├── .gitignore
├── LICENSE
├── README.md
├── data
    └── titanic_train.csv
├── digits.py
├── glove_helper.py
├── rl
    └── atari-rl.py
├── seq2seq
    ├── data.py
    ├── generate_data.py
    ├── seq2seq.py
    └── timeline.py
├── titanic.py
├── titanic_all_features.py
├── titanic_all_features_with_fc.py
├── titanic_categorical_variables.py
└── vm_example.py


/.gitignore:
--------------------------------------------------------------------------------
 1 | # virtualenv
 2 | .env/
 3 | env/
 4 | .DS_Store
 5 | 
 6 | # data
 7 | MNIST-data/
 8 | */MNIST-data/
 9 | 
10 | # models
11 | */models/
12 | models/
13 | 
14 | # Ipython
15 | .ipynb_checkpoints/
16 | 
17 | # Byte-compiled / optimized / DLL files
18 | __pycache__/
19 | *.py[cod]
20 | 
21 | # C extensions
22 | *.so
23 | 
24 | # Distribution / packaging
25 | .Python
26 | env/
27 | build/
28 | develop-eggs/
29 | dist/
30 | downloads/
31 | eggs/
32 | .eggs/
33 | lib/
34 | lib64/
35 | parts/
36 | sdist/
37 | var/
38 | *.egg-info/
39 | .installed.cfg
40 | *.egg
41 | 
42 | # PyInstaller
43 | #  Usually these files are written by a python script from a template
44 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
45 | *.manifest
46 | *.spec
47 | 
48 | # Installer logs
49 | pip-log.txt
50 | pip-delete-this-directory.txt
51 | 
52 | # Unit test / coverage reports
53 | htmlcov/
54 | .tox/
55 | .coverage
56 | .coverage.*
57 | .cache
58 | nosetests.xml
59 | coverage.xml
60 | *,cover
61 | 
62 | # Translations
63 | *.mo
64 | *.pot
65 | 
66 | # Django stuff:
67 | *.log
68 | 
69 | # Sphinx documentation
70 | docs/_build/
71 | 
72 | # PyBuilder
73 | target/
74 | 


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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # TensorFlow examples
2 | 
3 | TensorFlow and TF.Learn examples
4 | 
5 | See also:
6 |   - GANs: https://github.com/ilblackdragon/GAN
7 |   - Adversarial training: https://github.com/ilblackdragon/adversarial_workshop
8 | 
9 | 


--------------------------------------------------------------------------------
/data/titanic_train.csv:
--------------------------------------------------------------------------------
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  3 | 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
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110 | 109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
111 | 110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
112 | 111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
113 | 112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
114 | 113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
115 | 114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
116 | 115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
117 | 116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
118 | 117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
119 | 118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
120 | 119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
121 | 120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
122 | 121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
123 | 122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
124 | 123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
125 | 124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
126 | 125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
127 | 126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
128 | 127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
129 | 128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
130 | 129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
131 | 130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
132 | 131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
133 | 132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
134 | 133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
135 | 134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
136 | 135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
137 | 136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
138 | 137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
139 | 138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
140 | 139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
141 | 140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
142 | 141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
143 | 142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
144 | 143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
145 | 144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
146 | 145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
147 | 146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
148 | 147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
149 | 148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
150 | 149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
151 | 150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
152 | 151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
153 | 152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
154 | 153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
155 | 154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
156 | 155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
157 | 156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
158 | 157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
159 | 158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
160 | 159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
161 | 160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
162 | 161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
163 | 162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
164 | 163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
165 | 164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
166 | 165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
167 | 166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
168 | 167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
169 | 168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
170 | 169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
171 | 170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
172 | 171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
173 | 172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
174 | 173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
175 | 174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
176 | 175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
177 | 176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
178 | 177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
179 | 178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
180 | 179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
181 | 180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
182 | 181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
183 | 182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
184 | 183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
185 | 184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
186 | 185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
187 | 186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
188 | 187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
189 | 188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
190 | 189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
191 | 190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
192 | 191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
193 | 192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
194 | 193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
195 | 194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
196 | 195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
197 | 196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
198 | 197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
199 | 198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
200 | 199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
201 | 200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
202 | 201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
203 | 202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
204 | 203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
205 | 204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
206 | 205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
207 | 206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
208 | 207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
209 | 208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
210 | 209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
211 | 210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
212 | 211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
213 | 212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
214 | 213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
215 | 214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
216 | 215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
217 | 216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
218 | 217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
219 | 218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
220 | 219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
221 | 220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
222 | 221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
223 | 222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
224 | 223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
225 | 224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
226 | 225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
227 | 226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
228 | 227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
229 | 228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
230 | 229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
231 | 230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
232 | 231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
233 | 232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S
234 | 233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
235 | 234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
236 | 235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
237 | 236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
238 | 237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
239 | 238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S
240 | 239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
241 | 240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
242 | 241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
243 | 242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
244 | 243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
245 | 244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
246 | 245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
247 | 246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
248 | 247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
249 | 248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
250 | 249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
251 | 250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S
252 | 251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
253 | 252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
254 | 253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S
255 | 254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S
256 | 255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
257 | 256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
258 | 257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
259 | 258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
260 | 259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
261 | 260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
262 | 261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
263 | 262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
264 | 263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
265 | 264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
266 | 265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
267 | 266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
268 | 267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
269 | 268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
270 | 269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
271 | 270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
272 | 271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
273 | 272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
274 | 273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
275 | 274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
276 | 275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
277 | 276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
278 | 277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
279 | 278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
280 | 279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
281 | 280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
282 | 281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
283 | 282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
284 | 283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
285 | 284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
286 | 285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
287 | 286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
288 | 287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
289 | 288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
290 | 289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
291 | 290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
292 | 291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
293 | 292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
294 | 293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
295 | 294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
296 | 295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
297 | 296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
298 | 297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
299 | 298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
300 | 299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
301 | 300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
302 | 301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
303 | 302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
304 | 303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
305 | 304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
306 | 305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
307 | 306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
308 | 307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
309 | 308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
310 | 309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
311 | 310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C
312 | 311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
313 | 312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
314 | 313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
315 | 314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
316 | 315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
317 | 316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
318 | 317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
319 | 318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
320 | 319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
321 | 320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
322 | 321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
323 | 322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
324 | 323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
325 | 324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
326 | 325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
327 | 326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
328 | 327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
329 | 328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
330 | 329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
331 | 330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
332 | 331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
333 | 332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
334 | 333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
335 | 334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S
336 | 335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
337 | 336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
338 | 337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
339 | 338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
340 | 339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
341 | 340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
342 | 341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
343 | 342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
344 | 343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
345 | 344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
346 | 345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
347 | 346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
348 | 347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
349 | 348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
350 | 349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
351 | 350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
352 | 351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
353 | 352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
354 | 353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
355 | 354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
356 | 355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
357 | 356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
358 | 357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
359 | 358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
360 | 359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
361 | 360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
362 | 361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
363 | 362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
364 | 363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
365 | 364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
366 | 365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
367 | 366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
368 | 367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
369 | 368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
370 | 369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
371 | 370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
372 | 371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
373 | 372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
374 | 373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
375 | 374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
376 | 375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
377 | 376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
378 | 377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
379 | 378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
380 | 379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
381 | 380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
382 | 381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
383 | 382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
384 | 383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
385 | 384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
386 | 385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
387 | 386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
388 | 387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
389 | 388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
390 | 389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
391 | 390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
392 | 391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
393 | 392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
394 | 393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
395 | 394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
396 | 395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S
397 | 396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
398 | 397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
399 | 398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
400 | 399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
401 | 400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
402 | 401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
403 | 402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
404 | 403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
405 | 404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
406 | 405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
407 | 406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
408 | 407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
409 | 408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
410 | 409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
411 | 410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
412 | 411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
413 | 412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
414 | 413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
415 | 414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
416 | 415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
417 | 416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
418 | 417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
419 | 418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
420 | 419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
421 | 420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
422 | 421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
423 | 422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
424 | 423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
425 | 424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
426 | 425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
427 | 426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
428 | 427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
429 | 428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
430 | 429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
431 | 430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
432 | 431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S
433 | 432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
434 | 433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S
435 | 434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
436 | 435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
437 | 436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S
438 | 437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
439 | 438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
440 | 439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
441 | 440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
442 | 441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
443 | 442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S
444 | 443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
445 | 444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S
446 | 445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
447 | 446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
448 | 447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S
449 | 448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
450 | 449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C
451 | 450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
452 | 451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S
453 | 452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
454 | 453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
455 | 454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C
456 | 455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
457 | 456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C
458 | 457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S
459 | 458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
460 | 459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S
461 | 460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
462 | 461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S
463 | 462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S
464 | 463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S
465 | 464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S
466 | 465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
467 | 466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
468 | 467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S
469 | 468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S
470 | 469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
471 | 470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
472 | 471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
473 | 472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S
474 | 473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S
475 | 474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
476 | 475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S
477 | 476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S
478 | 477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S
479 | 478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S
480 | 479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
481 | 480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
482 | 481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S
483 | 482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S
484 | 483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S
485 | 484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S
486 | 485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
487 | 486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
488 | 487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
489 | 488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
490 | 489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
491 | 490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
492 | 491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
493 | 492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
494 | 493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
495 | 494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
496 | 495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
497 | 496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
498 | 497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
499 | 498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
500 | 499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
501 | 500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
502 | 501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
503 | 502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q
504 | 503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
505 | 504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
506 | 505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
507 | 506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C
508 | 507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S
509 | 508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
510 | 509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S
511 | 510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
512 | 511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
513 | 512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
514 | 513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
515 | 514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
516 | 515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S
517 | 516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
518 | 517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S
519 | 518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
520 | 519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
521 | 520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S
522 | 521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
523 | 522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
524 | 523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
525 | 524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
526 | 525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
527 | 526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
528 | 527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
529 | 528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
530 | 529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
531 | 530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S
532 | 531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
533 | 532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
534 | 533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
535 | 534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
536 | 535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
537 | 536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
538 | 537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
539 | 538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C
540 | 539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
541 | 540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C
542 | 541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S
543 | 542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S
544 | 543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
545 | 544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
546 | 545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
547 | 546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
548 | 547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S
549 | 548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
550 | 549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S
551 | 550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
552 | 551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
553 | 552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S
554 | 553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
555 | 554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
556 | 555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
557 | 556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
558 | 557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
559 | 558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
560 | 559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S
561 | 560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S
562 | 561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
563 | 562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
564 | 563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
565 | 564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
566 | 565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
567 | 566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S
568 | 567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S
569 | 568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
570 | 569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
571 | 570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
572 | 571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
573 | 572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S
574 | 573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S
575 | 574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
576 | 575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
577 | 576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
578 | 577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
579 | 578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S
580 | 579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
581 | 580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S
582 | 581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S
583 | 582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C
584 | 583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S
585 | 584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C
586 | 585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
587 | 586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S
588 | 587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S
589 | 588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C
590 | 589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S
591 | 590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
592 | 591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
593 | 592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
594 | 593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S
595 | 594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
596 | 595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S
597 | 596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S
598 | 597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S
599 | 598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S
600 | 599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
601 | 600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C
602 | 601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S
603 | 602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
604 | 603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
605 | 604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S
606 | 605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C
607 | 606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S
608 | 607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S
609 | 608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S
610 | 609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C
611 | 610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S
612 | 611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S
613 | 612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
614 | 613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
615 | 614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
616 | 615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
617 | 616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S
618 | 617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
619 | 618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S
620 | 619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S
621 | 620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S
622 | 621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
623 | 622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S
624 | 623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C
625 | 624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
626 | 625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S
627 | 626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
628 | 627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q
629 | 628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S
630 | 629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S
631 | 630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
632 | 631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S
633 | 632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S
634 | 633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C
635 | 634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S
636 | 635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S
637 | 636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
638 | 637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S
639 | 638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S
640 | 639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S
641 | 640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
642 | 641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S
643 | 642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C
644 | 643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S
645 | 644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
646 | 645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
647 | 646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C
648 | 647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S
649 | 648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C
650 | 649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
651 | 650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S
652 | 651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
653 | 652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S
654 | 653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S
655 | 654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
656 | 655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
657 | 656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S
658 | 657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
659 | 658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q
660 | 659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S
661 | 660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C
662 | 661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S
663 | 662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C
664 | 663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S
665 | 664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S
666 | 665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S
667 | 666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S
668 | 667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
669 | 668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
670 | 669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S
671 | 670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S
672 | 671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S
673 | 672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S
674 | 673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S
675 | 674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
676 | 675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
677 | 676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
678 | 677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
679 | 678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
680 | 679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
681 | 680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
682 | 681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
683 | 682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
684 | 683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
685 | 684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
686 | 685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
687 | 686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
688 | 687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
689 | 688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
690 | 689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
691 | 690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
692 | 691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
693 | 692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
694 | 693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
695 | 694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
696 | 695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
697 | 696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
698 | 697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
699 | 698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
700 | 699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
701 | 700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
702 | 701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
703 | 702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
704 | 703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
705 | 704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
706 | 705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
707 | 706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
708 | 707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
709 | 708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
710 | 709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
711 | 710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
712 | 711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
713 | 712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
714 | 713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
715 | 714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
716 | 715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
717 | 716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
718 | 717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
719 | 718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
720 | 719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
721 | 720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
722 | 721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
723 | 722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
724 | 723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
725 | 724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
726 | 725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
727 | 726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
728 | 727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
729 | 728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
730 | 729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
731 | 730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
732 | 731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
733 | 732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
734 | 733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
735 | 734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
736 | 735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
737 | 736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
738 | 737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
739 | 738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
740 | 739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
741 | 740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
742 | 741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
743 | 742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
744 | 743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
745 | 744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
746 | 745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
747 | 746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
748 | 747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
749 | 748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
750 | 749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
751 | 750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
752 | 751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
753 | 752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
754 | 753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
755 | 754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
756 | 755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
757 | 756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
758 | 757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
759 | 758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
760 | 759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
761 | 760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
762 | 761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
763 | 762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
764 | 763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
765 | 764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
766 | 765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
767 | 766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
768 | 767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
769 | 768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
770 | 769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
771 | 770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
772 | 771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
773 | 772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
774 | 773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
775 | 774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
776 | 775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
777 | 776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
778 | 777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
779 | 778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
780 | 779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
781 | 780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
782 | 781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
783 | 782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
784 | 783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
785 | 784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
786 | 785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
787 | 786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
788 | 787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
789 | 788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
790 | 789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
791 | 790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
792 | 791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
793 | 792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
794 | 793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
795 | 794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
796 | 795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
797 | 796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
798 | 797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
799 | 798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
800 | 799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
801 | 800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
802 | 801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
803 | 802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
804 | 803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
805 | 804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
806 | 805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
807 | 806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
808 | 807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
809 | 808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
810 | 809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
811 | 810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
812 | 811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
813 | 812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
814 | 813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
815 | 814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
816 | 815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
817 | 816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
818 | 817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
819 | 818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
820 | 819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
821 | 820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
822 | 821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
823 | 822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
824 | 823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
825 | 824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
826 | 825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
827 | 826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
828 | 827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
829 | 828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
830 | 829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
831 | 830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
832 | 831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
833 | 832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
834 | 833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
835 | 834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
836 | 835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
837 | 836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
838 | 837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
839 | 838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
840 | 839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
841 | 840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
842 | 841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
843 | 842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
844 | 843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
845 | 844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
846 | 845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
847 | 846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
848 | 847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
849 | 848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
850 | 849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
851 | 850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
852 | 851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
853 | 852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
854 | 853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
855 | 854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
856 | 855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
857 | 856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
858 | 857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
859 | 858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
860 | 859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
861 | 860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
862 | 861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
863 | 862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
864 | 863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
865 | 864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
866 | 865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
867 | 866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
868 | 867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
869 | 868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
870 | 869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
871 | 870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
872 | 871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
873 | 872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
874 | 873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
875 | 874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
876 | 875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
877 | 876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
878 | 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
879 | 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
880 | 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
881 | 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
882 | 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
883 | 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
884 | 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
885 | 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
886 | 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
887 | 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
888 | 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
889 | 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
890 | 889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
891 | 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
892 | 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
893 | 


--------------------------------------------------------------------------------
/digits.py:
--------------------------------------------------------------------------------
 1 | import random
 2 | from sklearn import datasets, cross_validation, metrics
 3 | import tensorflow as tf
 4 | from tensorflow.contrib import layers
 5 | from tensorflow.contrib import learn
 6 | 
 7 | random.seed(42)
 8 | 
 9 | # Load dataset and split it into train / test subsets.
10 | 
11 | digits = datasets.load_digits()
12 | X = digits.images
13 | y = digits.target
14 | 
15 | X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y,
16 |     test_size=0.2, random_state=42)
17 | 
18 | # TensorFlow model using Scikit Flow ops
19 | 
20 | def conv_model(features, target):
21 |     target = tf.one_hot(target, 10, 1.0, 0.0)
22 |     features = tf.expand_dims(features, 3)
23 |     features = tf.reduce_max(layers.conv2d(features, 12, [3, 3]), [1, 2])
24 |     features = tf.reshape(features, [-1, 12])
25 |     prediction, loss = learn.models.logistic_regression(features, target)
26 |     train_op = layers.optimize_loss(loss,
27 |         tf.contrib.framework.get_global_step(), optimizer='SGD',
28 |         learning_rate=0.01)
29 |     return tf.argmax(prediction, dimension=1), loss, train_op
30 | 
31 | # Create a classifier, train and predict.
32 | classifier = learn.Estimator(model_fn=conv_model)
33 | classifier.fit(X_train, y_train, steps=1000, batch_size=128)
34 | score = metrics.accuracy_score(classifier.predict(X_test), y_test)
35 | print('Accuracy: %f' % score)
36 | 


--------------------------------------------------------------------------------
/glove_helper.py:
--------------------------------------------------------------------------------
 1 | """
 2 | GloVe embeddings in Tensorflow
 3 | 
 4 | Usage:
 5 |   python glove_helper.py path/to/glove.txt path/to/save/glove path/to/save/vocab
 6 | 
 7 | Convert GloVe embeddings from https://nlp.stanford.edu/projects/glove/
 8 | And use embeddings in your model after you created embeddings Tensor:
 9 |   tf.contrib.framework.init_from_checkpoint(path_to_saved_here, {
10 |     'embeddings': 'embeddings'})
11 | """
12 | 
13 | import sys
14 | import tensorflow as tf
15 | 
16 | args = sys.argv
17 | 
18 | f = open(args[1])
19 | vocab = []
20 | embeddings = []
21 | for line in f:
22 |     tokens = line.split()
23 |     vocab.append(tokens[0])
24 |     embeddings.append([float(val) for val in tokens[1:]])
25 | 
26 | with tf.Session() as sess:
27 |     v = tf.Variable(tf.constant(embeddings, name="embeddings"))
28 |     sess.run(tf.global_variables_initializer())
29 |     embedding_saver = tf.train.Saver({"embeddings": v})
30 |     embedding_saver.save(sess, args[2])
31 | 
32 | with open(args[3], 'w') as f:
33 |   for word in vocab:
34 |      f.write(word + '\n')
35 | 
36 | 


--------------------------------------------------------------------------------
/rl/atari-rl.py:
--------------------------------------------------------------------------------
  1 | import random
  2 | import time
  3 | 
  4 | import numpy as np
  5 | import skimage.transform
  6 | import skimage.color
  7 | import gym
  8 | import tensorflow as tf
  9 | 
 10 | 
 11 | def run_episode(env, agent, max_steps, shape=(64, 64), render_every=None):
 12 |   observation = env.reset()
 13 |   agent.reset(observation)
 14 |   reward = 0.0
 15 |   done = False
 16 |   for step in range(max_steps):
 17 |     action = agent.act(observation, reward)
 18 |     observation, reward, done, _ = env.step(action)
 19 |     if done:
 20 |       break
 21 |     if render_every is not None and step % render_every == 0:
 22 |       env.render()
 23 | 
 24 | 
 25 | class RandomAgent(object):
 26 |   
 27 |   def __init__(self, action_space):
 28 |     self.action_space = action_space
 29 | 
 30 |   def act(self, observation, reward):
 31 |     return self.action_space.sample()
 32 | 
 33 | 
 34 | def sample_final_epsilon():
 35 |     """
 36 |     Sample a final epsilon value to anneal towards from a distribution.
 37 |     These values are specified in section 5.1 of http://arxiv.org/pdf/1602.01783v1.pdf
 38 |     """
 39 |     final_epsilons = np.array([.1,.01,.5])
 40 |     probabilities = np.array([0.4,0.3,0.3])
 41 |     return np.random.choice(final_epsilons, 1, p=list(probabilities))[0]
 42 | 
 43 | 
 44 | def process_observation(observation, shape):
 45 |   return skimage.transform.resize(skimage.color.rgb2gray(observation), shape)
 46 | 
 47 | 
 48 | class TFAgent(object):
 49 | 
 50 |   def __init__(self, model_fn, action_space, trace_length, shape=(64, 64)):
 51 |     self.action_space = action_space
 52 |     self.model_fn = model_fn
 53 |     self.trace_length = trace_length
 54 |     self.shape = shape
 55 |     self.epsilon = 1.0
 56 |     self.final_epsilon = sample_final_epsilon() 
 57 |     self.graph = self._create_graph()
 58 |     self.episode = 0
 59 |     self.episode_reward = 0.0
 60 |     with self.graph.as_default():
 61 |       self.session = tf.contrib.learn.monitored_session.MonitoredSession()
 62 | 
 63 |   def _create_graph(self):
 64 |     graph = tf.Graph()
 65 |     with graph.as_default():
 66 |       params = {'n_actions': self.action_space.n}
 67 |       self.features = tf.placeholder(
 68 |         shape=[None, self.trace_length] + list(self.shape), dtype=tf.float32, name='observation')
 69 |       self.targets = {
 70 |         'reward': tf.placeholder(shape=[None], dtype=tf.float32, name='reward'),
 71 |         'action': tf.placeholder(shape=[None], dtype=tf.int64, name='action')}
 72 |       self.prediction, self.loss, self.train_op = self.model_fn(
 73 |         self.features, self.targets, 'train', params)
 74 |     return graph
 75 | 
 76 |   def reset(self, observation):
 77 |     print("Episode %d, Reward: %.2f, Epsilon: %.4f" % (self.episode, self.episode_reward, self.epsilon))
 78 |     observation = process_observation(observation, self.shape)
 79 |     self.observation_trace = [observation] * self.trace_length
 80 |     self.last_action = None
 81 |     self.episode_reward = 0.0
 82 |     self.final_epsilon = sample_final_epsilon()
 83 |     self.episode += 1
 84 | 
 85 |   def act(self, observation, reward):
 86 |     observation = process_observation(observation, self.shape)
 87 |     if self.last_action is not None:
 88 |       _, loss = self.session.run([self.train_op, self.loss], {
 89 |         self.features: [self.observation_trace],
 90 |         self.targets['action']: [self.last_action],
 91 |         self.targets['reward']: [reward]})
 92 |     self.observation_trace = self.observation_trace[1:] + [observation]
 93 |     if random.random() <= self.epsilon:
 94 |       action = self.action_space.sample()
 95 |     else:
 96 |       action = self.session.run(self.prediction, {
 97 |         self.features: [self.observation_trace]})[0]
 98 |     if self.epsilon > self.final_epsilon:
 99 |       self.epsilon -= (1.0 - self.final_epsilon) / 10000
100 |     self.last_action = action
101 |     self.episode_reward += reward
102 |     return action
103 |  
104 | 
105 | def simple_model(features, targets, mode, params):
106 |   n_actions = params.pop('n_actions')
107 | 
108 |   # DQN model.
109 |   features = tf.contrib.layers.convolution2d(features, 16, 
110 |     kernel_size=[8, 8], stride=[4, 4], padding='SAME',
111 |     activation_fn=tf.nn.relu)
112 |   features = tf.contrib.layers.convolution2d(features, 8, 
113 |     kernel_size=[4, 4], stride=[2, 2], padding='SAME',
114 |     activation_fn=tf.nn.relu)
115 |   features = tf.contrib.layers.flatten(features)
116 |   features = tf.contrib.layers.fully_connected(
117 |     features, 256, activation_fn=tf.nn.relu)
118 |   q_values = tf.contrib.layers.fully_connected(
119 |     features, n_actions, activation_fn=None)
120 |   prediction = tf.argmax(q_values, dimension=1)
121 | 
122 |   # Compute loss and add optimizer.
123 |   reward, action = targets['reward'], targets['action']
124 |   action = tf.one_hot(action, n_actions, 1.0, 0.0)
125 |   action_q_values = tf.reduce_sum(
126 |     tf.mul(q_values, action), reduction_indices=[1])
127 |   loss = tf.contrib.losses.mean_squared_error(action_q_values, reward) 
128 |   train_op = tf.contrib.layers.optimize_loss(
129 |     loss, tf.contrib.framework.get_global_step(),
130 |     learning_rate=0.01, optimizer='Adam')
131 |   return prediction, loss, train_op
132 | 
133 | 
134 | def main():
135 |   env = gym.make('Breakout-v0')
136 | #  agent = RandomAgent(env.action_space)
137 |   agent = TFAgent(simple_model, env.action_space, 10, (64, 64))
138 |   for i in range(100):
139 |     run_episode(env, agent, 100, render_every=10)
140 |     print("episode", i)
141 | 
142 | 
143 | if __name__ == "__main__":
144 |   main()
145 | 
146 | 


--------------------------------------------------------------------------------
/seq2seq/data.py:
--------------------------------------------------------------------------------
 1 | import tensorflow as tf
 2 | from tensorflow.contrib import layers
 3 | from tensorflow.contrib import learn
 4 | 
 5 | 
 6 | def make_input_fn(mode, filename_in, filename_out, in_vocab_file, out_vocab_file, batch_size, vocab_size,
 7 |                   input_max_length, output_max_length, queue_capacity=10000, num_threads=10):
 8 |     def input_fn():
 9 |         num_epochs = None if mode == tf.estimator.ModeKeys.TRAIN else 1
10 |         filename_in_queue = tf.train.string_input_producer(
11 |             [filename_in], num_epochs=num_epochs)
12 |         filename_out_queue = tf.train.string_input_producer(
13 |             [filename_out], num_epochs=num_epochs)
14 |         reader_in = tf.TextLineReader()
15 |         reader_out = tf.TextLineReader()
16 |         in_list, out_list = [], []
17 |         for _ in range(num_threads):
18 |             in_list.append(reader_in.read(filename_in_queue)[1])
19 |             out_list.append(reader_out.read(filename_out_queue)[1])
20 |         tensor_in = reader_in.read(filename_in_queue)[1]
21 |         tensor_out = reader_out.read(filename_out_queue)[1]
22 |         if mode == tf.estimator.ModeKeys.TRAIN:
23 |             inputs, outputs = tf.train.shuffle_batch(
24 |                 (tensor_in, tensor_out), batch_size, capacity=queue_capacity,
25 |                 min_after_dequeue=batch_size * 3,
26 |                 enqueue_many=True
27 |             )
28 |         else:
29 |             inputs, outputs = tf.train.batch(
30 |                 (tensor_in, tensor_out), batch_size, capacity=queue_capacity,
31 |                 allow_smaller_final_batch=True)
32 | 
33 |         # Preprocess inputs.
34 |         inputs = utils.sparse_to_dense_trim(tf.string_split(inputs), output_shape=[batch_size, input_max_length], default_value='<\S>')
35 |         outputs = utils.sparse_to_dense_trim(tf.string_split(outputs), output_shape=[batch_size, output_max_length], default_value='<\S>')
36 |         tf.identity(inputs[0], name='inputs')
37 |         tf.identity(outputs[0], name='outputs')
38 |         in_vocab = tf.contrib.lookup.index_table_from_file(in_vocab_file, vocab_size=vocab_size, default_value=2)
39 |         input_ids = in_vocab.lookup(inputs)
40 |         out_vocab = tf.contrib.lookup.index_table_from_file(out_vocab_file, vocab_size=vocab_size, default_value=2)
41 |         output_ids = out_vocab.lookup(outputs)
42 |         return {'inputs': inputs_ids, 'outputs': outputs_ids}, None
43 |     return input_fn
44 | 
45 | 


--------------------------------------------------------------------------------
/seq2seq/generate_data.py:
--------------------------------------------------------------------------------
 1 | import random
 2 | 
 3 | examples = 10000
 4 | symbols = 100
 5 | length = 10
 6 | 
 7 | with open('vocab', 'w') as f:
 8 |     f.write("<S>\n</S>\n<UNK>\n")
 9 |     for i in range(100):
10 |         f.write("%d\n" % i)
11 | 
12 | 
13 | with open('input', 'w') as fin:
14 |     with open('output', 'w') as fout:
15 |         for i in range(examples):
16 |             inp = [random.randint(0, symbols) + 3 for _ in range(length)]
17 |             out = [(x + 5) % 100 + 3 for x in inp]
18 |             fin.write(' '.join([str(x) for x in inp]) + '\n')
19 |             fout.write(' '.join([str(x) for x in out]) + '\n')
20 | 
21 | 


--------------------------------------------------------------------------------
/seq2seq/seq2seq.py:
--------------------------------------------------------------------------------
  1 | import logging
  2 | 
  3 | import numpy as np
  4 | import tensorflow as tf
  5 | from tensorflow.contrib import layers
  6 | 
  7 | import timeline
  8 | 
  9 | 
 10 | GO_TOKEN = 0
 11 | END_TOKEN = 1
 12 | UNK_TOKEN = 2
 13 | 
 14 | 
 15 | def seq2seq(mode, features, labels, params):
 16 |     vocab_size = params['vocab_size']
 17 |     embed_dim = params['embed_dim']
 18 |     num_units = params['num_units']
 19 |     input_max_length = params['input_max_length']
 20 |     output_max_length = params['output_max_length']
 21 | 
 22 |     inp = features['input']
 23 |     output = features['output']
 24 |     batch_size = tf.shape(inp)[0]
 25 |     start_tokens = tf.zeros([batch_size], dtype=tf.int64)
 26 |     train_output = tf.concat([tf.expand_dims(start_tokens, 1), output], 1)
 27 |     input_lengths = tf.reduce_sum(tf.to_int32(tf.not_equal(inp, 1)), 1)
 28 |     output_lengths = tf.reduce_sum(tf.to_int32(tf.not_equal(train_output, 1)), 1)
 29 |     input_embed = layers.embed_sequence(
 30 |         inp, vocab_size=vocab_size, embed_dim=embed_dim, scope='embed')
 31 |     output_embed = layers.embed_sequence(
 32 |         train_output, vocab_size=vocab_size, embed_dim=embed_dim, scope='embed', reuse=True)
 33 |     with tf.variable_scope('embed', reuse=True):
 34 |         embeddings = tf.get_variable('embeddings')
 35 | 
 36 |     cell = tf.contrib.rnn.GRUCell(num_units=num_units)
 37 |     encoder_outputs, encoder_final_state = tf.nn.dynamic_rnn(cell, input_embed, dtype=tf.float32)
 38 | 
 39 |     train_helper = tf.contrib.seq2seq.TrainingHelper(output_embed, output_lengths)
 40 |     # train_helper = tf.contrib.seq2seq.ScheduledEmbeddingTrainingHelper(
 41 |     #     output_embed, output_lengths, embeddings, 0.3
 42 |     # )
 43 |     pred_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
 44 |         embeddings, start_tokens=tf.to_int32(start_tokens), end_token=1)
 45 | 
 46 |     def decode(helper, scope, reuse=None):
 47 |         with tf.variable_scope(scope, reuse=reuse):
 48 |             attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(
 49 |                 num_units=num_units, memory=encoder_outputs,
 50 |                 memory_sequence_length=input_lengths)
 51 |             cell = tf.contrib.rnn.GRUCell(num_units=num_units)
 52 |             attn_cell = tf.contrib.seq2seq.AttentionWrapper(
 53 |                 cell, attention_mechanism, attention_layer_size=num_units / 2)
 54 |             out_cell = tf.contrib.rnn.OutputProjectionWrapper(
 55 |                 attn_cell, vocab_size, reuse=reuse
 56 |             )
 57 |             decoder = tf.contrib.seq2seq.BasicDecoder(
 58 |                 cell=out_cell, helper=helper,
 59 |                 initial_state=out_cell.zero_state(
 60 |                     dtype=tf.float32, batch_size=batch_size))
 61 |                 #initial_state=encoder_final_state)
 62 |             outputs = tf.contrib.seq2seq.dynamic_decode(
 63 |                 decoder=decoder, output_time_major=False,
 64 |                 impute_finished=True, maximum_iterations=output_max_length
 65 |             )
 66 |             return outputs[0]
 67 |     train_outputs = decode(train_helper, 'decode')
 68 |     pred_outputs = decode(pred_helper, 'decode', reuse=True)
 69 | 
 70 |     tf.identity(train_outputs.sample_id[0], name='train_pred')
 71 |     weights = tf.to_float(tf.not_equal(train_output[:, :-1], 1))
 72 |     loss = tf.contrib.seq2seq.sequence_loss(
 73 |         train_outputs.rnn_output, output, weights=weights)
 74 |     train_op = layers.optimize_loss(
 75 |         loss, tf.train.get_global_step(),
 76 |         optimizer=params.get('optimizer', 'Adam'),
 77 |         learning_rate=params.get('learning_rate', 0.001),
 78 |         summaries=['loss', 'learning_rate'])
 79 | 
 80 |     tf.identity(pred_outputs.sample_id[0], name='predictions')
 81 |     return tf.estimator.EstimatorSpec(
 82 |         mode=mode,
 83 |         predictions=pred_outputs.sample_id,
 84 |         loss=loss,
 85 |         train_op=train_op
 86 |     )
 87 | 
 88 | 
 89 | def tokenize_and_map(line, vocab):
 90 |     return [vocab.get(token, UNK_TOKEN) for token in line.split(' ')]
 91 | 
 92 | 
 93 | def make_input_fn(
 94 |         batch_size, input_filename, output_filename, vocab,
 95 |         input_max_length, output_max_length,
 96 |         input_process=tokenize_and_map, output_process=tokenize_and_map):
 97 | 
 98 |     def input_fn():
 99 |         inp = tf.placeholder(tf.int64, shape=[None, None], name='input')
100 |         output = tf.placeholder(tf.int64, shape=[None, None], name='output')
101 |         tf.identity(inp[0], 'input_0')
102 |         tf.identity(output[0], 'output_0')
103 |         return {
104 |             'input': inp,
105 |             'output': output,
106 |         }, None
107 | 
108 |     def sampler():
109 |         while True:
110 |             with open(input_filename) as finput:
111 |                 with open(output_filename) as foutput:
112 |                     for in_line in finput:
113 |                         out_line = foutput.readline()
114 |                         yield {
115 |                             'input': input_process(in_line, vocab)[:input_max_length - 1] + [END_TOKEN],
116 |                             'output': output_process(out_line, vocab)[:output_max_length - 1] + [END_TOKEN]
117 |                         }
118 | 
119 |     sample_me = sampler()
120 | 
121 |     def feed_fn():
122 |         inputs, outputs = [], []
123 |         input_length, output_length = 0, 0
124 |         for i in range(batch_size):
125 |             rec = sample_me.next()
126 |             inputs.append(rec['input'])
127 |             outputs.append(rec['output'])
128 |             input_length = max(input_length, len(inputs[-1]))
129 |             output_length = max(output_length, len(outputs[-1]))
130 |         # Pad me right with </S> token.
131 |         for i in range(batch_size):
132 |             inputs[i] += [END_TOKEN] * (input_length - len(inputs[i]))
133 |             outputs[i] += [END_TOKEN] * (output_length - len(outputs[i]))
134 |         return {
135 |             'input:0': inputs,
136 |             'output:0': outputs
137 |         }
138 | 
139 |     return input_fn, feed_fn
140 | 
141 | 
142 | def load_vocab(filename):
143 |     vocab = {}
144 |     with open(filename) as f:
145 |         for idx, line in enumerate(f):
146 |             vocab[line.strip()] = idx
147 |     return vocab
148 | 
149 | 
150 | def get_rev_vocab(vocab):
151 |     return {idx: key for key, idx in vocab.iteritems()}
152 | 
153 | 
154 | def get_formatter(keys, vocab):
155 |     rev_vocab = get_rev_vocab(vocab)
156 | 
157 |     def to_str(sequence):
158 |         tokens = [
159 |             rev_vocab.get(x, "<UNK>") for x in sequence]
160 |         return ' '.join(tokens)
161 | 
162 |     def format(values):
163 |         res = []
164 |         for key in keys:
165 |             res.append("%s = %s" % (key, to_str(values[key])))
166 |         return '\n'.join(res)
167 |     return format
168 | 
169 | 
170 | def train_seq2seq(
171 |         input_filename, output_filename, vocab_filename,
172 |         model_dir):
173 |     vocab = load_vocab(vocab_filename)
174 |     params = {
175 |         'vocab_size': len(vocab),
176 |         'batch_size': 32,
177 |         'input_max_length': 30,
178 |         'output_max_length': 30,
179 |         'embed_dim': 100,
180 |         'num_units': 256
181 |     }
182 |     est = tf.estimator.Estimator(
183 |         model_fn=seq2seq,
184 |         model_dir=model_dir, params=params)
185 | 
186 |     input_fn, feed_fn = make_input_fn(
187 |         params['batch_size'],
188 |         input_filename,
189 |         output_filename,
190 |         vocab, params['input_max_length'], params['output_max_length'])
191 | 
192 |     # Make hooks to print examples of inputs/predictions.
193 |     print_inputs = tf.train.LoggingTensorHook(
194 |         ['input_0', 'output_0'], every_n_iter=100,
195 |         formatter=get_formatter(['input_0', 'output_0'], vocab))
196 |     print_predictions = tf.train.LoggingTensorHook(
197 |         ['predictions', 'train_pred'], every_n_iter=100,
198 |         formatter=get_formatter(['predictions', 'train_pred'], vocab))
199 | 
200 |     timeline_hook = timeline.TimelineHook(model_dir, every_n_iter=100)
201 |     est.train(
202 |         input_fn=input_fn,
203 |         hooks=[tf.train.FeedFnHook(feed_fn), print_inputs, print_predictions,
204 |                timeline_hook],
205 |         steps=10000)
206 | 
207 | 
208 | def main():
209 |     tf.logging._logger.setLevel(logging.INFO)
210 |     train_seq2seq('input', 'output', 'vocab', 'model/seq2seq')
211 | 
212 | 
213 | if __name__ == "__main__":
214 |     main()
215 | 
216 | 


--------------------------------------------------------------------------------
/seq2seq/timeline.py:
--------------------------------------------------------------------------------
 1 | """Module with tools for timeline tracking."""
 2 | 
 3 | import os
 4 | 
 5 | import tensorflow as tf
 6 | from tensorflow.python.client import timeline
 7 | from tensorflow.python.training import basic_session_run_hooks
 8 | 
 9 | 
10 | def save_timeline(path, run_metadata):
11 |     fetched_timeline = timeline.Timeline(run_metadata.step_stats)
12 |     chrome_trace = fetched_timeline.generate_chrome_trace_format()
13 |     with open(path, 'w') as f:
14 |         f.write(chrome_trace)
15 | 
16 | 
17 | class TimelineHook(tf.train.SessionRunHook):
18 | 
19 |     def __init__(self, timeline_dir, every_n_iter=None, every_n_secs=None):
20 |         if (every_n_iter is None and every_n_secs is None) or (
21 |             every_n_iter is not None and every_n_secs is not None):
22 |             raise ValueError(
23 |                 "Either every_n_iter or every_n_secs should be used.")
24 |         self._timeline_dir = timeline_dir
25 |         self._timer = basic_session_run_hooks.SecondOrStepTimer(
26 |             every_secs=every_n_secs, every_steps=every_n_iter)
27 |         self._iter_count = 0
28 | 
29 |     def begin(self):
30 |         self._timer.reset()
31 |         self._iter_count = 0
32 | 
33 |     def before_run(self, run_context):
34 |         self._should_trigger = self._timer.should_trigger_for_step(self._iter_count)
35 |         if self._should_trigger:
36 |             options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
37 |             return tf.train.SessionRunArgs([], options=options)
38 |         return None
39 | 
40 |     def after_run(self, run_context, run_values):
41 |         if self._should_trigger:
42 |             self._timer.update_last_triggered_step(self._iter_count)
43 |             save_timeline(os.path.join(
44 |                 self._timeline_dir, "timeline-%d.json" % self._iter_count),
45 |                 run_values.run_metadata)
46 |         self._iter_count += 1
47 | 
48 | 


--------------------------------------------------------------------------------
/titanic.py:
--------------------------------------------------------------------------------
 1 | import random
 2 | import pandas
 3 | from sklearn.linear_model import LogisticRegression
 4 | from sklearn.metrics import accuracy_score
 5 | from sklearn.utils import check_array
 6 | from sklearn.cross_validation import train_test_split
 7 | 
 8 | import tensorflow as tf
 9 | from tensorflow.contrib import layers
10 | from tensorflow.contrib import learn
11 | 
12 | 
13 | train = pandas.read_csv('data/titanic_train.csv')
14 | y, X = train['Survived'], train[['Age', 'SibSp', 'Fare']].fillna(0)
15 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
16 | 
17 | lr = LogisticRegression()
18 | lr.fit(X_train, y_train)
19 | print(accuracy_score(lr.predict(X_test), y_test))
20 | 
21 | 
22 | # Linear classifier.
23 | 
24 | random.seed(42)
25 | tflr = learn.LinearClassifier(n_classes=2,
26 |     feature_columns=learn.infer_real_valued_columns_from_input(X_train),
27 |     optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.05))
28 | tflr.fit(X_train, y_train, batch_size=128, steps=500)
29 | print(accuracy_score(tflr.predict(X_test), y_test))
30 | 
31 | # 3 layer neural network with rectified linear activation.
32 | 
33 | random.seed(42)
34 | classifier = learn.DNNClassifier(hidden_units=[10, 20, 10],
35 |     n_classes=2,
36 |     feature_columns=learn.infer_real_valued_columns_from_input(X_train),
37 |     optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.05))
38 | classifier.fit(X_train, y_train, batch_size=128, steps=500)
39 | print(accuracy_score(classifier.predict(X_test), y_test))
40 | 
41 | # 3 layer neural network with hyperbolic tangent activation.
42 | 
43 | def dnn_tanh(features, target):
44 |     target = tf.one_hot(target, 2, 1.0, 0.0)
45 |     logits = layers.stack(features, layers.fully_connected, [10, 20, 10],
46 |         activation_fn=tf.tanh)
47 |     prediction, loss = learn.models.logistic_regression(logits, target)
48 |     train_op = layers.optimize_loss(loss,
49 |         tf.contrib.framework.get_global_step(), optimizer='SGD', learning_rate=0.05)
50 |     return tf.argmax(prediction, dimension=1), loss, train_op
51 | 
52 | random.seed(42)
53 | classifier = learn.Estimator(model_fn=dnn_tanh)
54 | classifier.fit(X_train, y_train, batch_size=128, steps=100)
55 | print(accuracy_score(classifier.predict(X_test), y_test))
56 | 
57 | 


--------------------------------------------------------------------------------
/titanic_all_features.py:
--------------------------------------------------------------------------------
 1 | import random
 2 | import pandas
 3 | from sklearn.cross_validation import train_test_split
 4 | from sklearn.linear_model import LogisticRegression
 5 | from sklearn.metrics import accuracy_score
 6 | from sklearn.preprocessing import LabelEncoder
 7 | from sklearn.utils import check_array
 8 | 
 9 | import tensorflow as tf
10 | from tensorflow.contrib import layers
11 | from tensorflow.contrib import learn
12 | 
13 | 
14 | train = pandas.read_csv('data/titanic_train.csv')
15 | y = train.pop('Survived')
16 | # Drop all unique columns. List all variables for future reference.
17 | categorical_vars = ['Pclass', 'Sex', 'Embarked']
18 | continues_vars = ['Age', 'SibSp', 'Parch', 'Fare']
19 | X = train[categorical_vars + continues_vars].fillna(0)
20 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
21 | 
22 | 
23 | # Pandas input functino. 
24 | def pandas_input_fn(x, y=None, batch_size=128, num_epochs=None):
25 |   def input_fn():
26 |     if y is not None:
27 |       x['target'] = y
28 |     queue = learn.dataframe.queues.feeding_functions.enqueue_data(
29 |       x, 1000, shuffle=num_epochs is None, num_epochs=num_epochs)
30 |     if num_epochs is None:
31 |       features = queue.dequeue_many(batch_size)
32 |     else:
33 |       features = queue.dequeue_up_to(batch_size)
34 |     features = dict(zip(['index'] + list(x.columns), features))
35 |     if y is not None:
36 |       target = features.pop('target')
37 |       return features, target
38 |     return features
39 |   return input_fn
40 | 
41 | 
42 | # Process categorical variables into ids.
43 | X_train = X_train.copy()
44 | X_test = X_test.copy()
45 | categorical_var_encoders = {}
46 | for var in categorical_vars:
47 |   le = LabelEncoder().fit(X_train[var])
48 |   X_train[var + '_ids'] = le.transform(X_train[var])
49 |   X_test[var + '_ids'] = le.transform(X_test[var])
50 |   X_train.pop(var)
51 |   X_test.pop(var)
52 |   categorical_var_encoders[var] = le
53 | 
54 | 
55 | CATEGORICAL_EMBED_SIZE = 10 # Note, you can customize this per variable.
56 | 
57 | 
58 | # 3 layer neural network with hyperbolic tangent activation.
59 | def dnn_tanh(features, target):
60 |     target = tf.one_hot(target, 2, 1.0, 0.0)
61 |     # Organize continues features.
62 |     final_features = [tf.expand_dims(tf.cast(features[var], tf.float32), 1) for var in continues_vars]
63 |     # Embed categorical variables into distributed representation.
64 |     for var in categorical_vars:
65 |         feature = learn.ops.categorical_variable(
66 |             features[var + '_ids'], len(categorical_var_encoders[var].classes_), 
67 |             embedding_size=CATEGORICAL_EMBED_SIZE, name=var)
68 |         final_features.append(feature)
69 |     # Concatenate all features into one vector.
70 |     features = tf.concat(1, final_features)
71 |     # Deep Neural Network
72 |     logits = layers.stack(features, layers.fully_connected, [10, 20, 10],
73 |         activation_fn=tf.tanh)
74 |     prediction, loss = learn.models.logistic_regression(logits, target)
75 |     train_op = layers.optimize_loss(loss,
76 |         tf.contrib.framework.get_global_step(), optimizer='SGD', learning_rate=0.05)
77 |     return tf.argmax(prediction, dimension=1), loss, train_op
78 | 
79 | random.seed(42)
80 | classifier = learn.Estimator(model_fn=dnn_tanh)
81 | # Note: not training this alomst at all.
82 | classifier.fit(input_fn=pandas_input_fn(X_train, y_train), steps=100)
83 | preds = list(classifier.predict(input_fn=pandas_input_fn(X_test, num_epochs=1), as_iterable=True))
84 | print(accuracy_score(y_test, preds))
85 | 


--------------------------------------------------------------------------------
/titanic_all_features_with_fc.py:
--------------------------------------------------------------------------------
  1 | import random
  2 | import pandas
  3 | from sklearn.cross_validation import train_test_split
  4 | from sklearn.linear_model import LogisticRegression
  5 | from sklearn.metrics import accuracy_score
  6 | from sklearn.preprocessing import LabelEncoder
  7 | from sklearn.utils import check_array
  8 | 
  9 | import tensorflow as tf
 10 | from tensorflow.contrib import layers
 11 | from tensorflow.contrib import learn
 12 | 
 13 | 
 14 | train = pandas.read_csv('data/titanic_train.csv')
 15 | y = train.pop('Survived')
 16 | # Drop all unique columns. List all variables for future reference.
 17 | categorical_vars = ['Pclass', 'Sex', 'Embarked']
 18 | continues_vars = ['Age', 'SibSp', 'Parch', 'Fare']
 19 | X = train[categorical_vars + continues_vars].fillna(0)
 20 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 21 | 
 22 | 
 23 | # Pandas input functino. 
 24 | def pandas_input_fn(x, y=None, batch_size=128, num_epochs=None):
 25 |   def input_fn():
 26 |     if y is not None:
 27 |       x['target'] = y
 28 |     queue = learn.dataframe.queues.feeding_functions.enqueue_data(
 29 |       x, 1000, shuffle=num_epochs is None, num_epochs=num_epochs)
 30 |     if num_epochs is None:
 31 |       features = queue.dequeue_many(batch_size)
 32 |     else:
 33 |       features = queue.dequeue_up_to(batch_size)
 34 |     features = dict(zip(['index'] + list(x.columns), features))
 35 |     if y is not None:
 36 |       target = features.pop('target')
 37 |       return features, target
 38 |     return features
 39 |   return input_fn
 40 | 
 41 | 
 42 | # Process categorical variables into ids.
 43 | X_train = X_train.copy()
 44 | X_test = X_test.copy()
 45 | categorical_var_encoders = {}
 46 | for var in categorical_vars:
 47 |   le = LabelEncoder().fit(X_train[var])
 48 |   X_train[var + '_ids'] = le.transform(X_train[var])
 49 |   X_test[var + '_ids'] = le.transform(X_test[var])
 50 |   X_train.pop(var)
 51 |   X_test.pop(var)
 52 |   categorical_var_encoders[var] = le
 53 | 
 54 | ### Note: Feature Columns currently (2016/10/22) not working, update is coming.
 55 | # Setup feature columns.
 56 | CATEGORICAL_EMBED_SIZE = 10 # Note, you can customize this per variable.
 57 | feature_columns = [
 58 |   layers.real_valued_column(var) for var in continues_vars
 59 | ] + [
 60 |   layers.embedding_column(
 61 |      layers.sparse_column_with_integerized_feature(
 62 |        var + '_ids', len(categorical_var_encoders[var].classes_)), 
 63 |      CATEGORICAL_EMBED_SIZE) for var in
 64 |   categorical_vars
 65 | ]
 66 | 
 67 | 
 68 | # Linear classifier.
 69 | '''
 70 | random.seed(42)
 71 | tflr = learn.LinearClassifier(n_classes=2,
 72 |     feature_columns=feature_columns,
 73 |     optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.05))
 74 | tflr.fit(input_fn=train_input_fn, steps=500)
 75 | print(list(tflr.predict(input_fn=test_input_fn, as_iterable=True)), y_test)
 76 | print(accuracy_score(y_test, list(tflr.predict(input_fn=test_input_fn, as_iterable=True))))
 77 | '''
 78 | 
 79 | # 3 layer neural network with rectified linear activation.
 80 | '''
 81 | random.seed(42)
 82 | classifier = learn.DNNClassifier(hidden_units=[10, 20, 10],
 83 |     n_classes=2,
 84 |     feature_columns=feature_columns,
 85 |     optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.05))
 86 | classifier.fit(X_train, y_train, batch_size=128, steps=500)
 87 | print(accuracy_score(y_test, classifier.predict(X_test)))
 88 | '''
 89 | 
 90 | # 3 layer neural network with hyperbolic tangent activation.
 91 | def dnn_tanh(features, target):
 92 |     target = tf.one_hot(target, 2, 1.0, 0.0)
 93 |     # Organize continues features.
 94 |     final_features = [tf.expand_dims(tf.cast(features[var], tf.float32), 1) for var in continues_vars]
 95 |     # Embed categorical variables into distributed representation.
 96 |     for var in categorical_vars:
 97 |         feature = learn.ops.categorical_variable(
 98 |             features[var + '_ids'], len(categorical_var_encoders[var].classes_), 
 99 |             embedding_size=CATEGORICAL_EMBED_SIZE, name=var)
100 |         final_features.append(feature)
101 |     # Concatenate all features into one vector.
102 |     features = tf.concat(1, final_features)
103 |     # Deep Neural Network
104 |     logits = layers.stack(features, layers.fully_connected, [10, 20, 10],
105 |         activation_fn=tf.tanh)
106 |     prediction, loss = learn.models.logistic_regression(logits, target)
107 |     train_op = layers.optimize_loss(loss,
108 |         tf.contrib.framework.get_global_step(), optimizer='SGD', learning_rate=0.05)
109 |     return tf.argmax(prediction, dimension=1), loss, train_op
110 | 
111 | random.seed(42)
112 | classifier = learn.Estimator(model_fn=dnn_tanh)
113 | classifier.fit(input_fn=pandas_input_fn(X_train, y_train), steps=100)
114 | preds = list(classifier.predict(input_fn=pandas_input_fn(X_test, num_epochs=1), as_iterable=True))
115 | print(accuracy_score(y_test, preds))
116 | 


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/titanic_categorical_variables.py:
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 1 | import random
 2 | import pandas
 3 | import numpy as np
 4 | from sklearn import metrics, cross_validation
 5 | 
 6 | import tensorflow as tf
 7 | from tensorflow.contrib import layers
 8 | from tensorflow.contrib import learn
 9 | 
10 | random.seed(42)
11 | 
12 | data = pandas.read_csv('data/titanic_train.csv')
13 | X = data[["Embarked"]]
14 | y = data["Survived"]
15 | X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2, random_state=42)
16 | 
17 | embarked_classes = X_train["Embarked"].unique()
18 | n_classes = len(embarked_classes) + 1
19 | print('Embarked has next classes: ', embarked_classes)
20 | 
21 | cat_processor = learn.preprocessing.CategoricalProcessor()
22 | X_train = np.array(list(cat_processor.fit_transform(X_train)))
23 | X_test = np.array(list(cat_processor.transform(X_test)))
24 | 
25 | ### Embeddings
26 | 
27 | EMBEDDING_SIZE = 3
28 | 
29 | def categorical_model(features, target):
30 |     target = tf.one_hot(target, 2, 1.0, 0.0)
31 |     features = learn.ops.categorical_variable(
32 |         features, n_classes, embedding_size=EMBEDDING_SIZE, name='embarked')
33 |     prediction, loss = learn.models.logistic_regression(tf.squeeze(features, [1]), target)
34 |     train_op = layers.optimize_loss(loss,
35 |         tf.contrib.framework.get_global_step(), optimizer='SGD', learning_rate=0.05)
36 |     return tf.argmax(prediction, dimension=1), loss, train_op
37 | 
38 | classifier = learn.Estimator(model_fn=categorical_model)
39 | classifier.fit(X_train, y_train, steps=1000)
40 | 
41 | print("Accuracy: {0}".format(metrics.accuracy_score(classifier.predict(X_test), y_test)))
42 | print("ROC: {0}".format(metrics.roc_auc_score(classifier.predict(X_test), y_test)))
43 | 
44 | ### One Hot
45 | 
46 | def one_hot_categorical_model(features, target):
47 |     target = tf.one_hot(target, 2, 1.0, 0.0)
48 |     features = tf.one_hot(features, n_classes, 1.0, 0.0)
49 |     prediction, loss = learn.models.logistic_regression(
50 |       tf.squeeze(features, [1]), target)
51 |     train_op = layers.optimize_loss(loss,
52 |         tf.contrib.framework.get_global_step(), optimizer='SGD',
53 |         learning_rate=0.01)
54 |     return tf.argmax(prediction, dimension=1), loss, train_op
55 | 
56 | classifier = learn.Estimator(model_fn=one_hot_categorical_model)
57 | classifier.fit(X_train, y_train, steps=1000)
58 | 
59 | print("Accuracy: {0}".format(metrics.accuracy_score(classifier.predict(X_test), y_test)))
60 | print("ROC: {0}".format(metrics.roc_auc_score(classifier.predict(X_test), y_test)))
61 | 
62 | 


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/vm_example.py:
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 1 | """
 2 | This is an example of how to use TensorFlow as "interpreter" of graph
 3 | functions.
 4 | """
 5 | 
 6 | import tensorflow as tf
 7 | 
 8 | def run_tf(func):
 9 |   def wrapper():
10 |     with tf.Graph().as_default() as graph:
11 |       x = func()
12 |       with tf.Session('') as session:
13 |         return session.run(x)
14 |   return wrapper
15 | 
16 | @run_tf
17 | def hello_world():
18 |   return tf.Print([], ["Hello world!"])
19 | 
20 | @run_tf
21 | def add_3_5():
22 |   return tf.constant(3) + tf.constant(5)
23 | 
24 | hello_world()
25 | print add_3_5()
26 | 


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