HW1_40001565.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# سوال اول"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "اولین مرحله اضافه کردن کتابخانه های مورد نیاز ماست"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#برای تست مدلم\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import sklearn.linear_model\n",
    "np.random.seed(1) \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "در این مرحله من دیتای خورد را با کتابخانه پانداس میگیرم و برای فهم بهتر نام ستون تعیین میکنم"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>age</th>\n",
       "      <th>name</th>\n",
       "      <th>kind</th>\n",
       "      <th>resave</th>\n",
       "      <th>chanel</th>\n",
       "      <th>time</th>\n",
       "      <th>measure</th>\n",
       "      <th>sex</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45341</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.13</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>112.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12958</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12.95</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18233</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31742</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14381</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>23.76</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  age  name  kind  resave  chanel  time  measure    sex  label\n",
       "0  45341   28     1     1   28.13       1    34        1  112.5      1\n",
       "1  12958   37     2     1   12.95       1    53        1   37.0      0\n",
       "2  18233   27     3     0    0.00       1    28        0   13.0      0\n",
       "3  31742   36     3     0    0.00       1     1        0   34.0      0\n",
       "4  14381   26     4     0   23.76       1    33        0   39.6      0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colNames = [\"id\", \"age\", \"name\",\"kind\",\"resave\", \"chanel\", \"time\",\"measure\", \"sex\", \"label\"]\n",
    "dataset = pd.read_csv(\"data.csv\", names=colNames,delimiter=',',header=None)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "برای درک بهتر ارتباطات در هر ستون و بررسی میزان اثر ان بر برچسب هر سطر ان را روی نمودار میبریم.\n",
    "برای این کار ای دی را نسبت به هر ویژگی و ارتباط ان رسم میکنم"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:487: RuntimeWarning: invalid value encountered in true_divide\n",
      "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
      "C:\\Users\\acer\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1662.38x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(dataset.drop(\"id\", axis=1), hue=\"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "برای مثال همانطور که میبینیم جنسیت فاکتور با ارزشی نیست"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "در این تمرین برای نرمالسازی و بردن داده ها به رنج خاصی از توابع متعدد و کتابخانه های متعدد می توان استفاده کرد برای مثال برای نرمالسازی داده ها و بردن ان ها به رنج -1 و +1 هستند و چون داده هایمان همگی مثبت هستند ابتدا باید با دستور زیر میانگین داده ها را 0 کرد  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x-=np.mean(x, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "بعد میتوان برای بردن داده ها به رنج منفی یک تا یک استفاده کرد یا از سیگموید برای بردن داده ها به رنج صفر و یک استفاده کردtanh(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "از انجا که داده هایمان زیاد نیست که برای پایین اوردن زمان اجرا و همچنین اختیاری بودن از نرمالسازی صرف نظر شد از سویی نرمالسازی ممکن است ارزش پارامتر برای مثال جنسیت را زیاد کند"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "یا دادگان را نرمالسازی کنیم به این صورت که استاندارد در هر ویژگی را حساب کرده تقسیم بر ان میکنیم\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x/=np.std(x,axis=0) فرمول"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "راه حل های دیگری نیز هست مانند تبدیل ماتریس کواریانس به یک ماتریس قطری یا تبدیل ماتریس کواریانس به یک ماتریس همانی"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "استفاده از مرحله پیش پردازش و نرمالسازی تاثیری در نتیجه ما ندارد فقط سرعت کار را بالا میبرد"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# سوال 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "برای تبدیل دادگان به دو دسته مجموعه اموزشی و مجموعه تست داریم"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"age\", \"name\",\"kind\",\"resave\", \"chanel\", \"time\",\"measure\", \"sex\"]\n",
    "x = pd.DataFrame(dataset, columns=columns)\n",
    "\n",
    "columns = ['label']\n",
    "y = pd.DataFrame(dataset, columns=columns)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (8, 49830)\n",
      "The shape of Y is: (1, 49830)\n",
      "I have m = 49830 training examples!\n"
     ]
    }
   ],
   "source": [
    "x=x_train.to_numpy()\n",
    "y=y_train.to_numpy()\n",
    "x=x.reshape(8,49830)\n",
    "y=y.reshape(1,49830)\n",
    "shape_X = x.shape\n",
    "shape_Y = y.shape\n",
    "m = shape_X[1] \n",
    "print ('The shape of X is: ' + str(shape_X))\n",
    "print ('The shape of Y is: ' + str(shape_Y))\n",
    "print ('I have m = %d training examples!' % (m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of test_set_x is: (8, 12458)\n",
      "The shape of test_set_y is: (1, 12458)\n"
     ]
    }
   ],
   "source": [
    "test_set_x=x_test.to_numpy()\n",
    "test_set_y=y_test.to_numpy()\n",
    "test_set_x=test_set_x.reshape(8,12458)\n",
    "test_set_y=test_set_y.reshape(1,12458)\n",
    "shape_test_set_x=test_set_x.shape\n",
    "shape_test_set_y=test_set_y.shape\n",
    "print ('The shape of test_set_x is: ' + str(shape_test_set_x))\n",
    "print ('The shape of test_set_y is: ' + str(shape_test_set_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# سوال 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "شبکه عصبی دولایه شبکه عصبی با یک لایه مخفی است "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "برای اینکار من یک لایه ورودی دو ماتریس وزن براساس تعداد نرون ها باید بسازم "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "تعداد نرون ها براساس مقالات به فاکتورهایی بستگی دارد از جمله این فاکتور ها تعداد نرون های ورودی تعداد نرون های خروجی عامل مقیاس بندی و همچنین تعداد نمونه های موجود در داده آموزشی که در اینجا تعداد نرون ها را 10 در نظر گرفتیم"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "e=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    s = 1 / (1 + np.exp(-z))    \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def layer_sizes(X, Y):\n",
    "    n_x = X.shape[0]\n",
    "    n_h = 10\n",
    "    n_y = Y.shape[0]\n",
    "    return (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    np.random.seed(2)\n",
    "    W1 = np.random.randn(n_h,n_x)* 0.01\n",
    "    b1 = np.zeros((n_h,1))\n",
    "    W2 = np.random.randn(n_y,n_h) * 0.01\n",
    "    b2 = np.zeros((n_y,1))    \n",
    "    print(\"w1:======\",W1,\"%\",\"w2:======\",W2)\n",
    "    assert (W1.shape == (n_h, n_x))\n",
    "    assert (b1.shape == (n_h, 1))\n",
    "    assert (W2.shape == (n_y, n_h))\n",
    "    assert (b2.shape == (n_y, 1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_propagation(X, parameters):\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    Z1 = np.dot(W1,X) + b1\n",
    "    A1 = np.tanh(Z1)\n",
    "    Z2 = np.dot(W2,A1) + b2\n",
    "    A2 = sigmoid(Z2)\n",
    "    assert(A2.shape == (1, X.shape[1]))    \n",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2}\n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cost(A2, Y, parameters):    \n",
    "    m = Y.shape[1] \n",
    "    logprobs = np.multiply(np.log(A2),Y) + np.multiply(np.log(1 - A2),1 - Y)\n",
    "    cost = - np.sum(logprobs) * (1 / m) \n",
    "    cost = np.squeeze(cost)\n",
    "    assert(isinstance(cost, float))\n",
    "#    e.append(cost)\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backward_propagation(parameters, cache, X, Y):\n",
    "    m = X.shape[1]\n",
    "    W1 = parameters[\"W1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    A1 = cache[\"A1\"]\n",
    "    A2 = cache[\"A2\"]\n",
    "    dZ2= A2 - Y\n",
    "    dW2 = 1 / m *(np.dot(dZ2,A1.T))\n",
    "    db2 = 1 / m *(np.sum(dZ2,axis = 1,keepdims = True))\n",
    "    dZ1 = np.dot(W2.T,dZ2) * (1 - np.power(A1, 2))\n",
    "    dW1 = 1 / m *(np.dot(dZ1,X.T))\n",
    "    db1 = 1 / m *(np.sum(dZ1,axis = 1,keepdims = True))\n",
    "    grads = {\"dW1\": dW1,\n",
    "             \"db1\": db1,\n",
    "             \"dW2\": dW2,\n",
    "             \"db2\": db2}\n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters(parameters, grads, learning_rate = 1.2):\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    dW1 = grads[\"dW1\"]\n",
    "    db1 = grads[\"db1\"]\n",
    "    dW2 = grads[\"dW2\"]\n",
    "    db2 = grads[\"db2\"]\n",
    "    W1 = W1 - learning_rate * dW1\n",
    "    b1 = b1 - learning_rate * db1\n",
    "    W2 = W2 - learning_rate * dW2\n",
    "    b2 = b2 - learning_rate * db2\n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nn_model(X, Y, n_h, num_iterations , print_cost=False):\n",
    "    np.random.seed(3)\n",
    "    n_x = layer_sizes(X, Y)[0]\n",
    "    n_y = layer_sizes(X, Y)[2]\n",
    "    parameters = initialize_parameters(n_x,n_h,n_y)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    for i in range(0, num_iterations):         \n",
    "        A2, cache = forward_propagation(X,parameters)        \n",
    "        cost = compute_cost(A2, Y, parameters)\n",
    "        grads = backward_propagation(parameters, cache, X, Y) \n",
    "        parameters = update_parameters(parameters, grads)\n",
    "        e.append(cost)\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(parameters, X):\n",
    "    A2, cache = forward_propagation(X,parameters)\n",
    "    predictions = (A2 > 0.5)    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-4.16757847e-03 -5.62668272e-04 -2.13619610e-02  1.64027081e-02\n",
      "  -1.79343559e-02 -8.41747366e-03  5.02881417e-03 -1.24528809e-02]\n",
      " [-1.05795222e-02 -9.09007615e-03  5.51454045e-03  2.29220801e-02\n",
      "   4.15393930e-04 -1.11792545e-02  5.39058321e-03 -5.96159700e-03]\n",
      " [-1.91304965e-04  1.17500122e-02 -7.47870949e-03  9.02525097e-05\n",
      "  -8.78107893e-03 -1.56434170e-03  2.56570452e-03 -9.88779049e-03]\n",
      " [-3.38821966e-03 -2.36184031e-03 -6.37655012e-03 -1.18761229e-02\n",
      "  -1.42121723e-02 -1.53495196e-03 -2.69056960e-03  2.23136679e-02]\n",
      " [-2.43476758e-02  1.12726505e-03  3.70444537e-03  1.35963386e-02\n",
      "   5.01857207e-03 -8.44213704e-03  9.76147160e-08  5.42352572e-03]\n",
      " [-3.13508197e-03  7.71011738e-03 -1.86809065e-02  1.73118467e-02\n",
      "   1.46767801e-02 -3.35677339e-03  6.11340780e-03  4.79705919e-04]\n",
      " [-8.29135289e-03  8.77102184e-04  1.00036589e-02 -3.81092518e-03\n",
      "  -3.75669423e-03 -7.44707629e-04  4.33496330e-03  1.27837923e-02]\n",
      " [-6.34679305e-03  5.08396243e-03  2.16116006e-03 -1.85861239e-02\n",
      "  -4.19316482e-03 -1.32328898e-03 -3.95702397e-04  3.26003433e-03]\n",
      " [-2.04032305e-02  4.62555231e-04 -6.77675577e-03 -1.43943903e-02\n",
      "   5.24296430e-03  7.35279576e-03 -6.53250268e-03  8.42456282e-03]\n",
      " [-3.81516482e-03  6.64890091e-04 -1.09873895e-02  1.58448706e-02\n",
      "  -2.65944946e-02 -9.14526229e-04  6.95119605e-03 -2.03346655e-02]] % w2:====== [[-0.00189469 -0.00077219  0.00824703  0.01248213 -0.00403892 -0.01384519\n",
      "   0.01367235  0.01217886 -0.00462005  0.00350888]]\n",
      "Cost after iteration 0: 0.690487\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(x, y, n_h = 10, num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 80%\n"
     ]
    }
   ],
   "source": [
    "predictions = predict(parameters, x)\n",
    "print ('Accuracy: %d' % float((np.dot(y,predictions.T) + np.dot(1-y,1-predictions.T))/float(y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# سوال 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "در این قسمت برای درک بهتر صحت مشتق گیری همین کد بالا را برای یک مقدار محاسبه میکنیم و اگر از مقدار کمتر بود یعنی بسیار ناچیز و محاسبات ما درست است"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_propagation(x, theta):\n",
    "    J = theta * x    \n",
    "    return J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "J = 8\n"
     ]
    }
   ],
   "source": [
    "x, theta = 2, 4\n",
    "J = forward_propagation(x, theta)\n",
    "print (\"J = \" + str(J))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backward_propagation(x, theta):\n",
    "    dtheta = x    \n",
    "    return dtheta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dtheta = 2\n"
     ]
    }
   ],
   "source": [
    "x, theta = 2, 4\n",
    "dtheta = backward_propagation(x, theta)\n",
    "print (\"dtheta = \" + str(dtheta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_check(x, theta, epsilon = 1e-7):\n",
    "    thetaplus = theta + epsilon                               \n",
    "    thetaminus = theta - epsilon                              \n",
    "    J_plus = forward_propagation(x, thetaplus)               \n",
    "    J_minus = forward_propagation(x, thetaminus)              \n",
    "    gradapprox = (J_plus - J_minus) / (2 * epsilon)          \n",
    "    grad = backward_propagation(x,theta)    \n",
    "    numerator = np.linalg.norm(grad - gradapprox)                   \n",
    "    denominator = np.linalg.norm(grad) + np.linalg.norm(gradapprox) \n",
    "    difference = numerator / denominator                               \n",
    "    if difference < 1e-7:\n",
    "        print (\"The gradient is correct!\")\n",
    "    else:\n",
    "        print (\"The gradient is wrong!\")\n",
    "    \n",
    "    return difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The gradient is correct!\n",
      "difference = 2.919335883291695e-10\n"
     ]
    }
   ],
   "source": [
    "x, theta = 2, 4\n",
    "difference = gradient_check(x, theta)\n",
    "print(\"difference = \" + str(difference))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# سوال 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "عملکرد مدل ما بر روی هر دو داده اموزشی و ازمایشی خوب است و اختلاف کمی دارد اگر اختلاف زیاد بود میتوانستیم تابع هزینه مربوط به وزن ها را اضافه کنیم یعنی همان ضریب تنظیم تا هر دو دسته به نسبت خوب باشند یا میشد از توابع پیچیده تر نیز استفاده شود"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-4.16757847e-03 -5.62668272e-04 -2.13619610e-02  1.64027081e-02\n",
      "  -1.79343559e-02 -8.41747366e-03  5.02881417e-03 -1.24528809e-02]\n",
      " [-1.05795222e-02 -9.09007615e-03  5.51454045e-03  2.29220801e-02\n",
      "   4.15393930e-04 -1.11792545e-02  5.39058321e-03 -5.96159700e-03]\n",
      " [-1.91304965e-04  1.17500122e-02 -7.47870949e-03  9.02525097e-05\n",
      "  -8.78107893e-03 -1.56434170e-03  2.56570452e-03 -9.88779049e-03]\n",
      " [-3.38821966e-03 -2.36184031e-03 -6.37655012e-03 -1.18761229e-02\n",
      "  -1.42121723e-02 -1.53495196e-03 -2.69056960e-03  2.23136679e-02]\n",
      " [-2.43476758e-02  1.12726505e-03  3.70444537e-03  1.35963386e-02\n",
      "   5.01857207e-03 -8.44213704e-03  9.76147160e-08  5.42352572e-03]\n",
      " [-3.13508197e-03  7.71011738e-03 -1.86809065e-02  1.73118467e-02\n",
      "   1.46767801e-02 -3.35677339e-03  6.11340780e-03  4.79705919e-04]\n",
      " [-8.29135289e-03  8.77102184e-04  1.00036589e-02 -3.81092518e-03\n",
      "  -3.75669423e-03 -7.44707629e-04  4.33496330e-03  1.27837923e-02]\n",
      " [-6.34679305e-03  5.08396243e-03  2.16116006e-03 -1.85861239e-02\n",
      "  -4.19316482e-03 -1.32328898e-03 -3.95702397e-04  3.26003433e-03]\n",
      " [-2.04032305e-02  4.62555231e-04 -6.77675577e-03 -1.43943903e-02\n",
      "   5.24296430e-03  7.35279576e-03 -6.53250268e-03  8.42456282e-03]\n",
      " [-3.81516482e-03  6.64890091e-04 -1.09873895e-02  1.58448706e-02\n",
      "  -2.65944946e-02 -9.14526229e-04  6.95119605e-03 -2.03346655e-02]] % w2:====== [[-0.00189469 -0.00077219  0.00824703  0.01248213 -0.00403892 -0.01384519\n",
      "   0.01367235  0.01217886 -0.00462005  0.00350888]]\n",
      "Cost after iteration 0: 0.690475\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(x, y, n_h = 10, num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 80%\n"
     ]
    }
   ],
   "source": [
    "predictions = predict(parameters, x)\n",
    "print ('Accuracy: %d' % int((np.dot(y,predictions.T) + np.dot(1-y,1-predictions.T))/float(y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-4.16757847e-03 -5.62668272e-04 -2.13619610e-02  1.64027081e-02\n",
      "  -1.79343559e-02 -8.41747366e-03  5.02881417e-03 -1.24528809e-02]\n",
      " [-1.05795222e-02 -9.09007615e-03  5.51454045e-03  2.29220801e-02\n",
      "   4.15393930e-04 -1.11792545e-02  5.39058321e-03 -5.96159700e-03]\n",
      " [-1.91304965e-04  1.17500122e-02 -7.47870949e-03  9.02525097e-05\n",
      "  -8.78107893e-03 -1.56434170e-03  2.56570452e-03 -9.88779049e-03]\n",
      " [-3.38821966e-03 -2.36184031e-03 -6.37655012e-03 -1.18761229e-02\n",
      "  -1.42121723e-02 -1.53495196e-03 -2.69056960e-03  2.23136679e-02]\n",
      " [-2.43476758e-02  1.12726505e-03  3.70444537e-03  1.35963386e-02\n",
      "   5.01857207e-03 -8.44213704e-03  9.76147160e-08  5.42352572e-03]\n",
      " [-3.13508197e-03  7.71011738e-03 -1.86809065e-02  1.73118467e-02\n",
      "   1.46767801e-02 -3.35677339e-03  6.11340780e-03  4.79705919e-04]\n",
      " [-8.29135289e-03  8.77102184e-04  1.00036589e-02 -3.81092518e-03\n",
      "  -3.75669423e-03 -7.44707629e-04  4.33496330e-03  1.27837923e-02]\n",
      " [-6.34679305e-03  5.08396243e-03  2.16116006e-03 -1.85861239e-02\n",
      "  -4.19316482e-03 -1.32328898e-03 -3.95702397e-04  3.26003433e-03]\n",
      " [-2.04032305e-02  4.62555231e-04 -6.77675577e-03 -1.43943903e-02\n",
      "   5.24296430e-03  7.35279576e-03 -6.53250268e-03  8.42456282e-03]\n",
      " [-3.81516482e-03  6.64890091e-04 -1.09873895e-02  1.58448706e-02\n",
      "  -2.65944946e-02 -9.14526229e-04  6.95119605e-03 -2.03346655e-02]] % w2:====== [[-0.00189469 -0.00077219  0.00824703  0.01248213 -0.00403892 -0.01384519\n",
      "   0.01367235  0.01217886 -0.00462005  0.00350888]]\n",
      "Cost after iteration 0: 0.690595\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(test_set_x, test_set_y, n_h = 10, num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 79%\n"
     ]
    }
   ],
   "source": [
    "predictions = predict(parameters, test_set_x)\n",
    "print ('Accuracy: %d' % int((np.dot(test_set_y,predictions.T) + np.dot(1-test_set_y,1-predictions.T))/float(test_set_y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = [79,80]\n",
    "# calculate mean\n",
    "m = sum(results) / len(results)\n",
    "\n",
    "# calculate variance using a list comprehension\n",
    "var_res = sum((xi - m) ** 2 for xi in results) / len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:=== 79.5\n",
      "variance:=== 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"mean:===\",m)\n",
    "print(\"variance:===\",var_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# سوال 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "e=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-4.16757847e-03 -5.62668272e-04 -2.13619610e-02  1.64027081e-02\n",
      "  -1.79343559e-02 -8.41747366e-03  5.02881417e-03 -1.24528809e-02]\n",
      " [-1.05795222e-02 -9.09007615e-03  5.51454045e-03  2.29220801e-02\n",
      "   4.15393930e-04 -1.11792545e-02  5.39058321e-03 -5.96159700e-03]\n",
      " [-1.91304965e-04  1.17500122e-02 -7.47870949e-03  9.02525097e-05\n",
      "  -8.78107893e-03 -1.56434170e-03  2.56570452e-03 -9.88779049e-03]\n",
      " [-3.38821966e-03 -2.36184031e-03 -6.37655012e-03 -1.18761229e-02\n",
      "  -1.42121723e-02 -1.53495196e-03 -2.69056960e-03  2.23136679e-02]\n",
      " [-2.43476758e-02  1.12726505e-03  3.70444537e-03  1.35963386e-02\n",
      "   5.01857207e-03 -8.44213704e-03  9.76147160e-08  5.42352572e-03]\n",
      " [-3.13508197e-03  7.71011738e-03 -1.86809065e-02  1.73118467e-02\n",
      "   1.46767801e-02 -3.35677339e-03  6.11340780e-03  4.79705919e-04]\n",
      " [-8.29135289e-03  8.77102184e-04  1.00036589e-02 -3.81092518e-03\n",
      "  -3.75669423e-03 -7.44707629e-04  4.33496330e-03  1.27837923e-02]\n",
      " [-6.34679305e-03  5.08396243e-03  2.16116006e-03 -1.85861239e-02\n",
      "  -4.19316482e-03 -1.32328898e-03 -3.95702397e-04  3.26003433e-03]\n",
      " [-2.04032305e-02  4.62555231e-04 -6.77675577e-03 -1.43943903e-02\n",
      "   5.24296430e-03  7.35279576e-03 -6.53250268e-03  8.42456282e-03]\n",
      " [-3.81516482e-03  6.64890091e-04 -1.09873895e-02  1.58448706e-02\n",
      "  -2.65944946e-02 -9.14526229e-04  6.95119605e-03 -2.03346655e-02]] % w2:====== [[-0.00189469 -0.00077219  0.00824703  0.01248213 -0.00403892 -0.01384519\n",
      "   0.01367235  0.01217886 -0.00462005  0.00350888]]\n",
      "Cost after iteration 0: 0.690487\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(x, y, n_h = 10, num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6904865164130443,\n",
       " 0.5362036553668209,\n",
       " 0.5313391289591991,\n",
       " 0.5151096284748454,\n",
       " 0.5161412729818964,\n",
       " 0.5038402705884971,\n",
       " 0.5027780020819809,\n",
       " 0.5006846387376667,\n",
       " 0.5001715107826581,\n",
       " 0.4996781601068703,\n",
       " 0.49949031309265446,\n",
       " 0.49934694018523595,\n",
       " 0.4992738698740525,\n",
       " 0.4992217268717696,\n",
       " 0.4991920430891374,\n",
       " 0.499171938695477,\n",
       " 0.49915789004107614,\n",
       " 0.49914656455662465,\n",
       " 0.49913751799152506,\n",
       " 0.4991298948575574,\n",
       " 0.4991232747327105,\n",
       " 0.49911697083760564,\n",
       " 0.4991113053289624,\n",
       " 0.499106521242587,\n",
       " 0.49910120696110827,\n",
       " 0.4990971383490677,\n",
       " 0.4990923157504105,\n",
       " 0.49908781480795467,\n",
       " 0.4990840626252547,\n",
       " 0.4990795842210007,\n",
       " 0.49907610861205015,\n",
       " 0.4990719024361405,\n",
       " 0.49906775714477974,\n",
       " 0.49906413997274773,\n",
       " 0.49905986203375685,\n",
       " 0.4990547186851238,\n",
       " 0.49904892841288195,\n",
       " 0.4990443805360186,\n",
       " 0.49904033369172474,\n",
       " 0.49903650117324905,\n",
       " 0.49903272182419445,\n",
       " 0.4990291218687323,\n",
       " 0.49902602836374443,\n",
       " 0.4990227032154403,\n",
       " 0.4990191125656772,\n",
       " 0.4990167667173854,\n",
       " 0.49901515371273075,\n",
       " 0.49901278773243135,\n",
       " 0.49901052132571316,\n",
       " 0.4990132894160826,\n",
       " 0.49901199385167144,\n",
       " 0.499012433136249,\n",
       " 0.4990149873101343,\n",
       " 0.49901558846190036,\n",
       " 0.4990186247153483,\n",
       " 0.49901915505317634,\n",
       " 0.4990223662498099,\n",
       " 0.4990223535335085,\n",
       " 0.4990253355539792,\n",
       " 0.4990245668225018,\n",
       " 0.49902691466817556,\n",
       " 0.499025180254908,\n",
       " 0.4990260345756787,\n",
       " 0.499021455046335,\n",
       " 0.49901910669540056,\n",
       " 0.49901630738725017,\n",
       " 0.49901688149368706,\n",
       " 0.49901464783987853,\n",
       " 0.49901497088687435,\n",
       " 0.49901284916091865,\n",
       " 0.4990129267186363,\n",
       " 0.4990108931954849,\n",
       " 0.499010780102558,\n",
       " 0.4990088777308889,\n",
       " 0.4990086776130702,\n",
       " 0.4990068809417343,\n",
       " 0.4990066485943739,\n",
       " 0.4990049520385166,\n",
       " 0.499004752574407,\n",
       " 0.4990031121355872,\n",
       " 0.49900297113666675,\n",
       " 0.49900136077491763,\n",
       " 0.4990013091073323,\n",
       " 0.4989996781691445,\n",
       " 0.4989997110221813,\n",
       " 0.4989980287234517,\n",
       " 0.49899814971700607,\n",
       " 0.49899636292044547,\n",
       " 0.49899654008467154,\n",
       " 0.4989946361968654,\n",
       " 0.49899488097503897,\n",
       " 0.4989928303183699,\n",
       " 0.4989931117036956,\n",
       " 0.49899097375198465,\n",
       " 0.4989913735535247,\n",
       " 0.49898909980621536,\n",
       " 0.4989894848342233,\n",
       " 0.49898722830686226,\n",
       " 0.4989878539522164,\n",
       " 0.49898519334948566]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "costt=[]\n",
    "for i in range(len(e)-1):\n",
    "    costt.append(e[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yGu+vAe8A2iR9MnNoIlCsVz4i1gBrqtJuyrwO4JPpVl12BbCiRnon8OZ67z2USgX3QMzMshoZwhoHnJjmPSmT/grw/jwqNRKVir4GYmaW1chqvI8Bj0m6KyJ+OFA+Sf8QEX8wpLUbQcrFgoewzMwyGr4GMljwSNW7oP4LrVSQh7DMzDKauYg+ppWKBd+JbmaW4QDSoHJRvhPdzCxjKANIriviDreSbyQ0M+un4QAi6XfqpP39kNRohEqGsNwDMTOraKYHsnSwtIi465hrM4IlQ1jugZiZVTRyI+FlwG8BUyXdnjk0keTxtmNCciPhmGmumVldjdxIuBPoBOYDGzLprwJ/mEelRqJyUZ6FZWaW0ciNhE8DT0v6UrpiLukCh9MrD4EaC0qFgmdhmZllNHMN5GFJE9NHzT4N3Cnp73Kq14jjpUzMzPprJoCcHBGvAO8D7oyIOcBv5lOtkSdZysQ9EDOzimYCSCl9HvmVwL/kVJ8Ry/eBmJn110wAuZnk2R0/iIj1ks4GtuZTrZHHS5mYmfXX8BMJI+IrwFcy+9uAK/Ko1EjkpUzMzPpr5k70aZLul7RL0o8l/ZOkafVLjg7JfSDugZiZVTQzhHUnsBo4E5gKfDVNG5SkeZK2SOqSdGON41dL6pG0Md0+kqa/K5O2UdLPJV2eHrtL0vbMsdlNtKMlyX0g7oGYmVU0PIQFtEVENmDcJekTgxWQVASWAe8GuoH1klZHxOaqrPdFxJJsQkQ8CsxOzzOZ5Pnn/5bJcn1ErGqi/sek5KVMzMz6aaYH8pKkD0oqptsHgd11yswFuiJiW0QcAFYCC1qo5/uBhyJiXwtlh0SpUODQ4SB5hLuZmTUTQH6fZArvj4AXSb7Ur6lTZiqwI7PfnaZVu0LSdyWtkjS9xvGrgHur0m5Jy9wq6bhaby5psaROSZ09PT11qjq4cjFZrd4zsczMEs0EkE8BiyKiLSJOJwkof16nTK1nhFR/A38VOCsizge+Dtzd7wTJvSdvIZlCXLEUOBd4GzAZuKHWm0fE8ojoiIiOtra2OlUdXKmY/Ko8E8vMLNFMADk/u/ZVROwBLqhTphvI9iimkSzOeERE7I6I/enuZ4E5Vee4Eri/sg5XWubFSOwnuZA/t4l2tKRUcA/EzCyrmQBSSBdRBI5c2K53EX490C5ppqRxJENRq7MZ0h5GxXzg2apzLKRq+KpSRpKAy4FnmmhHS8qVHohnYpmZAc3Nwvo/wOOSVpEMQ10J3DJYgYjolbSEZPipCKyIiE2SbgY6I2I1cJ2k+STPFtkDXF0pL+kskh7MY1Wn/qKkNpIhso3AtU20oyWl9BqIZ2KZmSWauRP9HkmdwCUkX9zvqzEdt1a5NcCaqrSbMq+XUvtph0TE89S46B4RlzRa76FSLiQ9EN8LYmaWaKYHQhow6gaN0ehID8TXQMzMgOaugYxpnoVlZtafA0iDyp6FZWbWjwNIg470QBxAzMwAB5CGVa6B+KmEZmYJB5AGVWZhuQdiZpZwAGlQ3yws90DMzMABpGFHFlP0jYRmZoADSMNKBS9lYmaW5QDSoJKXczcz68cBpEFl30hoZtaPA0iDKsu5exaWmVnCAaRBlR6IF1M0M0s4gDTIy7mbmfXnANIgz8IyM+vPAaRBZc/CMjPrxwGkQV7O3cysv9wDiKR5krZI6pJ0Y43jV0vqkbQx3T6SOXYok746kz5T0pOStkq6L33eeq5KXs7dzKyfXAOIpCKwDLgMmAUslDSrRtb7ImJ2ut2RSf9ZJn1+Jv3TwK0R0Q7sBT6cVxsqyl7O3cysn7x7IHOBrojYFhEHgJXAgmM5oSSRPJd9VZp0N3D5MdWyAcWCkDyEZWZWkXcAmQrsyOx3p2nVrpD0XUmrJE3PpI+X1ClpnaRKkDgVeDkieuucE0mL0/KdPT09x9iUZEl3D2GZmSXyDiCqkVb9DfxV4KyIOB/4OkmPomJGRHQAvwvcJulXGjxnkhixPCI6IqKjra2t+dpXKRXlabxmZqm8A0g3kO1RTAN2ZjNExO6I2J/ufhaYkzm2M/25DfgmcAHwEnCKpNJA58xLqSDfSGhmlso7gKwH2tNZU+OAq4DV2QySpmR25wPPpumTJB2Xvj4NuAjYHBEBPAq8Py2zCHgw11akysWClzIxM0uV6mdpXUT0SloCrAWKwIqI2CTpZqAzIlYD10maD/QCe4Cr0+LnAf9X0mGSQPc3EbE5PXYDsFLSXwLfAT6XZzsqkiEs90DMzCDnAAIQEWuANVVpN2VeLwWW1ij3OPCWAc65jWSG1+uqVChw0LOwzMwA34nelLJ7IGZmRziANKFULPg+EDOzlANIE0oF+T4QM7OUA0gTysWC7wMxM0s5gDShVPR9IGZmFQ4gTUiWMnEPxMwMHECa4vtAzMz6OIA0oVQscNBDWGZmgANIU046rkT3nn28tr+3fmYzs1HOAaQJH/6vM9mz7wC3PfzccFfFzGzYOYA04a0zJrFw7gzufPx5Nu38yXBXx8xsWDmANOmG95zLpOPL/PH9z3DI10PMbAxzAGnSyceX+ZP3zuLpHS9z71MvDHd1zMyGjQNICxbMPpMLz57M3z+yleTxJGZmY48DSAskcdmbp9Dz6n5+/Mr++gXMzEYhB5AWnTdlIgDPvvjKMNfEzGx4OIC06NwpJwGw2QHEzMao3AOIpHmStkjqknRjjeNXS+qRtDHdPpKmz5b0hKRNkr4r6QOZMndJ2p4pMzvvdlSbOL7M9MkTHEDMbMzK9ZG2korAMuDdQDewXtLqzLPNK+6LiCVVafuAD0XEVklnAhskrY2Il9Pj10fEqjzrX895vzTRQ1hmNmbl3QOZC3RFxLaIOACsBBY0UjAinouIrenrncAuoC23mrZg1pkT2f7ST9l3wEubmNnYk3cAmQrsyOx3p2nVrkiHqVZJml59UNJcYBzwg0zyLWmZWyUdV+vNJS2W1Cmps6en5xiaUdt5UyYSAVt+9OqQn9vMbKTLO4CoRlr1jRNfBc6KiPOBrwN39zuBNAX4PHBNRFQexrEUOBd4GzAZuKHWm0fE8ojoiIiOtrah77zMSmdi+TqImY1FeQeQbiDbo5gG7MxmiIjdEVG5meKzwJzKMUkTga8BfxIR6zJlXozEfuBOkqGy1920SRM4aXzJ10HMbEzKO4CsB9olzZQ0DrgKWJ3NkPYwKuYDz6bp44D7gXsi4iu1ykgScDnwTG4tGIQkzvuliWze6QBiZmNPrrOwIqJX0hJgLVAEVkTEJkk3A50RsRq4TtJ8oBfYA1ydFr8SeCdwqqRK2tURsRH4oqQ2kiGyjcC1ebZjMLPOnMiXO3dw+HBQKNQasTMzG51yDSAAEbEGWFOVdlPm9VKSaxrV5b4AfGGAc14yxNVs2XlTTmLfgUO8sGcfZ512wnBXx8zsdeM70Y/RrCknA76QbmZjjwPIMWo/40SKBflCupmNOQ4gx2h8ucivtJ3A5p2vcKD3MI9+fxd3P/68l3k3s1Ev92sgY8F5Uyby8OYfM/evvs7L+w4CcP60k7lgxqRhrpmZWX7cAxkCv35OG+VigV8/p41bP/CrADy5fc8w18rMLF/ugQyB9711Gu9767Qj+//4jS6e2r6Ha3/9V4axVmZm+XIPJAdzZ57K+u17OHTY10HMbPRyAMnBhWdP5tX9vZ6ZZWajmgNIDubOnAz4OoiZjW4OIDmYcvIEZkw+nie37R7uqpiZ5cYBJCdvnzmZ9c/v4bCvg5jZKOUAkpO5Myezd99Btu56bbirYmaWCweQnFx49qkAPLXdw1hmNjo5gORk2qQJTDl5POt8Id3MRikHkJxI4u0zJ/PU9j1eF8vMRiUHkBy9/exT6Xl1P7d87Vm+/yPfE2Jmo4sDSI7ee/4UfvO8M7jz8eeZd9u3mHfbv/PYcz3DXS0zsyGRewCRNE/SFkldkm6scfxqST2SNqbbRzLHFknamm6LMulzJH0vPeft6bPRR5yJ48vcsaiDp/74N/jUgjdx8NBhFq14ipsefIafHTg03NUzMzsmynN8XlIReA54N9ANrAcWRsTmTJ6rgY6IWFJVdjLQCXQAAWwA5kTEXklPAR8H1pE8Lvf2iHhosLp0dHREZ2fnUDWtJT8/eIj/vXYLn/uP7ZzddgJXdkznjWecRPsZJzL1lAmM0DhoZmOYpA0R0VHrWN6r8c4FuiJiW1qRlcACYPOgpRLvAR6OiD1p2YeBeZK+CUyMiCfS9HuAy4FBA8hIML5c5E9/exaXnHs6f/rAM/zNQ98/cuyEcUXecPqJvOH0kzjzlPFMHF9m4oQSJx5X5vjjipwwrsTx44qUiwXKRVEuFigVRbEgSoUCRQkVoChRkJBINtLXJBf2k584WJnZMcs7gEwFdmT2u4G318h3haR3kvRW/jAidgxQdmq6dddIP4qkxcBigBkzZrTYhKF30RtO4xt/dDE/2XeQrbte5fs/epWuXa/Rtes1/qOrh55X9/N63cBeCS7Ja2Vepz/JBJp+ebPJfTt95foHqX7hSjVfHqlD9bmq8w103r5kDVK2r87VMXSg9xhIv/cY4PcxUP5GDDQ4ENQ+0MpgQrNlKn+Y1Dw2QLvrna+p/E2/Q/N/LDX9Hi1Uaqj+fGu0bff/93dw0vjyEL1rn7wDSK3WVf+T/Spwb0Tsl3QtcDdwySBlGzlnkhixHFgOyRBWo5V+vZx8fJmOsybTcdbkfumHDwc/PdDLKz/v5af7k23fgUPsO3CIg4cOc/DQYQ70Hqb3cCTbocNEwOEIDh1Ovl4ORxABUfkJ6c++faLvq6hyrPIa+v9Ss8ezB/rniUze/mX78kfN9GrZodVmzhVUn7dGnuCoL+EYsE0D1G+AX8JATWp1qHigL4gBvzZa+jI7ulAQR6UHMWADW2lds7+T1t6jyfxNn7/5WjVdoqlvvcqh/p9fsZDPiEPeAaQbmJ7ZnwbszGaIiOyt2p8FPp0pe3FV2W+m6dOq0vud8xddoSBOGl/O5S8GM7OhkvcsrPVAu6SZksYBVwGrsxkkTcnszgeeTV+vBS6VNEnSJOBSYG1EvAi8KunCdPbVh4AHc26HmZlVybUHEhG9kpaQBIMisCIiNkm6GeiMiNXAdZLmA73AHuDqtOweSZ8iCUIAN1cuqAMfA+4CJpBcPB/xF9DNzEabXKfxjiQjYRqvmdkvmsGm8fpOdDMza4kDiJmZtcQBxMzMWuIAYmZmLXEAMTOzloyZWViSeoAftlj8NOClIazOLwq3e2xxu8eeRtr+yxHRVuvAmAkgx0JS50DT2EYzt3tscbvHnmNtu4ewzMysJQ4gZmbWEgeQxiwf7goME7d7bHG7x55jaruvgZiZWUvcAzEzs5Y4gJiZWUscQAYhaZ6kLZK6JN043PXJk6Tpkh6V9KykTZI+nqZPlvSwpK3pz0nDXdehJqko6TuS/iXdnynpybTN96XPshl1JJ0iaZWk76ef+6+Nkc/7D9N/489IulfS+NH4mUtaIWmXpGcyaTU/XyVuT7/rvivprY28hwPIACQVgWXAZcAsYKGkWcNbq1z1Av8zIs4DLgT+R9reG4FHIqIdeCTdH20+Tt+DzCB5KuataZv3Ah8ellrl7++Bf42Ic4FfJfkdjOrPW9JU4DqgIyLeTPKcoqsYnZ/5XcC8qrSBPt/LgPZ0Wwx8ppE3cAAZ2FygKyK2RcQBYCWwYJjrlJuIeDEi/l/6+lWSL5OpJG2+O812N3D58NQwH5KmAe8F7kj3BVwCrEqzjLo2A0iaCLwT+BxARByIiJcZ5Z93qgRMkFQCjgdeZBR+5hHx7yQP6csa6PNdANwTiXXAKVVPi63JAWRgU4Edmf3uNG3Uk3QWcAHwJHBG+hhh0p+nD1/NcnEb8L+Aw+n+qcDLEdGb7o/Wz/1soAe4Mx2+u0PSCYzyzzsi/hP4W+AFksDxE2ADY+Mzh4E/35a+7xxABqYaaaN+zrOkE4F/Aj4REa8Md33yJOm3gV0RsSGbXCPraPzcS8Bbgc9ExAXATxllw1W1pGP+C4CZwJnACSTDN9VG42c+mJb+3TuADKwbmJ7ZnwbsHKa6vC4klUmCxxcj4p/T5B9XurLpz13DVb8cXATMl/Q8yRDlJSQ9klPS4Q0YvZ97N9AdEfLDN9YAAAMlSURBVE+m+6tIAspo/rwBfhPYHhE9EXEQ+GfgHYyNzxwG/nxb+r5zABnYeqA9nZ0xjuRC2+phrlNu0rH/zwHPRsTfZQ6tBhalrxcBD77edctLRCyNiGkRcRbJ5/uNiPg94FHg/Wm2UdXmioj4EbBD0hvTpN8ANjOKP+/UC8CFko5P/81X2j3qP/PUQJ/vauBD6WysC4GfVIa6BuM70Qch6bdI/iItAisi4pZhrlJuJP0X4FvA9+i7HvDHJNdBvgzMIPnP9zsRUX1h7heepIuBP4qI35Z0NkmPZDLwHeCDEbF/OOuXB0mzSSYPjAO2AdeQ/FE5qj9vSX8BfIBk5uF3gI+QjPePqs9c0r3AxSRLtv8Y+DPgAWp8vmkw/UeSWVv7gGsiorPueziAmJlZKzyEZWZmLXEAMTOzljiAmJlZSxxAzMysJQ4gZmbWEgcQsxZJOiRpY2Ybsju5JZ2VXUXVbCQq1c9iZgP4WUTMHu5KmA0X90DMhpik5yV9WtJT6faGNP2XJT2SPm/hEUkz0vQzJN0v6el0e0d6qqKkz6bPrvg3SRPS/NdJ2pyeZ+UwNdPMAcTsGEyoGsL6QObYKxExl+Tu3tvStH8kWTL7fOCLwO1p+u3AYxHxqyTrUW1K09uBZRHxJuBl4Io0/UbggvQ81wJI6pB0Rz7NNKvNd6KbtUjSaxFxYo3054FLImJbukDljyLiVEkvAVMi4mCa/mJEnCapB5iWXTojXVL/4fTBP0i6AShHxF9K+lfgNZJlKR6IiNdybqpZTe6BmOUjBng9UJ5asmsxHaLvmuV7SZ6WOQfYkFlF1ux15QBilo8PZH4+kb5+nGTVX4DfA/4jff0I8DE48nz2iQOdVFIBmB4Rj5I8COsU4KhekNnrwX+5mLVugqSNmf1/jYjKVN7jJD1J8kfawjTtOmCFpOtJngZ4TZr+cWC5pA+T9DQ+RvK0vFqKwBcknUzyEKBbI+JlSR3AtRHxkaFqnFk9vgZiNsTSayAdEfHScNfFLE8ewjIzs5a4B2JmZi1xD8TMzFriAGJmZi1xADEzs5Y4gJiZWUscQMzMrCX/H7Z6azdfXtuFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt1\n",
    "\n",
    "# plotting Loss\n",
    "plt1.plot(costt)\n",
    "plt1.ylabel('cost_train')\n",
    "plt1.xlabel(\"Epochs:\")\n",
    "plt1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "e=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-4.16757847e-03 -5.62668272e-04 -2.13619610e-02  1.64027081e-02\n",
      "  -1.79343559e-02 -8.41747366e-03  5.02881417e-03 -1.24528809e-02]\n",
      " [-1.05795222e-02 -9.09007615e-03  5.51454045e-03  2.29220801e-02\n",
      "   4.15393930e-04 -1.11792545e-02  5.39058321e-03 -5.96159700e-03]\n",
      " [-1.91304965e-04  1.17500122e-02 -7.47870949e-03  9.02525097e-05\n",
      "  -8.78107893e-03 -1.56434170e-03  2.56570452e-03 -9.88779049e-03]\n",
      " [-3.38821966e-03 -2.36184031e-03 -6.37655012e-03 -1.18761229e-02\n",
      "  -1.42121723e-02 -1.53495196e-03 -2.69056960e-03  2.23136679e-02]\n",
      " [-2.43476758e-02  1.12726505e-03  3.70444537e-03  1.35963386e-02\n",
      "   5.01857207e-03 -8.44213704e-03  9.76147160e-08  5.42352572e-03]\n",
      " [-3.13508197e-03  7.71011738e-03 -1.86809065e-02  1.73118467e-02\n",
      "   1.46767801e-02 -3.35677339e-03  6.11340780e-03  4.79705919e-04]\n",
      " [-8.29135289e-03  8.77102184e-04  1.00036589e-02 -3.81092518e-03\n",
      "  -3.75669423e-03 -7.44707629e-04  4.33496330e-03  1.27837923e-02]\n",
      " [-6.34679305e-03  5.08396243e-03  2.16116006e-03 -1.85861239e-02\n",
      "  -4.19316482e-03 -1.32328898e-03 -3.95702397e-04  3.26003433e-03]\n",
      " [-2.04032305e-02  4.62555231e-04 -6.77675577e-03 -1.43943903e-02\n",
      "   5.24296430e-03  7.35279576e-03 -6.53250268e-03  8.42456282e-03]\n",
      " [-3.81516482e-03  6.64890091e-04 -1.09873895e-02  1.58448706e-02\n",
      "  -2.65944946e-02 -9.14526229e-04  6.95119605e-03 -2.03346655e-02]] % w2:====== [[-0.00189469 -0.00077219  0.00824703  0.01248213 -0.00403892 -0.01384519\n",
      "   0.01367235  0.01217886 -0.00462005  0.00350888]]\n",
      "Cost after iteration 0: 0.690544\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(test_set_x, test_set_y, n_h = 10, num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 0.5055528971876698,\n",
       " 0.5056970963706349,\n",
       " 0.5056521084266484,\n",
       " 0.5058237815889969,\n",
       " 0.5057158987491034,\n",
       " 0.5058640850672976,\n",
       " 0.5058213234124863,\n",
       " 0.5061581243966532,\n",
       " 0.5059615217183226,\n",
       " 0.5060948122620965,\n",
       " 0.5059745708417337,\n",
       " 0.5063697389488968,\n",
       " 0.5060492920374721,\n",
       " 0.5059049962746276,\n",
       " 0.5057735370150731,\n",
       " 0.5058526613328743,\n",
       " 0.5057974120379506,\n",
       " 0.505659634001293,\n",
       " 0.5056588770198929,\n",
       " 0.5057251289491279,\n",
       " 0.5055377143173422]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "costt=[]\n",
    "for i in range(len(e)-1):\n",
    "    costt.append(e[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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": [
    "import matplotlib.pyplot as plt1\n",
    "\n",
    "# plotting Loss\n",
    "plt1.plot(costt)\n",
    "plt1.ylabel('cost_test')\n",
    "plt1.xlabel(\"Epochs:\")\n",
    "plt1.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# سوال 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "در این بخش خواهیم دید که اگر مقدار ما خیلی بزرگ باشد مقادیر اول پایین می اید ولی بعد از جواب بهینه ما دور میشود همچنین اگر بسیار کم باشد خیلی دیر به جواب بهینه میرسیم ولی نمودار محدب است به جواب میرسیم اما خیلی دیر"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#learning_rate=0.1\n",
    "e=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters(parameters, grads, learning_rate = 0.0001):\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    dW1 = grads[\"dW1\"]\n",
    "    db1 = grads[\"db1\"]\n",
    "    dW2 = grads[\"dW2\"]\n",
    "    db2 = grads[\"db2\"]\n",
    "    W1 = W1 - learning_rate * dW1\n",
    "    b1 = b1 - learning_rate * db1\n",
    "    W2 = W2 - learning_rate * dW2\n",
    "    b2 = b2 - learning_rate * db2\n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-4.16757847e-03 -5.62668272e-04 -2.13619610e-02  1.64027081e-02\n",
      "  -1.79343559e-02 -8.41747366e-03  5.02881417e-03 -1.24528809e-02]\n",
      " [-1.05795222e-02 -9.09007615e-03  5.51454045e-03  2.29220801e-02\n",
      "   4.15393930e-04 -1.11792545e-02  5.39058321e-03 -5.96159700e-03]\n",
      " [-1.91304965e-04  1.17500122e-02 -7.47870949e-03  9.02525097e-05\n",
      "  -8.78107893e-03 -1.56434170e-03  2.56570452e-03 -9.88779049e-03]\n",
      " [-3.38821966e-03 -2.36184031e-03 -6.37655012e-03 -1.18761229e-02\n",
      "  -1.42121723e-02 -1.53495196e-03 -2.69056960e-03  2.23136679e-02]\n",
      " [-2.43476758e-02  1.12726505e-03  3.70444537e-03  1.35963386e-02\n",
      "   5.01857207e-03 -8.44213704e-03  9.76147160e-08  5.42352572e-03]\n",
      " [-3.13508197e-03  7.71011738e-03 -1.86809065e-02  1.73118467e-02\n",
      "   1.46767801e-02 -3.35677339e-03  6.11340780e-03  4.79705919e-04]\n",
      " [-8.29135289e-03  8.77102184e-04  1.00036589e-02 -3.81092518e-03\n",
      "  -3.75669423e-03 -7.44707629e-04  4.33496330e-03  1.27837923e-02]\n",
      " [-6.34679305e-03  5.08396243e-03  2.16116006e-03 -1.85861239e-02\n",
      "  -4.19316482e-03 -1.32328898e-03 -3.95702397e-04  3.26003433e-03]\n",
      " [-2.04032305e-02  4.62555231e-04 -6.77675577e-03 -1.43943903e-02\n",
      "   5.24296430e-03  7.35279576e-03 -6.53250268e-03  8.42456282e-03]\n",
      " [-3.81516482e-03  6.64890091e-04 -1.09873895e-02  1.58448706e-02\n",
      "  -2.65944946e-02 -9.14526229e-04  6.95119605e-03 -2.03346655e-02]] % w2:====== [[-0.00189469 -0.00077219  0.00824703  0.01248213 -0.00403892 -0.01384519\n",
      "   0.01367235  0.01217886 -0.00462005  0.00350888]]\n",
      "Cost after iteration 0: 0.690487\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(x, y, n_h = 10, num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6904865164130443,\n",
       " 0.6904630772545437,\n",
       " 0.6904396340650909,\n",
       " 0.6904161868064652,\n",
       " 0.6903927354408939,\n",
       " 0.6903692799310445,\n",
       " 0.6903458202400171,\n",
       " 0.6903223563313359,\n",
       " 0.6902988881689404,\n",
       " 0.6902754157171757,\n",
       " 0.6902519389407831,\n",
       " 0.6902284578048893,\n",
       " 0.6902049722749971,\n",
       " 0.6901814823169736,\n",
       " 0.6901579878970409,\n",
       " 0.6901344889817631,\n",
       " 0.6901109855380377,\n",
       " 0.6900874775330826,\n",
       " 0.6900639649344265,\n",
       " 0.6900404477098947,\n",
       " 0.6900169258276018,\n",
       " 0.6899933992559365,\n",
       " 0.6899698679635515,\n",
       " 0.6899463319193511,\n",
       " 0.6899227910924793,\n",
       " 0.6898992454523072,\n",
       " 0.6898756949684202,\n",
       " 0.6898521396106059,\n",
       " 0.6898285793488397,\n",
       " 0.6898050141532728,\n",
       " 0.6897814439942168,\n",
       " 0.6897578688421302,\n",
       " 0.6897342886676044,\n",
       " 0.6897107034413476,\n",
       " 0.6896871131341693,\n",
       " 0.6896635177169661,\n",
       " 0.6896399171607038,\n",
       " 0.6896163114364017,\n",
       " 0.6895927005151149,\n",
       " 0.689569084367919,\n",
       " 0.6895454629658894,\n",
       " 0.6895218362800862,\n",
       " 0.6894982042815352,\n",
       " 0.6894745669412097,\n",
       " 0.6894509242300131,\n",
       " 0.6894272761187599,\n",
       " 0.6894036225781591,\n",
       " 0.689379963578796,\n",
       " 0.6893562990911148,\n",
       " 0.6893326290854027,\n",
       " 0.6893089535317741,\n",
       " 0.6892852724001546,\n",
       " 0.6892615856602692,\n",
       " 0.6892378932816268,\n",
       " 0.6892141952335104,\n",
       " 0.689190491484967,\n",
       " 0.6891667820047983,\n",
       " 0.6891430667615553,\n",
       " 0.6891193457235328,\n",
       " 0.6890956188587664,\n",
       " 0.6890718861350331,\n",
       " 0.689048147519852,\n",
       " 0.6890244029804888,\n",
       " 0.6890006524839623,\n",
       " 0.6889768959970529,\n",
       " 0.6889531334863139,\n",
       " 0.6889293649180855,\n",
       " 0.6889055902585105,\n",
       " 0.688881809473552,\n",
       " 0.688858022529015,\n",
       " 0.6888342293905677,\n",
       " 0.6888104300237661,\n",
       " 0.6887866243940798,\n",
       " 0.6887628124669204,\n",
       " 0.6887389942076686,\n",
       " 0.6887151695817044,\n",
       " 0.6886913385544381,\n",
       " 0.6886675010913411,\n",
       " 0.6886436571579754,\n",
       " 0.6886198067200268,\n",
       " 0.6885959497433335,\n",
       " 0.6885720861939181,\n",
       " 0.6885482160380144,\n",
       " 0.6885243392420974,\n",
       " 0.6885004557729091,\n",
       " 0.688476565597483,\n",
       " 0.6884526686831686,\n",
       " 0.6884287649976518,\n",
       " 0.688404854508975,\n",
       " 0.6883809371855537,\n",
       " 0.6883570129961917,\n",
       " 0.688333081910094,\n",
       " 0.6883091438968773,\n",
       " 0.6882851989265771,\n",
       " 0.6882612469696554,\n",
       " 0.6882372879970022,\n",
       " 0.6882133219799386,\n",
       " 0.6881893488902155,\n",
       " 0.6881653687000102,\n",
       " 0.6881413813819242]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#learning rate=0.0001\n",
    "# calculate mean\n",
    "m = sum(e) / len(e)\n",
    "\n",
    "# calculate variance using a list comprehension\n",
    "var_res = sum((xi - m) ** 2 for xi in e) / len(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:=== 0.6893184674424324\n",
      "variance:=== 4.6737188005550175e-07\n"
     ]
    }
   ],
   "source": [
    "print(\"mean:===\",m)\n",
    "print(\"variance:===\",var_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learning_rate=1\n",
    "e=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters(parameters, grads, learning_rate = 2):\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    dW1 = grads[\"dW1\"]\n",
    "    db1 = grads[\"db1\"]\n",
    "    dW2 = grads[\"dW2\"]\n",
    "    db2 = grads[\"db2\"]\n",
    "    W1 = W1 - learning_rate * dW1\n",
    "    b1 = b1 - learning_rate * db1\n",
    "    W2 = W2 - learning_rate * dW2\n",
    "    b2 = b2 - learning_rate * db2\n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-4.16757847e-03 -5.62668272e-04 -2.13619610e-02  1.64027081e-02\n",
      "  -1.79343559e-02 -8.41747366e-03  5.02881417e-03 -1.24528809e-02]\n",
      " [-1.05795222e-02 -9.09007615e-03  5.51454045e-03  2.29220801e-02\n",
      "   4.15393930e-04 -1.11792545e-02  5.39058321e-03 -5.96159700e-03]\n",
      " [-1.91304965e-04  1.17500122e-02 -7.47870949e-03  9.02525097e-05\n",
      "  -8.78107893e-03 -1.56434170e-03  2.56570452e-03 -9.88779049e-03]\n",
      " [-3.38821966e-03 -2.36184031e-03 -6.37655012e-03 -1.18761229e-02\n",
      "  -1.42121723e-02 -1.53495196e-03 -2.69056960e-03  2.23136679e-02]\n",
      " [-2.43476758e-02  1.12726505e-03  3.70444537e-03  1.35963386e-02\n",
      "   5.01857207e-03 -8.44213704e-03  9.76147160e-08  5.42352572e-03]\n",
      " [-3.13508197e-03  7.71011738e-03 -1.86809065e-02  1.73118467e-02\n",
      "   1.46767801e-02 -3.35677339e-03  6.11340780e-03  4.79705919e-04]\n",
      " [-8.29135289e-03  8.77102184e-04  1.00036589e-02 -3.81092518e-03\n",
      "  -3.75669423e-03 -7.44707629e-04  4.33496330e-03  1.27837923e-02]\n",
      " [-6.34679305e-03  5.08396243e-03  2.16116006e-03 -1.85861239e-02\n",
      "  -4.19316482e-03 -1.32328898e-03 -3.95702397e-04  3.26003433e-03]\n",
      " [-2.04032305e-02  4.62555231e-04 -6.77675577e-03 -1.43943903e-02\n",
      "   5.24296430e-03  7.35279576e-03 -6.53250268e-03  8.42456282e-03]\n",
      " [-3.81516482e-03  6.64890091e-04 -1.09873895e-02  1.58448706e-02\n",
      "  -2.65944946e-02 -9.14526229e-04  6.95119605e-03 -2.03346655e-02]] % w2:====== [[-0.00189469 -0.00077219  0.00824703  0.01248213 -0.00403892 -0.01384519\n",
      "   0.01367235  0.01217886 -0.00462005  0.00350888]]\n",
      "Cost after iteration 0: 0.690487\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(x, y, n_h = 10, num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6904865164130443,\n",
       " 0.5073063195196039,\n",
       " 0.5516808202838386,\n",
       " 0.6683248835079965,\n",
       " 0.9834341270252938,\n",
       " 0.5423142576645285,\n",
       " 0.7443663824561055,\n",
       " 0.7538788732718914,\n",
       " 1.4823048186726107,\n",
       " 0.6570954724181776,\n",
       " 0.7620142052004868,\n",
       " 1.3701456653131125,\n",
       " 0.610267047398637,\n",
       " 0.8108078746117507,\n",
       " 1.5569760287821133,\n",
       " 0.7248159550359455,\n",
       " 0.7024750480669713,\n",
       " 1.2761537893279307,\n",
       " 0.5564033332561882,\n",
       " 0.6709071044364326,\n",
       " 1.1483298864581724,\n",
       " 0.5155174567770274,\n",
       " 0.545128692315139,\n",
       " 0.6804691473407668,\n",
       " 0.6312740869767436,\n",
       " 0.9525945376300962,\n",
       " 0.502033750504547,\n",
       " 0.507825481921182,\n",
       " 0.5207649604949619,\n",
       " 0.5701866154292599,\n",
       " 0.5908999091106196,\n",
       " 0.7712148488975817,\n",
       " 0.5297723349245146,\n",
       " 0.6024329859287788,\n",
       " 0.5942732013319478,\n",
       " 0.760984349052102,\n",
       " 0.523429908770547,\n",
       " 0.5672054491628196,\n",
       " 0.5669609339962196,\n",
       " 0.6858997457417408,\n",
       " 0.5472243451334246,\n",
       " 0.6326994138310026,\n",
       " 0.5641350202060015,\n",
       " 0.6775171047022065,\n",
       " 0.5497316142982723,\n",
       " 0.6390838520125166,\n",
       " 0.5620920386330266,\n",
       " 0.6718227370949319,\n",
       " 0.5515313438304917,\n",
       " 0.6437427935779326,\n",
       " 0.5606136454367365,\n",
       " 0.6677932819092972,\n",
       " 0.5528254033572504,\n",
       " 0.6471113643768462,\n",
       " 0.5595213758016168,\n",
       " 0.664836995150626,\n",
       " 0.5537647421343426,\n",
       " 0.6495613197320218,\n",
       " 0.5587094049737913,\n",
       " 0.6626474994318087,\n",
       " 0.5544492324699877,\n",
       " 0.6513467298930233,\n",
       " 0.5580996922393975,\n",
       " 0.6610042942220204,\n",
       " 0.5549457111942785,\n",
       " 0.6526390580132904,\n",
       " 0.5576398191738787,\n",
       " 0.6597633912897515,\n",
       " 0.5553099178551462,\n",
       " 0.6535799008613509,\n",
       " 0.5572934282232965,\n",
       " 0.6588175356317055,\n",
       " 0.5555616198703814,\n",
       " 0.6542161098067626,\n",
       " 0.5570284778676338,\n",
       " 0.658082316896423,\n",
       " 0.5557594274909237,\n",
       " 0.6547063398138415,\n",
       " 0.5568371888443131,\n",
       " 0.657556068719449,\n",
       " 0.555901272208663,\n",
       " 0.6550700337152234,\n",
       " 0.5566938814425886,\n",
       " 0.6571553489608085,\n",
       " 0.5560071094669237,\n",
       " 0.6553304226282916,\n",
       " 0.5565850571342755,\n",
       " 0.6568434898495796,\n",
       " 0.556084015811526,\n",
       " 0.6555146838717433,\n",
       " 0.5564967392917912,\n",
       " 0.6565867240752719,\n",
       " 0.5561334223615604,\n",
       " 0.6556283658529785,\n",
       " 0.5564233686078275,\n",
       " 0.6563805544494321,\n",
       " 0.5561628723269384,\n",
       " 0.6556962498201027,\n",
       " 0.5563650664659712,\n",
       " 0.6562185494821708]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learning rate=2\n",
    "# calculate mean\n",
    "m = sum(e) / len(e)\n",
    "\n",
    "# calculate variance using a list comprehension\n",
    "var_res = sum((xi - m) ** 2 for xi in e) / len(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:=== 0.6575423558925211\n",
      "variance:=== 0.03436728994982522\n"
     ]
    }
   ],
   "source": [
    "print(\"mean:===\",m)\n",
    "print(\"variance:===\",var_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# سوال 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "در این تمرین دیدیم که انتخاب وزن خیلی اهمیت دارد اما در مراتب بعد میتوانیم ان را بهبود دهیم و در دوره های زیاد تاثیر ان را کم کنیم اما وجود معیار درست برای مقداردهی به وزن بسیار با اهمیت است "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "دادن اعداد خیلی بزرگ باعث اشباع و اعداد خیلی کوچک مقدار فعالیت نرون شبکه های بزرگ صفر می شود "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "e=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    np.random.seed(2)\n",
    "    W1 = np.random.randn(n_h,n_x)* 0.01\n",
    "    b1 = np.zeros((n_h,1))\n",
    "    W2 = np.random.randn(n_y,n_h) * 0.01\n",
    "    b2 = np.zeros((n_y,1))    \n",
    "    print(\"w1:======\",W1,\"%\",\"w2:======\",W2)\n",
    "    assert (W1.shape == (n_h, n_x))\n",
    "    assert (b1.shape == (n_h, 1))\n",
    "    assert (W2.shape == (n_y, n_h))\n",
    "    assert (b2.shape == (n_y, 1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-4.16757847e-03 -5.62668272e-04 -2.13619610e-02  1.64027081e-02\n",
      "  -1.79343559e-02 -8.41747366e-03  5.02881417e-03 -1.24528809e-02]\n",
      " [-1.05795222e-02 -9.09007615e-03  5.51454045e-03  2.29220801e-02\n",
      "   4.15393930e-04 -1.11792545e-02  5.39058321e-03 -5.96159700e-03]\n",
      " [-1.91304965e-04  1.17500122e-02 -7.47870949e-03  9.02525097e-05\n",
      "  -8.78107893e-03 -1.56434170e-03  2.56570452e-03 -9.88779049e-03]\n",
      " [-3.38821966e-03 -2.36184031e-03 -6.37655012e-03 -1.18761229e-02\n",
      "  -1.42121723e-02 -1.53495196e-03 -2.69056960e-03  2.23136679e-02]\n",
      " [-2.43476758e-02  1.12726505e-03  3.70444537e-03  1.35963386e-02\n",
      "   5.01857207e-03 -8.44213704e-03  9.76147160e-08  5.42352572e-03]\n",
      " [-3.13508197e-03  7.71011738e-03 -1.86809065e-02  1.73118467e-02\n",
      "   1.46767801e-02 -3.35677339e-03  6.11340780e-03  4.79705919e-04]\n",
      " [-8.29135289e-03  8.77102184e-04  1.00036589e-02 -3.81092518e-03\n",
      "  -3.75669423e-03 -7.44707629e-04  4.33496330e-03  1.27837923e-02]\n",
      " [-6.34679305e-03  5.08396243e-03  2.16116006e-03 -1.85861239e-02\n",
      "  -4.19316482e-03 -1.32328898e-03 -3.95702397e-04  3.26003433e-03]\n",
      " [-2.04032305e-02  4.62555231e-04 -6.77675577e-03 -1.43943903e-02\n",
      "   5.24296430e-03  7.35279576e-03 -6.53250268e-03  8.42456282e-03]\n",
      " [-3.81516482e-03  6.64890091e-04 -1.09873895e-02  1.58448706e-02\n",
      "  -2.65944946e-02 -9.14526229e-04  6.95119605e-03 -2.03346655e-02]] % w2:====== [[-0.00189469 -0.00077219  0.00824703  0.01248213 -0.00403892 -0.01384519\n",
      "   0.01367235  0.01217886 -0.00462005  0.00350888]]\n",
      "Cost after iteration 0: 0.690487\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(x, y, n_h = 10, num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy  80.08830022075055\n"
     ]
    }
   ],
   "source": [
    "predictions = predict(parameters, x)\n",
    "accuracy = float((np.dot(y,predictions.T) + np.dot(1-y,1-predictions.T))/float(y.size)*100)\n",
    "print (\"Accuracy \", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6904865164130443,\n",
       " 0.5362036553668209,\n",
       " 0.5313391289591991,\n",
       " 0.5151096284748454,\n",
       " 0.5161412729818964,\n",
       " 0.5038402705884971,\n",
       " 0.5027780020819809,\n",
       " 0.5006846387376667,\n",
       " 0.5001715107826581,\n",
       " 0.4996781601068703,\n",
       " 0.49949031309265446,\n",
       " 0.49934694018523595,\n",
       " 0.4992738698740525,\n",
       " 0.4992217268717696,\n",
       " 0.4991920430891374,\n",
       " 0.499171938695477,\n",
       " 0.49915789004107614,\n",
       " 0.49914656455662465,\n",
       " 0.49913751799152506,\n",
       " 0.4991298948575574,\n",
       " 0.4991232747327105,\n",
       " 0.49911697083760564,\n",
       " 0.4991113053289624,\n",
       " 0.499106521242587,\n",
       " 0.49910120696110827,\n",
       " 0.4990971383490677,\n",
       " 0.4990923157504105,\n",
       " 0.49908781480795467,\n",
       " 0.4990840626252547,\n",
       " 0.4990795842210007,\n",
       " 0.49907610861205015,\n",
       " 0.4990719024361405,\n",
       " 0.49906775714477974,\n",
       " 0.49906413997274773,\n",
       " 0.49905986203375685,\n",
       " 0.4990547186851238,\n",
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       " 0.4990031121355872,\n",
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       " 0.4990013091073323,\n",
       " 0.4989996781691445,\n",
       " 0.4989997110221813,\n",
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       " 0.49899814971700607,\n",
       " 0.49899636292044547,\n",
       " 0.49899654008467154,\n",
       " 0.4989946361968654,\n",
       " 0.49899488097503897,\n",
       " 0.4989928303183699,\n",
       " 0.4989931117036956,\n",
       " 0.49899097375198465,\n",
       " 0.4989913735535247,\n",
       " 0.49898909980621536,\n",
       " 0.4989894848342233,\n",
       " 0.49898722830686226,\n",
       " 0.4989878539522164,\n",
       " 0.49898519334948566]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "costt_1=[]\n",
    "for i in range(len(e)-1):\n",
    "    costt_1.append(e[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       " 0.4989878539522164]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costt_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "e=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    np.random.seed(2)\n",
    "    W1 = np.random.randn(n_h,n_x)* 2\n",
    "    b1 = np.zeros((n_h,1))\n",
    "    W2 = np.random.randn(n_y,n_h) * 2\n",
    "    b2 = np.zeros((n_y,1))    \n",
    "    print(\"w1:======\",W1,\"%\",\"w2:======\",W2)\n",
    "    assert (W1.shape == (n_h, n_x))\n",
    "    assert (b1.shape == (n_h, 1))\n",
    "    assert (W2.shape == (n_y, n_h))\n",
    "    assert (b2.shape == (n_y, 1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-8.33515695e-01 -1.12533654e-01 -4.27239219e+00  3.28054162e+00\n",
      "  -3.58687117e+00 -1.68349473e+00  1.00576283e+00 -2.49057617e+00]\n",
      " [-2.11590444e+00 -1.81801523e+00  1.10290809e+00  4.58441603e+00\n",
      "   8.30787860e-02 -2.23585089e+00  1.07811664e+00 -1.19231940e+00]\n",
      " [-3.82609930e-02  2.35000244e+00 -1.49574190e+00  1.80505019e-02\n",
      "  -1.75621579e+00 -3.12868341e-01  5.13140904e-01 -1.97755810e+00]\n",
      " [-6.77643932e-01 -4.72368062e-01 -1.27531002e+00 -2.37522457e+00\n",
      "  -2.84243445e+00 -3.06990391e-01 -5.38113920e-01  4.46273358e+00]\n",
      " [-4.86953515e+00  2.25453010e-01  7.40889073e-01  2.71926773e+00\n",
      "   1.00371441e+00 -1.68842741e+00  1.95229432e-05  1.08470514e+00]\n",
      " [-6.27016394e-01  1.54202348e+00 -3.73618131e+00  3.46236933e+00\n",
      "   2.93535602e+00 -6.71354677e-01  1.22268156e+00  9.59411837e-02]\n",
      " [-1.65827058e+00  1.75420437e-01  2.00073177e+00 -7.62185035e-01\n",
      "  -7.51338846e-01 -1.48941526e-01  8.66992660e-01  2.55675846e+00]\n",
      " [-1.26935861e+00  1.01679249e+00  4.32232013e-01 -3.71722477e+00\n",
      "  -8.38632964e-01 -2.64657797e-01 -7.91404794e-02  6.52006867e-01]\n",
      " [-4.08064610e+00  9.25110463e-02 -1.35535115e+00 -2.87887805e+00\n",
      "   1.04859286e+00  1.47055915e+00 -1.30650054e+00  1.68491256e+00]\n",
      " [-7.63032964e-01  1.32978018e-01 -2.19747789e+00  3.16897411e+00\n",
      "  -5.31889891e+00 -1.82905246e-01  1.39023921e+00 -4.06693309e+00]] % w2:====== [[-0.37893853 -0.15443733  1.64940601  2.49642584 -0.80778454 -2.76903733\n",
      "   2.73447085  2.43577127 -0.9240107   0.70177699]]\n",
      "Cost after iteration 0: 1.794631\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(x, y, n_h = 10, num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy  80.08830022075055\n"
     ]
    }
   ],
   "source": [
    "predictions = predict(parameters, x)\n",
    "accuracy = float((np.dot(y,predictions.T) + np.dot(1-y,1-predictions.T))/float(y.size)*100)\n",
    "print (\"Accuracy \", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       " 0.5029440062919537,\n",
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       " 0.5024725033193922,\n",
       " 0.5023643403312231]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "costt_2=[]\n",
    "for i in range(len(e)-1):\n",
    "    costt_2.append(e[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costt_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#مقدار اولیه رندوم برای وزن ها\n",
    "#میانگین و انحراف معیار برای 100 ایپوک اول برای تابع هزیته\n",
    "# calculate mean\n",
    "m = sum(costt_1) / len(costt_1)\n",
    "\n",
    "# calculate variance using a list comprehension\n",
    "var_res = sum((xi - m) ** 2 for xi in costt_1) / len(costt_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:=== 0.5021397005802354\n",
      "variance:=== 0.0003910868791641077\n"
     ]
    }
   ],
   "source": [
    "print(\"mean:===\",m)\n",
    "print(\"variance:===\",var_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#مقدار دوم رندوم برای وزن ها\n",
    "#میانگین و انحراف معیار برای 100 ایپوک اول برای تابع هزیته\n",
    "# calculate mean\n",
    "m = sum(costt_2) / len(costt_2)\n",
    "\n",
    "# calculate variance using a list comprehension\n",
    "var_res = sum((xi - m) ** 2 for xi in costt_2) / len(costt_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:=== 0.5940373929431298\n",
      "variance:=== 0.03577364806060617\n"
     ]
    }
   ],
   "source": [
    "print(\"mean:===\",m)\n",
    "print(\"variance:===\",var_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy= [80.08830022075055 , 80.08830022075055]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "#میانگین و انحراف معیار برای 100 ایپوک اول برای دقت\n",
    "# calculate mean\n",
    "m = sum(accuracy) / len(accuracy)\n",
    "\n",
    "# calculate variance using a list comprehension\n",
    "var_res = sum((xi - m) ** 2 for xi in accuracy) / len(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:=== 80.08830022075055\n",
      "variance:=== 0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"mean:===\",m)\n",
    "print(\"variance:===\",var_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "مهم نیست چه وزنی انتخاب کنیم در مراحل بالاتر جواب درست را به ما نشان می دهد \n",
    "ولی تغییرات در هر دو متفاوت است "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# سوال 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "در این قسمت شبکه عصبی سه لایه است را پیاده سازی می کنیم لایه اول شامل 10 نرون و لایه دوم شامل 5 نرون است توابع فعالساز در هر لایه مخفی را تابع رلو در نظر گرفتیم و تابع فعالساز اخر را سیگموید در نظر می گیریم "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "در این مثال خواهیم دید با اضافه شدن یک لایه جدید مقادیر تابع هزینه متفاوت از دو لایه است و وجود لایه جدید باعث به دست امدن ویژگی های مفید تر است "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "همچنین دقت نسبت به دولایه بالاتر و رشد سریع تری نسبت به دولایه دارد"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#برای تست مدلم\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import sklearn.linear_model\n",
    "np.random.seed(1) \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>age</th>\n",
       "      <th>name</th>\n",
       "      <th>kind</th>\n",
       "      <th>resave</th>\n",
       "      <th>chanel</th>\n",
       "      <th>time</th>\n",
       "      <th>measure</th>\n",
       "      <th>sex</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45341</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.13</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>112.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12958</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12.95</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18233</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31742</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14381</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>23.76</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  age  name  kind  resave  chanel  time  measure    sex  label\n",
       "0  45341   28     1     1   28.13       1    34        1  112.5      1\n",
       "1  12958   37     2     1   12.95       1    53        1   37.0      0\n",
       "2  18233   27     3     0    0.00       1    28        0   13.0      0\n",
       "3  31742   36     3     0    0.00       1     1        0   34.0      0\n",
       "4  14381   26     4     0   23.76       1    33        0   39.6      0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colNames = [\"id\", \"age\", \"name\",\"kind\",\"resave\", \"chanel\", \"time\",\"measure\", \"sex\", \"label\"]\n",
    "dataset = pd.read_csv(\"data.csv\", names=colNames,delimiter=',',header=None)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"age\", \"name\",\"kind\",\"resave\", \"chanel\", \"time\",\"measure\", \"sex\"]\n",
    "x = pd.DataFrame(dataset, columns=columns)\n",
    "\n",
    "columns = ['label']\n",
    "y = pd.DataFrame(dataset, columns=columns)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (8, 49830)\n",
      "The shape of Y is: (1, 49830)\n",
      "I have m = 49830 training examples!\n"
     ]
    }
   ],
   "source": [
    "x=x_train.to_numpy()\n",
    "y=y_train.to_numpy()\n",
    "x=x.reshape(8,49830)\n",
    "y=y.reshape(1,49830)\n",
    "shape_X = x.shape\n",
    "shape_Y = y.shape\n",
    "m = shape_X[1] \n",
    "print ('The shape of X is: ' + str(shape_X))\n",
    "print ('The shape of Y is: ' + str(shape_Y))\n",
    "print ('I have m = %d training examples!' % (m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of test_set_x is: (8, 12458)\n",
      "The shape of test_set_y is: (1, 12458)\n"
     ]
    }
   ],
   "source": [
    "test_set_x=x_test.to_numpy()\n",
    "test_set_y=y_test.to_numpy()\n",
    "test_set_x=test_set_x.reshape(8,12458)\n",
    "test_set_y=test_set_y.reshape(1,12458)\n",
    "shape_test_set_x=test_set_x.shape\n",
    "shape_test_set_y=test_set_y.shape\n",
    "print ('The shape of test_set_x is: ' + str(shape_test_set_x))\n",
    "print ('The shape of test_set_y is: ' + str(shape_test_set_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "e=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    s = 1 / (1 + np.exp(-z))    \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(x):\n",
    "    s=np.maximum(0, x)\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def layer_sizes(X, Y):\n",
    "    n_x = X.shape[0]\n",
    "    n_h_1 = 10\n",
    "    n_h_2 = 5\n",
    "    n_y = Y.shape[0]\n",
    "    return (n_x, n_h_1,n_h_2, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters(n_x, n_h_1,n_h_2, n_y):\n",
    "    np.random.seed(2)\n",
    "    W1 = np.random.randn(n_h_1,n_x)* 0.01\n",
    "    b1 = np.zeros((n_h_1,1))\n",
    "    W2 = np.random.randn(n_h_2,n_h_1) * 0.01\n",
    "    b2 = np.zeros((n_h_2,1))\n",
    "    W3 = np.random.randn(1,n_h_2)* 0.01\n",
    "    b3 = np.zeros((n_y,1))\n",
    "    print(\"w1:======\",W1,\"%\",\"w2:======\",W2)\n",
    "    assert (W1.shape == (n_h_1,n_x))\n",
    "    assert (b1.shape == (n_h_1,1))\n",
    "    assert (W2.shape == (n_h_2,n_h_1))\n",
    "    assert (b2.shape == (n_h_2,1))\n",
    "    assert (W3.shape == (1,n_h_2))\n",
    "    assert (b3.shape == (n_y,1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2,\n",
    "                  \"W3\": W3,\n",
    "                  \"b3\": b3}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_propagation(X, parameters):\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    b3 = parameters[\"b3\"]\n",
    "    Z1 = np.dot(W1,X) + b1\n",
    "    A1 = relu(Z1)\n",
    "    \n",
    "    Z2 = np.dot(W2,A1) + b2\n",
    "    A2 = relu(Z2)\n",
    "    \n",
    "    Z3 = np.dot(W3,A2) + b3\n",
    "    A3 = sigmoid(Z3)\n",
    "    \n",
    "    assert(A3.shape == (1, X.shape[1]))\n",
    "    \n",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2,\n",
    "             \"Z3\": Z3,\n",
    "             \"A3\": A3}\n",
    "    return A3, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cost(A3, Y, parameters):    \n",
    "    m = Y.shape[1] \n",
    "    logprobs = np.multiply(np.log(A3),Y) + np.multiply(np.log(1 - A3),1 - Y)\n",
    "    cost = - np.sum(logprobs) * (1 / m) \n",
    "    cost = np.squeeze(cost)\n",
    "    assert(isinstance(cost, float))\n",
    "#    e.append(cost)\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backward_propagation(parameters, cache, X, Y):\n",
    "    m = X.shape[1]\n",
    "    W1 = parameters[\"W1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    A1 = cache[\"A1\"]\n",
    "    A2 = cache[\"A2\"]\n",
    "    A3 = cache[\"A3\"]\n",
    "    dZ3 = A3 - Y\n",
    "    dW3 = 1./m * np.dot(dZ3, A2.T)\n",
    "    db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)\n",
    "    \n",
    "    dA2 = np.dot(W3.T, dZ3)\n",
    "    dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n",
    "    dW2 = 1./m * np.dot(dZ2, A1.T) \n",
    "    db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)\n",
    "    \n",
    "    dA1 = np.dot(W2.T, dZ2)\n",
    "    dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n",
    "    dW1 = 1./m * np.dot(dZ1, X.T)\n",
    "    db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)\n",
    "    \n",
    "    grads = {\"dW1\": dW1,\n",
    "             \"db1\": db1,\n",
    "             \"dW2\": dW2,\n",
    "             \"db2\": db2,\n",
    "             \"dW3\": dW3,\n",
    "             \"db3\": db3}\n",
    "    \n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters(parameters, grads, learning_rate = 1.2):\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    b3 = parameters[\"b3\"]\n",
    "    \n",
    "    dW1 = grads[\"dW1\"]\n",
    "    db1 = grads[\"db1\"]\n",
    "    dW2 = grads[\"dW2\"]\n",
    "    db2 = grads[\"db2\"]\n",
    "    dW3 = grads[\"dW3\"]\n",
    "    db3 = grads[\"db3\"]\n",
    "    \n",
    "    W1 = W1 - learning_rate * dW1\n",
    "    b1 = b1 - learning_rate * db1\n",
    "    W2 = W2 - learning_rate * dW2\n",
    "    b2 = b2 - learning_rate * db2\n",
    "    W3 = W3 - learning_rate * dW3\n",
    "    b3 = b3 - learning_rate * db3\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2,\n",
    "                  \"W3\": W3,\n",
    "                  \"b3\": b3}    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nn_model(X, Y, n_h_1,n_h_2, num_iterations , print_cost=False):\n",
    "    np.random.seed(3)\n",
    "    n_x = layer_sizes(X, Y)[0]\n",
    "    n_y = layer_sizes(X, Y)[3]\n",
    "    parameters = initialize_parameters(n_x, n_h_1,n_h_2, n_y)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    b3 = parameters[\"b3\"]\n",
    "    for i in range(0, num_iterations):         \n",
    "        A3, cache = forward_propagation(X,parameters) \n",
    "        cost = compute_cost(A3, Y, parameters)\n",
    "        grads = backward_propagation(parameters, cache, X, Y) \n",
    "        parameters = update_parameters(parameters, grads)\n",
    "        e.append(cost)\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(parameters, X):\n",
    "    A3, cache = forward_propagation(X,parameters)\n",
    "    predictions = (A3 > 0.5)    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-4.16757847e-03 -5.62668272e-04 -2.13619610e-02  1.64027081e-02\n",
      "  -1.79343559e-02 -8.41747366e-03  5.02881417e-03 -1.24528809e-02]\n",
      " [-1.05795222e-02 -9.09007615e-03  5.51454045e-03  2.29220801e-02\n",
      "   4.15393930e-04 -1.11792545e-02  5.39058321e-03 -5.96159700e-03]\n",
      " [-1.91304965e-04  1.17500122e-02 -7.47870949e-03  9.02525097e-05\n",
      "  -8.78107893e-03 -1.56434170e-03  2.56570452e-03 -9.88779049e-03]\n",
      " [-3.38821966e-03 -2.36184031e-03 -6.37655012e-03 -1.18761229e-02\n",
      "  -1.42121723e-02 -1.53495196e-03 -2.69056960e-03  2.23136679e-02]\n",
      " [-2.43476758e-02  1.12726505e-03  3.70444537e-03  1.35963386e-02\n",
      "   5.01857207e-03 -8.44213704e-03  9.76147160e-08  5.42352572e-03]\n",
      " [-3.13508197e-03  7.71011738e-03 -1.86809065e-02  1.73118467e-02\n",
      "   1.46767801e-02 -3.35677339e-03  6.11340780e-03  4.79705919e-04]\n",
      " [-8.29135289e-03  8.77102184e-04  1.00036589e-02 -3.81092518e-03\n",
      "  -3.75669423e-03 -7.44707629e-04  4.33496330e-03  1.27837923e-02]\n",
      " [-6.34679305e-03  5.08396243e-03  2.16116006e-03 -1.85861239e-02\n",
      "  -4.19316482e-03 -1.32328898e-03 -3.95702397e-04  3.26003433e-03]\n",
      " [-2.04032305e-02  4.62555231e-04 -6.77675577e-03 -1.43943903e-02\n",
      "   5.24296430e-03  7.35279576e-03 -6.53250268e-03  8.42456282e-03]\n",
      " [-3.81516482e-03  6.64890091e-04 -1.09873895e-02  1.58448706e-02\n",
      "  -2.65944946e-02 -9.14526229e-04  6.95119605e-03 -2.03346655e-02]] % w2:====== [[-0.00189469 -0.00077219  0.00824703  0.01248213 -0.00403892 -0.01384519\n",
      "   0.01367235  0.01217886 -0.00462005  0.00350888]\n",
      " [ 0.00381866  0.00566275  0.00204208  0.01406696 -0.0173796   0.01040824\n",
      "   0.00380472 -0.00217135  0.01173531 -0.02343603]\n",
      " [ 0.01161521  0.00386078 -0.01133133  0.00433093 -0.00304086  0.02585295\n",
      "   0.01835333  0.0044069  -0.00719254 -0.00583415]\n",
      " [-0.0032505  -0.00560235 -0.00902246 -0.00590972 -0.00276179 -0.00516884\n",
      "  -0.0069859  -0.00928892  0.02550438 -0.01473173]\n",
      " [-0.01021415  0.00432396 -0.0032358   0.00423825  0.0079918   0.01262614\n",
      "   0.00751965 -0.00993761  0.01109143 -0.01764918]]\n",
      "Cost after iteration 0: 0.693049\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(x, y, n_h_1=10 ,n_h_2=5 , num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 80%\n"
     ]
    }
   ],
   "source": [
    "predictions = predict(parameters, x)\n",
    "print ('Accuracy: %d' % float((np.dot(y,predictions.T) + np.dot(1-y,1-predictions.T))/float(y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy  80.10234798314269\n"
     ]
    }
   ],
   "source": [
    "\n",
    "predictions = predict(parameters, x)\n",
    "accuracy = float((np.dot(y,predictions.T) + np.dot(1-y,1-predictions.T))/float(y.size)*100)\n",
    "print (\"Accuracy \", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6930485334634853,\n",
       " 0.6004256523011937,\n",
       " 0.5541395901534045,\n",
       " 0.5298405042427816,\n",
       " 0.5162101893366018,\n",
       " 0.5079163181966363,\n",
       " 0.5029878647915711,\n",
       " 0.5014996024313526,\n",
       " 0.5010895536589558,\n",
       " 0.5007564306275608,\n",
       " 0.5004818182108846,\n",
       " 0.50025351093176,\n",
       " 0.5000624948707827,\n",
       " 0.499901973213655,\n",
       " 0.4997664569651146,\n",
       " 0.49965179095128676,\n",
       " 0.4995544331993776,\n",
       " 0.49947157936898245,\n",
       " 0.4994009749925682,\n",
       " 0.4993406073761479,\n",
       " 0.4992889192092432,\n",
       " 0.49924461523670416,\n",
       " 0.4992064263789562,\n",
       " 0.49917350365256463,\n",
       " 0.49914509843403587,\n",
       " 0.4991208064449491,\n",
       " 0.49909972479513315,\n",
       " 0.49908139864451095,\n",
       " 0.4990653432745635,\n",
       " 0.4990512637574924,\n",
       " 0.49903888042138567,\n",
       " 0.4990282864192895,\n",
       " 0.4990191173937542,\n",
       " 0.4990109664255576,\n",
       " 0.4990036007188728,\n",
       " 0.4989970418073226,\n",
       " 0.49899118300144935,\n",
       " 0.49898587992978743,\n",
       " 0.49898103049886017,\n",
       " 0.4989765570426522,\n",
       " 0.4989724737479861,\n",
       " 0.4989687208788241,\n",
       " 0.49896517785146066,\n",
       " 0.49896195305834334,\n",
       " 0.49895908285866425,\n",
       " 0.4989563956998982,\n",
       " 0.49895375890809057,\n",
       " 0.49895121542826154,\n",
       " 0.49894872572063015,\n",
       " 0.4989462811514793,\n",
       " 0.49894410175367276,\n",
       " 0.4989422499084102,\n",
       " 0.4989404883488753,\n",
       " 0.4989388900383115,\n",
       " 0.4989373603290392,\n",
       " 0.4989358066853667,\n",
       " 0.4989344143643549,\n",
       " 0.4989329311465129,\n",
       " 0.4989314067655738,\n",
       " 0.4989299443746855,\n",
       " 0.49892845515113665,\n",
       " 0.4989270104633289,\n",
       " 0.49892587649329595,\n",
       " 0.4989247053975285,\n",
       " 0.4989235879499101,\n",
       " 0.49892250908224617,\n",
       " 0.4989216275418988,\n",
       " 0.498921002312752,\n",
       " 0.49892057957625013,\n",
       " 0.49892019661101894,\n",
       " 0.498919822383359,\n",
       " 0.4989194632840249,\n",
       " 0.49891911897672764,\n",
       " 0.49891875830392957,\n",
       " 0.4989183835574125,\n",
       " 0.4989179752264429,\n",
       " 0.4989175616635482,\n",
       " 0.4989171725265281,\n",
       " 0.49891678828315833,\n",
       " 0.4989163791051132,\n",
       " 0.4989159539708736,\n",
       " 0.4989154995750487,\n",
       " 0.49891504002192105,\n",
       " 0.4989146296512338,\n",
       " 0.49891426564135544,\n",
       " 0.4989138736920148,\n",
       " 0.49891345979558327,\n",
       " 0.4989131495761045,\n",
       " 0.4989128683329305,\n",
       " 0.49891259875933075,\n",
       " 0.49891237730884513,\n",
       " 0.4989121770610019,\n",
       " 0.49891195469203525,\n",
       " 0.49891175432329204,\n",
       " 0.49891156597843883,\n",
       " 0.498911381734216,\n",
       " 0.4989111615472463,\n",
       " 0.49891094308613415,\n",
       " 0.4989107218767457,\n",
       " 0.49891050957925126]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loss_train\n",
    "# calculate mean\n",
    "m = sum(e) / len(e)\n",
    "\n",
    "# calculate variance using a list comprehension\n",
    "var_res = sum((xi - m) ** 2 for xi in e) / len(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:=== 0.5032293376188285\n",
      "variance:=== 0.0005055821484323391\n"
     ]
    }
   ],
   "source": [
    "print(\"mean:===\",m)\n",
    "print(\"variance:===\",var_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SOYpRRUR/uiZkXZp3VURsknQj0BkRaxgOEpuBAeDa0la3kr5AEoAAboyIXen7TwN3AM0kd1nlfqcVQEtzPQf7BznQNzD0iHYzs1pUybBVA3B8mveETPprwEfGKhwRa0lup82m3ZB5H8Dn0ld52VXAqhHSO4F3VFD3SdXSXA/Aa/v7HDzMrKZV8lTdR4BHJN0RES+Mlk/SlyPiDya1dtPM7DR4vLq/j1NamsbIbWY2c1U853GkwJEaa/L8mFcKHq8d8LyHmdW2aibMa15LU9JR86S5mdU6B48qZIetzMxq2WQGj6PycMKpNDRstd+rzM2stlUcPCT9zhhpfzUpNZrGWtzzMDMDqut5rDhSWkTcMeHaTHP1hTpmNRQcPMys5lWySPCDwG8CcyXdkjnVQrIlbU3xKnMzs8oWCW4HOoFlwIZM+uvAH+ZRqelsdnM9rzl4mFmNq2SR4E+An0j6ZvrwQtJnTc0r7cFRS1qa3PMwM6tmzuNBSS3pDn8/AW6X9Jc51WvaavGwlZlZVcFjdkS8BnwIuD0izgF+PZ9qTV+zm+t5/UDNTfWYmR2imuBRTLeAvQT4h5zqM+21NBfd8zCzmldN8LiR5PHpP4uI9ZJOA57Np1rT1+zmet7o7ad/wHuZm1ntqngnwYj4NvDtzPFW4MN5VGo6K60yf/1AP63HNUxxbczMpkY1K8zbJd0r6WVJv5D095Laxy45s/j5VmZm1Q1b3U6yhexbgLnAA2naEUlaKmmLpC5J149w/gpJPZI2pq9PpukfyKRtlHRA0sXpuTskPZc5d3YV7ZiQliYHDzOzioetgLaIyAaLOyR99kgFJBWAlcAFQDewXtKaiNhclvWeiLg6mxARDwFnp9eZA3QB/5jJcm1ErK6i/pNi9izv6WFmVk3P4xVJH5NUSF8fA3aOUWYJ0BURWyPiIHA3cNE46vkR4HsRsW8cZSeVh63MzKoLHr9Pcpvuz4EdJL/QrxyjzFxgW+a4O00r92FJT0paLWneCOcvBb5VlnZTWuZmSY0VtWASeNjKzKy64PEF4PKIaIuIU0iCyR+PUWakPT6i7PgBYEFEnAX8E3DnIRdI1pa8k+Q24ZIVwBnAe4A5wHUjfri0XFKnpM6enp4xqloZ9zzMzKoLHmdln2UVEbuAxWOU6QayPYl2kgctDomInRHRmx5+FTin7BqXAPeWnquVltkRiV6SSfslI314RNwaER0R0dHW1jZGVSvTVF9HQ6HOG0KZWU2rJnjUpQ9EBIYmsceacF8PLJK0UFIDyfDTmmyGtGdRsgx4uuwal1E2ZFUqI0nAxcBTVbRjQiR5lbmZ1bxq7rb6P8CjklaTDD1dAtx0pAIR0S/papIhpwKwKiI2SboR6IyINcA1kpaR7A2yC7iiVF7SApKeyyNll/6GpDaSYbGNwFVVtGPCWvxYdjOrcdWsML9LUidwPskv7Q+NcMvtSOXWAmvL0m7IvF/ByLsUEhHPM8IEe0ScX2m98zC7ud636ppZTaum50EaLMYMGDPd7OZ6du09ONXVMDObMtXMeVjKG0KZWa1z8BgHb0VrZrXOwWMckjmPfiLKl6yYmdUGB49xaGkuMjAYvNHrtR5mVpscPMahtMr8NW9Ha2Y1ysFjHIYeUbLP8x5mVpscPMahxc+3MrMa5+AxDqUn63qhoJnVKgePcfCTdc2s1jl4jMPQboIOHmZWoxw8xuH4hiKSex5mVrscPMahrk60NHmVuZnVLgePcZrd7OdbmVntcvAYJ28IZWa1zMFjnErPtzIzq0UOHuM0u7mePfu8p4eZ1abcg4ekpZK2SOqSdP0I56+Q1CNpY/r6ZObcQCZ9TSZ9oaQfSXpW0j3p/uhH1Ztbmtnx6gE/WdfMalKuwUNSAVgJfBA4E7hM0pkjZL0nIs5OX7dl0vdn0pdl0r8I3BwRi4DdwCfyasNo2lub2XdwgN1+vpWZ1aC8ex5LgK6I2BoRB4G7gYsmckFJItlHfXWadCdw8YRqOQ7trc0AdO/ed7Q/2sxsyuUdPOYC2zLH3WlauQ9LelLSaknzMulNkjolPS6pFCBOAvZERGm2erRr5qq9dRYAL+3ef7Q/2sxsyuUdPDRCWvkkwQPAgog4C/gnkp5EyfyI6AB+F/iSpP9Q4TWTD5eWp8Gns6enp/raH8HcoZ6Hg4eZ1Z68g0c3kO1JtAPbsxkiYmdE9KaHXwXOyZzbnv7cCjwMLAZeAU6UVBztmpnyt0ZER0R0tLW1Tbw1GbOb6zmhqehhKzOrSXkHj/XAovTuqAbgUmBNNoOkUzOHy4Cn0/RWSY3p+5OBc4HNkdze9BDwkbTM5cD9ubZiFO2ts9zzMLOaVBw7y/hFRL+kq4F1QAFYFRGbJN0IdEbEGuAaScuAfmAXcEVa/O3A30gaJAlyfx4Rm9Nz1wF3S/pT4Anga3m2YzTtrc28uNM9DzOrPbkGD4CIWAusLUu7IfN+BbBihHKPAu8c5ZpbSe7kmlLtrc082vUKEUFyE5iZWW3wCvMJaG+dxd6DA37GlZnVHAePCZh7ou+4MrPa5OAxAV4oaGa1ysFjAualCwXd8zCzWuPgMQEtzUVOaCw6eJhZzXHwmABJzG1t9rCVmdUcB48J8kJBM6tFDh4T1N7azEu793tfDzOrKQ4eE9Te2szrvf28tt9b0ppZ7XDwmKDS7brbPO9hZjXEwWOC2n27rpnVIAePCfJCQTOrRQ4eEzS7uZ7jGgrueZhZTXHwmCBJtLfO4qU9Dh5mVjscPCZBe2uzex5mVlMcPCbBvDmzeGHnXgYGvdbDzGpD7sFD0lJJWyR1Sbp+hPNXSOqRtDF9fTJNP1vSY5I2SXpS0kczZe6Q9FymzNl5t+NI3jVvNvsODvDML16fymqYmR01ue4kKKkArAQuALqB9ZLWZLaTLbknIq4uS9sHfDwinpX0FmCDpHURsSc9f21ErM6z/pV69/xWAH784m7efmrLFNfGzCx/efc8lgBdEbE1Ig4CdwMXVVIwIp6JiGfT99uBl4G23Go6AfPnzGLOcQ088eKesTObmc0AeQePucC2zHF3mlbuw+nQ1GpJ88pPSloCNAA/yyTflJa5WVLjpNa6SpJ49/wT+fGLu6eyGmZmR03ewUMjpJXPKj8ALIiIs4B/Au485ALSqcDfAldGxGCavAI4A3gPMAe4bsQPl5ZL6pTU2dPTM/5WVGDx/Fa29uxlz76DuX6Omdl0kHfw6AayPYl2YHs2Q0TsjIje9PCrwDmlc5JagO8CfxQRj2fK7IhEL3A7yfDYYSLi1ojoiIiOtrZ8R7xK8x4eujKzWpB38FgPLJK0UFIDcCmwJpsh7VmULAOeTtMbgHuBuyLi2yOVkSTgYuCp3FpQobPaZ1MneMJDV2ZWA3K92yoi+iVdDawDCsCqiNgk6UagMyLWANdIWgb0A7uAK9LilwDvB06SVEq7IiI2At+Q1EYyLLYRuCrPdlTiuMYiZ7y5hR+752FmNSDX4AEQEWuBtWVpN2TeryCZwygv93Xg66Nc8/xJruakePdbT+S+J7YzMBgU6kaa7jEzmxm8wnwSLZ7Xyhu9/XS9/MZUV8XMLFcOHpPo3W8dXixoZjaTOXhMogUnzaJ1Vj0/fsHBw8xmNgePSSSJxfNbeWKbJ83NbGZz8JhkSxbOoevlN9i2yzsLmtnM5eAxyX7rncmylfs3vjTFNTEzy4+DxySbN2cWSxbO4TtPvESE9/cws5nJwSMHH1o8l609e/lJ96tTXRUzs1w4eOTgg+88lYZiHff+uHuqq2JmlgsHjxzMbq7ngjPfxANP7qBvYHDsAmZmxxgHj5x8aPFcdu09yCNb8n0UvJnZVHDwyMn7T2/jpOMauPcJ33VlZjOPg0dO6gt1/Od3vYUHn/4FP3/1wFRXx8xsUjl45Oj3z12IgC/8w+aproqZ2aRy8MjR/JNmcfUH3sZ3f7qDR57x3IeZzRwOHjlb/p9O47STj+Pz9z/Fgb6Bqa6OmdmkcPDIWWOxwBcufgfP79zHVx7+2VRXx8xsUuQePCQtlbRFUpek60c4f4WkHkkb09cnM+cul/Rs+ro8k36OpJ+m17wl3ct82jr3bSez7F1v4f8+3MWff+/f2flG71RXycxsQnLdhlZSAVgJXAB0A+slrYmI8hnkeyLi6rKyc4DPAx1AABvSsruBrwDLgcdJtrhdCnwvz7ZM1J8s+2UC+Jt/+Rl3Pvo8ly2Zz+L5J/LWk2Yxf84sZjfXM81joJnZkLz3MF8CdEXEVgBJdwMXAZXcfvQbwIMRsSst+yCwVNLDQEtEPJam3wVczDQPHq3HNfDlyxbzmV9bxMqHurjj0edY9cPh87MaCrx5dhOnzm6i7fhG2k5o5OTjG2k9roE5sxpoPa6e2c31tDTX09JUT1N9YeoaY2Y1L+/gMRfYljnuBn5lhHwflvR+4BngDyNi2yhl56av7hHSjwlvO+V4bv7o2dz0X97Bi7v28cLOfWzbtY8drx7g568eYMer+9nw4m56Xu/lQN/ojzapL4jjG4sc11jk+MYisxoKzGoo0txQoKm+QHN9HU31BRqLyc+GQh0NxeFXfaGOhkLys1gQDenPQp0o1iXvi3WiTkrSJerqkp+FuuH3dYK6NF+dkg2xCnVCQJ2EBBLp+SRdaT4zO3blHTxG+g1R/pzyB4BvRUSvpKuAO4Hzj1C2kmsmHy4tJxneYv78+ZXW+aiY1VDkjDe3cMabW0Y8HxHsPTjA7r0H2b3vILv2HuS1A/28ur+P1/b38UZvP28c6OeN3n729vazv2+Avb397Nx7kAN9AxzoG2B/3wC9fYMc6B9gOj4dXiINJpmgQpKYPS7lI5M3OTg0PXvNw/Km5zJHI6aPnj+t25hlxg6Ko2UZNX3Ef/Kj5z+8/GifV0FdK/uICRSo3tH+s+NY/UMnW+vv/Nf3cUJT/aReP+/g0Q3Myxy3A9uzGSJiZ+bwq8AXM2XPKyv7cJrefqRrZq59K3ArQEdHxzT89Tk6KelZHN9YZN6cWRO6VkTQNxD0DQxysH+Qg+nPvoHBofT+waB/6GfQNzjI4GAwUHpF5v1gMBjBwCAMRhDpuYChcxEMHZfqMDAIwfC5GMqXTUuOKcsDw+eT93FYQIyIob8ihq6TOR56f0iZQ64wSnp5+bGvW0nZQzNVlVzxXjGV1KnasqPmPwp/oRz1/4mPqd8aw8r/nRXqJj8A5h081gOLJC0EXgIuBX43m0HSqRGxIz1cBjydvl8H/Jmk1vT4QmBFROyS9Lqk9wI/Aj4OfDnndhzTJNFQFA3FOo5rnOramNlMkGvwiIh+SVeTBIICsCoiNkm6EeiMiDXANZKWAf3ALuCKtOwuSV8gCUAAN5Ymz4FPA3cAzSQT5dN6stzMbKZRrWyV2tHREZ2dnVNdDTOzY4qkDRHRUZ7uFeZmZlY1Bw8zM6uag4eZmVXNwcPMzKrm4GFmZlVz8DAzs6rVzK26knqAF8ZZ/GTglUmszrGgFtsMtdnuWmwz1Ga7x9Pmt0ZEW3lizQSPiZDUOdJ9zjNZLbYZarPdtdhmqM12T2abPWxlZmZVc/AwM7OqOXhU5taprsAUqMU2Q222uxbbDLXZ7klrs+c8zMysau55mJlZ1Rw8jkDSUklbJHVJun6q65MXSfMkPSTpaUmbJH0mTZ8j6UFJz6Y/W8e61rFGUkHSE5L+IT1eKOlHaZvvkdQw1XWcbJJOlLRa0r+n3/mvzvTvWtIfpv+2n5L0LUlNM/G7lrRK0suSnsqkjfjdKnFL+vvtSUnvruazHDxGIakArAQ+CJwJXCbpzKmtVW76gf8eEW8H3gv8t7St1wM/iIhFwA/S45nmMwxvQAbJTpY3p23eDXxiSmqVr78Cvh8RZwDvImn/jP2uJc0FrgE6IuIdJHsLXcrM/K7vAJaWpY323X4QWJS+lgNfqeaDHDxGtwToioitEXEQuBu4aIrrlIuI2BERP07fv07yy2QuSXvvTLPdCVw8NTXMh6R24LeA29JjAecDq9MsM7HNLcD7ga8BRMTBiNjDDP+uSTa+a5ZUBGYBO5iB33VE/AvJpnpZo323FwF3ReJx4ERJp1b6WQ4eo5sLbMscd6dpM5qkBcBiki1+31TaIjj9ecrU1SwXXwL+J6UzdjYAAAPtSURBVDCYHp8E7ImI/vR4Jn7npwE9wO3pcN1tko5jBn/XEfES8BfAiyRB41VgAzP/uy4Z7bud0O84B4/RjbRj/Iy+NU3S8cDfA5+NiNemuj55kvTbwMsRsSGbPELWmfadF4F3A1+JiMXAXmbQENVI0jH+i4CFwFuA40iGbMrNtO96LBP69+7gMbpuYF7muB3YPkV1yZ2kepLA8Y2I+E6a/ItSNzb9+fJU1S8H5wLLJD1PMiR5PklP5MR0aANm5nfeDXRHxI/S49UkwWQmf9e/DjwXET0R0Qd8B3gfM/+7Lhntu53Q7zgHj9GtBxald2Q0kEywrZniOuUiHev/GvB0RPxl5tQa4PL0/eXA/Ue7bnmJiBUR0R4RC0i+23+OiN8DHgI+kmabUW0GiIifA9sk/VKa9GvAZmbwd00yXPVeSbPSf+ulNs/o7zpjtO92DfDx9K6r9wKvloa3KuFFgkcg6TdJ/hotAKsi4qYprlIuJP1H4F+BnzI8/v+/SOY9/g6YT/I/4O9ERPlk3DFP0nnA/4iI35Z0GklPZA7wBPCxiOidyvpNNklnk9wk0ABsBa4k+UNyxn7Xkv4E+CjJnYVPAJ8kGd+fUd+1pG8B55E8PfcXwOeB+xjhu00D6V+T3J21D7gyIjor/iwHDzMzq5aHrczMrGoOHmZmVjUHDzMzq5qDh5mZVc3Bw8zMqubgYTZOkgYkbcy8Jm2ltqQF2Sejmk03xbGzmNko9kfE2VNdCbOp4J6H2SST9LykL0r6t/T1tjT9rZJ+kO6d8ANJ89P0N0m6V9JP0tf70ksVJH013YfiHyU1p/mvkbQ5vc7dU9RMq3EOHmbj11w2bPXRzLnXImIJyQreL6Vpf03yCOyzgG8At6TptwCPRMS7SJ4ztSlNXwSsjIhfBvYAH07TrwcWp9e5CkBSh6Tb8mmm2eG8wtxsnCS9ERHHj5D+PHB+RGxNHzj584g4SdIrwKkR0Zem74iIkyX1AO3ZR2Okj8Z/MN3AB0nXAfUR8aeSvg+8QfLYifsi4o2cm2p2GPc8zPIRo7wfLc9Iss9ZGmB4jvK3SHa5PAfYkHkyrNlR4+Bhlo+PZn4+lr5/lOQJvgC/B/y/9P0PgE/D0J7qLaNdVFIdMC8iHiLZyOpE4LDej1ne/BeL2fg1S9qYOf5+RJRu122U9COSP9AuS9OuAVZJupZkN78r0/TPALdK+gRJD+PTJDvejaQAfF3SbJLNfG6OiD2SOoCrIuKTk9U4syPxnIfZJEvnPDoi4pWprotZXjxsZWZmVXPPw8zMquaeh5mZVc3Bw8zMqubgYWZmVXPwMDOzqjl4mJlZ1Rw8zMysav8fhaGo5X0pIrcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt1\n",
    "\n",
    "# plotting Loss\n",
    "plt1.plot(e)\n",
    "plt1.ylabel('cost_train')\n",
    "plt1.xlabel(\"Epochs:\")\n",
    "plt1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "e=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1:====== [[-4.16757847e-03 -5.62668272e-04 -2.13619610e-02  1.64027081e-02\n",
      "  -1.79343559e-02 -8.41747366e-03  5.02881417e-03 -1.24528809e-02]\n",
      " [-1.05795222e-02 -9.09007615e-03  5.51454045e-03  2.29220801e-02\n",
      "   4.15393930e-04 -1.11792545e-02  5.39058321e-03 -5.96159700e-03]\n",
      " [-1.91304965e-04  1.17500122e-02 -7.47870949e-03  9.02525097e-05\n",
      "  -8.78107893e-03 -1.56434170e-03  2.56570452e-03 -9.88779049e-03]\n",
      " [-3.38821966e-03 -2.36184031e-03 -6.37655012e-03 -1.18761229e-02\n",
      "  -1.42121723e-02 -1.53495196e-03 -2.69056960e-03  2.23136679e-02]\n",
      " [-2.43476758e-02  1.12726505e-03  3.70444537e-03  1.35963386e-02\n",
      "   5.01857207e-03 -8.44213704e-03  9.76147160e-08  5.42352572e-03]\n",
      " [-3.13508197e-03  7.71011738e-03 -1.86809065e-02  1.73118467e-02\n",
      "   1.46767801e-02 -3.35677339e-03  6.11340780e-03  4.79705919e-04]\n",
      " [-8.29135289e-03  8.77102184e-04  1.00036589e-02 -3.81092518e-03\n",
      "  -3.75669423e-03 -7.44707629e-04  4.33496330e-03  1.27837923e-02]\n",
      " [-6.34679305e-03  5.08396243e-03  2.16116006e-03 -1.85861239e-02\n",
      "  -4.19316482e-03 -1.32328898e-03 -3.95702397e-04  3.26003433e-03]\n",
      " [-2.04032305e-02  4.62555231e-04 -6.77675577e-03 -1.43943903e-02\n",
      "   5.24296430e-03  7.35279576e-03 -6.53250268e-03  8.42456282e-03]\n",
      " [-3.81516482e-03  6.64890091e-04 -1.09873895e-02  1.58448706e-02\n",
      "  -2.65944946e-02 -9.14526229e-04  6.95119605e-03 -2.03346655e-02]] % w2:====== [[-0.00189469 -0.00077219  0.00824703  0.01248213 -0.00403892 -0.01384519\n",
      "   0.01367235  0.01217886 -0.00462005  0.00350888]\n",
      " [ 0.00381866  0.00566275  0.00204208  0.01406696 -0.0173796   0.01040824\n",
      "   0.00380472 -0.00217135  0.01173531 -0.02343603]\n",
      " [ 0.01161521  0.00386078 -0.01133133  0.00433093 -0.00304086  0.02585295\n",
      "   0.01835333  0.0044069  -0.00719254 -0.00583415]\n",
      " [-0.0032505  -0.00560235 -0.00902246 -0.00590972 -0.00276179 -0.00516884\n",
      "  -0.0069859  -0.00928892  0.02550438 -0.01473173]\n",
      " [-0.01021415  0.00432396 -0.0032358   0.00423825  0.0079918   0.01262614\n",
      "   0.00751965 -0.00993761  0.01109143 -0.01764918]]\n",
      "Cost after iteration 0: 0.693044\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(test_set_x, test_set_y, n_h_1=10 ,n_h_2=5 , num_iterations = 100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 79%\n"
     ]
    }
   ],
   "source": [
    "predictions = predict(parameters, test_set_x)\n",
    "print ('Accuracy: %d' % float((np.dot(test_set_y,predictions.T) + np.dot(1-test_set_y,1-predictions.T))/float(test_set_y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy  79.58741371006582\n"
     ]
    }
   ],
   "source": [
    "predictions = predict(parameters, test_set_x)\n",
    "accuracy = float((np.dot(test_set_y,predictions.T) + np.dot(1-test_set_y,1-predictions.T))/float(test_set_y.size)*100)\n",
    "print (\"Accuracy \", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6930439221890862,\n",
       " 0.6035501717665223,\n",
       " 0.5588292521540104,\n",
       " 0.5353409449738162,\n",
       " 0.5220890613349186,\n",
       " 0.513909596533525,\n",
       " 0.5093096290848472,\n",
       " 0.5083189330020241,\n",
       " 0.5078694418704345,\n",
       " 0.5075257630356798,\n",
       " 0.5072524147626226,\n",
       " 0.5070289818185835,\n",
       " 0.5068449705106395,\n",
       " 0.5066924842918862,\n",
       " 0.5065654927104066,\n",
       " 0.5064595796527334,\n",
       " 0.5063697459119675,\n",
       " 0.5062927428356955,\n",
       " 0.5062278137664499,\n",
       " 0.5061712486113411,\n",
       " 0.5061233296649443,\n",
       " 0.5060792900511704,\n",
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       " 0.5060041512852153,\n",
       " 0.5059777579767235,\n",
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       " 0.505917316600623,\n",
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       " 0.5057653765260037,\n",
       " 0.5057637600596674,\n",
       " 0.5057623409417876,\n",
       " 0.5057608300879568,\n",
       " 0.505759418789582,\n",
       " 0.5057578516227704,\n",
       " 0.5057558859120272,\n",
       " 0.5057539873959929,\n",
       " 0.5057518294157999,\n",
       " 0.5057493259668168,\n",
       " 0.5057469988618635,\n",
       " 0.5057449533452811,\n",
       " 0.505743034738635,\n",
       " 0.5057416254429211,\n",
       " 0.5057403140117792,\n",
       " 0.5057392356198085,\n",
       " 0.5057381737296269,\n",
       " 0.5057366904676055,\n",
       " 0.5057350694256612,\n",
       " 0.5057334812063143,\n",
       " 0.5057315157458514,\n",
       " 0.5057292360293333,\n",
       " 0.5057275763540201,\n",
       " 0.5057263370474565,\n",
       " 0.5057253365580386,\n",
       " 0.5057242408850999,\n",
       " 0.5057235196195594,\n",
       " 0.5057224158545216,\n",
       " 0.505721432287459,\n",
       " 0.5057205255716922,\n",
       " 0.5057194322866477,\n",
       " 0.5057181779216213,\n",
       " 0.505716768360034,\n",
       " 0.5057154821275174,\n",
       " 0.5057140780796188,\n",
       " 0.5057129062381357,\n",
       " 0.5057116074321171,\n",
       " 0.5057103738142288,\n",
       " 0.5057089012657499,\n",
       " 0.505707566154757,\n",
       " 0.5057065971065056,\n",
       " 0.5057049472822673,\n",
       " 0.505703201042667,\n",
       " 0.5057012040131119,\n",
       " 0.5056987390994638,\n",
       " 0.505696915542769]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt1\n",
    "\n",
    "# plotting Loss\n",
    "plt1.plot(e)\n",
    "plt1.ylabel('cost_test')\n",
    "plt1.xlabel(\"Epochs:\")\n",
    "plt1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learning rate=0.0001\n",
    "# calculate mean\n",
    "m = sum(e) / len(e)\n",
    "\n",
    "# calculate variance using a list comprehension\n",
    "var_res = sum((xi - m) ** 2 for xi in e) / len(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:=== 0.5098857114299079\n",
      "variance:=== 0.00046995949627522945\n"
     ]
    }
   ],
   "source": [
    "print(\"mean:===\",m)\n",
    "print(\"variance:===\",var_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=[80.10234798314269 , 79.58741371006582]\n",
    "\n",
    "#accuracy\n",
    "# calculate mean\n",
    "m = sum(result) / len(result)\n",
    "\n",
    "# calculate variance using a list comprehension\n",
    "var_res = sum((xi - m) ** 2 for xi in result) / len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:=== 79.84488084660425\n",
      "variance:=== 0.06628932639730245\n"
     ]
    }
   ],
   "source": [
    "print(\"mean:===\",m)\n",
    "print(\"variance:===\",var_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# سوال 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "تمامی کد ها بر این اساس نوشته شده است \n",
    "در صورت سوال بیشتر در تحویل حضوری پاسخ خواهم داد"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}