[1]\n",
    "\n",
    "print (\"Train veri sayısı: m_train = \" + str(m_train))\n",
    "print (\"Test veri sayısı: m_test = \" + str(m_test))\n",
    "print (\"Her bir resmin en/boy değeri: num_px = \" + str(num_px))\n",
    "print (\"Her bir resmin boyutu: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n",
    "print (\"train_set_x boyut: \" + str(train_set_x_orig.shape))\n",
    "print (\"train_set_y boyut: \" + str(train_set_y.shape))\n",
    "print (\"test_set_x boyut: \" + str(test_set_x_orig.shape))\n",
    "print (\"test_set_y boyut: \" + str(test_set_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Görüldüğü gibi, 259 resmin 50 tanesi test için ayrılmıştır. Bundan sonra yapmamız gereken, bir resimdeki her pikseli o resmin değişkeni olacak biçimde veriye dönüştürmektir. Yani 64 piksellik her resim **64 x 64 x 3 = 12288** adet piksel bilgisine sahiptir. Bu da her bir resim verisi için 12288 adet değişken bulunacak demektir. Değişkenler, $x_1$'den $x_{12288}$'e kadar gidecektir. Aynı şekilde 12288 adet w değeri tespit edilmesi gerekecektir. \n",
    "\n",
    "Aşağıda **flatten** olarak isimlendirilen matrisler bu türdendir. Eğitim için 209 resim olduğundan **12288 x 209**'luk bir matris, test için de 50 resime karşılık olarak **12288 x 50**'lik bir matris oluşturulacaktır. Diğer bir ifadeyle bir resmin tüm pikselleri, tek sütunlu bir vektör matrisine dönüştürülmektedir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/zkcplk/Deep-Learning/main/images/4_logistic_regression_image_recognition.png\" width=\"700px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_set_x_flatten shape: (12288, 209)\n",
      "train_set_y shape: (1, 209)\n",
      "test_set_x_flatten shape: (12288, 50)\n",
      "test_set_y shape: (1, 50)\n"
     ]
    }
   ],
   "source": [
    "# flatten matrisler\n",
    "train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T\n",
    "test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T\n",
    "\n",
    "print (\"train_set_x_flatten shape: \" + str(train_set_x_flatten.shape))\n",
    "print (\"train_set_y shape: \" + str(train_set_y.shape))\n",
    "print (\"test_set_x_flatten shape: \" + str(test_set_x_flatten.shape))\n",
    "print (\"test_set_y shape: \" + str(test_set_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu örnekte standardizasyon işlemini, her piksel bilgisini 255'e bölerek yapacağız. Çünkü piksellerin alabileceği en büyük değer, 255'tir. Maksimum 255 olan pikseller 1, Minimum 0 olan pikseller de, yine aynı bölme işlemine göre 0 olacaktır. Böylece tüm değişkenlerin değerlerini 0 ile 1 arasına çekmiş olacağız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardizasyon ile eğitim ve test verilerinin son kez düzenlenmesi.\n",
    "train_set_x = train_set_x_flatten/255.\n",
    "test_set_x = test_set_x_flatten/255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 , Cost: 0.693147\n",
      "Epoch: 100 , Cost: 0.565191\n",
      "Epoch: 200 , Cost: 0.519663\n",
      "Epoch: 300 , Cost: 0.486091\n",
      "Epoch: 400 , Cost: 0.458920\n",
      "Epoch: 500 , Cost: 0.435919\n",
      "Epoch: 600 , Cost: 0.415925\n",
      "Epoch: 700 , Cost: 0.398233\n",
      "Epoch: 800 , Cost: 0.382376\n",
      "Epoch: 900 , Cost: 0.368020\n",
      "Epoch: 1000 , Cost: 0.354920\n",
      "Epoch: 1100 , Cost: 0.342888\n",
      "Epoch: 1200 , Cost: 0.331775\n",
      "Epoch: 1300 , Cost: 0.321462\n",
      "Epoch: 1400 , Cost: 0.311852\n",
      "Train Accuracy: 92.3444976076555 %\n",
      "Test Accuracy: 74.0 %\n"
     ]
    }
   ],
   "source": [
    "# her şey hazır olduğuna göre modelimizi çalıştırabiliriz.\n",
    "d3 = model(train_set_x, train_set_y, test_set_x, test_set_y, epoch = 1500, learning_rate = 0.0017, print_cost = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Çıkan doğruluk oranı, böylesi basit bir model için fena değildir. Daha sonra değineceğimiz gizli katmanlı ve daha fazla nörona sahip yapay sinir ağı modelleri, bundan daha iyi sonuçlar verebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "costs = np.squeeze(d3['costs'])\n",
    "plt.plot(costs)\n",
    "plt.ylabel('Cost')\n",
    "plt.xlabel('Epoch (per hundreds)')\n",
    "plt.title(\"Learning rate = \" + str(d3[\"learning_rate\"]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'costs': [0.6931471805599453,\n",
       "  0.5651905642386902,\n",
       "  0.5196625730202545,\n",
       "  0.48609075367460486,\n",
       "  0.45891999959720525,\n",
       "  0.4359190886290494,\n",
       "  0.4159249142704356,\n",
       "  0.3982333545152499,\n",
       "  0.3823758393050739,\n",
       "  0.36802024484432744,\n",
       "  0.35492045473257366,\n",
       "  0.34288796277598554,\n",
       "  0.3317746412074617,\n",
       "  0.32146167287234695,\n",
       "  0.31185212093674286],\n",
       " 'Y_hat_test': array([[1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0., 1.,\n",
       "         0., 1., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0., 0., 1., 0., 1.,\n",
       "         1., 0., 1., 0., 0., 1., 0., 0., 1., 1., 1., 0., 1., 0., 0., 1.,\n",
       "         1., 0.]]),\n",
       " 'Y_hat_train': array([[0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 1., 0., 0.,\n",
       "         0., 0., 0., 1., 0., 0., 0., 0., 1., 1., 0., 1., 0., 1., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 1.,\n",
       "         0., 0., 1., 0., 0., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "         0., 0., 0., 1., 1., 0., 0., 0., 1., 0., 0., 0., 0., 1., 1., 0.,\n",
       "         0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 1., 1., 1., 1.,\n",
       "         0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0.,\n",
       "         1., 1., 0., 0., 0., 1., 1., 0., 1., 1., 0., 0., 0., 0., 1., 0.,\n",
       "         0., 1., 1., 0., 1., 1., 1., 0., 0., 1., 0., 1., 1., 0., 0., 0.,\n",
       "         0., 0., 1., 0., 1., 0., 1., 0., 0., 0., 1., 1., 0., 0., 1., 1.,\n",
       "         0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
       "         0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
       "         0.]]),\n",
       " 'w': array([[ 0.00514683],\n",
       "        [-0.01180574],\n",
       "        [-0.00555095],\n",
       "        ...,\n",
       "        [-0.00604268],\n",
       "        [-0.01545623],\n",
       "        [ 0.00987126]]),\n",
       " 'b': -0.014285023905884361,\n",
       " 'learning_rate': 0.0017,\n",
       " 'epoch': 1500}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "::: DOĞRU TAHMİN ::: Kedi\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD7CAYAAACscuKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO19a4wk13XeOVXV75737CyXXFLUY62HbYkSGMmKDIeWLINRDAv5ocACHCiBAP5xAhlxYEkJEMABDDAIYDg/ggBE7FiAHSeCH6EgGLYJxkJgwJG1siiLFCWS5muXO7uzO+9+d3Xf/Jieud/5Znp2yN3tYdjnAxZbNVV169atqq5z7nfOdzSEIA6H462P5LQ74HA4JgN/2R2OKYG/7A7HlMBfdodjSuAvu8MxJfCX3eGYEtzSy66qD6vqD1X1BVX90u3qlMPhuP3QN8qzq2oqIs+JyCdF5LKIfEtEPhtC+P7t657D4bhdyG7h2A+LyAshhBdFRFT1f4jIp0Vk7MteqsyE2uyZ0ZqabcNh/NHptgdm26Aft9nfJvtDlaaxzWLZGi1pFrepwrltNyTN4nFZZjdi+9hGyG1/++38YLnVHFIbY7svMAQyNx9vTVaw/QjDceNhG8FteMyhU1Mb+AE47ltgeqV8P8duGt/eCffLc9spPFeS2n0HcGt6+BzZ2yKisO1Qv2LHUrKFcXzMfun455uRQJsFOI7HvgfXPaSx2n8Eu511yfuNI0fyVl72e0TkEqxfFpGPHHdAbfaMfPKzvy4iIkp3pbkb78pLT++YbRtX+wfL/V7cT9Xesdn5wsHyfReqZtv8ctyGL35WtHdvbql8sLx0tmLbn4ttFOAFzNcbZr+rT904WP7WX7dsP2bj+aj70oJr+9Q/Xj5YXj5jb1PeiT8mec82MuzGB6Lfidt6XfoBHcCD07dPVb8fjxvifgP6cYUfRlE7jp1e3LcAmxJ6WfAdSOnHNYE3C8+8vpab/RrNeG0z8/a52tqO215bjc9Ru22vJUnjeo9elRJc51zNXkAvh2cJLnRmxt6zdie2r/QW12pxeQV+5Ptte28vrcdr6dJzu9bY2/bsxUdlHG7FZz/q1+PQz5eqPqKqF1X1Yre9ewunczgct4Jb+bJfFpF7Yf28iFzhnUIIj4nIYyIiCyvvCPtmVTK0v1oz9fi78/6PzJttO+vdg+VeI/46F8i8nV0uHSynRbutC7/+AWy7ftf2Y3ejE9tP7W9XMYvWQlKPQ0cfNWOl7DTsF7WYxTbrFdvHShHdhLhfzl9e+GoOe3bbII/Xkw/gC02mbw77sUkbQuwHmp9sieK5h2IbScAcHSbw1exxI3E9tUMlKZgBA/gu7ezaL3sbv4BkxjdgX3RlsoyslEpsPynaNmbgyz7s2OM6YD3V4dxhaC8mg4ekk9uxakMbO7vQR7LV+3Bcs0puwtv2Oh2+N94XupUv+7dE5IKqvl1ViyLyCyLytVtoz+Fw3EG84S97CCFX1X8hIn8me7+nvx1CeOa29czhcNxW3IoZLyGEPxGRP7lNfXE4HHcQt/SyvxHs+4dBaTYUZheLNCtbuCv64kmIDlVKbWSleDmBXJcMZnZxtjkfWN9qCH7RzlbPbCuWYCY9iX0q0Yx4uxXXB+ToboMPXy8XzLazZ6PTl8CdOUSbgf8dyM/F9aFZpjaGR+8nQtQbUow0pjjtMqA5gQEMHfqaPZojqcPsdmD6DvrVhXvWolnqHpxLG3ZbF+YIKjBHkufWgx2Av10r2Wsp432nOQGFiYbFpbgx0Hg0O3Guqde3fWw1475bO3F5btFOHvTgHnbIn9/v4XHspYfLOhxTAn/ZHY4pwUTN+BBE8lHgSIGosRRCy4plspWQCgLrKCHzNgGKp1Sxl4ZRaJ3daFJ1+vb3DqkmDkTZAVougy7qsG/264CpurJo29/eiVTQgKJqllZiQE8Rxmc4sPuZCDqxCHL0WJEVLwHMwECtoOuBwYEDy3iZ4JAh2fiNZg7L6E/Yc5XAleE28LoN3UgXY+4ZRcTgWJUrKexn720B2r+7Zp+dzcj8mn6IiCxAoNXcXGy/TRRdEYJ4kgIF5sDjkwN32MlsP3YTcD9v2GeuvLl3PdrlJyLCv+wOx5TAX3aHY0rgL7vDMSWYOPW27x4mifWtVIwzbrfBpgI4y5yVhiG4HEqLv2sJ+Fnaso5oh2gdxBD83OZWPC6lOE8MRb3vPoq9DHHIl1Ys9ba4AiG44Hv3O+OzAJX9XOg+Uox9psbA5eMwWPTNMcOMfWU885D9fjgOQ5IHlASCWYEVm3dk5g76g6P/LmKz2fha+kDLlQpx4yGqENY3tyjRBq50ds6+MjMzRyc25eTbY5IPRWFLD6hbTHLaadn73oCw3bJYJKOQWz0uu27sFofD8ZaCv+wOx5Rgoma8isi+9V5masxkfBEtB6Z7WozHFawVLBlQbwPKFEvA1C4V8dxE1Qhkx5FFhPnVPaCFOnSuDFwUjKoSEVlciJF3c8s0BmCmDYD265E5h9FvnLHWBZOwC5RRTpQMmuc8VvkQIwDj3ylg0VwnK0+UynG90kc6kERFMNf9GCGRBMLpeD8044mllD66HrBYI5ehHVlV2aKIQoycrFTY/cSMtfj33R2KzITDGiRosg1Rf02IvqxV7bMzB/dFKdWyNXIbjrHi/cvucEwL/GV3OKYEE5+N15ENdmiuHM0SCvJPYWYdg4rQbBexskAJbRvI0bZvuUK/d7DKEWNhePRM/YCiperQ5tysHeIymGZF0ilD4YluC2aibbCUiQobUFJFF4QQcBac5av6YPp2iYHo5kfbgvxlMNp4h4TbQIQBXbRjUjW6lCSTgGuAs+WDYxJ3BsQY4LW0YBwXZ62JPDsLSTLUBiZRsbYcmuTrm/GB4Uclh2ep1bLtIwOEUYQcrYfMixJjNRjd3+M0A/3L7nBMCfxldzimBP6yOxxTgslmvUmMtOqRD5kYvWzrFxnfEDXCi9bvwoyvjDKL0NcaQJpRQueqQ5ssRtltoxAjdsTsJguLcVjrM7aPRaCkWP8c/e0uUEH9Qz5qXB6QX4fiEOjPsw+JIodbFDHWxAwtiPwq0JjivEiT6KQGUIclaGNuxrZRQT1/20VJgWMzFB3Nx6Aqec7UG4zP2nq8zoz4O8xYY214bHNzx56gCbToLoxBgRI3MaKzSNGdM7W4cxP9d7ppqZHWpnmt0aqLVzgcDn/ZHY5pwWTN+GGQzsjsORSdBlRNUuZuHR0FxZF2CeiMJxm7ArHNHMzRftvqzKEJVyARgwDCajlEgmUFm+wym0ZzsVix9twAQrx6bFp3FZYhuYP2Q/ZnSPQglgjqdZC+swO+ClVV1smMRxcLaa4i3RekRHd3KWkDSmCV4V5sblEFnnrcVi5ydB3qEsa/c8RiB6MGiabswqWhHt0rq3bHOdCB43oBfaj60qXxRqqvZErf2P0q8BzsNu021PfHJJ+EaD4sHdbq2IciH71QLESC8C+7wzEl8Jfd4ZgS+MvucEwJJu6z5yNqgdgT6YE/UirxbxD6vZhBRRlIqH+RsL8T15My0izW3x6C01es2n4USlEyoA0+ab9r/fIM6tYN1PqyV1+JVV17lKKl0MddoHhWL3fNfih2mdJ1DoGnY4EGxDZQRkpPQREmLm5sxOvcaJKOPvioLEqBwiIK7bXJpx7sxussUv21DEJCM7johNLvcD6izXTpGJEOzgLsbcQdOSQWb1Og72MlJjFKFeqvlYleQ5rVCHCKnRcxgqEUNm78dLq1+wKlPI+FuOmXXVV/W1XXVPVp+Nuiqj6hqs+P/l+4WTsOh+N0cRIz/ndE5GH625dE5MkQwgUReXK07nA43sS4qRkfQvg/qno//fnTIvLQaPkrIvINEfniSU64r/GdE580GGJ00HihhSxDM9X+VikqOaQcwoSb4nEFKn0LOgsmMktEpALuBWqaD/uWj9FKNPeTor2W7//wxsHy9qa1aZeXYidzsDn/9pmG2Q9FKQKZz3g1uK1etmN1BqL87jprVUBQeAKpoJdeo1LJoL1+34Wq2bZ0BtwjOIxLdgG7dkiQoQvae61tKLPdsf3AxD+Sgzd6gIkJjSMzG01msoSxj/xMoL5/A1yjBpnZ+LRzRKTN1DuabhQRyZAWpm2lEU3Mjz3ijU7QnQ0hrIqIjP5feYPtOByOCeGOz8ar6iOqelFVL/a6jZsf4HA47gje6Gz8NVU9F0JYVdVzIrI2bscQwmMi8piISG3mbaExmvWsLVrTMdSiOdSjCKkUIp9SmLFNSLghFTQ/aRuYTjhTn3LCTD2anzmpRuSQ3JGAy8CaaGh+DpokSwx6Y42GteeqYBIO4FoSnmHVoyPc9tahNBQMY8p9hKiwjNiPxWUYAzBv17daZr8NMK3nVqy48Uc+dU/sP5iWvbbtcLken4Na3TIjRajKO4Rn4vorW2a/576zfrB89VXbxx0ok4Qz8JwIg08cPzsoPMFmfN/IWIPEN/kCyE7w81Iw4x+X6ySsgsIt5bJtZKd958QrviYinxstf05EHn+D7TgcjgnhJNTb74vIX4nIu1X1sqp+XkQeFZFPqurzIvLJ0brD4XgT4ySz8Z8ds+kTt7kvDofjDmKiEXR5P8iNq3u8UUoZTsN5oLIGnP2EvhC2Z/1hLA2VknOFvswQ6JiQUZQcRn6pbaOHx4HPngpphANddeNq224DH69ImXnYxy7MDxzS0YdRyEk43vhsZoWoTuR/yImcAZ89KcVtW9v2OludeG3PP7VttvUhE61aj49Zt23v2Y98aPFg+Z3vXzbbls7HWK35xSj0Pn+3FX1fedfcwXJz217n9VejmPvqc5sHy70tG5XY3o392t22czUb0GZGpcZxjDN45th3LkOXy3Tf0e/HDDguYYbM5AaJaNwYlQJnQRSEx8Y7HFMCf9kdjinBRM34YRBpjwQVWN+7D6FPqZBum9GNB11tot4M7UR1cNIELhVM35ySUTC6LiMhsWEZzC0wgwO3AWb37qZNHhkAj8MliDD6CRMiSuRqdBXdCdsGWvzhmKQhjIzj1IkMeLoZqFp6/rylxnbB9H35kjWLn/6/GwfLOKZc7XXtShTb29ywJv7V1djmhfdFE79atzRfWoptVhesq3H/fNz33vfOHyw3N617de2F2N9Lz+6Ybb003sN73z1jzw0DfumFGEeyu2VdgUop3txlop374LI1QFMeaVoRm2w0SOwzMV/fa5+TeBD+ZXc4pgT+sjscUwJ/2R2OKcFka72FcOArMkGAEYrsX2KYamr05Q81D+2Np7VQyC8jHwddIc4gKkGZaRSN6BCVkmItNqJCikA5lvg4aLNSiMfNzVJ53vb4/veMcDpQdMdkGXIduDTD9uM1F4kuveds3La6Zn3UJohdDmCOJFB475WXovri9SuvmG2z86sHy/ddiL7yj//kXWa/+TNRQWIWSmKLWK37IRTv623bsNoElCTnF+3cxAA4rwsfPWe2lefjvmcvx2tZv2TzQIrwxJdJ0KTXiucewDj2EjuHUYTMxQqVc9734bO/G//99i+7wzEl8Jfd4ZgSTNaM1xisxZrYCUS/9cn0zdGsB42xlMxgjFzLc2sqaR9EAcBWT4nWwjaV6A2MeCuDhjr3o7EWTcQBKRVkKdKDZpMkYOphOSIOiiqClcnlnBMw3XHY2Nyfqcf2V84TlQVRYu2deIImRZbheM/W7Vj1gBYNx+iiDSBtr9+2F7rVi9RbYz3SX6sv7Zr9zr2jdrD8jh+3CmlViADMOrG9EpU8xtLdzSbRWgtxwKuzljYrVqPbsHJvPG52ye6H7hD7n91W7Nc8ZOa1KSuyUov9UKpRpSN35Zvfsi4Iwr/sDseUwF92h2NKMFEzXlUPEmCKJTubaMxnnmHGqqVg05bJBB8j5bW3DczFkKOpTjPiKFlMZYCKIDKA5aTSzA5jeyOaZXmfbXUQqKCZ6TZc6BCi5LhqKWrhtakMEE70YhXQhPTdFsBNqJTsGGy8FqPL+jBTvLthzfgtKPlUonEsgTWJrhIRLdIFt4xdEiz/hJp869dsVOL6WhzvV0iv713n4715349Hc798vmb26w/jfskmm89wf1l5AhNhYOa/VC7QfuBeUcxiEaIUi+DypBUb5YfPGbq9InKQJcPPM8K/7A7HlMBfdodjSuAvu8MxJZg49bZf5pd0IYwrxGINW2tQKhk0w8+QiMFsMfpJTO0hzYW69JwkhHMHXIaqWos0C84PsN45dr9PPjWKXRKzJ6jFgUeVSPO9C2IHfcoetO7g+GhD9O0uv2ijyXrNePYKZPoNSAgUs7K4jHKtfPQ49qi/w5DCst1WQHoJMguZskRfX0k4fhboqjLQjRkJjWI/srJ9OGfuqcNx9MoMQYQU/pxS+GVusjDJZweB+AK0zyXMwgCjEm03DjLnjlGc9C+7wzEl8Jfd4ZgSTNSMT1SkNKLe0mOqXHZJW/zl78eIqQFomFUqlt4oA0XSbdk2CpDEguVemQYpgI5YpWKHB9dbbdQKs3Ylatw1di2fNAAzkyT0BIP+ShCht7Nrd2zCuUmjw0ThGS10Mu+2oPRUY8tuOweloapgjqfkXl3fBhqxyyIgkIQD9zZQQg4+Bgk9E2WI5OshXUr3DC/triX7/ULt9dBHl4QoS3w8itYEn70LKDtyqfqQQJODbc3JXGiSH6q0ii4hNF8k8ZQcnLsBlcAajB6mO6Eb73A4/j+Dv+wOx5TAX3aHY0owYepND6itkFufKYDIJAtJbkLGUwI+35C00JGT6bEPCaWThxhSyill4E4ViJ7B9QRCW7ls8hBOcIgmgvNtbFIfwWedgyZ3SSMcHfUihwyD34jlilPi+RTaWFywvuE9d8e5EJwT6LNOPxx2XKngNoTchmS8L8sUI7aPYiRVqoFWxnGbo3oBMB7b6yAMQeImOcQZpzU7F2SyzcRCsT4BxD9zyLcJqaZPLNYSQCGRIU3IYCZnr20FPvepN34WEScp/3Svqv6Fqj6rqs+o6hdGf19U1SdU9fnR/ws3a8vhcJweTmLG5yLyKyGE94rIT4jIL6nq+0TkSyLyZAjhgog8OVp3OBxvUpyk1tuqiKyOlndV9VkRuUdEPi0iD412+4qIfENEvniTxg406FpNS42VKtEc4sydPpjMBTCPuDwOWjAD0nLPgbIbQJTcgEylHohNDClMCc34eTAXt7bIJQETkaOgcmDi+hR1JpBl1wC6jcU8DHNDZhua8TgEfC2YPVit0Dgm2A9wjagfFTC7czJwjRYelPMqUqlhDCMsUKIY3sMm6KnzF6oAZbzZjJ9Zio94EyIDX/2uzY7rwH154FP3mm2YCcn1DhAJuFRJZi/G3DKuwA27Do+LFAS6LXDZr/16BLcrgk5V7xeRD4rIN0Xk7OiHYP8HYeX1tOVwOCaLE7/sqloXkT8UkV8OIezcbH847hFVvaiqFwd54+YHOByOO4ITveyqWpC9F/33Qgh/NPrzNVU9N9p+TkTWjjo2hPBYCOHBEMKDaVY/aheHwzEB3NRn173Yvt8SkWdDCL8Bm74mIp8TkUdH/z9+07OpHmijM63QB18lJ+FBzBwrgLAh0zgYoqlKtAXQRkNQJeGwyR6EPzYalt5AUcIy0DEJif9hqCTPCWDGXZF8VFSgwZLHOYVGJnBtHC6rgnQbhLqSUr/Ceqdj508uX0JRT6zTZsdqpgZijuT3N2MJN0lAdadEPnupxGRWRABaq9GITvVMndrAkF4a0xkI/e1Bae0GzRmd/5H4IZo/awU4A3wTWVwUpYHwWVIab0uJjVe7QZ89z22oNY5jgSjXZPQ88hwR4iQ8+8dE5J+KyPdU9anR3/6N7L3kX1XVz4vIqyLymRO05XA4TgknmY3/SzkcS7CPT9ze7jgcjjuFyQpOCmT1EEUQwFLtcKlaMG0KkOhfpnJEJciS4kiiFOgkJGeY/iqA5dSizLmtzWjWz8BhLIBRqoIYAZmtlT6enUwx0DJHM5P0GOiXlyLSULAQM6horBYXor3LkWttyKoz2vNkwhaqEP1GrkwC+7ZA/5zLctXAJOdtyDFWyvHGLJ21jy0exYIgGVx3BiKYeW7PNbNcPVhOy8dor/PNhhOWMJqOXTssJ04mPkYmdnbjM8ZReFh+rN20Jn5aGt3PYzT6PTbe4ZgS+MvucEwJJpsII3Jgc3ECP5rdnbadfcaZe0wWGdKMOya/kCyccQXCICbWzFEGxxBm3Ltdaz+3YpFOE01XKNthXLgrmoTzy9YkXN2Jem8VK6EneQ5abX1Mphk/s3so8QHGVcEtWJqnkkZgPucUGZfBjC7q3nMyzQBs/AK5CQLRalVwE9hSz2BWuUjuRAui5mZBP25x0Y43ioVUSXAEq2/1OjhbbjF7NgpUVOfsjUngujkSEcUTiyU4d8YMTVwOlFC0eyNSF42tqBVfm7cVadNKfJayArFNFG13FPzL7nBMCfxldzimBP6yOxxTggn77OFANIEpEtQMZ8FJBEYIsU/dbIAPSZrvqy/GuPwEWIv3/D0STAA6qUDpSR1w78vduJKSyEUVhC/nyO+6rtEnyw4xPOCLg2/I0YCYFTggZw1dxQron9+9ZG91FXxsjgrD+nGb23G5Tf5q2sd5Czv30QbqcKaGuvFmNxPlV6B+HGRyifXTKzWiAEEtku879qPVjO3NUHZcZSFGzaVFe2MSQerQ9h8fZKTl+JlAYdP+0PrsVcj43OqPF8DA+ROmAA8ekfHMm3/ZHY5pgb/sDseUYOIadLofWcSRSIAelyG2ag0HS40NW7q3uIhr9tKuvBTN5wLwP297r21jLo8m3JDMyhzyYnIwt3BZRGQI5mdQSrQBDqZSslkbhVLctwjRgMMBmWywOkM01OJ8bFNBC2+uanYzblS1bn/zO72jzUVOssD9iqT51+vE66xBskt91prPS3fF8Watf3xEVlbidZkSykIRkdSPALRiHei7tr3t0oREm3m6nwXj8pAdD31E0z3jsuPgcoaePfnMDAi3YLIR8ZSm7DhzmMfZ7yP4l93hmBL4y+5wTAn8ZXc4pgQTz3rbp3mYZlGguXq98bF/+OvU3LSZPwLH1easP7y1Hf0d1EtgH2+IgpNUa2sAwobtNtYJJjFH4JcGFIqKlNohoUe4ugxEDlk0ogL04AfeXzPbUAd/Zw18b8q0SlK8FuujXroafcpeN+43T3RVik8PUYA4rlhSeZ6oyJW3x/5vXLKlowfg5+K8Qkr+cB9Cc/l+KoY1w42vzdhrWb8WY6HrZ2w/5iAjrlC05y7Cc4zPdE7jkXfjmHK9O0yWQ3n5XQidFRGZPxP7cSgcfP+6vdabw+Hwl93hmBJMmHqLJhjl9ouCDvahcrpgmyQoRkBsA2rDb5P5jJFUATLnunQuNGkHZJ6nCWjX5eNNR9DJOERXYaZbnSgvLFlcLMZMqDqVO3r7O2O01/s+NGe2PfdUFP6toK4+m3dAAb521bpDNzbA5YEnZKtpdpNCKdqfed+2US8dXb54YcXquy2djabpoGUjyxQyFdFy53oBHVMjgOlSyHSDh65es2Z8HyI4b6xa8eQMzlefs/03HgW6ol07HpidmJWsK4ONLN4zf7B85WXbj92t6ArUZ20b+67AMdoV/mV3OKYF/rI7HFOCyZrxIc5GcyD/ID96eW9nNEchQYTseDTm2hSNhRFMGdg6LJTRakRTSRM7oz+Amdi8B5FlnKgCJvIMsQKVKiTQsKYb/PTWYKZ+Yd4mZnz040sHy4t3WbPyyg/A9AMxiM0Ne51YsqpNbsiZ2Xjc4lzsxzqZ8Ve34pgWcjveP3p/vO4cLmz5nBWGqM7Ea5tdstfZ24n3ogSRaxXS9Wvis8SVT/uYHAVuEo39LMhH90h4Yut6TKIa0kx6CfycrBjHuMCJKjAGw0MS6LGN2nx0a5bP2XNtrEWWYJ0YlPrc3tgdU/3Jv+wOx7TAX3aHY0rgL7vDMSWYOPW2L8TAQold0G/nsrjo4qDL1GjZ/TKIcOuQTzOAyLgB0HdY7klEJIdSSH0SQmjsQPuNeFyJqLEy6MaXKEOrCOKURRJ8GELk3dxs9HnPv8v6ufe9J9Jt/Y7NoFpYBpEHyLgbUkRXsxXX33mP9ZWvX4u0UR5AEDK1bWxDBOMZiq6bm4/rSSVeS5lYpwK0Obtg5x+6G5F+xLLM2zdYtASiARv2fnYhAnAZRCmSoW2jCqFri/fOm23bN+I8SK9ho9qG/XhcfSHep5TqT+OTGoju7Q8wug6y9Obtfccqza+9Ymm5/ay9/gmjT4+EqpZV9a9V9buq+oyq/tro74uq+oSqPj/6f+FmbTkcjtPDScz4roh8PITwARF5QEQeVtWfEJEviciTIYQLIvLkaN3hcLxJcZJab0FE9rmHwuhfEJFPi8hDo79/RUS+ISJfPL4xOaBGWMurD+YzR9BhBJOAiT8kukeBMumQAMYAKTvwC1iTHSk63bHbrm1HE7G9HU2vs/dZZYi3vXvmYHn5Lmu3XgVzt0klfASuGyuTvvdD1mg6e0+sOHr5uetmWwFOV1mK55pfIJMQxjElvXYsifXci1Gxo0h00ixQkQnd0DUwtd/5ntipKpnxM0BFzlF0mrTjub/9V1sHy5dWram+AgIeA6oAvLYWxzgDt6xeJ335XmyzvmCTi8pwnY11az73BrH9NI3HpZREhW4rJheJiIQeuE0w9pxMg7r6K+fsM/faS7v7J5JxOGl99nRUwXVNRJ4IIXxTRM6GEFZHF7IqIisnacvhcJwOTvSyhxAGIYQHROS8iHxYVX/spCdQ1UdU9aKqXsz7jZsf4HA47gheF/UWQtiSPXP9YRG5pqrnRERG/6+NOeaxEMKDIYQHs0L9qF0cDscEcFOfXVXPiEg/hLClqhUR+RkR+Q8i8jUR+ZyIPDr6//GbtRVCOPBJBkQRoMgk+9GYOYb62wMqu4uCkF2i5cYl9XcorLaxHf2n4dD6XZdfjPGiay/H0EWmUu59Z/xR41BX9OGVwiYH4DfeuBYvZoH8swx8SKULW7obwj43IdzUXopgibsW+bkrd0Xa6KVXYxs7TdpvFrPe7Bi8di1ey4UPxL/XZ+0jt3w+zkdUZu3HYO3V6B8/9b043m3q7z2L8Zt1/minRwMAACAASURBVIL1+/tAuW5vom68pcYyGKAktX0sQI215JC2PZwLBE1KFXvfFRU+1c7V4BxJDvRru2VpVRRCqc7Z9u8eiYAcqrkHOAnPfk5EvqKqqexZAl8NIXxdVf9KRL6qqp8XkVdF5DMnaMvhcJwSTjIb/7ci8sEj/r4uIp+4E51yOBy3HxMv2bzPljH90GmiOX1MBF2ACDdyBQZQ8rhPpaEwE03BFWg27H4lyIzKyGTbATNwEzK+GjuWCkqgVPKQXJIC0CdF0jNbezW2gxp0BbLBMWOwUODovbhvdzP+vUfuygDcphtr1qzEUsxngNZqkrgEVsdapgg6PF0K/T9UKms+RgNW6rNm29plcEMC0pJmN0EtCLxHIra0UqsHUZo0XVVdiK5Snts2es0YyTekkmBonrd2435FMuOzYhxHDfbc6L51IQuz26ToTjDjKzN2EKqze9RqwqowAI+NdzimBP6yOxxTgomLV+wrTPBsvJkVJ51cNE2w6mcgwS1MoOlT9FEwdXpAhII16Ew/bPcbUCKoBYk2LXJJsPRPlxJtCjDipJFgZmX7YLKh7LOISAamcG3RhqT1wCQcgtl6/eWu2a8LrsdOw45BCq5AEca+Qgk/OKNfX7Db7jkTzdgy6L11SSgDzdakSPcM7s3H/0Gcqd/Zsi7JNsiE7zSsS3Lu/jg+szDgSY1ERcAs7jVtskveiW0WytY8T6HPGH2Zk/uGUtvMoCArk6L7Ru5KH5JwOg17P/dPx24jwr/sDseUwF92h2NK4C+7wzElmKjPHiSSalziCam4QPRGhu4V+unkU+Nhw0OilUB5QRZdRlRFDkFLbbG+IUqjo/YBMTWSg688HFDmEvi9wTYvRYh+6oPvxfSagsBlqWJv4dxSzG7bmY9U0NqwY/ZLQDjjrjMktABjotfjxb2rbv3V6nw895mz5ANDpBzSVUxTblzfPli+e95Sb+/9+3fH/j93+WB56zu7Zj+MqFtcsf1YOht99vIMCHusWL39IdzQ1q71hzMorZ0k9l4UgFJLC5BJSPcM1zS1NGVWiuM6GMYHsFJgyjUu90gotTuK3uPnbVwfHA7HWxj+sjscU4JTiKDbMzPIgpUh0GZKOuyYCJOM0ZAXOf6XC6uYmja4hBTQPQPaiIkIqB/OiTt9oOUKNMIzy9HMZjGIV34QU4CLEHXGpjqCo/wwQq+2FI9bebul6IYwHuV5a/oOh7HNGdAuL1XtCFegEirq4YuIdMBc37wS/Z9uYs3P3U2omEom6Hs+euFgOYVyUlevv2L2yypHC1SIiLR24r04+7ZI31XvsgIVu0Cb9Sj6MkD9gEDbsJbAzBwpcyCQ+c3GU8tGm5GezVIZ7hM9+/urXG7MnGd87xwOx1sJ/rI7HFMCf9kdjinBZKm3EP1bku02JZDZH1H4TUqPyeoJg/HZZuPQbVOoKISmksaAdMEXR31FrhfX2I7UzZmzdojLoCO/sGB9vAWgjVCwsELa8wq+fkqTAoVi7OPsMsRb0pj2QOCzQOV/sT5aGXzUWt36g5i1l3J5a8hADNBFVdJr78SxCy1LD9aWFw+W73vP+YPlxpYNZ730XBSjbO1YwYfuFuj71yLFVajYay6ACgVnIw6Ac8VagCIiBagDUJuHNun5C2a+x7bRh5BqxbmglH17FNiw2/bnU1gQxRw/dovD4XhLwV92h2NKcArU256ZwdlmSPekRElJivuNpyZQzOJQIJEeTd+xnlkCJZOSQ1l1qP0W0dixpulzT0cKrVaZMdv21L32UC3b41og4DG7BFFbFHGFY8Dlq1IoY1SHaLpC1d7qDmSH9Qf2OrUT20e9u2LF7peB6c4ZcWWgBMsQhbdE/ZiFMlclEunIgPKqg8jF0n1nzH4bUI66Omt9r+xeiDZcjPeiQAL2VTCLc9Ie3AFdws6udTWyQhS9GIKpLgOKvgR3ZUgPbq8b28cthYKNWAxIH/OzKeMj5w6OuekeDofjLQF/2R2OKcHExSt0f1aYKrViFFqgqDC0WND8VzJl+pCAwrOhFhiFR4IJMGtNnoZJXEHtMWYWXno+zhY3d62Z/aMfjObo+ftt4sfsYpw9L0DyRUrSxqhxVyQxBRws1CxL6E5jEkuXKt5mWWw/g+Qc0hQR7cdrq9St6ZuChHa2Fvebs1awVKDRQt26PFkSTe00i6ZusW6j387cu3SwXK4x+xHbaMINTSnSLoObWKpSVGIrnptytCQB1gTLkQXyI/uwjWfSscIwkiZpal0SfDa5/f1zc3Vk09exWxwOx1sK/rI7HFMCf9kdjinBZH12BcqAA30MNUbRQSXUUMdyy7YJ9Eu5JLTxd4BeEyW6B+YL2i3SpQc/ySQnUdQSijWwJvu116LTur1tI6k6QB3OQEkjFthAvUIUKBSxkYg50HJYEltEpACZdBUSXxyg0CG6gOyzm5JGdhv6skUQn+xctuOhQIGVqtYXz4FOwm6UaZ7i7P3LB8sLK8tm2zaIRzavRyF99r2L5diP3oBEQsG/L1d4/gQbgmeY6OMMfXvyq41oCcwrdNt2rI7L+Az7cwLHTFWd+Ms+Ktv8HVX9+mh9UVWfUNXnR/8v3KwNh8Nxeng9ZvwXRORZWP+SiDwZQrggIk+O1h0Ox5sUJzLjVfW8iPwjEfl1EflXoz9/WkQeGi1/RfZKOX/x2IZCjP5inTnUdecIOqR/yhCplRN91+tB8gVRb6g3nwDdw+V8cojkywrsa0QY1i+x5yrV4DeU+rEJiRnbW9aMxySfNpT+6fWtWYmWe79LpZsMNYmiCCTIgCWIKJlGISlkAOdW4t4KkFgyILNyCHReDiKCLdKGnwcXrcYcJpTlxearNVvVdhYSiroD28erq1HjDgVHkoyuGa+NknqyIrgaFdKng3uG1n+hZNvHSESk2kREFGhWBdqTdebQFcjpmQiH/OLDOOmX/TdF5FdFTEze2RDCqojI6P+VE7blcDhOATd92VX150RkLYTw7TdyAlV9RFUvqurFwaBx8wMcDscdwUnM+I+JyM+r6qdEpCwis6r6uyJyTVXPhRBWVfWciKwddXAI4TEReUxEpFS+72RJ5g6H47bjJPXZvywiXxYRUdWHRORfhxB+UVX/o4h8TkQeHf3/+ElOuO9b5Dn5LeCUUZKXocNQjzu1jJE0d1gsHs8bgb4bi2EgRcd+KGYa6ZhlEZEilAkOfdsG1qDb2bD+X7uNGXdwDIkcFkD/PKNMsQB+r5nrCPZWo4/XWG/RtogqnovDduEGBJo/Qd3+HB6zfI4yvuBetBrW8itUYtaepHF+oD5rw4wLWdzvtRcumW1bN2KbKczBFMokkAL3sE+iJTg3VCyTLw7P4xAz3ZReLXzMqHbfsHd0xho/V/jODAb83bx9PvtReFREPqmqz4vIJ0frDofjTYrXFVQTQviG7M26SwhhXUQ+cfu75HA47gQmXv5pvzzNkLPewGTWjKOPjtbVZsMlR5OZKDU19BiKUFAZYhiRvGftuXG6dmxuibkW6qMpK223zZ+JFA9Ki3OWlEL/M/J5sEksTcTRhrubMbIMhRVE7Diim9MJtB+YlVzuaKYOIhrL0QTXM3a/IUSuddpWW04z0IyD9ktgtotYPf9Om8QlYCA7EJHWoYjCchWi8phG7KNrZMcb3U8T+Hk4hBMWKeISODuMHk3pXHkPs+PoHRm5c65B53A4/GV3OKYFExev2M8lYTNeTLSX3WTL8UCCCPUeLadAprWCOVcwggwUyRcw+YITXLDBuHhoRh82YpKDiEipjJFa/FsLUti9aNp1OnbWvlqLCSPsWSjMkGMb/a6N1kN3KOFyRNAomo47N2w/OijDTWPQWYlRbnedj7PnCc3oY/XefNua8WkpZuSUqqAfR75RpxNn3He3LLNg/Be4riZVasVwsWLJ3nd0IbgCq3H1UDuFNOjweTlU3gyeA3QrOZkG2achRxselwGz395N93A4HG8J+MvucEwJ/GV3OKYEE6bewoGvyNQBiihyxhoKImL2GtNmKEbJwnvoFyFNNCT/KYCGOvv944KUBiT+h+dmoYIOXMsPntox25bm4wkWF8ZH4aGIAYtXSCHu282jP4nlpEREKljKqWH9P/RnA4xx3rXXmYFPub1uKa/djbje2IrtJaR8uXJf9MWrRY46w7mVuC0h/7TZju03G9YXz8F3Rhouo3mKXjvOaYScns1idJYLNI5DuPeYiTbILW1bAMENpf7rmBLigek7FHih275fzovfHYR/2R2OKYG/7A7HlGDi5Z/2KY4hmShofiQsEAbmCx7X7bB2NkSukQmegLk+xmra70lcYp0v1LaHZWZB1FAplPgBfb5GVUtnKpFqwuHJKdIOE1AKZdLJ60VzNANuMqlb3bbttUhXDXPbBkr0oXYdjwean70Oma0gMtJpwnWS5l9lJpZyWrhryWwrlutxGUpBFegbdWMtVnFtc3kmSFzJweSuztjyT13Qhu/R/UzBPczI1UDactgHIQty7TKkmg9VIob2UaCCOoLP2WHqeoy2I/Z1/CaHw/FWgr/sDseUwF92h2NKMHGffbjvEzNDgOuU3I8uPGpe9CnpH4UhAqV5YZgmhrNmFM5q6m41x3cR5xGyMv1mAiXFrtXAhKKSKGYW1wsQGslZaVjeWomDQT86AVou75K/Df0vlkk7H8JFr1+Og1CftX4/Up0LZ60PnIBvi2xQbcGKRdagjHKpVjfbhiCSia5on4RP1td3D5Y3rtpw2YW74/kwe5Cz9FAxZdDlSRiY7yFfPAV6MICQSN6y9wyfzSIJmWYoTolzGkQLDwLUnKPvdHwMPOvN4Zh6+MvucEwJTqFk8z5FQIIMEEGXcsYatXEAFsCAdaa8UAN+FsoVc5BcC0xE3ob2qIl6op9M1G4nq09SoGraVM4ZNfR0BbLXOIMKzNuExhHXu70YTdZt2HOVqnEMBn12ZaLpPjMX+1GuW9G/diOalUmBSkgNY5uY+bd8fs7sV52NZnyf1DxMGTCg/dokEteCjD4uc9XajNsKoA0oGVFjJXRR7HOFgiOhZ/uIzwha3RzIlgCXOqTxNkolw/FRoOZcXBFsvPxi7MPNd3E4HG8F+MvucEwJJj4bPzhIhOForLiMpXJErPmCR/XJRkaNOLbAUyNeEbeWyyReAZFa2xtcYicCI/Jo0tREvAWaOS7XoimZUNmoASSaoK5aYLMS2uTEDIFSS0Fw9taiDxVeZcguT2yzOhPbazeo+iiYxV1iDMoQsVdfiKb6PEXJYfmtPunHzc5Fkz8D1mG9bSWnYahk9oyd7e+bSqgwpik9O7gXjQeyN2pJBwmgH5cDO5RSok0ODwWXbsIKskbSmlwGW8GLvtMJ3+HD8C+7wzEl8Jfd4ZgS+MvucEwJTkE3/ugIH1NOiQUc4RDMejssFnlMOhv4NFg6J6XSunUjDMEUIAhbwLlCwj4vrlifGsUmmB4sQyRepRr3K5CyJgomDDOmMCHjDsY0pUjB/i74/VRCCo+rAEV3XNZboWKj62qL0Xeugc+eFqzTm8K1oX66iEgR9sUsw9Vr62a/bhfptfGPdB8y81okclGFa0OxFBGRIdyn6qztP4pg4NwSU29DKJGdKmUqwjONtC3PgxialcQoDya9jsl6O2l99pdFZFdEBiKShxAeVNVFEfmfInK/iLwsIv8khLB5kvYcDsfk8XrM+J8OITwQQnhwtP4lEXkyhHBBRJ4crTscjjcpbsWM/7SIPDRa/ors1YD74rFHBDksCj8CVkg9lCAC3BaanKy3hbkvrH8xTgyisW2jsVCMIE2ZgkFxDDjGnspEfg2JHkQzbXfHmouDQbT/a3Px1pTq1nREFwKTUURE+kbwABNhrG58H2i+Ss2a4Eka9duxBFGVdcxBm61JEXqzi1HbPgUBCdQaFLEuQ6Fk+5GC7txOKya4rF3dMPthlducnonEaL/F9tobVv+v3Yztc5JMDa6l1eCkpHg9JXg2O5RMU6zEscpoDALo3+VwHD/DpQpGFNK9GJ36uFquJ/2yBxH5c1X9tqo+Mvrb2RDCqojI6P+VE7blcDhOASf9sn8shHBFVVdE5AlV/cFJTzD6cXhERCRJFt5AFx0Ox+3Aib7sIYQro//XROSPReTDInJNVc+JiIz+Xxtz7GMhhAdDCA9qUjtqF4fDMQHc9MuuqjURSUIIu6PlnxWRfy8iXxORz4nIo6P/Hz/JCQ9CDzmzDdYD+bm9LgpORp+m1xsf8si+C9JcPfRrKX0oAN3RYxEDU+o5IiHqbZAjPUi68TAf0Sd/ewDzGRUINy0SNYZ64uyzo/iGoA4Cl/KFVe5HZSaeO8+h9hiF5uJxZaK8TE00pClZaBRiQMtl+zEowAV876XVg+W167tmv3YbQlEHRCNCtmMGcx/FORtWu3Ut+vD1ss3gK8K1KWXLbW9EX7/djL53l7LvkkLcNjNjxwrLQOPy4ZBvCE8menA/y3DMlNhe2+M3HeCsiPzx6GXMROS/hxD+VFW/JSJfVdXPi8irIvKZE7TlcDhOCTd92UMIL4rIB474+7qIfOJOdMrhcNx+TF6Dbp8aYTM+GbciZmYBM5ACW9mmLi5twbJRuEx0Ur8T7aCeZauMiYRUIZdlzkpQZojMZyxHJBwBCJRdsQrZa3QtqEnO1B7qt2N/C2XSO2/FfrSaXM4ZjoMoQi45lJVAYCOQOwQ3TSGkMM2siTyEDL5SVjbbtndiFtzTz1w9WL76mtWZwzJdWdE+FNUZLIEcl2sLFbNfC8Ztc8uWjs6A8ipX7ThWZ6DPWPZry2bwrYM2XrFktfaqs0eLqbBJji5bj0pxtTa6h/ZheGy8wzEl8Jfd4ZgS+MvucEwJJl/r7UA2frxSDdNV6LvkqA1Pvizud4i2QN8c9itS1phUj/H78VxIXQ04vBd8dvLnC0C9Udkz068kRZqPs/viMlf1xUwpzFJT8qnRf+UQ1lYTao+Ba1ugDMFyLW5cX7N+7sxypO/Qf08LNiQWa5YlYgfkldVrB8uvvRzrubVaNJkCl1aftX4/6rqjfmWB7nt9PlJxPA/S2Kbz4amh0RJk/lXq9pnodaOIZ3PXhmhXa0izAs1MzxWGkXNW3bWX9+hIrqVg+jp2i8PheEvBX3aHY0owcfGKfZokORTqg3Y8ZfRAOhsaKViiOZ5hv41jmsesMTKV8NePs+oMs4cRf+RPYPYduyRG8IGot3Yj0kZbG1FcgUtC43VylB9mgLWb0VzMSBAE26zWqfwTPBUlcEMSos06ECVWrlrzPCuOMeOLlNk2jOfeallBiWd/+NrBcnM7UlktumasCVCs2G09jCgEF0fp+cPhqZIr0AIqbueGdVcQC2dBLISiDVFzv92wbsHuVhzwuSVwJ+j5w+jRXeqHjvw5PSQtGuFfdodjSuAvu8MxJZioGa+i0fxlUx1mR3NOcEETHH6fAoXQmcg4nkmHP/QgyijYQCczS0uFYI2BZKpQkSvQ68MMc892JAGTM1AnMSrq6muxY31yV9AFyg+5MhDxhqWg2qRBDtdWpKiwUjUmjJTr0azMh9zfOP6VGZtYgpGCKEoRgjVvhxrP/dIrNnHy2pXtg2VMEuIKvUNwBZCtERHpY8Ri++hEJhGRHMx9XBYRaYO7kpDLpmBaX78S9ewXVmxSD+py5FT+CQUwkKVixgprCTQpQm9pZe8EGbNLAP+yOxxTAn/ZHY4pgb/sDseUYMIRdOEg/CshfxWjmw5TTaaFuHxIcBJ8Zcpmw/UB7FdgfW84N0enYSRbihRakaPTYJnEC7EwGV9nEerOdZpIw9ksrxmghjpNG41lkupQ05z9RJxnID+0PBcj40y5Yvo2ILVXIMqrgCGRIBzZpvmYK1ejBvyrl26YbVhEz9Txo6zIFIQystTOCeQwfxI0zltwlFynE9c31y2tVYQsRqUxyLtQShroUn6+8XFMSvS8ZBxKud93O6ab1+KcQIlqFJZGNQQ5YdScZ/wmh8PxVoK/7A7HlGCy1FuiUhqVOGLdNkwKSdkWwcQPFAhgGgSOY+otoNmKJajIjG83ommaW7aKMmHgzxSNhUFifSrhg31myq7TBkoN6J7LL1rNtfveCd2gNpCNHICFzzQfnnt4qGQzlHwCn4TN57nlWNapOj9jtuVQ23gDEkleu2JLN924ERNENq83zbarr0SztbkbxzEheikFXbg+lcgOu/HcmAyF5r2IyPY6ROiRazRzX7w2dg+bYGpjmagmJc/U5mIEHT/7mGCEbsH2mqXX+kCfzizYaMZwrGL86Lw33cPhcLwl4C+7wzEl8Jfd4ZgSTNRnTxKV6kgzmzN6MFvrsF42hJj2kAqyCCak0m7DsFI8dY98N1xn8QDsmQmXpRDNci36U30SBmxCqeQBUW831uMf6hAuu3yP9d1W7omhmJRcJR0QdhjAtRwqgw1+Y7Fqa8kN4RuQAG3W7dkOzy/HCj+l2qLZ9p2/eelgefVK1GTf2LC0Fs5pPP831p+/9EKcq1i5P17z3FkrFoka6lmJyi3Dzc5g3gLnR0REdq7HfpVnrD+MWvwZDXgJQo17QN9heWgRkZ2duF4s8WsXtyUJCF+yIAjU/+M6APtzQcf57v5ldzimBP6yOxxTgsmKV4RwUHpJD9ngcfEw84ZRUEcfc7gRMnPAmu6jeZuRO2Ei6FjP62g3IWREx4Ame0LX0gV3ZUjU3sJS5OxWr0Rz/L51u2MG51M6QQG03Etgne/uWLNS03jrr16zohGFTcxYAy17MmEXzsZz7RLVdPEvXzlYbrVAy576e+NSpNe+/y0bQdfvx+uszsexmV20bgfq/A1JNx3pttdejdRet233q9ZiG9U6Rcn1QPRC7XEFKNdUg7JOaw2KbIRnoptYVyOH6MOByb6z58LIzCFT1wcP5C2KV6jqvKr+gar+QFWfVdWPquqiqj6hqs+P/vcSrQ7HmxgnNeP/k4j8aQjhPbJXCupZEfmSiDwZQrggIk+O1h0Ox5sUJ6niOisiPyUi/0xEJITQE5Geqn5aRB4a7fYVEfmGiHzxuLZCiFFdPBuPuQADFmQAUwwj6DgRBuWSUy4vBeZNH6uskkowzqyztpyN0ANTekAzoCY6zW7CKKsebZxdjmbxXefirdm4YTvZApN8btHqpVUX46x1GyZz//Lxl8x+5dl4rrklaxaXYXUW9Onueduc2Q8Tg159acNse/mFKP1cwlJWNKZrq9GF6B9SbEatvWhKsxSzQrJRRtVNy5XY/85WNOMXZ+1+paXIJtQq9hs4aMaB7HdJTnsOxUKiqzFH1cm3MZKSvcNmZFsajdjezCztByouxY5NjuqXRlF+x1RxPcmX/R0icl1E/puqfkdV/+uodPPZEMKqiMjo/5UTtOVwOE4JJ3nZMxH5kIj8lxDCB0WkKa/DZFfVR1T1oqpeHA4aNz/A4XDcEZzkZb8sIpdDCN8crf+B7L3811T1nIjI6P+1ow4OITwWQngwhPBgktaP2sXhcEwAJ6nPflVVL6nqu0MIP5S9muzfH/37nIg8Ovr/8ZOccF8rkDPFBuzHAFIsYwT7cZQcrnLWG2aAYdZRr2V9t15vvL9ty0sdLTQhYqPkOEvK9PGQcEb87f3Iw9GHXH3ZZoM1d6JzO7dA/nY9RpdtQfbWD561bdQha+pdP2YfgzbMCRSGcduhbK1iPPdLz79qtg1hHiMFamxnm8IGYXxY2z6BAUpQaNS2IN0G+MMp+9vRmnzPTOzj/St2gmBQiXMMzcQSS8u1K7EfRB1moFi62Y+O+k7JOu0/unT9YLnds22UhtH/vjqIH8Tlqh2rrUH0lFcSO0dyqTNKhTxcxzz2dewWi38pIr+nqkUReVFE/rnsWQVfVdXPi8irIvKZE7blcDhOASd62UMIT4nIg0ds+sTt7Y7D4bhTmHgV131TkK0NTAopUERaasxHoM2OOxHZ+AmUYTJ6d0yvwX7cPraI1GG/Z8+F0Vnlmo06w2sJJBqxvRnN0bP3Rd5l5Zw1Cau1eNtqszYppDIbzcAdoL9uXLdRclvb0VQ/e49t4+wSUDwQIVYo2gQR0bj+wrM2iaUNFFgJBBkGpKs2sxDpql2KXOvBcVoYL6KRg2BFr2mjDfvDaK6XzhsnyuzX7MX2N8TSmUkSKccB0b3pIB7XDPGeNdXq6GdJNPe7YqnD7V4cxzSN7b+6O2/200rsV61q79m+J3ko6BPgsfEOx5TAX3aHY0rgL7vDMSWYuM++z70dkoUAF4o1sTE7rI8+02H1irjI4bJA8RxH0ZlsNi7ZLEfTclybrttBsQOeEwDBSRK9WN+I/mUHKMGadc+kUot+7syCjV3IqlEccTCIdNvujvVlqyDQ0CORDtTBR331jMotN0DM8flnrpttnS5oqMM8CAt21CGbbf5u6+fuboHgQ9nU3Db7Yc05FgupLMTxeG4Yl692SIAziTQi1/hbC+fH9h8zMjMQpeBw8CvdeO4uPS89KNWNz0RKc1cZaJis9+08zn4W42A4/vvtX3aHY0rgL7vDMSXQwGFod/JkqtdF5BURWRaRGzfZfRLwflh4PyzeDP14vX14WwjhzFEbJvqyH5xU9WII4aggHe+H98P7cYf64Ga8wzEl8Jfd4ZgSnNbL/tgpnZfh/bDwfli8Gfpx2/pwKj67w+GYPNyMdzimBBN92VX1YVX9oaq+oKoTU6NV1d9W1TVVfRr+NnEpbFW9V1X/YiTH/YyqfuE0+qKqZVX9a1X97qgfv3Ya/YD+pCN9w6+fVj9U9WVV/Z6qPqWqF0+xH3dMtn1iL7uqpiLyn0XkH4rI+0Tks6r6vgmd/ndE5GH622lIYeci8ishhPeKyE+IyC+NxmDSfemKyMdDCB8QkQdE5GFV/YlT6Mc+viB78uT7OK1+/HQI4QGguk6jH3dOtj2EMJF/IvJREfkzWP+yiHx5gue/X0SehvUfisi50fI5EfnhpPoCfXhcRD55mn0RkaqI/I2I+L/9zgAAAi9JREFUfOQ0+iEi50cP8MdF5OundW9E5GURWaa/TbQfIjIrIi/JaC7tdvdjkmb8PSJyCdYvj/52WjhVKWxVvV9EPigi3zyNvoxM56dkTyj0ibAnKHoaY/KbIvKrYtXUT6MfQUT+XFW/raqPnFI/7qhs+yRf9qOEZaaSClDVuoj8oYj8cghh52b73wmEEAYhhAdk78v6YVX9sUn3QVV/TkTWQgjfnvS5j8DHQggfkj0385dU9adOoQ+3JNt+M0zyZb8sIvfC+nkRuTJm30ngRFLYtxuqWpC9F/33Qgh/dJp9EREJIWzJXjWfh0+hHx8TkZ9X1ZdF5H+IyMdV9XdPoR8SQrgy+n9NRP5YRD58Cv24Jdn2m2GSL/u3ROSCqr59pFL7CyLytQmen/E12ZPAFnkdUti3At2rGfVbIvJsCOE3TqsvqnpGVedHyxUR+RkR+cGk+xFC+HII4XwI4X7Zex7+dwjhFyfdD1WtqerM/rKI/KyIPD3pfoQQrorIJVV99+hP+7Ltt6cfd3rigyYaPiUiz4nI34nIv53geX9fRFZFpC97v56fF5El2ZsYen70/+IE+vGTsue6/K2IPDX696lJ90VE3i8i3xn142kR+Xejv098TKBPD0mcoJv0eLxDRL47+vfM/rN5Ss/IAyJycXRv/peILNyufngEncMxJfAIOodjSuAvu8MxJfCX3eGYEvjL7nBMCfxldzimBP6yOxxTAn/ZHY4pgb/sDseU4P8BwVT7Ix8gCtIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test verisindeki herhangi bir resmin doğru tahmin edilip edilmediğini kontrol etmek.\n",
    "# bu kısmı tekrar tekrar çalıştırarak, birkaç resim için inceleme yapılabilir.\n",
    "\n",
    "index = random.randint(0, 49)\n",
    "plt.imshow(test_set_x_flatten[:, index].reshape((num_px, num_px, 3)))\n",
    "cindex = int(np.squeeze(d3[\"Y_hat_test\"])[index])\n",
    "\n",
    "if cindex: pred = \"Kedi\"\n",
    "else: pred = \"Kedi değil\"\n",
    "\n",
    "if test_set_y[0,index] == cindex: sonuc = \"::: DOĞRU TAHMİN ::: \" + pred\n",
    "else: sonuc = \"::: YANLIŞ TAHMİN ::: \" + pred\n",
    "\n",
    "print(sonuc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi de learning_rate parametresinin önemine değinelim. Bu parametre, her veri seti için akıllıca seçilmelidir. Çünkü çok büyük seçilmesi halinde, türevler w ve b değerlerini çok hızlı değiştirecek ve minimum cost değeri kaçırılmış olabilecektir. Aşağıda bu örneğe dair bir gif bulunmaktadır.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/zkcplk/Deep-Learning/main/images/5_bad_learning_rate_gradient_descent.gif\">\n",
    "\n",
    "Eğer bu değer çok küçük seçilirse, o zaman da cost değerinin küçültülebilmesi için daha fazla epoch gerekecek, bu da daha fazla zaman harcatacaktır. \n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/zkcplk/Deep-Learning/main/images/6_good_learning_rate_gradient_descent.gif\">\n",
    "\n",
    "\n",
    "Aşağıda farklı learning_rate değerleri, aynı modelde test edilerek sonuçlar tek bir grafikte sunulmuştur. Learning_rate değerleri değiştikçe, modelin test ve train doğruluk değerlerinin nasıl değiştiğini inceleyebilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate:  0.01\n",
      "Train Accuracy: 99.52153110047847 %\n",
      "Test Accuracy: 68.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "Learning rate:  0.005\n",
      "Train Accuracy: 97.60765550239235 %\n",
      "Test Accuracy: 70.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "Learning rate:  0.003\n",
      "Train Accuracy: 96.17224880382776 %\n",
      "Test Accuracy: 74.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "Learning rate:  0.002\n",
      "Train Accuracy: 93.77990430622009 %\n",
      "Test Accuracy: 74.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "Learning rate:  0.0018\n",
      "Train Accuracy: 92.82296650717703 %\n",
      "Test Accuracy: 74.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "Learning rate:  0.0017\n",
      "Train Accuracy: 92.3444976076555 %\n",
      "Test Accuracy: 74.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "Learning rate:  0.0016\n",
      "Train Accuracy: 91.86602870813397 %\n",
      "Test Accuracy: 72.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "Learning rate:  0.001\n",
      "Train Accuracy: 88.99521531100478 %\n",
      "Test Accuracy: 64.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "Learning rate:  0.0005\n",
      "Train Accuracy: 82.77511961722487 %\n",
      "Test Accuracy: 56.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "Learning rate:  0.0001\n",
      "Train Accuracy: 68.42105263157895 %\n",
      "Test Accuracy: 36.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# farklı learning_rate değerlerinin denenmesi.\n",
    "models = {}\n",
    "learning_rates = [0.01, 0.005, 0.003, 0.002, 0.0018, 0.0017, 0.0016, 0.001, 0.0005, 0.0001]\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "for i in learning_rates:\n",
    "    print (\"Learning rate: \", i)\n",
    "    models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, \n",
    "                           epoch = 1500, learning_rate = i, print_cost = False)\n",
    "    print ('\\n' + \"-------------------------------------------------------\" + '\\n')\n",
    "    plt.plot(np.squeeze(models[str(i)][\"costs\"]), label=str(models[str(i)][\"learning_rate\"]))\n",
    "\n",
    "\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('epochs (x100)')\n",
    "\n",
    "legend = plt.legend(loc='upper right', shadow=True)\n",
    "frame = legend.get_frame()\n",
    "frame.set_facecolor('1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi de son olarak, bilgisayarımızdan 5 farklı resim seçelim, o resimleri modelimizde test edelim. \n",
    "<br>Modelimiz, seçtiğimiz resimlerin kedi resmi olup olmadığını tahmin etsin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelimiz bu resmin KEDİ OLMADIĞINI tahmin etti.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelimiz bu resmin KEDİ OLDUĞUNU tahmin etti.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelimiz bu resmin KEDİ OLDUĞUNU tahmin etti.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelimiz bu resmin KEDİ OLMADIĞINI tahmin etti.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelimiz bu resmin KEDİ OLDUĞUNU tahmin etti.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# veri setinde olmayan resimlerle, modelin denenmesi.\n",
    "from PIL import Image\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "for kedi in range(5):\n",
    "    # resim dosyasının dizini\n",
    "    fname = \"images/kedi\" + str(kedi) +\".jpg\" \n",
    "\n",
    "    # resim dosyası açılır, üstte belirlenen num_px değerine resize olur.\n",
    "    img = Image.open(fname)\n",
    "    im_resized = img.resize(size=(num_px, num_px))\n",
    "    \n",
    "    # resmin piksellerinin standardizasyonu\n",
    "    arr_img = np.asarray(im_resized)\n",
    "    arr_img = arr_img/255.\n",
    "    \n",
    "    # resmin piksellerinin predict() fonksiyonunda kullanılabilir hale getirilmesi.\n",
    "    my_image = arr_img.reshape((1, num_px*num_px*3)).T\n",
    "    \n",
    "    # tahmin işlemi.\n",
    "    my_predicted_image = predict(d3[\"w\"], d3[\"b\"], my_image)\n",
    "\n",
    "    # tahmin etiketinin belirlenmesi.\n",
    "    if np.squeeze(my_predicted_image):\n",
    "        pred = \"KEDİ OLDUĞUNU\"\n",
    "    else:\n",
    "        pred = \"KEDİ OLMADIĞINI\"\n",
    "    \n",
    "    # sonuç.\n",
    "    print(\"Modelimiz bu resmin\", pred ,\"tahmin etti.\")\n",
    "\n",
    "    # tahmin edilen resmin ekranda gösterilmesi.\n",
    "    plt.figure()\n",
    "    plt.imshow(np.asarray(im_resized))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelimiz, verdiğimiz rastgele 5 resmin 4'ünü doğru tahmin etti."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 style=\"text-align:right\">Zeki ÇIPLAK</h4>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}