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‎Artificial Neural Networks.ipynb

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{
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"cells": [
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"# first neural network with keras tutorial\n",
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"from numpy import loadtxt\n",
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"import pandas as pd\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Dense"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Pregnancies</th>\n",
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" <th>Glucose</th>\n",
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" <th>BloodPressure</th>\n",
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" <th>SkinThickness</th>\n",
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" <th>Insulin</th>\n",
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" <th>BMI</th>\n",
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" <th>DiabetesPedigreeFunction</th>\n",
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" <th>Age</th>\n",
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" <th>Outcome</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>6</td>\n",
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" <td>148</td>\n",
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" <td>72</td>\n",
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" <td>35</td>\n",
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" <td>0</td>\n",
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" <td>33.6</td>\n",
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" <td>0.627</td>\n",
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" <td>50</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>85</td>\n",
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" <td>66</td>\n",
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" <td>29</td>\n",
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" <td>0</td>\n",
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" <td>26.6</td>\n",
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" <td>0.351</td>\n",
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" <td>31</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>64</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>23.3</td>\n",
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" <td>0.672</td>\n",
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" <td>32</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1</td>\n",
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" <td>89</td>\n",
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" <td>66</td>\n",
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" <td>23</td>\n",
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" <td>94</td>\n",
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" <td>28.1</td>\n",
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" <td>0.167</td>\n",
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" <td>21</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>0</td>\n",
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" <td>40</td>\n",
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" <td>35</td>\n",
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" <td>168</td>\n",
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" <td>43.1</td>\n",
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" <td>2.288</td>\n",
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" <td>33</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>...</td>\n",
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" <tr>\n",
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" <th>763</th>\n",
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" <td>10</td>\n",
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" <td>101</td>\n",
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" <td>76</td>\n",
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" <td>48</td>\n",
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" <td>180</td>\n",
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" <td>32.9</td>\n",
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" <td>0.171</td>\n",
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" <td>63</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
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" <th>764</th>\n",
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" <td>2</td>\n",
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" <td>70</td>\n",
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" <td>27</td>\n",
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" <td>0</td>\n",
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" <td>36.8</td>\n",
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" <td>0.340</td>\n",
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" <td>27</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>765</th>\n",
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" <td>5</td>\n",
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" <td>72</td>\n",
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" <td>23</td>\n",
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" <td>112</td>\n",
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" <td>0.245</td>\n",
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" <td>60</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>30.1</td>\n",
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" <td>0.349</td>\n",
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" <td>47</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>767</th>\n",
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" <td>1</td>\n",
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" <td>70</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>768 rows ×ばつ 9 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
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"0 6 148 72 35 0 33.6 \n",
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"1 1 85 66 29 0 26.6 \n",
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"2 8 183 64 0 0 23.3 \n",
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"3 1 89 66 23 94 28.1 \n",
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"4 0 137 40 35 168 43.1 \n",
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".. ... ... ... ... ... ... \n",
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"763 10 101 76 48 180 32.9 \n",
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"764 2 122 70 27 0 36.8 \n",
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"765 5 121 72 23 112 26.2 \n",
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"766 1 126 60 0 0 30.1 \n",
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"767 1 93 70 31 0 30.4 \n",
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"\n",
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" DiabetesPedigreeFunction Age Outcome \n",
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"0 0.627 50 1 \n",
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"1 0.351 31 0 \n",
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"2 0.672 32 1 \n",
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"3 0.167 21 0 \n",
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"4 2.288 33 1 \n",
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".. ... ... ... \n",
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"763 0.171 63 0 \n",
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"764 0.340 27 0 \n",
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"765 0.245 30 0 \n",
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"766 0.349 47 1 \n",
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"767 0.315 23 0 \n",
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"\n",
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"[768 rows x 9 columns]"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# load the dataset\n",
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"dataset = pd.read_csv('~/Downloads/Data Science/data set/Diabetes.csv')\n",
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"dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# split into input (X) and output (y) variables\n",
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"X = dataset.iloc[:,0:8]\n",
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"y = dataset.iloc[:,8]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# make keras model\n",
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"model = Sequential()\n",
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"model.add(Dense(12, input_dim=8, activation='relu'))\n",
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"model.add(Dense(8, activation='relu'))\n",
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"model.add(Dense(1, activation='sigmoid'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# compile keras model\n",
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"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<keras.callbacks.callbacks.History at 0x1046d5c10>"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# fit model\n",
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"model.fit(X, y, epochs=150, batch_size=10,verbose=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy: 77.86\n"
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]
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}
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],
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"source": [
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"# evaluate the model\n",
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"_, accuracy = model.evaluate(X, y,verbose=0)\n",
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"print('Accuracy: %.2f' % (accuracy*100))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# make class predictions\n",
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"predictions = model.predict_classes(X)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[6.0, 148.0, 72.0, 35.0, 0.0, 33.6, 0.627, 50.0] => 1 (expected 1)\n",
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"[1.0, 85.0, 66.0, 29.0, 0.0, 26.6, 0.35100000000000003, 31.0] => 0 (expected 0)\n",
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"[8.0, 183.0, 64.0, 0.0, 0.0, 23.3, 0.672, 32.0] => 1 (expected 1)\n",
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"[1.0, 89.0, 66.0, 23.0, 94.0, 28.1, 0.16699999999999998, 21.0] => 0 (expected 0)\n",
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"[0.0, 137.0, 40.0, 35.0, 168.0, 43.1, 2.2880000000000003, 33.0] => 1 (expected 1)\n"
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]
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}
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],
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"source": [
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"# summary the first 5 cases\n",
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"for i in range(5):\n",
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" print('%s => %d (expected %d)' % (X.iloc[i].tolist(), predictions[i], y.iloc[i]))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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