├── ANN_Model_Code.ipynb
├── Data.xlsx
├── Features.xlsx
├── MicroGridModel.slx
├── Project.pdf
└── README.md
/ANN_Model_Code.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "nbformat": 4,
3 | "nbformat_minor": 0,
4 | "metadata": {
5 | "colab": {
6 | "name": "ANN Model Code.ipynb",
7 | "provenance": [],
8 | "collapsed_sections": [],
9 | "include_colab_link": true
10 | },
11 | "kernelspec": {
12 | "name": "python3",
13 | "display_name": "Python 3"
14 | },
15 | "accelerator": "GPU"
16 | },
17 | "cells": [
18 | {
19 | "cell_type": "markdown",
20 | "metadata": {
21 | "id": "view-in-github",
22 | "colab_type": "text"
23 | },
24 | "source": [
25 | "
"
26 | ]
27 | },
28 | {
29 | "cell_type": "code",
30 | "metadata": {
31 | "id": "vh_2PFdoc83n",
32 | "colab_type": "code",
33 | "colab": {
34 | "base_uri": "https://localhost:8080/",
35 | "height": 34
36 | },
37 | "outputId": "3c535a50-5853-468d-b9f9-5a1d148a13ea"
38 | },
39 | "source": [
40 | "import numpy as np\n",
41 | "import pandas as pd\n",
42 | "from keras.models import Sequential\n",
43 | "from keras.layers import Dense\n",
44 | "from keras.layers import Dropout\n",
45 | "from keras.wrappers.scikit_learn import KerasClassifier\n",
46 | "from sklearn.model_selection import cross_val_score\n",
47 | "from sklearn.preprocessing import LabelEncoder\n",
48 | "from sklearn.model_selection import StratifiedKFold\n",
49 | "from sklearn.preprocessing import StandardScaler\n",
50 | "from sklearn.pipeline import Pipeline"
51 | ],
52 | "execution_count": null,
53 | "outputs": [
54 | {
55 | "output_type": "stream",
56 | "text": [
57 | "Using TensorFlow backend.\n"
58 | ],
59 | "name": "stderr"
60 | }
61 | ]
62 | },
63 | {
64 | "cell_type": "code",
65 | "metadata": {
66 | "id": "p4mPCB1wwZQC",
67 | "colab_type": "code",
68 | "colab": {}
69 | },
70 | "source": [
71 | "seed=7\n",
72 | "np.random.seed(seed)"
73 | ],
74 | "execution_count": null,
75 | "outputs": []
76 | },
77 | {
78 | "cell_type": "code",
79 | "metadata": {
80 | "id": "dOR-kUC5dPB7",
81 | "colab_type": "code",
82 | "colab": {
83 | "base_uri": "https://localhost:8080/",
84 | "height": 360
85 | },
86 | "outputId": "eb51bf24-927c-49ee-851f-91541dde974c"
87 | },
88 | "source": [
89 | "df=pd.read_excel('Data.xlsx')\n",
90 | "df1=df.values\n",
91 | "df"
92 | ],
93 | "execution_count": null,
94 | "outputs": [
95 | {
96 | "output_type": "error",
97 | "ename": "FileNotFoundError",
98 | "evalue": "ignored",
99 | "traceback": [
100 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
101 | "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
102 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Data.xlsx'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
103 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36mread_excel\u001b[0;34m(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m \u001b[0mio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 305\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 306\u001b[0m raise ValueError(\n",
104 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, io, engine)\u001b[0m\n\u001b[1;32m 822\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstringify_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 823\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 824\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 825\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 826\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__fspath__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
105 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_xlrd.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0merr_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Install xlrd >= 1.0.0 for Excel support\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mimport_optional_dependency\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"xlrd\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextra\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
106 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[1;32m 351\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 354\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
107 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_xlrd.py\u001b[0m in \u001b[0;36mload_workbook\u001b[0;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mopen_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mopen_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
108 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/xlrd/__init__.py\u001b[0m in \u001b[0;36mopen_workbook\u001b[0;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0mpeek\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfile_contents\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0mpeek\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpeek\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34mb\"PK\\x03\\x04\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# a ZIP file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
109 | "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Data.xlsx'"
110 | ]
111 | }
112 | ]
113 | },
114 | {
115 | "cell_type": "code",
116 | "metadata": {
117 | "id": "rOOARm0VdcBp",
118 | "colab_type": "code",
119 | "colab": {}
120 | },
121 | "source": [
122 | "po=pd.DataFrame(columns=['current','load','result','Time'])"
123 | ],
124 | "execution_count": null,
125 | "outputs": []
126 | },
127 | {
128 | "cell_type": "code",
129 | "metadata": {
130 | "id": "fbVDvAuRdjpn",
131 | "colab_type": "code",
132 | "colab": {}
133 | },
134 | "source": [
135 | "for i in range(23):\n",
136 | " po=po.append({'current':df.at[i,\"Current 1\"],'load':df.at[i,\"P_L 1\"],'result':1,'Time':df.at[i,\"Time\"]},ignore_index=True)"
137 | ],
138 | "execution_count": null,
139 | "outputs": []
140 | },
141 | {
142 | "cell_type": "code",
143 | "metadata": {
144 | "id": "oGNxLOtXdlhY",
145 | "colab_type": "code",
146 | "colab": {
147 | "base_uri": "https://localhost:8080/",
148 | "height": 470
149 | },
150 | "outputId": "13485257-89c0-4378-e9c9-c63977b1cff6"
151 | },
152 | "source": [
153 | "po"
154 | ],
155 | "execution_count": null,
156 | "outputs": [
157 | {
158 | "output_type": "execute_result",
159 | "data": {
160 | "text/html": [
161 | "\n",
162 | "\n",
175 | "
\n",
176 | " \n",
177 | " \n",
178 | " | \n",
179 | " 0 | \n",
180 | " 1 | \n",
181 | " 2 | \n",
182 | " 3 | \n",
183 | " 4 | \n",
184 | " 5 | \n",
185 | " 6 | \n",
186 | " 7 | \n",
187 | " 8 | \n",
188 | " 9 | \n",
189 | " 10 | \n",
190 | " 11 | \n",
191 | " 12 | \n",
192 | " 13 | \n",
193 | " 14 | \n",
194 | " 15 | \n",
195 | " 16 | \n",
196 | " 17 | \n",
197 | " 18 | \n",
198 | " 19 | \n",
199 | " 20 | \n",
200 | " 21 | \n",
201 | " 22 | \n",
202 | " 23 | \n",
203 | " 24 | \n",
204 | " 25 | \n",
205 | " 26 | \n",
206 | " 27 | \n",
207 | " 28 | \n",
208 | " 29 | \n",
209 | " 30 | \n",
210 | " 31 | \n",
211 | " 32 | \n",
212 | " 33 | \n",
213 | " 34 | \n",
214 | " 35 | \n",
215 | " 36 | \n",
216 | " 37 | \n",
217 | " 38 | \n",
218 | " 39 | \n",
219 | " ... | \n",
220 | " 3097 | \n",
221 | " 3098 | \n",
222 | " 3099 | \n",
223 | " 3100 | \n",
224 | " 3101 | \n",
225 | " 3102 | \n",
226 | " 3103 | \n",
227 | " 3104 | \n",
228 | " 3105 | \n",
229 | " 3106 | \n",
230 | " 3107 | \n",
231 | " 3108 | \n",
232 | " 3109 | \n",
233 | " 3110 | \n",
234 | " 3111 | \n",
235 | " 3112 | \n",
236 | " 3113 | \n",
237 | " 3114 | \n",
238 | " 3115 | \n",
239 | " 3116 | \n",
240 | " 3117 | \n",
241 | " 3118 | \n",
242 | " 3119 | \n",
243 | " 3120 | \n",
244 | " 3121 | \n",
245 | " 3122 | \n",
246 | " 3123 | \n",
247 | " 3124 | \n",
248 | " 3125 | \n",
249 | " 3126 | \n",
250 | " 3127 | \n",
251 | " 3128 | \n",
252 | " 3129 | \n",
253 | " 3130 | \n",
254 | " 3131 | \n",
255 | " 3132 | \n",
256 | " 3133 | \n",
257 | " 3134 | \n",
258 | " 3135 | \n",
259 | " label | \n",
260 | "
\n",
261 | " \n",
262 | " image | \n",
263 | " | \n",
264 | " | \n",
265 | " | \n",
266 | " | \n",
267 | " | \n",
268 | " | \n",
269 | " | \n",
270 | " | \n",
271 | " | \n",
272 | " | \n",
273 | " | \n",
274 | " | \n",
275 | " | \n",
276 | " | \n",
277 | " | \n",
278 | " | \n",
279 | " | \n",
280 | " | \n",
281 | " | \n",
282 | " | \n",
283 | " | \n",
284 | " | \n",
285 | " | \n",
286 | " | \n",
287 | " | \n",
288 | " | \n",
289 | " | \n",
290 | " | \n",
291 | " | \n",
292 | " | \n",
293 | " | \n",
294 | " | \n",
295 | " | \n",
296 | " | \n",
297 | " | \n",
298 | " | \n",
299 | " | \n",
300 | " | \n",
301 | " | \n",
302 | " | \n",
303 | " | \n",
304 | " | \n",
305 | " | \n",
306 | " | \n",
307 | " | \n",
308 | " | \n",
309 | " | \n",
310 | " | \n",
311 | " | \n",
312 | " | \n",
313 | " | \n",
314 | " | \n",
315 | " | \n",
316 | " | \n",
317 | " | \n",
318 | " | \n",
319 | " | \n",
320 | " | \n",
321 | " | \n",
322 | " | \n",
323 | " | \n",
324 | " | \n",
325 | " | \n",
326 | " | \n",
327 | " | \n",
328 | " | \n",
329 | " | \n",
330 | " | \n",
331 | " | \n",
332 | " | \n",
333 | " | \n",
334 | " | \n",
335 | " | \n",
336 | " | \n",
337 | " | \n",
338 | " | \n",
339 | " | \n",
340 | " | \n",
341 | " | \n",
342 | " | \n",
343 | " | \n",
344 | "
\n",
345 | " \n",
346 | " \n",
347 | " \n",
348 | " train0.jpg | \n",
349 | " 165 | \n",
350 | " 166 | \n",
351 | " 174 | \n",
352 | " 174 | \n",
353 | " 175 | \n",
354 | " 193 | \n",
355 | " 196 | \n",
356 | " 194 | \n",
357 | " 200 | \n",
358 | " 193 | \n",
359 | " 197 | \n",
360 | " 194 | \n",
361 | " 190 | \n",
362 | " 198 | \n",
363 | " 194 | \n",
364 | " 200 | \n",
365 | " 190 | \n",
366 | " 201 | \n",
367 | " 197 | \n",
368 | " 201 | \n",
369 | " 208 | \n",
370 | " 209 | \n",
371 | " 201 | \n",
372 | " 203 | \n",
373 | " 203 | \n",
374 | " 199 | \n",
375 | " 192 | \n",
376 | " 193 | \n",
377 | " 182 | \n",
378 | " 192 | \n",
379 | " 180 | \n",
380 | " 181 | \n",
381 | " 173 | \n",
382 | " 160 | \n",
383 | " 182 | \n",
384 | " 186 | \n",
385 | " 188 | \n",
386 | " 189 | \n",
387 | " 192 | \n",
388 | " 184 | \n",
389 | " ... | \n",
390 | " 203 | \n",
391 | " 194 | \n",
392 | " 197 | \n",
393 | " 192 | \n",
394 | " 184 | \n",
395 | " 201 | \n",
396 | " 200 | \n",
397 | " 192 | \n",
398 | " 188 | \n",
399 | " 191 | \n",
400 | " 196 | \n",
401 | " 201 | \n",
402 | " 192 | \n",
403 | " 186 | \n",
404 | " 194 | \n",
405 | " 185 | \n",
406 | " 149 | \n",
407 | " 181 | \n",
408 | " 181 | \n",
409 | " 160 | \n",
410 | " 161 | \n",
411 | " 151 | \n",
412 | " 166 | \n",
413 | " 166 | \n",
414 | " 144 | \n",
415 | " 162 | \n",
416 | " 145 | \n",
417 | " 145 | \n",
418 | " 140 | \n",
419 | " 103 | \n",
420 | " 100 | \n",
421 | " 97 | \n",
422 | " 95 | \n",
423 | " 114 | \n",
424 | " 119 | \n",
425 | " 69 | \n",
426 | " 79 | \n",
427 | " 90 | \n",
428 | " 78 | \n",
429 | " 0 | \n",
430 | "
\n",
431 | " \n",
432 | " train1.jpg | \n",
433 | " 27 | \n",
434 | " 44 | \n",
435 | " 61 | \n",
436 | " 78 | \n",
437 | " 96 | \n",
438 | " 109 | \n",
439 | " 120 | \n",
440 | " 123 | \n",
441 | " 130 | \n",
442 | " 136 | \n",
443 | " 144 | \n",
444 | " 150 | \n",
445 | " 163 | \n",
446 | " 169 | \n",
447 | " 172 | \n",
448 | " 165 | \n",
449 | " 157 | \n",
450 | " 163 | \n",
451 | " 169 | \n",
452 | " 170 | \n",
453 | " 173 | \n",
454 | " 173 | \n",
455 | " 166 | \n",
456 | " 170 | \n",
457 | " 173 | \n",
458 | " 175 | \n",
459 | " 179 | \n",
460 | " 185 | \n",
461 | " 186 | \n",
462 | " 184 | \n",
463 | " 183 | \n",
464 | " 177 | \n",
465 | " 178 | \n",
466 | " 180 | \n",
467 | " 178 | \n",
468 | " 176 | \n",
469 | " 181 | \n",
470 | " 177 | \n",
471 | " 169 | \n",
472 | " 169 | \n",
473 | " ... | \n",
474 | " 150 | \n",
475 | " 147 | \n",
476 | " 149 | \n",
477 | " 145 | \n",
478 | " 140 | \n",
479 | " 141 | \n",
480 | " 143 | \n",
481 | " 140 | \n",
482 | " 146 | \n",
483 | " 144 | \n",
484 | " 141 | \n",
485 | " 147 | \n",
486 | " 152 | \n",
487 | " 149 | \n",
488 | " 143 | \n",
489 | " 141 | \n",
490 | " 147 | \n",
491 | " 147 | \n",
492 | " 155 | \n",
493 | " 160 | \n",
494 | " 166 | \n",
495 | " 165 | \n",
496 | " 166 | \n",
497 | " 168 | \n",
498 | " 162 | \n",
499 | " 158 | \n",
500 | " 151 | \n",
501 | " 140 | \n",
502 | " 134 | \n",
503 | " 125 | \n",
504 | " 116 | \n",
505 | " 108 | \n",
506 | " 90 | \n",
507 | " 72 | \n",
508 | " 54 | \n",
509 | " 33 | \n",
510 | " 13 | \n",
511 | " 2 | \n",
512 | " 1 | \n",
513 | " 1 | \n",
514 | "
\n",
515 | " \n",
516 | " train2.jpg | \n",
517 | " 0 | \n",
518 | " 0 | \n",
519 | " 0 | \n",
520 | " 0 | \n",
521 | " 0 | \n",
522 | " 0 | \n",
523 | " 0 | \n",
524 | " 0 | \n",
525 | " 0 | \n",
526 | " 0 | \n",
527 | " 0 | \n",
528 | " 0 | \n",
529 | " 0 | \n",
530 | " 0 | \n",
531 | " 0 | \n",
532 | " 0 | \n",
533 | " 0 | \n",
534 | " 0 | \n",
535 | " 0 | \n",
536 | " 0 | \n",
537 | " 0 | \n",
538 | " 0 | \n",
539 | " 0 | \n",
540 | " 0 | \n",
541 | " 1 | \n",
542 | " 0 | \n",
543 | " 0 | \n",
544 | " 0 | \n",
545 | " 0 | \n",
546 | " 0 | \n",
547 | " 0 | \n",
548 | " 0 | \n",
549 | " 0 | \n",
550 | " 0 | \n",
551 | " 0 | \n",
552 | " 0 | \n",
553 | " 0 | \n",
554 | " 0 | \n",
555 | " 0 | \n",
556 | " 0 | \n",
557 | " ... | \n",
558 | " 0 | \n",
559 | " 0 | \n",
560 | " 0 | \n",
561 | " 0 | \n",
562 | " 0 | \n",
563 | " 0 | \n",
564 | " 0 | \n",
565 | " 0 | \n",
566 | " 0 | \n",
567 | " 0 | \n",
568 | " 0 | \n",
569 | " 0 | \n",
570 | " 0 | \n",
571 | " 0 | \n",
572 | " 0 | \n",
573 | " 0 | \n",
574 | " 0 | \n",
575 | " 0 | \n",
576 | " 0 | \n",
577 | " 0 | \n",
578 | " 0 | \n",
579 | " 0 | \n",
580 | " 0 | \n",
581 | " 0 | \n",
582 | " 0 | \n",
583 | " 0 | \n",
584 | " 0 | \n",
585 | " 0 | \n",
586 | " 0 | \n",
587 | " 0 | \n",
588 | " 0 | \n",
589 | " 0 | \n",
590 | " 0 | \n",
591 | " 0 | \n",
592 | " 0 | \n",
593 | " 0 | \n",
594 | " 0 | \n",
595 | " 0 | \n",
596 | " 0 | \n",
597 | " 1 | \n",
598 | "
\n",
599 | " \n",
600 | " train3.jpg | \n",
601 | " 197 | \n",
602 | " 210 | \n",
603 | " 204 | \n",
604 | " 199 | \n",
605 | " 206 | \n",
606 | " 210 | \n",
607 | " 208 | \n",
608 | " 207 | \n",
609 | " 207 | \n",
610 | " 205 | \n",
611 | " 203 | \n",
612 | " 204 | \n",
613 | " 198 | \n",
614 | " 189 | \n",
615 | " 176 | \n",
616 | " 175 | \n",
617 | " 175 | \n",
618 | " 172 | \n",
619 | " 162 | \n",
620 | " 157 | \n",
621 | " 134 | \n",
622 | " 134 | \n",
623 | " 135 | \n",
624 | " 136 | \n",
625 | " 138 | \n",
626 | " 149 | \n",
627 | " 145 | \n",
628 | " 140 | \n",
629 | " 141 | \n",
630 | " 146 | \n",
631 | " 158 | \n",
632 | " 159 | \n",
633 | " 170 | \n",
634 | " 171 | \n",
635 | " 170 | \n",
636 | " 162 | \n",
637 | " 174 | \n",
638 | " 164 | \n",
639 | " 152 | \n",
640 | " 161 | \n",
641 | " ... | \n",
642 | " 165 | \n",
643 | " 166 | \n",
644 | " 153 | \n",
645 | " 146 | \n",
646 | " 161 | \n",
647 | " 168 | \n",
648 | " 174 | \n",
649 | " 176 | \n",
650 | " 179 | \n",
651 | " 178 | \n",
652 | " 174 | \n",
653 | " 173 | \n",
654 | " 174 | \n",
655 | " 175 | \n",
656 | " 164 | \n",
657 | " 160 | \n",
658 | " 157 | \n",
659 | " 162 | \n",
660 | " 176 | \n",
661 | " 181 | \n",
662 | " 184 | \n",
663 | " 197 | \n",
664 | " 193 | \n",
665 | " 193 | \n",
666 | " 197 | \n",
667 | " 192 | \n",
668 | " 197 | \n",
669 | " 203 | \n",
670 | " 200 | \n",
671 | " 201 | \n",
672 | " 198 | \n",
673 | " 201 | \n",
674 | " 203 | \n",
675 | " 198 | \n",
676 | " 211 | \n",
677 | " 199 | \n",
678 | " 196 | \n",
679 | " 196 | \n",
680 | " 197 | \n",
681 | " 2 | \n",
682 | "
\n",
683 | " \n",
684 | " train4.jpg | \n",
685 | " 128 | \n",
686 | " 119 | \n",
687 | " 133 | \n",
688 | " 115 | \n",
689 | " 109 | \n",
690 | " 123 | \n",
691 | " 138 | \n",
692 | " 131 | \n",
693 | " 143 | \n",
694 | " 133 | \n",
695 | " 133 | \n",
696 | " 130 | \n",
697 | " 140 | \n",
698 | " 138 | \n",
699 | " 137 | \n",
700 | " 135 | \n",
701 | " 138 | \n",
702 | " 141 | \n",
703 | " 134 | \n",
704 | " 128 | \n",
705 | " 125 | \n",
706 | " 128 | \n",
707 | " 98 | \n",
708 | " 118 | \n",
709 | " 112 | \n",
710 | " 116 | \n",
711 | " 116 | \n",
712 | " 115 | \n",
713 | " 121 | \n",
714 | " 117 | \n",
715 | " 112 | \n",
716 | " 118 | \n",
717 | " 142 | \n",
718 | " 122 | \n",
719 | " 118 | \n",
720 | " 111 | \n",
721 | " 92 | \n",
722 | " 94 | \n",
723 | " 103 | \n",
724 | " 110 | \n",
725 | " ... | \n",
726 | " 169 | \n",
727 | " 167 | \n",
728 | " 163 | \n",
729 | " 164 | \n",
730 | " 173 | \n",
731 | " 168 | \n",
732 | " 167 | \n",
733 | " 156 | \n",
734 | " 146 | \n",
735 | " 156 | \n",
736 | " 154 | \n",
737 | " 135 | \n",
738 | " 129 | \n",
739 | " 149 | \n",
740 | " 146 | \n",
741 | " 150 | \n",
742 | " 152 | \n",
743 | " 142 | \n",
744 | " 138 | \n",
745 | " 141 | \n",
746 | " 141 | \n",
747 | " 130 | \n",
748 | " 130 | \n",
749 | " 120 | \n",
750 | " 130 | \n",
751 | " 110 | \n",
752 | " 117 | \n",
753 | " 117 | \n",
754 | " 129 | \n",
755 | " 121 | \n",
756 | " 116 | \n",
757 | " 120 | \n",
758 | " 126 | \n",
759 | " 119 | \n",
760 | " 129 | \n",
761 | " 132 | \n",
762 | " 134 | \n",
763 | " 131 | \n",
764 | " 123 | \n",
765 | " 1 | \n",
766 | "
\n",
767 | " \n",
768 | " ... | \n",
769 | " ... | \n",
770 | " ... | \n",
771 | " ... | \n",
772 | " ... | \n",
773 | " ... | \n",
774 | " ... | \n",
775 | " ... | \n",
776 | " ... | \n",
777 | " ... | \n",
778 | " ... | \n",
779 | " ... | \n",
780 | " ... | \n",
781 | " ... | \n",
782 | " ... | \n",
783 | " ... | \n",
784 | " ... | \n",
785 | " ... | \n",
786 | " ... | \n",
787 | " ... | \n",
788 | " ... | \n",
789 | " ... | \n",
790 | " ... | \n",
791 | " ... | \n",
792 | " ... | \n",
793 | " ... | \n",
794 | " ... | \n",
795 | " ... | \n",
796 | " ... | \n",
797 | " ... | \n",
798 | " ... | \n",
799 | " ... | \n",
800 | " ... | \n",
801 | " ... | \n",
802 | " ... | \n",
803 | " ... | \n",
804 | " ... | \n",
805 | " ... | \n",
806 | " ... | \n",
807 | " ... | \n",
808 | " ... | \n",
809 | " ... | \n",
810 | " ... | \n",
811 | " ... | \n",
812 | " ... | \n",
813 | " ... | \n",
814 | " ... | \n",
815 | " ... | \n",
816 | " ... | \n",
817 | " ... | \n",
818 | " ... | \n",
819 | " ... | \n",
820 | " ... | \n",
821 | " ... | \n",
822 | " ... | \n",
823 | " ... | \n",
824 | " ... | \n",
825 | " ... | \n",
826 | " ... | \n",
827 | " ... | \n",
828 | " ... | \n",
829 | " ... | \n",
830 | " ... | \n",
831 | " ... | \n",
832 | " ... | \n",
833 | " ... | \n",
834 | " ... | \n",
835 | " ... | \n",
836 | " ... | \n",
837 | " ... | \n",
838 | " ... | \n",
839 | " ... | \n",
840 | " ... | \n",
841 | " ... | \n",
842 | " ... | \n",
843 | " ... | \n",
844 | " ... | \n",
845 | " ... | \n",
846 | " ... | \n",
847 | " ... | \n",
848 | " ... | \n",
849 | " ... | \n",
850 | "
\n",
851 | " \n",
852 | " train7691.jpg | \n",
853 | " 192 | \n",
854 | " 189 | \n",
855 | " 187 | \n",
856 | " 188 | \n",
857 | " 193 | \n",
858 | " 193 | \n",
859 | " 192 | \n",
860 | " 193 | \n",
861 | " 193 | \n",
862 | " 192 | \n",
863 | " 191 | \n",
864 | " 193 | \n",
865 | " 196 | \n",
866 | " 196 | \n",
867 | " 190 | \n",
868 | " 188 | \n",
869 | " 187 | \n",
870 | " 183 | \n",
871 | " 176 | \n",
872 | " 174 | \n",
873 | " 178 | \n",
874 | " 179 | \n",
875 | " 180 | \n",
876 | " 179 | \n",
877 | " 182 | \n",
878 | " 183 | \n",
879 | " 178 | \n",
880 | " 180 | \n",
881 | " 180 | \n",
882 | " 184 | \n",
883 | " 183 | \n",
884 | " 180 | \n",
885 | " 183 | \n",
886 | " 183 | \n",
887 | " 181 | \n",
888 | " 184 | \n",
889 | " 180 | \n",
890 | " 180 | \n",
891 | " 182 | \n",
892 | " 181 | \n",
893 | " ... | \n",
894 | " 167 | \n",
895 | " 162 | \n",
896 | " 161 | \n",
897 | " 154 | \n",
898 | " 155 | \n",
899 | " 157 | \n",
900 | " 159 | \n",
901 | " 159 | \n",
902 | " 155 | \n",
903 | " 161 | \n",
904 | " 157 | \n",
905 | " 153 | \n",
906 | " 149 | \n",
907 | " 148 | \n",
908 | " 144 | \n",
909 | " 146 | \n",
910 | " 146 | \n",
911 | " 147 | \n",
912 | " 156 | \n",
913 | " 165 | \n",
914 | " 168 | \n",
915 | " 175 | \n",
916 | " 173 | \n",
917 | " 174 | \n",
918 | " 174 | \n",
919 | " 176 | \n",
920 | " 175 | \n",
921 | " 180 | \n",
922 | " 179 | \n",
923 | " 178 | \n",
924 | " 179 | \n",
925 | " 181 | \n",
926 | " 179 | \n",
927 | " 176 | \n",
928 | " 177 | \n",
929 | " 177 | \n",
930 | " 180 | \n",
931 | " 181 | \n",
932 | " 181 | \n",
933 | " 4 | \n",
934 | "
\n",
935 | " \n",
936 | " train7692.jpg | \n",
937 | " 0 | \n",
938 | " 0 | \n",
939 | " 0 | \n",
940 | " 0 | \n",
941 | " 0 | \n",
942 | " 0 | \n",
943 | " 0 | \n",
944 | " 0 | \n",
945 | " 0 | \n",
946 | " 1 | \n",
947 | " 1 | \n",
948 | " 2 | \n",
949 | " 1 | \n",
950 | " 2 | \n",
951 | " 2 | \n",
952 | " 2 | \n",
953 | " 2 | \n",
954 | " 2 | \n",
955 | " 2 | \n",
956 | " 3 | \n",
957 | " 4 | \n",
958 | " 7 | \n",
959 | " 10 | \n",
960 | " 13 | \n",
961 | " 15 | \n",
962 | " 16 | \n",
963 | " 17 | \n",
964 | " 17 | \n",
965 | " 17 | \n",
966 | " 16 | \n",
967 | " 14 | \n",
968 | " 11 | \n",
969 | " 8 | \n",
970 | " 5 | \n",
971 | " 2 | \n",
972 | " 2 | \n",
973 | " 2 | \n",
974 | " 2 | \n",
975 | " 2 | \n",
976 | " 2 | \n",
977 | " ... | \n",
978 | " 8 | \n",
979 | " 16 | \n",
980 | " 23 | \n",
981 | " 29 | \n",
982 | " 36 | \n",
983 | " 42 | \n",
984 | " 48 | \n",
985 | " 52 | \n",
986 | " 55 | \n",
987 | " 57 | \n",
988 | " 60 | \n",
989 | " 59 | \n",
990 | " 54 | \n",
991 | " 47 | \n",
992 | " 40 | \n",
993 | " 34 | \n",
994 | " 27 | \n",
995 | " 20 | \n",
996 | " 11 | \n",
997 | " 6 | \n",
998 | " 1 | \n",
999 | " 1 | \n",
1000 | " 0 | \n",
1001 | " 0 | \n",
1002 | " 1 | \n",
1003 | " 0 | \n",
1004 | " 1 | \n",
1005 | " 0 | \n",
1006 | " 0 | \n",
1007 | " 0 | \n",
1008 | " 0 | \n",
1009 | " 0 | \n",
1010 | " 0 | \n",
1011 | " 0 | \n",
1012 | " 0 | \n",
1013 | " 0 | \n",
1014 | " 0 | \n",
1015 | " 0 | \n",
1016 | " 0 | \n",
1017 | " 3 | \n",
1018 | "
\n",
1019 | " \n",
1020 | " train7693.jpg | \n",
1021 | " 96 | \n",
1022 | " 97 | \n",
1023 | " 100 | \n",
1024 | " 105 | \n",
1025 | " 110 | \n",
1026 | " 114 | \n",
1027 | " 114 | \n",
1028 | " 112 | \n",
1029 | " 113 | \n",
1030 | " 119 | \n",
1031 | " 124 | \n",
1032 | " 118 | \n",
1033 | " 118 | \n",
1034 | " 120 | \n",
1035 | " 121 | \n",
1036 | " 122 | \n",
1037 | " 120 | \n",
1038 | " 116 | \n",
1039 | " 116 | \n",
1040 | " 121 | \n",
1041 | " 123 | \n",
1042 | " 125 | \n",
1043 | " 130 | \n",
1044 | " 124 | \n",
1045 | " 133 | \n",
1046 | " 124 | \n",
1047 | " 109 | \n",
1048 | " 123 | \n",
1049 | " 130 | \n",
1050 | " 133 | \n",
1051 | " 136 | \n",
1052 | " 131 | \n",
1053 | " 134 | \n",
1054 | " 137 | \n",
1055 | " 138 | \n",
1056 | " 146 | \n",
1057 | " 139 | \n",
1058 | " 145 | \n",
1059 | " 142 | \n",
1060 | " 139 | \n",
1061 | " ... | \n",
1062 | " 141 | \n",
1063 | " 140 | \n",
1064 | " 146 | \n",
1065 | " 147 | \n",
1066 | " 150 | \n",
1067 | " 159 | \n",
1068 | " 151 | \n",
1069 | " 146 | \n",
1070 | " 126 | \n",
1071 | " 106 | \n",
1072 | " 139 | \n",
1073 | " 143 | \n",
1074 | " 161 | \n",
1075 | " 156 | \n",
1076 | " 164 | \n",
1077 | " 159 | \n",
1078 | " 173 | \n",
1079 | " 176 | \n",
1080 | " 172 | \n",
1081 | " 167 | \n",
1082 | " 173 | \n",
1083 | " 170 | \n",
1084 | " 182 | \n",
1085 | " 165 | \n",
1086 | " 185 | \n",
1087 | " 169 | \n",
1088 | " 168 | \n",
1089 | " 170 | \n",
1090 | " 164 | \n",
1091 | " 165 | \n",
1092 | " 166 | \n",
1093 | " 187 | \n",
1094 | " 185 | \n",
1095 | " 193 | \n",
1096 | " 163 | \n",
1097 | " 188 | \n",
1098 | " 189 | \n",
1099 | " 188 | \n",
1100 | " 170 | \n",
1101 | " 4 | \n",
1102 | "
\n",
1103 | " \n",
1104 | " train7694.jpg | \n",
1105 | " 116 | \n",
1106 | " 98 | \n",
1107 | " 142 | \n",
1108 | " 158 | \n",
1109 | " 168 | \n",
1110 | " 162 | \n",
1111 | " 156 | \n",
1112 | " 155 | \n",
1113 | " 157 | \n",
1114 | " 160 | \n",
1115 | " 153 | \n",
1116 | " 147 | \n",
1117 | " 142 | \n",
1118 | " 143 | \n",
1119 | " 142 | \n",
1120 | " 150 | \n",
1121 | " 151 | \n",
1122 | " 161 | \n",
1123 | " 167 | \n",
1124 | " 201 | \n",
1125 | " 172 | \n",
1126 | " 169 | \n",
1127 | " 167 | \n",
1128 | " 172 | \n",
1129 | " 173 | \n",
1130 | " 173 | \n",
1131 | " 170 | \n",
1132 | " 168 | \n",
1133 | " 171 | \n",
1134 | " 146 | \n",
1135 | " 171 | \n",
1136 | " 169 | \n",
1137 | " 164 | \n",
1138 | " 144 | \n",
1139 | " 133 | \n",
1140 | " 137 | \n",
1141 | " 162 | \n",
1142 | " 163 | \n",
1143 | " 155 | \n",
1144 | " 144 | \n",
1145 | " ... | \n",
1146 | " 153 | \n",
1147 | " 149 | \n",
1148 | " 151 | \n",
1149 | " 144 | \n",
1150 | " 167 | \n",
1151 | " 168 | \n",
1152 | " 171 | \n",
1153 | " 175 | \n",
1154 | " 169 | \n",
1155 | " 163 | \n",
1156 | " 169 | \n",
1157 | " 188 | \n",
1158 | " 159 | \n",
1159 | " 152 | \n",
1160 | " 152 | \n",
1161 | " 153 | \n",
1162 | " 156 | \n",
1163 | " 154 | \n",
1164 | " 148 | \n",
1165 | " 147 | \n",
1166 | " 157 | \n",
1167 | " 168 | \n",
1168 | " 175 | \n",
1169 | " 175 | \n",
1170 | " 163 | \n",
1171 | " 149 | \n",
1172 | " 166 | \n",
1173 | " 179 | \n",
1174 | " 188 | \n",
1175 | " 177 | \n",
1176 | " 179 | \n",
1177 | " 172 | \n",
1178 | " 160 | \n",
1179 | " 175 | \n",
1180 | " 161 | \n",
1181 | " 151 | \n",
1182 | " 161 | \n",
1183 | " 170 | \n",
1184 | " 154 | \n",
1185 | " 5 | \n",
1186 | "
\n",
1187 | " \n",
1188 | " train7695.jpg | \n",
1189 | " 0 | \n",
1190 | " 0 | \n",
1191 | " 0 | \n",
1192 | " 1 | \n",
1193 | " 2 | \n",
1194 | " 2 | \n",
1195 | " 2 | \n",
1196 | " 2 | \n",
1197 | " 2 | \n",
1198 | " 3 | \n",
1199 | " 4 | \n",
1200 | " 19 | \n",
1201 | " 38 | \n",
1202 | " 56 | \n",
1203 | " 70 | \n",
1204 | " 85 | \n",
1205 | " 96 | \n",
1206 | " 107 | \n",
1207 | " 115 | \n",
1208 | " 123 | \n",
1209 | " 125 | \n",
1210 | " 130 | \n",
1211 | " 132 | \n",
1212 | " 135 | \n",
1213 | " 135 | \n",
1214 | " 135 | \n",
1215 | " 139 | \n",
1216 | " 140 | \n",
1217 | " 138 | \n",
1218 | " 137 | \n",
1219 | " 135 | \n",
1220 | " 133 | \n",
1221 | " 130 | \n",
1222 | " 127 | \n",
1223 | " 125 | \n",
1224 | " 120 | \n",
1225 | " 114 | \n",
1226 | " 104 | \n",
1227 | " 93 | \n",
1228 | " 80 | \n",
1229 | " ... | \n",
1230 | " 151 | \n",
1231 | " 154 | \n",
1232 | " 158 | \n",
1233 | " 159 | \n",
1234 | " 161 | \n",
1235 | " 161 | \n",
1236 | " 160 | \n",
1237 | " 161 | \n",
1238 | " 163 | \n",
1239 | " 163 | \n",
1240 | " 162 | \n",
1241 | " 162 | \n",
1242 | " 162 | \n",
1243 | " 162 | \n",
1244 | " 160 | \n",
1245 | " 158 | \n",
1246 | " 156 | \n",
1247 | " 153 | \n",
1248 | " 151 | \n",
1249 | " 147 | \n",
1250 | " 143 | \n",
1251 | " 138 | \n",
1252 | " 133 | \n",
1253 | " 127 | \n",
1254 | " 119 | \n",
1255 | " 107 | \n",
1256 | " 93 | \n",
1257 | " 74 | \n",
1258 | " 55 | \n",
1259 | " 30 | \n",
1260 | " 9 | \n",
1261 | " 2 | \n",
1262 | " 2 | \n",
1263 | " 2 | \n",
1264 | " 1 | \n",
1265 | " 1 | \n",
1266 | " 1 | \n",
1267 | " 0 | \n",
1268 | " 0 | \n",
1269 | " 1 | \n",
1270 | "
\n",
1271 | " \n",
1272 | "
\n",
1273 | "
7696 rows × 3137 columns
\n",
1274 | "
"
1275 | ],
1276 | "text/plain": [
1277 | " 0 1 2 3 4 ... 3132 3133 3134 3135 label\n",
1278 | "image ... \n",
1279 | "train0.jpg 165 166 174 174 175 ... 69 79 90 78 0\n",
1280 | "train1.jpg 27 44 61 78 96 ... 33 13 2 1 1\n",
1281 | "train2.jpg 0 0 0 0 0 ... 0 0 0 0 1\n",
1282 | "train3.jpg 197 210 204 199 206 ... 199 196 196 197 2\n",
1283 | "train4.jpg 128 119 133 115 109 ... 132 134 131 123 1\n",
1284 | "... ... ... ... ... ... ... ... ... ... ... ...\n",
1285 | "train7691.jpg 192 189 187 188 193 ... 177 180 181 181 4\n",
1286 | "train7692.jpg 0 0 0 0 0 ... 0 0 0 0 3\n",
1287 | "train7693.jpg 96 97 100 105 110 ... 188 189 188 170 4\n",
1288 | "train7694.jpg 116 98 142 158 168 ... 151 161 170 154 5\n",
1289 | "train7695.jpg 0 0 0 1 2 ... 1 1 0 0 1\n",
1290 | "\n",
1291 | "[7696 rows x 3137 columns]"
1292 | ]
1293 | },
1294 | "metadata": {
1295 | "tags": []
1296 | },
1297 | "execution_count": 6
1298 | }
1299 | ]
1300 | },
1301 | {
1302 | "cell_type": "code",
1303 | "metadata": {
1304 | "id": "mI2aTsIydoJ7",
1305 | "colab_type": "code",
1306 | "colab": {}
1307 | },
1308 | "source": [
1309 | "for i in range(23):\n",
1310 | " po=po.append({'current':df.at[i,\"Current 2\"],'load':df.at[i,\"P_L 2\"],'result':2,'Time':df.at[i,\"Time\"]},ignore_index=True)"
1311 | ],
1312 | "execution_count": null,
1313 | "outputs": []
1314 | },
1315 | {
1316 | "cell_type": "code",
1317 | "metadata": {
1318 | "id": "6UzQk6EOdq3Q",
1319 | "colab_type": "code",
1320 | "colab": {}
1321 | },
1322 | "source": [
1323 | "for i in range(23):\n",
1324 | " po=po.append({'current':df.at[i,\"Current 3\"],'load':df.at[i,\"P_L 3\"],'result':3,'Time':df.at[i,\"Time\"]},ignore_index=True)"
1325 | ],
1326 | "execution_count": null,
1327 | "outputs": []
1328 | },
1329 | {
1330 | "cell_type": "code",
1331 | "metadata": {
1332 | "id": "DjyG5rm2dz2m",
1333 | "colab_type": "code",
1334 | "colab": {
1335 | "base_uri": "https://localhost:8080/",
1336 | "height": 34
1337 | },
1338 | "outputId": "4ba00cec-1721-4da0-e0f4-09f6e2a0878a"
1339 | },
1340 | "source": [
1341 | "for i in range(23):\n",
1342 | " po=po.append({'current':df.at[i,\"Current Ideal\"],'load':df.at[i,\"P_L Ideal\"],'result':0,'Time':df.at[i,\"Time\"]},ignore_index=True)"
1343 | ],
1344 | "execution_count": null,
1345 | "outputs": [
1346 | {
1347 | "output_type": "stream",
1348 | "text": [
1349 | "Found 8490 images belonging to 7 classes.\n"
1350 | ],
1351 | "name": "stdout"
1352 | }
1353 | ]
1354 | },
1355 | {
1356 | "cell_type": "code",
1357 | "metadata": {
1358 | "id": "r-5cNtPCd2YG",
1359 | "colab_type": "code",
1360 | "colab": {
1361 | "base_uri": "https://localhost:8080/",
1362 | "height": 122
1363 | },
1364 | "outputId": "8ad1e5ba-02c1-4bb2-b69e-93516aaabf3a"
1365 | },
1366 | "source": [
1367 | "po"
1368 | ],
1369 | "execution_count": null,
1370 | "outputs": [
1371 | {
1372 | "output_type": "stream",
1373 | "text": [
1374 | "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
1375 | "Instructions for updating:\n",
1376 | "If using Keras pass *_constraint arguments to layers.\n",
1377 | "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_1_0_224_tf.h5\n",
1378 | "17227776/17225924 [==============================] - 3s 0us/step\n"
1379 | ],
1380 | "name": "stdout"
1381 | }
1382 | ]
1383 | },
1384 | {
1385 | "cell_type": "code",
1386 | "metadata": {
1387 | "id": "t00qelT1d6lX",
1388 | "colab_type": "code",
1389 | "colab": {}
1390 | },
1391 | "source": [
1392 | "po = pd.concat([po,pd.get_dummies(po['Time'], prefix='Time',dummy_na=True)],axis=1).drop(['Time'],axis=1)\n"
1393 | ],
1394 | "execution_count": null,
1395 | "outputs": []
1396 | },
1397 | {
1398 | "cell_type": "code",
1399 | "metadata": {
1400 | "id": "GtP2JRm5eBDI",
1401 | "colab_type": "code",
1402 | "colab": {}
1403 | },
1404 | "source": [
1405 | "po"
1406 | ],
1407 | "execution_count": null,
1408 | "outputs": []
1409 | },
1410 | {
1411 | "cell_type": "code",
1412 | "metadata": {
1413 | "id": "JU9SttS7eC1y",
1414 | "colab_type": "code",
1415 | "colab": {}
1416 | },
1417 | "source": [
1418 | "poo=po.values"
1419 | ],
1420 | "execution_count": null,
1421 | "outputs": []
1422 | },
1423 | {
1424 | "cell_type": "code",
1425 | "metadata": {
1426 | "id": "6YoYMY0heEkG",
1427 | "colab_type": "code",
1428 | "colab": {}
1429 | },
1430 | "source": [
1431 | "yy=poo[:,2]\n",
1432 | "po.drop(['result'],axis=\"columns\",inplace=True)"
1433 | ],
1434 | "execution_count": null,
1435 | "outputs": []
1436 | },
1437 | {
1438 | "cell_type": "code",
1439 | "metadata": {
1440 | "id": "xQnkBOb_eGou",
1441 | "colab_type": "code",
1442 | "colab": {
1443 | "base_uri": "https://localhost:8080/",
1444 | "height": 360
1445 | },
1446 | "outputId": "788eb09f-38c6-4068-e3a0-bcf76239f1ea"
1447 | },
1448 | "source": [
1449 | "XX=poo[:,:]\n"
1450 | ],
1451 | "execution_count": null,
1452 | "outputs": [
1453 | {
1454 | "output_type": "stream",
1455 | "text": [
1456 | "Epoch 1/30\n",
1457 | "119/770 [===>..........................] - ETA: 42:32 - loss: 2.5548 - acc: 0.3437"
1458 | ],
1459 | "name": "stdout"
1460 | },
1461 | {
1462 | "output_type": "error",
1463 | "ename": "KeyboardInterrupt",
1464 | "evalue": "ignored",
1465 | "traceback": [
1466 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1467 | "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
1468 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
1469 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 1294\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1295\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1296\u001b[0;31m steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m 1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1298\u001b[0m def evaluate_generator(self,\n",
1470 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtarget_steps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m \u001b[0mbatch_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_next_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 222\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbatch_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_dataset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1471 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36m_get_next_batch\u001b[0;34m(generator)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0;34m\"\"\"Retrieves the next batch of input data.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 364\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1472 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 781\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 782\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 784\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 785\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1473 | "\u001b[0;32m/usr/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 638\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1474 | "\u001b[0;32m/usr/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1475 | "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
1476 | "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 296\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1477 | "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
1478 | ]
1479 | }
1480 | ]
1481 | },
1482 | {
1483 | "cell_type": "code",
1484 | "metadata": {
1485 | "id": "LP99lYcEeIhq",
1486 | "colab_type": "code",
1487 | "colab": {}
1488 | },
1489 | "source": [
1490 | "def neural_net():\n",
1491 | " model = Sequential()\n",
1492 | " model.add(Dense(16, input_dim=27, kernel_initializer='normal', activation='relu'))\n",
1493 | " model.add(Dropout(0.2))\n",
1494 | " model.add(Dense(8, kernel_initializer='normal', activation='relu'))\n",
1495 | " model.add(Dense(4, kernel_initializer='normal',activation='softmax'))\n",
1496 | " model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
1497 | " return model"
1498 | ],
1499 | "execution_count": null,
1500 | "outputs": []
1501 | },
1502 | {
1503 | "cell_type": "code",
1504 | "metadata": {
1505 | "id": "hZb562n-yJzJ",
1506 | "colab_type": "code",
1507 | "colab": {}
1508 | },
1509 | "source": [
1510 | "from keras.utils import np_utils"
1511 | ],
1512 | "execution_count": null,
1513 | "outputs": []
1514 | },
1515 | {
1516 | "cell_type": "code",
1517 | "metadata": {
1518 | "id": "aVL4rZZDyeuy",
1519 | "colab_type": "code",
1520 | "colab": {}
1521 | },
1522 | "source": [
1523 | "encoder = LabelEncoder()\n",
1524 | "encoder.fit(yy)\n",
1525 | "encoded_Y = encoder.transform(yy)\n",
1526 | "dummy_y = np_utils.to_categorical(encoded_Y)"
1527 | ],
1528 | "execution_count": null,
1529 | "outputs": []
1530 | },
1531 | {
1532 | "cell_type": "code",
1533 | "metadata": {
1534 | "id": "S9LaHICzygN1",
1535 | "colab_type": "code",
1536 | "colab": {}
1537 | },
1538 | "source": [
1539 | "dummy_y"
1540 | ],
1541 | "execution_count": null,
1542 | "outputs": []
1543 | },
1544 | {
1545 | "cell_type": "code",
1546 | "metadata": {
1547 | "id": "i0oRbw7uyh8N",
1548 | "colab_type": "code",
1549 | "colab": {}
1550 | },
1551 | "source": [
1552 | "dummy_XX=XX"
1553 | ],
1554 | "execution_count": null,
1555 | "outputs": []
1556 | },
1557 | {
1558 | "cell_type": "code",
1559 | "metadata": {
1560 | "id": "tLdxBr17ylks",
1561 | "colab_type": "code",
1562 | "colab": {}
1563 | },
1564 | "source": [
1565 | "scaler=StandardScaler()"
1566 | ],
1567 | "execution_count": null,
1568 | "outputs": []
1569 | },
1570 | {
1571 | "cell_type": "code",
1572 | "metadata": {
1573 | "id": "XLkUP8O-ypti",
1574 | "colab_type": "code",
1575 | "colab": {}
1576 | },
1577 | "source": [
1578 | "dummy_XX=scaler.fit_transform(dummy_XX)"
1579 | ],
1580 | "execution_count": null,
1581 | "outputs": []
1582 | },
1583 | {
1584 | "cell_type": "code",
1585 | "metadata": {
1586 | "id": "EQCCA6QkyrvO",
1587 | "colab_type": "code",
1588 | "colab": {}
1589 | },
1590 | "source": [
1591 | "dummy_XX"
1592 | ],
1593 | "execution_count": null,
1594 | "outputs": []
1595 | },
1596 | {
1597 | "cell_type": "code",
1598 | "metadata": {
1599 | "id": "w-snfrWbytzP",
1600 | "colab_type": "code",
1601 | "colab": {}
1602 | },
1603 | "source": [
1604 | "mm=neural_net()\n",
1605 | "history=mm.fit(XX,dummy_y,epochs=500)"
1606 | ],
1607 | "execution_count": null,
1608 | "outputs": []
1609 | }
1610 | ]
1611 | }
--------------------------------------------------------------------------------
/Data.xlsx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/AgHarsh/Fault-Detection-in-Power-Microgrid/9c5cc97c3c099701fdb121a2b44ea4fb2acd1367/Data.xlsx
--------------------------------------------------------------------------------
/Features.xlsx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/AgHarsh/Fault-Detection-in-Power-Microgrid/9c5cc97c3c099701fdb121a2b44ea4fb2acd1367/Features.xlsx
--------------------------------------------------------------------------------
/MicroGridModel.slx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/AgHarsh/Fault-Detection-in-Power-Microgrid/9c5cc97c3c099701fdb121a2b44ea4fb2acd1367/MicroGridModel.slx
--------------------------------------------------------------------------------
/Project.pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/AgHarsh/Fault-Detection-in-Power-Microgrid/9c5cc97c3c099701fdb121a2b44ea4fb2acd1367/Project.pdf
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Fault-Detection-in-Power-Microgrid
2 | This project presents the concept of fault detection and location in a Power Microgrid making use of the machine learning concepts like Artificial Neural Network. The electronic equipment used in microgrids is in essential need of more secure protection against short circuit faults. Due to the high current at the time of fault occurrence, the whole system might be de-energized which would have a severely negative impact on the entire system. A fault occurs when two or more conductors come in contact with each other or ground. Ground faults are considered as one of the main problems in power systems and account for more than 80% of all faults. An effective method to detect, isolate, and protect the power microgrid system against the effects of short circuit faults is extremely important. In this project we worked on a highly effective new method to protect the microgrid system using an Artificial Neural Network (ANN) that will detect and find the location of the fault before it affects other parts of the system. It would, therefore, be more dependable for microgrid protection. This protection network is distributed all along the power microgrid system protecting the entire microgrid network and is connected to the other protective devices in the system. This project focuses on detecting faults and identifying the location of the faults on electric power transmission lines in the power microgrid network.
3 |
--------------------------------------------------------------------------------