NeuralNetworkFinal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "[5 rows x 400 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xls = pd.ExcelFile('ex3d1.xlsx')\n",
    "df = pd.read_excel(xls, 'X', header = None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 400)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.read_excel(xls, 'y', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 1)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def z_calc(X, theta):\n",
    "    return np.dot(X, theta.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    return 1/(1+ np.exp(-z)) - 0.00001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_len = 10\n",
    "m = 5000\n",
    "l = 3\n",
    "b1 = np.random.randn(25,)\n",
    "b2 = np.random.randn(10,)\n",
    "b = [b1, b2]\n",
    "def hypothesis(df, theta):\n",
    "    a = []\n",
    "    z = []\n",
    "    for i in range (0, l-1):\n",
    "        z1 = z_calc(df, theta[i]) + b[i]\n",
    "        out = sigmoid(z1)\n",
    "        a.append(out)\n",
    "        z.append(z1)\n",
    "        df = out\n",
    "    return out, a, z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid_grad(z):\n",
    "    return sigmoid(z)*(1 - sigmoid(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randInitializeWeights(Lin, Lout):\n",
    "    epi = (6**1/2) / (Lin + Lout)**0.5\n",
    "    w = np.random.rand(Lout, Lin)*(2*epi) -epi\n",
    "    return w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_layer = 25\n",
    "output =10\n",
    "theta1 = randInitializeWeights(len(df.T), hidden_layer)\n",
    "theta2 = randInitializeWeights(hidden_layer, output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "b1 = np.random.randn(25,)\n",
    "b2 = np.random.randn(10,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "z1 =z_calc(df, theta1) + b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = sigmoid(z1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "z2 = z_calc(a1, theta2) + b2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "a2 = sigmoid(z2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = [theta1, theta2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 400)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 25)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  1,  2,  3,  4,  5,  6,  7,  8,  9], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_arr = y[0].unique()\n",
    "y_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "      <th>6</th>\n",
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       "      <th>9</th>\n",
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       "     0    1    2    3    4    5    6    7    8    9\n",
       "0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "1  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "2  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "3  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "4  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1 = np.zeros([len(df), len(y_arr)])\n",
    "y1 = pd.DataFrame(y1)\n",
    "for i in range(0, len(y_arr)):\n",
    "    for j in range(0, len(y1)):\n",
    "        if y[0][j] == y_arr[i]:\n",
    "            y1.iloc[j, i] = 1\n",
    "        else: \n",
    "            y1.iloc[j, i] = 0\n",
    "y1.head()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "del3 = y1-a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 10)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "del2 = np.dot(del3, theta2) * a1*(1 - a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta1 = np.dot(del2.T, pd.DataFrame(a1)) * 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta2 = np.dot(del3.T, pd.DataFrame(a2)) * 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost_function(y, y_calc, l):\n",
    "    return (np.sum(np.sum(-np.log(y_calc)*y - np.log(1-y_calc)*(1-y))))/m    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = [theta1, theta2]\n",
    "l=3\n",
    "m = len(df)\n",
    "def backpropagation(df, theta, y1, alpha):\n",
    "    out, a, z = hypothesis(df, theta)\n",
    "    delta = []\n",
    "    delta.append(y1-a[-1])\n",
    "    i = l - 2\n",
    "    while i > 0:\n",
    "        delta.append(np.dot(delta[-i], theta[-i])*sigmoid_grad(z[-(i+1)]))\n",
    "        i -= 1\n",
    "    theta[0] = np.dot(delta[-1].T, df) * alpha\n",
    "    for i in range(1, len(theta)):\n",
    "        theta[i] = np.dot(delta[-(i+1)].T, pd.DataFrame(a[0])) * alpha\n",
    "    out, a, z = hypothesis(df, theta)\n",
    "    cost = cost_function(y1, a[-1], 1)\n",
    "    return theta, cost  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in log\n",
      "  \n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in exp\n",
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      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta1 = randInitializeWeights(len(df.T), hidden_layer)\n",
    "theta2 = randInitializeWeights(hidden_layer, output)\n",
    "theta = [theta1, theta2]\n",
    "cost_list = []\n",
    "for i in range(400):\n",
    "    theta, cost= backpropagation(df, theta, y1, 0.005)\n",
    "    cost_list.append(cost)\n",
    "cost_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in exp\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "out, a, z = hypothesis(df, theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
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     "execution_count": 55,
     "metadata": {},
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   "source": [
    "accuracy= 0\n",
    "for i in range(0, len(out)):\n",
    "    for j in range(0, len(out[i])):\n",
    "        if out[i][j] >= 0.5 and y1.iloc[i, j] == 1:\n",
    "            accuracy += 1\n",
    "accuracy/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in log\n",
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      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta1 = randInitializeWeights(len(df.T), hidden_layer)\n",
    "theta2 = randInitializeWeights(hidden_layer, output)\n",
    "theta = [theta1, theta2]\n",
    "cost_list = []\n",
    "for i in range(500):\n",
    "    theta, cost= backpropagation(df, theta, y1, 0.001)\n",
    "    cost_list.append(cost)\n",
    "cost_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "out, a, z = hypothesis(df, theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy= 0\n",
    "for i in range(0, len(out)):\n",
    "    for j in range(0, len(out[i])):\n",
    "        if out[i][j] >= 0.5 and y1.iloc[i, j] == 1:\n",
    "            accuracy += 1\n",
    "accuracy/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in log\n",
      "  \n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in exp\n",
      "  \n"
     ]
    },
    {
     "data": {
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       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841,\n",
       " 6.104148482605234,\n",
       " 62.7128512381391,\n",
       " 16.61591751672536,\n",
       " 14.703920101047721,\n",
       " 11.731155482720531,\n",
       " 77.50154308056841]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta1 = randInitializeWeights(len(df.T), hidden_layer)\n",
    "theta2 = randInitializeWeights(hidden_layer, output)\n",
    "theta = [theta1, theta2]\n",
    "cost_list = []\n",
    "for i in range(300):\n",
    "    theta, cost= backpropagation(df, theta, y1, 0.005)\n",
    "    cost_list.append(cost)\n",
    "cost_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": [
    "plt.figure()\n",
    "plt.scatter(range(0, len(cost_list)), cost_list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in exp\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "out, a, z = hypothesis(df, theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy= 0\n",
    "for i in range(0, len(out)):\n",
    "    for j in range(0, len(out[i])):\n",
    "        if out[i][j] >= 0.5 and y1.iloc[i, j] == 1:\n",
    "            accuracy += 1\n",
    "accuracy/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in log\n",
      "  \n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in exp\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.276461755678473,\n",
       " 55.84662579998282,\n",
       " 0.0,\n",
       " 22.44119895768736,\n",
       " 0.0,\n",
       " 50.753031454338526,\n",
       " 0.0,\n",
       " 40.76429743442143,\n",
       " 0.0,\n",
       " 22.47867390303841,\n",
       " 0.0,\n",
       " 50.82047966093026,\n",
       " 0.0,\n",
       " 40.7643298792305,\n",
       " 0.0,\n",
       " 22.478673915810116,\n",
       " 0.0,\n",
       " 50.82047966090141,\n",
       " 0.0,\n",
       " 40.764329879230004]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta1 = randInitializeWeights(len(df.T), hidden_layer)\n",
    "theta2 = randInitializeWeights(hidden_layer, output)\n",
    "theta = [theta1, theta2]\n",
    "cost_list = []\n",
    "for i in range(20):\n",
    "    theta, cost= backpropagation(df, theta, y1, 0.003)\n",
    "    cost_list.append(cost)\n",
    "cost_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in exp\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "out, a, z = hypothesis(df, theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy= 0\n",
    "for i in range(0, len(out)):\n",
    "    for j in range(0, len(out[i])):\n",
    "        if out[i][j] >= 0.5 and y1.iloc[i, j] == 1:\n",
    "            accuracy += 1\n",
    "accuracy/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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 plt\n",
    "plt.figure()\n",
    "plt.scatter(range(0, len(cost_list)), cost_list)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}