In [362]:

    

import tensorflow as tf
import numpy as np
import os
from collections import Counter
from nltk.tokenize import TweetTokenizer
import codecs
from random import randint


    

In [6]:

    

tf.__version__


    

    
Out[6]:





'1.4.0'

In [222]:

    

with codecs.open(os.path.join('../data', 'sent.csv'), 'r', encoding='utf-8') as f:
    corpus_line_by_line = f.read().lower().split("\n")


    

In [223]:

    

corpus_line_by_line = corpus_line_by_line[:1000]


    

In [224]:

    

corpus_line_by_line = [line.rstrip('\r').split(',') for line in corpus_line_by_line]


    

In [225]:

    

corpus_line_by_line[0]


    

    
Out[225]:





['0', '                     is so sad for my apl friend.............']

In [226]:

    

tw = TweetTokenizer()


    

In [227]:

    

corpus_tokenized = list(map(lambda line: (line[0], tw.tokenize(line[1])), corpus_line_by_line))


    

In [301]:

    

corpus_tokenized[0]


    

    
Out[301]:





('0', ['is', 'so', 'sad', 'for', 'my', 'apl', 'friend', '...'])

In [228]:

    

corpus = []
for sent in corpus_tokenized:
    corpus.extend([word for word in sent[1]])


    

In [229]:

    

def build_vocab(words, vocab_size):
    """ Build vocabulary of VOCAB_SIZE most frequent words """
    dictionary = dict()
    count = [('<UNK>', -1)]
    count.extend(Counter(words).most_common(vocab_size - 1))
    index = 0
    for word, _ in count:
        dictionary[word] = index
        index += 1
    index_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
    return dictionary, index_dictionary


    

In [230]:

    

vocabulary_size = len(set(corpus))


    

In [231]:

    

vocabulary_size


    

    
Out[231]:





3198

In [232]:

    

vocabulary, reverse_vocabulary = build_vocab(corpus, vocabulary_size)


    

In [233]:

    

for word, index in zip(reverse_vocabulary.values(), vocabulary.values()):
    print(word, index)


    

    




<UNK> 953
. 151
! 1968
i 954
" 955
to 956
... 957
the 958
- 412
my 959
a 960
is 413
? 335
and 961
in 962
you 963
of 564
on 414
.. 415
it 964
me 965
for 186
that 967
so 968
this 276
just 969
have 970
* 565
( 971
: 972
i'm 973
get 974
now 975
not 976
) 977
at 978
with 417
all 979
day 980
no 240
be 981
are 664
but 982
was 566
out 983
' 538
up 2839
am 985
go 215
want 170
& 103
what 337
night 216
today 986
2 567
well 241
like 987
do 418
love 568
one 887
will 989
know 990
good 125
twitter 569
can 991
about 992
work 993
u 994
if 995
think 2841
got 998
can't 999
why 1000
much 1001
your 1002
going 1003
sad 1004
really 1005
lol 21
im 1006
new 1007
see 1008
miss 1009
has 9
more 338
it's 1010
back 1011
how 1012
from 1013
last 1014
<3 1015
an 1016
its 1017
don't 419
/ 1018
@ 1019
some 339
too 1020
when 1021
him 1022
oh 152
feel 1046
feeling 153
we 570
home 920
tonight 1025
off 571
days 572
here 1026
time 1027
as 1028
better 1029
3 1030
that's 573
happy 1492
getting 420
tomorrow 1032
ur 48
never 2846
need 8
only 1033
win 1035
any 15
right 1036
bad 421
#imisscath 1546
very 1038
make 1039
should 1041
1 672
over 1043
hope 1044
who 1045
people 1047
being 1048
they 422
by 1049
gonna 1050
;) 137
great 1051
soon 1052
fuck 1053
did 1054
again 1055
head 574
still 1056
come 1057
dont 1058
weekend 1060
wanna 1061
i'll 1062
thanks 194
say 575
sleep 423
there 1064
guys 424
doing 1065
= 1066
morning 1067
> 1068
cool 2127
he 1792
â 1071
been 2883
friends 701
or 1073
didn't 1074
where 1075
had 1076
down 576
leave 1077
; 1078
4 1079
cry 577
even 2486
b 138
$ 1081
s 1082
guess 1083
things 1084
damn 1085
via 1086
her 578
away 2488
another 1087
must 1088
missing 1089
found 1090
everyone 1091
bed 1092
phone 115
said 579
try 1093
awesome 1094
though 425
those 1095
tell 1096
always 1097
year 177
week 366
hate 2859
™ 1101
€ 2860
wait 242
then 1103
school 1104
cant 1105
talk 154
wrong 1106
someone 71
first 1107
#mcflyforgermany 580
:'( 1108
% 1109
o 1110
way 1111
makes 1112
already 1113
sunday 1114
every 1115
would 1116
she 581
stupid 582
pretty 1117
making 1118
us 1119
world 1120
boo 1121
w 1122
movie 583
sucks 336
didnt 1123
i've 218
gone 1124
x 1125
missed 1126
lost 104
while 584
life 1127
n 705
wish 585
nice 277
< 1129
doesn't 1130
computer 426
sure 1131
anyone 1132
mean 2155
car 1133
# 444
could 340
went 1134
won't 586
happened 1136
beautiful 243
fun 1137
best 361
made 3079
thank 1138
give 244
ok 1140
summer 1141
you're 1142
myself 278
something 1143
yay 1144
wanted 1145
sorry 1146
till 1147
f 588
<--- 105
baby 1148
tv 1149
#shortstack 1150
tweet 2996
finally 2418
~ 2216
done 1152
mom 1153
cause 1154
looking 1155
#tokiohotel 1156
big 1157
aww 1158
may 1159
¿ 1467
awake 1161
maybe 590
] 2507
man 1163
[ 140
pain 1164
his 591
around 81
working 1165
look 1166
thing 1515
does 2290
sick 1169
told 1170
iphone 90
call 1171
follow 593
face 594
hard 1172
watch 1173
church 1174
gotta 595
died 1175
early 280
haven't 1176
2009 1177
almost 1178
were 596
next 597
news 1179
jonas 1180
heart 1181
worry 1182
such 1183
everything 1184
sold 428
coming 341
meet 598
god 342
because 2876
forgot 1186
please 1187
bored 436
bye 187
weeks 171
lmao 599
having 343
game 600
text 1189
turn 1190
song 1191
start 1192
killing 2990
find 1193
¸ 1194
air 2441
#barakatday 1196
dance 1239
=d 1198
shit 1199
house 1200
r 1201
rest 1202
into 1203
reality 1204
smile 1205
2day 1206
sitting 601
picture 1207
them 1208
omg 1209
saw 1210
seen 602
else 1211
after 262
family 2515
account 344
whats 24
aw 1215
ticket 603
french 1216
ugh 1217
put 847
fail 2153
c 1219
à 126
than 1220
y 1221
results 605
yeah 606
music 1222
these 2564
quite 1223
airport 1224
. . . 1225
seriously 1226
boring 1227
years 1512
m 1229
funny 1230
mood 1231
concert 1232
long 1233
started 1234
name 1235
help 172
take 1236
office 1237
@littlecharva 188
other 1238
ever 607
both 2633
let 1240
mi 429
dies 1241
angry 524
since 1242
#squarespace 1243
left 608
hear 1245
haha 19
hours 1246
keep 1247
july 609
j 1248
leaving 1249
story 1250
design 245
times 22
played 1251
flowers 2717
room 610
raining 2722
nothing 3071
we'll 1254
goodnight 400
yes 611
city 1256
isn't 776
black 219
congrats 1258
drive 1259
monday 612
goes 1260
broke 1261
amazing 1262
birthday 1263
beach 613
video 1264
forever 1059
studio 1266
| 1267
send 614
gave 615
internet 2458
sooo 1269
two 430
whole 1270
moment 1271
five 346
busy 616
grandma 617
sigh 1272
idc 1273
france 618
moms 1274
credit 1275
ya 1276
anybody 1277
bf 1278
rain 1279
da 1280
« 1281
xd 23
friend 1282
lets 1283
probably 1284
driving 246
eating 1285
weather 1099
crap 1286
content 1287
horrible 1288
jus 1289
tweets 1290
kind 1291
less 135
mad 1292
word 1293
fine 431
lot 1294
farrah 620
youtube 917
wrote 347
fawcett 1295
myspace 2890
soo 622
break 432
cold 281
): 1297
@ymptweet 623
live 1298
pop 1299
contest 624
class 1300
drops 1301
trust 247
female 1302
talking 1303
many 625
photo 1304
dinner 1305
girls 348
loves 1306
enjoy 1307
complicated 1308
ï 1309
shut 1310
bit 1311
peace 1841
comments 1313
proud 1314
little 1315
becuz 1316
ass 626
rip 1317
5 1318
closed 1319
dad 1320
uk 1808
looks 1322
emo 1323
dog 1324
our 627
eyes 1325
enough 1326
far 1327
girl 1328
same 97
feels 1329
shopping 628
#trackle 1330
hang 1331
couldn't 1332
lady 155
money 1333
worse 2535
depressed 3171
nobody 1335
tears 1336
nite 629
yummy 512
dead 1337
fucking 1338
comes 1339
+ 1340
ti 1341
wonder 37
@spiral_galaxy 1342
graduate 630
open 1343
party 1344
thursday 1345
kid 1346
person 1347
sister 1348
release 433
random 1349
passed 1350
stop 1351
bring 434
status 1352
hug 631
use 435
friday 173
brother 50
plan 1353
million 1354
spam 1355
lunch 282
em 2321
favorite 632
summertime 1356
booo 1357
food 1358
hugs 633
brothers 1359
@sevgli 1360
cake 1361
believe 1362
:[ 1363
hilarious 1364
eat 1365
dvd 1366
sickness 1367
ladies 1368
cali 1369
--> 1370
davis 634
bday 1371
loads 1372
poor 116
harry 1373
everton 635
typical 1374
frank 1375
studies 1376
¦ 1377
hurts 437
hey 1378
cheating 1379
wtf 1380
expensive 2611
frustrated 349
celebrate 2904
boss 283
geez 2905
short 438
spelt 636
twice 2906
floor 1386
goodbye 117
knew 1387
pleased 440
boom 2908
husband 1389
thats 350
ended 1390
fact 1391
likes 156
against 1392
12 1393
suppose 1394
upset 1395
til 637
tour 1396
anything 1397
amor 1398
:: 1399
predict 441
moving 1400
thx 2
½ 1401
national 638
gona 639
chicken 1312
bedtime 1403
¥ 1404
grr 640
close 1405
excited 1406
lonely 1407
pass 1408
mind 1409
sat 1410
=( 1411
miley 1412
thought 1413
own 1414
:-/ 1415
yep 1416
fly 1417
feelin 1418
drunk 1420
exercise 641
dads 1422
mothers 1423
ugly 1424
62 1425
show 1426
council 642
massive 897
gets 2796
strong 1428
dude 127
number 1429
cook 1430
@jonasbrothers 91
suspended 1431
card 63
annoyed 1432
swollen 1433
cheese 2187
gunna 1435
report 1436
stuff 1437
taking 442
cries 643
crown 2517
‡ 644
kinda 645
k 1439
spend 2188
point 1441
following 1442
tumblr 1443
dressed 663
red 11
update 1444
pic 1445
trip 1446
prince 443
build 1448
wide 646
^ 1449
cough 1450
;( 96
chocolate 1452
date 1453
catch 2944
hair 1454
forward 1455
strike 1586
growing 1456
he's 1457
answer 648
everyones 1458
came 1459
band 1460
lived 649
worst 650
awwwwwh 2190
angels 1462
cut 189
hot 1463
riding 1464
bought 1465
tight 1466
move 1468
sort 1469
yourself 1470
london 651
decided 1471
childhood 1472
sometime 1473
late 1474
business 1475
youu 652
beats 1476
camera 1477
care 1478
yall 1479
few 1480
lizzie 1481
ball 1482
50 1483
bitch 1484
dreams 357
stranger 1485
6 1486
play 1487
least 220
pi 1488
read 1489
apple 1490
type 653
end 654
pull 2558
stack 3
yea 352
afraid 655
gigs 353
post 174
gota 656
blame 2880
happiness 1493
train 92
msn 657
believing 1494
starting 1495
either 1496
paper 1497
buy 75
brown 445
there's 446
visit 1498
hungry 1499
york 76
fat 1891
mexico 658
dt 1501
graduation 1502
state 1197
answering 659
losing 1504
exams 2137
sytycd 1506
fix 660
boy 284
which 1507
deep 1508
let's 1509
@jessdelight 1510
voltron 1511
inside 2171
@liveguy 661
exam 447
tied 662
hand 1516
pub 1517
=] 1518
what's 1519
literally 1520
also 1521
added 54
rainy 354
_ 1522
tear 1523
atm 1822
@poisongirl10 1525
neither 1526
rule 351
due 1527
scary 1528
dying 1529
walk 1530
pictures 1531
haley 355
sniff 2070
germany 2926
outside 665
wishes 190
cried 1534
luv 1535
dentist 1536
woot 1537
complete 106
demons 1538
store 761
single 157
check 1539
talkin 1540
problem 1541
light 1542
i'd 107
aren't 1544
failing 1545
#fail 1547
aiden 2138
aint 449
upside 285
finding 666
called 1549
matt 1550
ma 667
noooo 1551
hi 1552
etc 3133
front 1553
30 1554
loss 1555
#followfriday 221
jersey 2191
nose 668
listen 1557
meeting 1558
@sween 2121
(: 1560
chilling 1561
rats 1562
crash 669
hoping 286
katie 1563
says 450
luck 161
asleep 1565
abt 1566
rained 1567
pens 1568
:/ 1569
roll 1570
followers 670
suck 671
cup 146
bbc 1042
bill 287
saying 1572
old 249
praise 222
arms 1573
useless 1574
voice 1575
huge 1576
baseball 1577
tired 673
model 1578
jb 356
yet 674
england 675
australia 1579
conan 676
link 1580
super 1419
tech 29
you'll 2327
glad 1582
cuz 677
court 1583
vets 1584
oreos 1585
chance 1587
breakfast 1588
small 1590
later 1591
huh 2932
smh 1593
cute 1594
plane 1595
mail 1596
slow 2568
kno 2209
bout 1599
nasty 494
wo 1600
alone 679
chest 451
ocean 1601
actually 1602
violent 1603
homework 1604
cancer 1605
case 2416
dark 680
through 682
iâ 2935
coz 1607
10 2210
become 1608
without 191
before 452
bummed 683
definately 684
women 1609
cannot 1610
app 516
change 453
canon 82
ily 158
watching 1612
shoot 2523
rock 1613
ones 1614
broken 1615
camp 1616
orlando 1617
wake 1618
badly 1619
@dwo34 1620
dream 1621
replacement 2566
sleeping 1623
cab 685
headed 1624
their 1625
boyle 1626
means 1627
connection 1628
gay 1629
#fb 1630
os 1631
minutes 1632
unsure 1633
boston 678
kris 1634
manchester 1635
afford 1636
... ... 2629
prepare 686
kandie 1637
three 1638
sprayed 1639
ive 687
penguin 53
pay 1640
creeps 454
finaly 1641
fulfilling 2938
bracelet 2691
most 1642
bebot 250
@dhempe 176
guardian 1514
@debraward 1643
wee 1644
australian 2939
@netfart 689
hitech 1645
divorce 193
@aaronxnow 1646
@mgph3nom 1647
position 1648
12-4 1589
stick 1650
@hitzproductions 1651
water 1652
hores 1653
joke 1654
#iranelection 1655
bleh 1656
gather 1657
motions 457
bacon 1658
@angryfeet 1811
ky 32
freaking 1659
grrrr 905
heartbroken 1661
#skydiving 289
vegetarian 458
bosco 1662
meteorologists 3168
negative 1663
allergies 1664
trains 20
@shaneaddinall 1063
t9ar5 1665
willows 1666
ayonna 1667
£ 1668
definitions 1669
positive 1670
shots 1671
bargain 359
9-7 1672
third 1673
exited 1674
night's 1675
laughter 159
09 1135
candy 290
bodies 1677
wats 360
mamma 1852
impact 1679
wantt 1680
leysh 1681
explains 784
mae 1683
</em> 691
fourth 401
@carole29 1685
limp 923
transfer 1687
haterade 42
realizes 1688
men 2885
laid 2424
flicked 1689
flippin 1690
“ 1691
@kvetchingeditor 1692
father's 1693
spazzing 291
fucked 459
<christy> 692
kristen 1694
obsolete 1696
vegas 1697
hrs 28
trams 1699
16 1700
@auroralee 2516
reason 1702
boro 693
100 1704
3.0 1705
walls 1706
tummy 1707
barber 195
@adamisacson 1708
ian 694
@alexjreid 1709
http://bit.ly/mumfh 695
hottest 696
@designrelated 1710
hands 1711
@technotetris 1712
currently 2476
nintendo 697
http://plurk.com/p/13s5mg 2590
ringtone 1715
gardens 1716
confusing 1717
@musicismysunshine 1718
@zoeboe 468
tiger 699
matter 2220
rather 1720
problems 2148
@kaitymarine 1722
t 2948
ironically 1724
http://tumblr.com/xsg1m3ufn 1725
annoying 1726
elf 1727
@lakers 1728
mia 700
½s 1072
bedroom 333
@sozi1 1730
torn 702
occured 1731
university 160
wooo 1732
revising 1733
t_t 1734
ella 0
nebody 703
pls 1735
pjs 1736
hedo 1737
http://tinyurl.com 1738
tourny 223
chat 292
groups 1739
dat 1740
@em91 1741
heard 1742
re-ripped 1743
able 461
@gigi4462 1034
darn 1745
normal 3067
http://tr.im/mp2j 119
http://bit.ly/gxh3q 1746
dan 2950
truth 1747
chills 1748
http://u.mavrev.com/bc7v 462
rihanna 2594
kicked 1749
@tommcfly 1750
confident 759
@lowridergrl 1751
goiing 1753
special 1754
lose 3035
susan 1755
@cortnee4christ 1756
aahh 2597
@ariaajaeger 1758
non-robsten 41
repairs 1759
vietnam 196
mathces 1760
machine 587
@bittenbybooks 803
#hello 1762
3k 1763
senator 1524
http://bit.ly/10o8lw 1764
places 2952
alice 362
cnt 2726
private 1766
t4 1767
@dirtyrose17 463
free 1768
symbol 1769
baddest 1770
@nibaq 1771
quits 1772
genuinely 1773
sucking 1774
poll 1775
yours 707
welcomee 708
mñ 1776
potter 1777
pill 1778
morrea 1779
batter 1780
path 1781
hoax 1782
log 1783
@shontelle_layne 1784
ny 1785
cheers 1786
sighs 709
600 294
basil 1787
providers 2521
pow 1128
@latuacatante17 1790
” 1791
mhmm 2401
woody 1793
interesting 1794
mustache 1795
mmm 1796
magazine 1797
@jeffersonreid 1798
tempermental 363
maintenance 710
cheered 1799
11am 1800
david 1801
ahh 1802
settle 1803
octo 1804
quilting 3177
hello 142
@pfont 1807
girl's 1809
kate 67
bless'd 364
fwd 2764
distracting 1810
country 1812
pollute 712
ride 1813
shrug 713
bae 1814
jubii 295
iero 1815
yeeeeeeeeeeeessshhhh 365
@lorinimus 714
scoobs 1817
@katiedontcry 715
fizz 251
rollin 716
@exoticbella_ 1818
@weelaura 3026
lower 464
necklace 1819
ast 1820
bitchesss 1821
kept 717
changin 143
cookies 2360
@theiancrawford 465
hom 1823
iphonegiveway 197
expect 1824
teacher 1543
hills 1825
@sharboubou 2961
steer 466
stress 296
eff 1827
staying 1828
diggâ 1829
hospital 1830
safety 1831
paradise 718
givin 1832
traveltips 1833
lisa 1834
pac 1836
im's 1837
twitterpated 83
66 1838
breath 1839
join 1840
@ahugeproduction 1788
sb 2963
http://bit.ly/onhyd 1843
cooler 719
birdie 64
jordans 1844
until 1845
fit 1846
@shaunamanu 252
@starhubcares 1848
hospitalized 1849
leg 1850
<- 2964
timing 1678
helps 720
@shinyhappyhead 1853
top 1698
religion 467
@fernandofelman 35
dammit 1854
http://plurk.com/p/1230lz 1855
http://blip.fm/~4lfyi 721
toolio 1761
hates 1856
shallow 1857
suddenly 1858
chris 1859
grin 1860
@benboychuk 1861
@trabalhosujo 2649
http://sbne.ws/r/vwc 1862
john 722
canadians 72
gosh 1863
dd 1703
imu 1865
waiting 2245
http://tr.im/mwkn 3086
effort 1867
feeding 44
nominated 1868
michigan 1869
skin 93
blowdrying 1870
burgers 248
21 1871
climate 297
space 723
google 1872
kevin's 1873
choice 1874
mh 128
politics 724
e-hugs 1876
marco 1877
anticipating 1878
dj 2886
catholic 1879
faltam 725
@nikkofelipe 1880
error 1881
bridget 726
http://tr.im/n8ai 298
@rob_in_grantham 1503
whyy 727
porter 1882
quarter 728
besides 1883
knows 2678
power's 1884
arrested 729
@codinghorror 1885
@tendaijoe 299
manuel 2244
18th 1886
heels 198
@jurgenphoto 1887
incessantly 1888
intrusion 300
mend 1889
remember 1890
beatings 1500
@iamyas 1892
447 1893
@snubbmatic 178
hippo 1894
figure 367
fonebook 1895
suit 1896
guna 405
wompppp 1898
talvez 1899
lord 1900
http://blip.fm/~5ytke 2278
lots 1901
rings 1902
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buck 882
wud 2795
squarespace 744
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popping 398
wht 2798
speak 2799
beard 2800
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marshmallows 2802
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va 2807
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@adbert 210
bordeirlne 1805
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reviews 399
bugs 2811
commission 2812
awww 883
felt 2813
comin 884
http://tumblr.com/xaj24dkly 267
@elysianfieldz 332
porn 184
:] 68
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kstew 45
mk 2816
mcnuggets 211
downloads 2817
display 2818
@standing_stones 1744
@isak 2820
8th 885
fringe 2821
item 2822
mousey 938
http://tr.im/mzvc 2824
blip.fm 2825
huz 2826
http://www.kansas.com/news/featured/story/860875.html 534
@baileygenine 2827
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playing 886
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trackle 2751
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@danregal 122
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79.75 2835
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rights 325
speeds 997
@sandyu 2842
karl 2843
http://blip.fm/~5zi9g 268
winona 2844
http://bit.ly/pxrzv 275
eureka 326
rushing 2845
http://tr.im/nyyv 553
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chilli 73
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shy 889
uof 2851
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once 1253
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lawn 2126
everythings 2855
tasks 2856
šà¹ 2857
doors 2858
author 1100
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haters 2861
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hotel 2862
2nd 540
afi 2863
delete 2864
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fresh 2867
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chain 2869
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device 427
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23 2875
final 1185
gm 185
ncaa 2877
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• 2878
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sweet 2879
@scorpio510 1676
bogan 3019
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friendly 7
nola 86
bing 1244
neighbor 2382
updating 17
shottas 1255
grateful 3158
giants 1973
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patrol 2887
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idk 2888
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explode 894
profile 2892
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11 2895
dunn 2896
scooby 2897
http://twitpic.com/6bo0t 2898
holler 2899
@pinkraygun 896
panties 2900
gathered 3078
blender 402
http://tr.im/lcel 134
crabs 14
diplomatic 2901
place 327
hosting 2902
reduce 2903
@alexrauchman 1682
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couldnt 439
golf 2907
sleepy 1388
icecream 541
homeless 328
@zheyamada 542
drinks 89
grandpa 1427
disappointed 2910
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afternoon 1684
served 110
xmas 898
unfortunately 899
apl 1757
spanking 900
doctors 2912
sf 2913
niggahz 901
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drizzle 2915
immune 2916
reviewed 74
gin 2917
roughplaces 2918
;] 2919
undies 2920
rat 2805
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gallery 12
crashed 2923
hollywood 3156
star 2925
thu 4
iï 1533
mayer 543
whatï 2927
sending 902
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blog 212
nutella 2928
double 2929
stretch 619
alr 2930
prawns 2931
fishes 1592
strawberry 2933
realms 2934
album 904
pouring 1606
uniform 2884
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daytona 2937
squared 545
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funeral 456
unless 2174
;d 403
writing 1660
meat 906
capitals 2940
6:30 2941
trio 16
#songstuckinmyheadwheniwokeup 2943
@patita 1686
@moneybagzfam1st 2945
none 2946
digital 907
everywhere 547
tough 533
@plusisaplus 2947
tonite 1723
somehow 548
bra's 2949
lays 621
btw 293
wings 101
http://tumblr.com/xpn1wrj48 2951
nyt 1765
hungover 270
@fontenot619 2953
maths 1789
sense 2955
poopie 2956
lampard 3165
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stout 2958
garros 908
cessna 2959
marcus 2960
@joseke 1826
alternatã 2962
sparks 1842
half 1851
designed 271
jam 2965
abusing 329
:p 2966
beautifulylost 237
@jazzgotsoul 2967
postman 909
ecl 404
tlkd 94
» 910
litle 2968
clashes 2969
http://nascar.com/racebuddy 911
system 2970
mum 2971
exhibition 2972
@lisabarone 2973
@squarespace 5
uprizing 1897
jamarcusssssss 2974
http://www.imediaconnection.com/content/23465.asp 912
hehe 2975
http://bit.ly/15ssci 2976
background 2977
ghostbusters 406
inn 549
@mightyboognish 2979
cate 913
sun 2980
upload 2981
hummer 914
mac 2982
twilight 2871
event 2983
coffee 31
warm 2984
@achaoticbeauty 2985
:| 1979
pie 915
jillian 2987
moval 123
http://bit.ly/whlbv 288
washington 2989
source 1024
genetics 2991
mixed 2992
jun 2017
epiccc 2993
cats 2994
nooooooooo 407
completely 330
@britespark 2995
@gigia 916
hub 2055
cartoons 2063
ftw 2998
practising 2999
tease 3000
hmv 550
interns 85
construction 592
@evertb 3002
80 3003
itchy 272
fcukk 2263
toy 3005
prices 3006
bears 3007
ben 918
trent 2128
reubens 1701
@prblog 2136
ughhhhhhhhhhhhhhhhhhhhhhhhhhhhh 3011
francisco 238
gr8 919
http://bit.ly/azfwv 3012
@mikehanes 551
@beckobviously 3013
region 3014
book 2160
boyfriends 2854
replies 2165
70-542 3017
@cranberryperson 124
boyfriend 3018
bummer 273
slept 2417
guyssss 3020
hehehe 2080
@hijack_king7 3021
handshake 3022
pressure 3023
sos 2429
award 3024
jazzy 3025
bookstore 2060
burst 3027
http://twitpic.com/75hsg 2222
fightiin 1447
fridge 922
waking 3030
alergic 3031
manners 95
st 3032
serious 408
@yhf 3033
recovery 3034
4me 924
whoops 2279
population 3036
delicious 3037
roommate 3038
pa 925
burn 2309
drugs--good 2310
sink 3040
bf's 3041
recital 3042
sadly 3043
referring 2322
crashing 3045
clocking 3046
battles 3047
bling 3048
hes 2434
chealea 927
@billyraycyrus 3050
tomorrows 3051
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newest 3052
@ignorantsheep 929
screen 3053
student 3054
@cnnbrk 930
wells 1188
white 3055
@shaunacland 3056
@rickswift 1695
awwwww 3058
hiding 3059
@mgapmp 3060
asshole 931
bank 2406
33 2410
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uva 3063
totally 3064
share 1713
curtain 3182
harddrive 3066
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board 3070
locations 2088
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blue 3103
epic 3075
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stay 99
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http://blip.fm/~6tfp6 2502
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bs 331
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‹ 3085
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waiit 3090
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woman 2582
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150 3097
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#battleground 40
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http://bit.ly/akqld 3100
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mr 3104
young 3105
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http://bit.ly/bxdvl 934
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http://tinyurl.com/ncqkm5 935
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206 2528
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http://blip.fm/~7d1qo 3136
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http://plurk.com/p/x8j86 563
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ove 3194
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15 3196
canaveral 3197

In [234]:

    

def index_words_in_corpus(corpus):
    return [vocabulary[token] if token in vocabulary else 0 for token in corpus]


    

In [235]:

    

corpus_indexed = [(line[0], index_words_in_corpus(line[1])) for line in corpus_tokenized]


    

In [236]:

    

len(corpus_indexed)


    

    
Out[236]:





1000

In [237]:

    

corpus_indexed[0]


    

    
Out[237]:





('0', [11, 23, 76, 21, 9, 2773, 460, 6])

In [238]:

    

vocabulary_size


    

    
Out[238]:





3198

In [239]:

    

def one_hot_encode(review):
    temp = np.zeros(vocabulary_size)
    for indx in review:
        temp[indx] = 1
    return temp


    

In [240]:

    

def one_hot_encode_class(sentiment):
    temp = np.zeros(2)
    if sentiment == 1:
        temp[1] = 1
    else:
        temp[0] = 1
    return temp


    

In [241]:

    

# data = np.array([(one_hot_encode(word), vocabulary.get(word)) for word in corpus])
data = np.array([one_hot_encode(word[1]) for word in corpus_indexed])


    

In [242]:

    

print("TRAIN: (Total number of words, Vocabulary size):", data.shape)


    

    




TRAIN: (Total number of words, Vocabulary size): (1000, 3198)

In [243]:

    

sample = data[0]


    

In [245]:

    

np.where(sample == 1)[0]


    

    
Out[245]:





array([   6,    9,   11,   21,   23,   76,  460, 2773], dtype=int64)

In [246]:

    

def decode_sentence(sample):
    sentence = []
    for i in range(sample.shape[0]):
        if sample[i] == 1:
            sentence.append(reverse_vocabulary[i])
    return ' '.join([i for i in sentence])


    

In [247]:

    

print(sample, decode_sentence(sample))


    

    




[ 0.  0.  0. ...,  0.  0.  0.] ... my is for so sad friend apl

In [ ]:

    

def get_batches(batch_size):
    idx = randint(data.shape[0]-3)
    return data[idx:idx+batch_size]


    

In [190]:

    

def xavier_init(n_inputs, n_outputs, uniform=True):
  """Set the parameter initialization using the method described.
  This method is designed to keep the scale of the gradients roughly the same
  in all layers.
  Xavier Glorot and Yoshua Bengio (2010):
           Understanding the difficulty of training deep feedforward neural
           networks. International conference on artificial intelligence and
           statistics.
  Args:
    n_inputs: The number of input nodes into each output.
    n_outputs: The number of output nodes for each input.
    uniform: If true use a uniform distribution, otherwise use a normal.
  Returns:
    An initializer.
  """
  if uniform:
    # 6 was used in the paper.
    init_range = tf.sqrt(6.0 / (n_inputs + n_outputs))
    return tf.random_uniform_initializer(-init_range, init_range)
  else:
    # 3 gives us approximately the same limits as above since this repicks
    # values greater than 2 standard deviations from the mean.
    stddev = tf.sqrt(3.0 / (n_inputs + n_outputs))
    return tf.truncated_normal_initializer(stddev=stddev)


    

In [191]:

    

'''A recent paper by He, Rang, Zhen and Sun they build on Glorot & Bengio and suggest using 2/size_of_input_neuron
''' 
def xavier_init(size):
    in_dim = size[0]
#     xavier_stddev = 1. / in_dim
#     xavier_stddev = 2. / in_dim
    xavier_stddev = 1. / tf.sqrt(in_dim / 2.)
    return tf.random_normal(shape=size, stddev=xavier_stddev)


    

In [363]:

    

with tf.name_scope('Input'):
    X = tf.placeholder(tf.float32, shape=[None, vocabulary_size])

with tf.name_scope('Discriminator_weights_biases'):    
    D_W1 = tf.Variable(xavier_init([vocabulary_size, 128]))
    D_b1 = tf.Variable(tf.zeros(shape=[128]))

    D_W2 = tf.Variable(xavier_init([128, 1]))
    D_b2 = tf.Variable(tf.zeros(shape=[1]))

    theta_D = [D_W1, D_W2, D_b1, D_b2]


    

In [364]:

    

def discriminator(x):
    D_h1 = tf.nn.relu(tf.matmul(x, D_W1) + D_b1)
    D_logit = tf.matmul(D_h1, D_W2) + D_b2
    D_prob = tf.nn.sigmoid(D_logit)

    return D_prob, D_logit


    

In [365]:

    

with tf.name_scope('Latent_space'):
    Z = tf.placeholder(tf.float32, shape=[None, 100])

with tf.name_scope('Generator_weights_biases'):
    G_W1 = tf.Variable(xavier_init([100, 128]))
    G_b1 = tf.Variable(tf.zeros(shape=[128]))

    G_W2 = tf.Variable(xavier_init([128, vocabulary_size]))
    G_b2 = tf.Variable(tf.zeros(shape=[vocabulary_size]))

    theta_G = [G_W1, G_W2, G_b1, G_b2]


    

In [195]:

    

def sample_Z(m, n):
    return np.random.uniform(0., 1., size=[m, n])


    

In [196]:

    

def generator(z):
    G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1)
    G_log_prob = tf.matmul(G_h1, G_W2) + G_b2
    G_prob = tf.nn.sigmoid(G_log_prob)

    return G_prob


    

In [197]:

    

G_sample = generator(Z)
D_real, D_logit_real = discriminator(X)
D_fake, D_logit_fake = discriminator(G_sample)


    

In [198]:

    

with tf.name_scope('cost'):
    D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real, labels=tf.ones_like(D_logit_real)))
    D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))
    D_loss = D_loss_real + D_loss_fake
    G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))


    

In [199]:

    

with tf.name_scope('train'):
    D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D)
    G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G)


    

In [288]:

    

minibatch_size = 128
Z_dim = 100

saver = tf.train.Saver(max_to_keep=1)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
i = 0

for it in range(100000):
    if it % 1000 == 0:
        samples = sess.run(G_sample, feed_dict={Z: sample_Z(1, Z_dim)})
        for sample in samples:
            sample[sample > 0.95] = 1
            print(samples.shape, decode_sentence(sample))
                  
    X_mb = get_batches(minibatch_size)

    _, D_loss_curr = sess.run([D_solver, D_loss], feed_dict={X: X_mb, Z: sample_Z(minibatch_size, Z_dim)})
    _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict={Z: sample_Z(minibatch_size, Z_dim)})
    

    if it % 1000 == 0:
        print('Iter: {}'.format(it))
        print('D loss: {:.4}'. format(D_loss_curr))
        print('G_loss: {:.4}'.format(G_loss_curr))
        print()

saver.save(sess, './tf_model/generator', global_step=1000000)


    

    




(1, 3198) 
Iter: 0
D loss: 1.432
G_loss: 6.979

(1, 3198) . ! i " to ... the - my a is and in you of on it me just ( i'm at all out am want night today one sad im see miss has its @ him feel home off 3 only any right gonna great come thanks friends cant :'( stupid pretty < mean fun you're yay sorry <--- tweet sick were rest sitting else whats let mi since five rain ): @ymptweet live girls far @spiral_galaxy status husband against =( :-/ report following hair walk talkin rats become wee explains david hospitalized guna ima nor jeans overdosed bothered strangely slope http://tumblr.com/xv31s2pi8 fetal suposed intercept :\ omgaga human magic wardrobe daily downloads level sannesias whatï blog writing meat hub uploading virus
Iter: 1000
D loss: 0.00104
G_loss: 10.05

(1, 3198) . ! i to ... the - my a is ? of me for have : i'm now ) with day no be ' what 2 like love will can think got much really new miss more from him oh we home days that's need over they gonna soon weekend damn tell year wrong o could won't till church 2009 leaving probably brother own bought msn dentist talkin leysh haterade fucked top dose wall http://tumblr.com/xv31s2pi8 isnt magic :-| semisonic banging brain pouring
Iter: 2000
D loss: 0.001163
G_loss: 10.64

(1, 3198) . i " ... - ? and for that so ( i'm now not ) at all day no be was up well like love one will work going new more it's last him we off that's never only they gonna been had away must already world could yay tv told omg seen hours room forever ): @spiral_galaxy massive taking cut listen lose heathers happier omgaga reviewed
Iter: 3000
D loss: 0.0001629
G_loss: 11.25

(1, 3198) . i " ... - ? and for that so ( i'm now not day no be up today well like love one will about work think new more last @ him home off that's never very by gonna guys been had already world could <--- tv please seen hours times room ): @spiral_galaxy taking cut listen <christy> hom heathers knw 4ever sweater omgaga downloads level saturday
Iter: 4000
D loss: 2.483e-05
G_loss: 13.83

(1, 3198) . i " ... - ? and for that so ( i'm now not day no be up want today well like love one will about work think new see more last @ him home off that's never very by gonna guys been had already world could <--- tv mom because please seen hours leaving times room ): @spiral_galaxy taking following listen <christy> hom guna heathers knw 4ever public slope 7:30 drink sweater omgaga downloads level winona sweet saturday virus
Iter: 5000
D loss: 1.029e-05
G_loss: 14.7

(1, 3198) . i " ... - ? and for that so ( i'm now not day no be ' up want & today well like do love one will about work think sad new see more back last @ him home off that's tomorrow never very by gonna guys had year already world could till <--- tv mom face because please seen whats leaving times room ): @spiral_galaxy moving following type listen <christy> hom guna knw painted 4ever t-mobile public slope 7:30 drink tonsils omgaga paying downloads level hmmmm winona sweet saturday white virus
Iter: 6000
D loss: 7.584e-06
G_loss: 14.26

(1, 3198) . i " ... - ? and for that so have ( i'm now not day no be ' up go want & today like do one will about work think going sad new see miss more back @ him home off tomorrow win any very by gonna dont thanks guys had year week o already world something till <--- tv mom face because please whats boring leaving times room ): @spiral_galaxy booo pleased thats moving number following type listen <christy> hom guna knw painted wove 4ever t-mobile public slope 7:30 drink tonsils handed paying downloads level hmmmm winona tasks sweet saturday white virus
Iter: 7000
D loss: 4.372e-06
G_loss: 16.01

(1, 3198) . i " ... - ? and for that so have ( i'm now not day no be ' up go want & today like do one will about work think going sad see miss more back @ home off tomorrow win any very by gonna fuck dont thanks say guys had year week :'( o world ok something wanted till <--- tv mom call face because please sitting whats boring leaving times room ): @spiral_galaxy booo pleased thats moving number gunna following type walk cried listen allergies <christy> hom guna knw painted wove 4ever t-mobile public slope 7:30 drink tonsils obs trae rose handed http://tumblr.com/xwp1yxhi6 paying downloads level hmmmm winona tasks sweet saturday blog white virus
Iter: 8000
D loss: 2.427e-06
G_loss: 15.67

(1, 3198) . i " ... - ? and for that so just have ( i'm now not all day no be are was ' up go want & what today like do one twitter about think going sad see miss more back <3 @ home off better happy tomorrow win any very who by gonna fuck dont thanks say sleep guys morning he been friends where had phone though year week :'( o w movie missed wish car went fun thank ok something wanted sorry till <--- tv mom call face because please game rest sitting whats boring mi leaving times room congrats probably ): hang kid hug million booo celebrate goodbye pleased thats moving number gunna following type pull neither walk cried talkin useless australia vets become headed stick allergies <christy> tourny hom manuel guna http://bit.ly/zoxzc knw v painted editors sucked wove pizza 4ever ood throwbie foreverr t-mobile public 3rd pose 7:30 drink tonsils fetal obs :\ trae fr earl athlete rose handed wardrobe http://tumblr.com/xwp1yxhi6 entry paying jin downloads level momacita hmmmm winona safina tasks haters sweet saturday grandpa whatï blog fightiin white backwards virus sequel
Iter: 9000
D loss: 1.975e-06
G_loss: 16.43

(1, 3198) . i " ... - ? and for that so just have ( i'm now not at all day no be are was ' up go want & what today like do one twitter about think going sad see miss back from <3 @ some too when him home tonight off better happy tomorrow win any very over who by gonna fuck dont weekend thanks say sleep guys morning he been friends where had phone though those tell year week hate cant :'( o w movie missed wish car went fun thank ok something wanted sorry till <--- tv mom call face because please game rest reality smile sitting whats yeah boring mi leaving times congrats probably fine girls hang kid hug plan million booo celebrate goodbye pleased thats moving fly number gunna following growing type pull hand neither walk cried talkin useless australia vets cute become headed stick allergies <christy> tourny quilting hom @shaunamanu manuel guna http://bit.ly/zoxzc knw v painted editors sucked wove enjoyable pizza 4ever ood throwbie sooooon foreverr t-mobile 3rd pose 7:30 drink tonsils fetal obs :\ trae fr earl athlete rose handed wardrobe http://tumblr.com/xwp1yxhi6 entry paying jin downloads level momacita hmmmm winona safina tasks haters sweet saturday place grandpa whatï blog half fightiin white backwards horsie virus sequel
Iter: 10000
D loss: 9.948e-07
G_loss: 18.08

(1, 3198) . i " ... - ? and .. for that so just have ( i'm now not ) at all day no be are was ' up go want & what today like do one twitter think much going sad see miss back from <3 @ some too when him home tonight off better happy tomorrow win any very over who by gonna fuck dont weekend thanks say sleep guys morning he been friends where had phone though those tell year week hate cant :'( already w movie missed wish car went fun thank ok something wanted sorry till <--- tv mom call face were please game rest reality smile sitting picture whats yeah boring let mi leaving times congrats probably jus fine girls hang kid hug plan million celebrate goodbye pleased thats moving fly number gunna following growing cut type pull hand neither walk cried talkin useless australia vets cute become headed stick allergies <christy> tourny re-ripped quilting hom @shaunamanu manuel guna http://bit.ly/zoxzc knw v painted editors sucked wove enjoyable pizza 4ever ood throwbie sooooon babes foreverr t-mobile 3rd hospitol pose 7:30 drink tonsils fetal rx obs :\ thunder trae showing earl athlete rose handed wardrobe http://tumblr.com/xwp1yxhi6 entry paying jin 112-102 downloads level momacita hmmmm winona safina tasks haters sweet saturday place golf grandpa whatï blog half fightiin white backwards uploading horsie virus sequel
Iter: 11000
D loss: 5.564e-07
G_loss: 17.49

(1, 3198) . i " ... - ? and for that so have ( i'm now not ) at all day no be are was ' up go today like love one twitter think much going sad really it's from him home off only win over who gonna weekend thanks morning he been friends didn't had those hate cant already she wish ok yay till cause were game boring mi leaving congrats fine fly growing cut type neither talkin cuz tourny re-ripped david @shaunamanu whyy http://bit.ly/zoxzc bball hospitol spaz strangely rx showing 112-102 level golf meantime uploading horsie sequel
Iter: 12000
D loss: 9.142e-07
G_loss: 16.98

(1, 3198) . i " ... - ? and for that so have ( i'm now not ) at all no be are was ' up go today like love one think much going sad really new more it's from last him feeling home off only win over who gonna soon weekend thanks morning he been friends didn't had those hate already she ok yay cause were game mi congrats growing cut type talkin cuz david whyy bball spaz strangely rx showing isnt 112-102 golf bf's meantime horsie tix
Iter: 13000
D loss: 2.634e-07
G_loss: 18.48

(1, 3198) . i " ... - ? and for that so have ( i'm now not ) at all no be are was ' up go today like love one think much going sad really new more it's from last him feeling home off days only win should over who gonna soon weekend thanks morning he been friends didn't had those hate already she yay cause were game mi congrats growing cut type talkin cuz david whyy bball spaz strangely rx showing isnt 112-102 golf bf's meantime horsie tix
Iter: 14000
D loss: 1.105e-07
G_loss: 19.68

(1, 3198) . i " ... - ? and for that so have ( i'm now not ) at all no be are was ' up go today like love one think much going sad really new more it's from last him feeling home off days only win should over who gonna soon weekend thanks morning he been friends didn't had those hate already she yay cause were game mi wonder growing cut bought type talkin cuz david whyy bball spaz strangely rx showing isnt 112-102 golf bf's meantime horsie tix
Iter: 15000
D loss: 6.076e-08
G_loss: 20.34

(1, 3198) . i " ... - ? and for that so have ( i'm now not ) at all no be are was ' up go today like love one think much going sad really new more it's from last him feeling home off days only win should over who gonna soon head weekend thanks morning he been friends didn't had those hate already she yay cause were game mi wonder growing cut bought type talkin cuz david whyy bball shall spaz strangely rx showing isnt 112-102 golf bf's meantime horsie tix
Iter: 16000
D loss: 3.357e-08
G_loss: 21.05

(1, 3198) . i " ... - ? and for that so have ( i'm now not ) at all no be are was up go today like love one think much going sad really new more it's from last him feeling home off days only win make should over who gonna soon head weekend thanks morning he been friends didn't had those hate already she yay cause were game start mi wonder growing cut bought type talkin cuz cab poll david whyy bball shall spaz strangely rx showing isnt 112-102 golf bf's meantime horsie tix
Iter: 17000
D loss: 1.989e-08
G_loss: 21.64

(1, 3198) . i " ... - ? and for that so have ( i'm now not ) at all no be are was up go today like love one think much going sad really new more it's from last him feel feeling home off days only win make should over who gonna soon head weekend thanks morning he been friends didn't had those hate already she yay cause were game start mi live wonder growing cut bought type talkin cuz cab poll david whyy bball shall spaz strangely rx showing isnt 112-102 golf bf's meantime horsie tix
Iter: 18000
D loss: 1.182e-08
G_loss: 22.2

(1, 3198) . i " ... - ? and for that so have ( i'm now not ) at all no be are but was up go today like love one if think much going sad really new more it's from last him feel feeling home off days only win make should over who gonna soon head weekend thanks morning he been friends didn't had those hate already she yay cause were game start mi live wonder growing cut bought type talkin cuz cab poll david whyy bball shall jeans spaz strangely rx showing isnt 112-102 beard golf bf's waiit meantime horsie tix
Iter: 19000
D loss: 7.455e-09
G_loss: 22.2

(1, 3198) . i " ... - ? and for that so have ( i'm now not ) at all no be are but was up go today like love one if think much going sad really new more it's from last him feel feeling home off days only win make should over who they gonna soon head weekend thanks morning he been friends didn't had those hate already she give yay cause were game start mi live far wonder growing cut bought type talkin cuz cab poll david whyy bball shall jeans spaz strangely rx showing isnt 112-102 beard golf apl bf's waiit meantime horsie tix
Iter: 20000
D loss: 4.748e-09
G_loss: 22.14

(1, 3198) . i to ... ? and for that have i'm now not ) at all no be but was up go today like love one if think much going sad really new more it's from last him feel feeling home off days 3 only make should over who they gonna soon head weekend thanks morning he didn't those wrong already she pretty could won't give yay cause were start mi live far wonder growing cut bought deep talkin cab poll david whyy ex bball shall jeans spaz strangely rx isnt 112-102 beard apl stout bf's waiit meantime tix
Iter: 21000
D loss: 5.485e-09
G_loss: 34.65

(1, 3198) . i to ... ? and for that have i'm now not ) at all no be but was up go today like love one if think much going sad really new more it's from last him feel feeling home off days 3 only make should over who they gonna soon head weekend thanks morning he didn't b away those wrong already she pretty could won't give yay cause were start mi live far wonder cut bought deep talkin cab special poll david whyy ex bball shall jeans spaz strangely rx isnt 112-102 beard xmas apl stout bf's waiit meantime tix
Iter: 22000
D loss: 2.002e-09
G_loss: 30.56

(1, 3198) . i to ... ? and for that have i'm now not ) at all no be but was go today like love one if think much going sad really new more it's from last him feel feeling home off days 3 only make should over who they gonna soon head weekend thanks morning he didn't b away those wrong makes already she pretty could won't give yay cause were start whats mi forever live far wonder cut bought deep talkin cab special poll david whyy ex bball shall jeans mother spaz strangely rx isnt 112-102 beard xmas apl stout bf's waiit meantime tix
Iter: 23000
D loss: 1.09e-09
G_loss: 28.47

(1, 3198) . i to ... ? and for that have i'm now not ) at all no be but was go today like love one if think much going sad really new more it's from last him feel feeling home off days 3 only make should over who they gonna soon head weekend thanks morning he didn't b away those wrong makes already she pretty could won't give yay were start whats mi forever live far wonder cut bought deep talkin cab special poll david whyy ex bball shall jeans mother spaz strangely rx isnt 112-102 beard xmas apl stout bf's waiit meantime tix
Iter: 24000
D loss: 6.641e-10
G_loss: 27.39

(1, 3198) . i to ... ? and for that have i'm now not ) at all no be but was go today like love one if think much going sad really new more it's from last him feel feeling home off days 3 only make should over who they gonna soon head weekend thanks morning > he didn't b away those wrong makes already she pretty could won't give yay were start whats mi forever live far wonder cut bought msn deep talkin cab fucked special poll david whyy ex bball shall jeans mother spaz strangely rx isnt 112-102 beard xmas apl stout bf's waiit meantime tix
Iter: 25000
D loss: 4.269e-10
G_loss: 26.78

(1, 3198) . ! i to ... the ? and for that this have i'm now not ) at all no be but was go today like love one if think much going sad really new more it's from last him feel feeling home tonight off days better 3 only make should over who they gonna soon again head come weekend wanna thanks morning > he didn't b away those wrong first makes already she pretty could won't give yay sick were start else whats mi forever da live far wonder brother wide cut bought msn deep talkin says cab fucked special t4 poll david whyy baithing ex motivation immediately bball shall jeans mother spaz strangely rx intercept isnt 112-102 beard 11 xmas apl stout bf's waiit meantime tix
Iter: 26000
D loss: 3.116e-10
G_loss: 24.71

(1, 3198) . ! i to ... the ? and for that this have i'm now not ) at all no be but was go today like love one if think much going sad really new more it's from last him feel feeling home tonight off days better 3 only make should over who they gonna soon again head come weekend wanna thanks morning > he didn't cry b away those wrong first makes already she pretty could won't give yay sick were start else whats mi forever da live far wonder brother wide cut bought msn deep talkin says cab fucked special t4 poll david whyy baithing ex motivation immediately bball shall jeans mother spaz strangely rx intercept isnt 112-102 beard 11 xmas apl stout bf's waiit meantime tix
Iter: 27000
D loss: 2.09e-10
G_loss: 25.56

(1, 3198) . ! i to ... the ? and for that this have i'm now not ) at all no be but was go today like love one work if think much going sad really new more it's from last him feel feeling home tonight off days better 3 tomorrow only make should over who they gonna soon again head come weekend wanna thanks morning > he didn't cry b away those wrong first way makes already she pretty could won't give yay sick were start else whats mi forever da live far wonder brother crown wide cough cut bought msn deep talkin says cab fucked special t4 poll david whyy baithing ex motivation immediately bball shall jeans mother spaz strangely rx intercept isnt 112-102 beard 11 xmas apl stout bf's waiit meantime tix
Iter: 28000
D loss: 1.51e-10
G_loss: 25.34

(1, 3198) . ! i to ... the ? and for that this just have i'm now not ) all no be but was go what today like love one can work if think much going sad really new more from last him feel feeling home tonight off days better 3 tomorrow only make should over who they gonna soon again head come weekend wanna thanks > he didn't cry b $ away those wrong first way makes already she pretty could won't give yay sick almost were start else whats forever da live trust far wonder brother harry goodbye crown wide cough cut bought msn deep pictures luv talkin says australia cab fucked tourny special t4 poll david whyy grrr baithing 30mins ex motivation wat straighten immediately bball shall jeans mother spaz strangely rx intercept isnt 112-102 muslims wompp beard 11 xmas apl stout bf's waiit tix
Iter: 29000
D loss: 1.404e-10
G_loss: 25.36

(1, 3198) . ! i to ... the ? and for that this just have i'm now not ) all no be but was go what today like love one can work if think much going sad really new more from last him feel feeling home tonight off days better 3 tomorrow only make should over who they gonna soon again head come weekend wanna thanks > he didn't cry b $ away those wrong first way makes already she pretty could won't give yay sick almost were start else whats forever da live trust far wonder brother harry goodbye crown wide cough cut bought msn deep pictures luv talkin says australia cab fucked tourny special t4 poll david whyy grrr baithing 30mins ex motivation wat straighten immediately bball shall jeans mother strangely rx intercept isnt 112-102 muslims wompp beard 11 xmas apl stout bf's waiit tix
Iter: 30000
D loss: 1.025e-10
G_loss: 25.46

(1, 3198) . ! i to ... the ? and for that this just have i'm now not ) all no be but was go what today like love one can work if think much going sad really new more last him feel feeling home tonight off days better 3 tomorrow only make should over who they gonna soon again head come weekend wanna thanks > he didn't cry b $ away those wrong first way makes already she pretty i've could won't give yay sick almost were start else whats put let forever da live trust far wonder brother harry goodbye boom crown wide cough cut bought msn deep pictures luv talkin says australia cab fucked tourny special t4 poll whyy grrr baithing 30mins ex motivation wat straighten immediately bball shall jeans mother strangely rx intercept isnt 112-102 muslims wompp beard sannesias 11 xmas apl stout bf's waiit tix
Iter: 31000
D loss: 8.821e-11
G_loss: 25.36

(1, 3198) . ! i to ... the ? and .. for that this just have i'm now not ) all no be but was go what today like love one can work if think much going sad really new more last him feel feeling home tonight off days better 3 tomorrow only make should over who they gonna soon again head come weekend wanna thanks > he didn't cry b $ away those wrong first way makes already she pretty i've could won't give yay sick almost were start else whats put let forever da live trust far wonder brother harry goodbye boom crown wide cough cut bought msn deep pictures luv talkin says australia cab fucked t_t tourny special t4 poll whyy grrr baithing 30mins ex motivation wat straighten immediately bball shall jeans mother strangely rx intercept isnt 112-102 muslims wompp beard sannesias 11 xmas apl stout bf's waiit tix
Iter: 32000
D loss: 6.385e-11
G_loss: 26.32

(1, 3198) . ! i to ... the ? and .. for that this just have i'm now not ) all no be but was go what today like love one can work if think much going sad really new more last him feel feeling home tonight off days better 3 tomorrow only make should over who they gonna soon again head come weekend wanna thanks > he didn't cry b $ away those wrong first way makes already she pretty i've could won't give yay sick almost were start else whats put funny let forever da live trust far wonder brother harry goodbye boom crown wide cough cut bought msn deep pictures luv talkin says australia cab fucked t_t tourny special t4 poll whyy grrr baithing 30mins ex motivation wat straighten immediately bball shall jeans mother strangely rx intercept isnt 112-102 muslims wompp beard sannesias 11 xmas apl stout bf's waiit tix
Iter: 33000
D loss: 4.992e-11
G_loss: 26.73

(1, 3198) . ! i to ... the ? and .. for that this just have i'm now not ) all no be but was go what today like love one can work if think much going sad really new more last him feel feeling home tonight off days better 3 tomorrow only make should over who they gonna soon again head come weekend wanna thanks > he didn't cry b $ away those wrong first way makes already she pretty i've could won't give yay sick almost were start else whats put airport funny let forever da live trust far wonder brother harry goodbye boom crown wide cough cut bought msn deep pictures luv talkin says australia cab fucked t_t tourny special t4 poll whyy grrr baithing 30mins ex motivation wat straighten immediately bball shall jeans mother strangely rx intercept isnt 112-102 muslims wompp beard sannesias 11 xmas apl stout half bf's waiit tix
Iter: 34000
D loss: 4.141e-11
G_loss: 26.69

(1, 3198) . ! i to ... the ? and .. for that this just have i'm now not ) all no be but was go what today like love one can work if think much going sad really new more last him feel feeling home tonight off days better 3 tomorrow only make should over who they gonna soon again head come weekend wanna thanks > he didn't cry b $ away those wrong first way makes already she pretty i've could won't give yay sick almost were start else whats put airport funny let forever da live trust far wonder brother harry goodbye boom crown wide cough cut bought msn deep pictures luv talkin says australia cab fucked t_t tourny special t4 poll whyy grrr baithing 30mins ex motivation wat straighten immediately bball shall jeans mother strangely rx intercept isnt 112-102 muslims wompp beard sannesias 11 xmas apl stout half bf's waiit tix
Iter: 35000
D loss: 3.459e-11
G_loss: 27.05

(1, 3198) . ! i to ... the ? and .. for that this just have i'm now not ) all no be but was go what today like love one can work if think much going sad really new more last him feel feeling home tonight off days better 3 tomorrow only make should over who they gonna soon again head come weekend wanna thanks > he didn't cry b $ away those wrong first way makes already she pretty i've could won't give yay sick almost were start else whats put airport funny let forever da live trust far wonder brother harry goodbye boom crown wide cough cut bought msn deep pictures luv talkin says australia cab fucked t_t tourny special t4 poll whyy grrr baithing 30mins ex motivation wat straighten immediately bball shall jeans mother strangely rx intercept isnt 112-102 muslims wompp beard sannesias 11 xmas apl stout half bf's waiit tix
Iter: 36000
D loss: 2.967e-11
G_loss: 27.29

(1, 3198) . ! i to ... the ? and .. for that this just have i'm now not ) all no be but was go what today like love one can work if think much going sad really new more last him feel feeling home tonight off days better 3 tomorrow only make should over who they gonna soon again head come weekend wanna thanks > he didn't cry b $ away those wrong first way makes already she pretty i've could won't give yay sick almost were start else whats put airport funny let forever da live trust far wonder brother harry goodbye boom crown wide cough cut bought msn deep pictures luv talkin says australia cab fucked t_t tourny special t4 poll whyy grrr baithing 30mins ex motivation wat straighten immediately bball shall jeans mother strangely rx intercept isnt 112-102 muslims wompp beard sannesias 11 xmas apl stout half bf's waiit tix
Iter: 37000
D loss: 2.594e-11
G_loss: 27.47

(1, 3198) . ! i to ... the - ? and .. for that this just i'm now not ) all no be but was ' go & what today like love one can work if think much going sad really miss more too oh feeling home tonight off better tomorrow over who they by gonna soon again come weekend wanna thanks > he b $ away tell year first way makes pretty i've could won't thank give something wanted sick almost else whats put funny let times forever da trust far kid brother million harry celebrate goodbye boom moving wide cough bought msn pictures luv talkin australia headed stick tourny special t4 grrr http://bit.ly/zoxzc baithing 30mins ex wat straighten immediately jeans mother strangely intercept isnt jin muslims wompp momacita sannesias haters saturday 11 stout half waiit tix sequel
Iter: 38000
D loss: 1.597e-09
G_loss: 41.19

(1, 3198) . i " to ... - ? and for that so just i'm now no be ' up & like love one work think going really miss more / too tonight off better tomorrow any over by gonna guys b away year o could thank something wanted smile whats boring let times fine kid celebrate goodbye moving wide cough australia headed stick tourny special grrr jin momacita winona haters saturday
Iter: 39000
D loss: 5.123e-10
G_loss: 23.01

(1, 3198) . i " to ... - ? and for that so just i'm now no be ' up & like love one work think going really miss more / off better tomorrow any over by fuck guys away year o thank something wanted smile whats boring times fine kid celebrate goodbye pleased thats moving wide pull australia headed stick tourny special grrr jin momacita winona haters saturday
Iter: 40000
D loss: 4.686e-10
G_loss: 24.03

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special grrr momacita haters saturday
Iter: 41000
D loss: 2.108e-10
G_loss: 24.02

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special grrr momacita haters saturday
Iter: 42000
D loss: 8.679e-11
G_loss: 25.02

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special grrr momacita haters saturday
Iter: 43000
D loss: 5.526e-11
G_loss: 25.5

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 44000
D loss: 4.24e-11
G_loss: 25.61

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 45000
D loss: 3.292e-11
G_loss: 26.0

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 46000
D loss: 2.705e-11
G_loss: 26.25

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 47000
D loss: 2.3e-11
G_loss: 26.43

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 48000
D loss: 2.002e-11
G_loss: 26.58

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 49000
D loss: 1.774e-11
G_loss: 26.71

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 50000
D loss: 1.593e-11
G_loss: 26.83

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 51000
D loss: 1.445e-11
G_loss: 26.93

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 52000
D loss: 1.324e-11
G_loss: 27.02

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 53000
D loss: 1.221e-11
G_loss: 27.1

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 54000
D loss: 1.133e-11
G_loss: 27.18

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 55000
D loss: 1.058e-11
G_loss: 27.25

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 56000
D loss: 9.915e-12
G_loss: 27.31

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 57000
D loss: 9.333e-12
G_loss: 27.38

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 58000
D loss: 8.815e-12
G_loss: 27.44

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 59000
D loss: 8.353e-12
G_loss: 27.49

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 60000
D loss: 7.937e-12
G_loss: 27.55

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 61000
D loss: 7.56e-12
G_loss: 27.6

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 62000
D loss: 7.219e-12
G_loss: 27.64

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 63000
D loss: 6.908e-12
G_loss: 27.69

(1, 3198) . i " ... - ? and for that so just i'm now no be ' up like love one work think going really miss more off better tomorrow over by fuck guys away year o thank something wanted smile omg whats boring times fine pleased thats pull australia special momacita haters saturday
Iter: 64000
D loss: 6.622e-12
G_loss: 27.73

(1, 3198) . i " ... - ? and for that so ( i'm now all no be up like love one work think going miss more off tomorrow by guys away o thank watch omg fine pleased thats australia special uniform
Iter: 65000
D loss: 2.756e-10
G_loss: 31.49

(1, 3198) . i " ... - ? and for that so ( i'm now all no be up like love one work think going miss more off tomorrow by guys away o thank watch omg fine pleased thats australia special uniform
Iter: 66000
D loss: 6.332e-11
G_loss: 29.94

(1, 3198) . i " ... - ? and for that so ( i'm now all no be up like love one work think going miss more off tomorrow by guys away o thank watch start omg fine pleased thats australia special uniform
Iter: 67000
D loss: 3.643e-11
G_loss: 28.89

(1, 3198) . i " ... - and for that so ( i'm now all no be up like love one work think going miss more off tomorrow by guys away o thank watch start omg fine pleased thats australia special uniform
Iter: 68000
D loss: 2.741e-11
G_loss: 27.09

(1, 3198) . i ... - and for that so ( i'm now all no be up like love one work going miss more off tomorrow by guys away o thank watch start omg fine thats australia special neighbor uniform
Iter: 69000
D loss: 5.569e-11
G_loss: 24.72

(1, 3198) . i ... - and for that so ( i'm now all no be up like love one work going miss more off tomorrow by guys away o thank watch start omg fine thats australia special neighbor uniform
Iter: 70000
D loss: 3.857e-11
G_loss: 25.38

(1, 3198) . i ... - and for that so ( i'm now all no be up like love one work going miss more off tomorrow by guys away o thank watch start omg fine thats australia special neighbor uniform
Iter: 71000
D loss: 3.001e-11
G_loss: 25.72

(1, 3198) . i ... - and for that so ( i'm now all no be up like love one work going miss more off tomorrow by guys away o thank watch start omg fine thats australia special neighbor uniform
Iter: 72000
D loss: 2.461e-11
G_loss: 25.96

(1, 3198) . i ... - and for that so ( i'm now all no be up like love one work going miss more off tomorrow by guys away o thank watch start omg fine thats australia special neighbor uniform
Iter: 73000
D loss: 2.087e-11
G_loss: 26.15

(1, 3198) . i ... - and for that so ( i'm now all no be up like love one work going miss more off tomorrow by guys away o thank watch start omg fine thats australia special neighbor uniform
Iter: 74000
D loss: 1.813e-11
G_loss: 26.3

(1, 3198) . i ... - and for that so ( i'm now all no be up like love one work miss more off by guys away o thank watch start omg fine special 3rd neighbor uniform
Iter: 75000
D loss: 8.798e-11
G_loss: 24.46

(1, 3198) . i ... - and for that so ( i'm now all no be up like love one work more off away o thank watch start omg fine special 3rd neighbor uniform
Iter: 76000
D loss: 1.672e-10
G_loss: 23.5

(1, 3198) . i ... - and for that so ( i'm now all no be up like love one work more off 3 down away o thank watch start omg fine special 3rd daily neighbor uniform
Iter: 77000
D loss: 1.147e-10
G_loss: 23.55

(1, 3198) . i ... - and for that so ( i'm now all no be up like love one work more it's off 3 that's head down away o thank watch start omg fine special squirrels 3rd daily neighbor uniform
Iter: 78000
D loss: 7.213e-11
G_loss: 24.25

(1, 3198) . i ... - and that so ( i'm now all no be up love one more it's off 3 that's head down away sucks face watch start omg fine special squirrels 3rd daily neighbor uniform
Iter: 79000
D loss: 1.569e-10
G_loss: 23.99

(1, 3198) . i ... - and that so ( i'm now all no be are up love one more it's off 3 that's head down away sucks face watch start omg fine special squirrels 3rd daily neighbor uniform
Iter: 80000
D loss: 7.645e-11
G_loss: 24.4

(1, 3198) . i ... - and that so ( i'm now all no be are up love one more it's off 3 that's head down away sucks face watch start omg fine special squirrels 3rd it'll daily neighbor uniform
Iter: 81000
D loss: 4.817e-11
G_loss: 25.03

(1, 3198) . i ... - and that so ( i'm now all no be are up love one more it's off 3 that's head down away sucks summer face watch start omg fine special squirrels 3rd it'll daily neighbor uniform
Iter: 82000
D loss: 3.817e-11
G_loss: 25.2

(1, 3198) . i ... - and that so ( i'm all no be are up love one more it's off 3 that's head down away sucks summer face watch start omg position re-ripped special squirrels 3rd it'll daily neighbor uniform
Iter: 83000
D loss: 9.419e-11
G_loss: 23.89

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks summer face watch start omg position re-ripped special squirrels 3rd it'll daily neighbor uniform
Iter: 84000
D loss: 5.314e-11
G_loss: 25.07

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks summer face watch start omg position re-ripped special squirrels 3rd it'll daily neighbor uniform
Iter: 85000
D loss: 3.396e-11
G_loss: 25.62

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks summer face watch start omg position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 86000
D loss: 2.701e-11
G_loss: 25.73

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks summer face watch start omg position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 87000
D loss: 2.093e-11
G_loss: 26.09

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks summer face watch start omg position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 88000
D loss: 1.721e-11
G_loss: 26.3

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks summer face watch start omg position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 89000
D loss: 1.466e-11
G_loss: 26.47

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks summer face watch start omg position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 90000
D loss: 1.278e-11
G_loss: 26.61

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks went summer face watch start omg position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 91000
D loss: 1.265e-11
G_loss: 26.36

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks went summer face watch start omg sooo position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 92000
D loss: 1.194e-11
G_loss: 26.35

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks went summer face watch start omg sooo position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 93000
D loss: 1.04e-11
G_loss: 26.64

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks went summer face watch start omg sooo position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 94000
D loss: 9.318e-12
G_loss: 26.84

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks went summer face watch start omg sooo position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 95000
D loss: 8.465e-12
G_loss: 26.99

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks went summer face watch start omg sooo position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 96000
D loss: 7.778e-12
G_loss: 27.12

(1, 3198) . i ... - and that so ( i'm all be are up love one more it's off 3 that's head down away sucks went summer face watch start omg sooo position re-ripped special squirrels 3rd it'll daily sannesias neighbor uniform
Iter: 97000
D loss: 7.203e-12
G_loss: 27.22

(1, 3198) . i ... - and that so have ( i'm all be are up love one more it's off 3 that's head down away sucks went summer face watch start omg sooo position re-ripped special squirrels 3rd standing it'll daily sannesias neighbor uniform
Iter: 98000
D loss: 1.256e-11
G_loss: 25.68

(1, 3198) . i ... - and that so have ( i'm all be are up love one more it's off 3 that's head down away sucks went summer face watch start omg sooo position re-ripped special squirrels 3rd standing it'll daily sannesias neighbor uniform
Iter: 99000
D loss: 9.381e-12
G_loss: 26.27







    
Out[288]:





'./tf_model/generator-1000000'

In [297]:

    

saver.restore(sess,  './tf_model/generator-1000000')


    

    




INFO:tensorflow:Restoring parameters from ./tf_model/generator-1000000

In [300]:

    

samples = sess.run(G_sample, feed_dict={Z: sample_Z(1, Z_dim)})
for sample in samples:
    print(sample)
    sample[sample == 1.] = 1
    sample[sample < 1.] = 0
    print(np.where(sample == 0)[0])
    print(decode_sentence(sample))


    

    




[  8.32440076e-21   1.00000000e+00   2.41504855e-34 ...,   6.45371080e-21
   1.25916747e-13   1.05300227e-26]
[   0    2    4 ..., 3195 3196 3197]
. i ... that so have i'm all be are up love more it's we off 3 that's head down away sucks went summer face watch start omg boring sooo position re-ripped special squirrels 3rd standing it'll daily sannesias neighbor place uniform

In [306]:

    

with codecs.open("../data/sentiment.vec", 'r', encoding='utf-8') as f:
    next(f)
    word_vectors_twitter = f.read().split('\n')


    

In [307]:

    

word_vectors_twitter[0]


    

    
Out[307]:





'to 0.28784 -0.16593 0.21775 -0.03476 0.17177 -0.24388 -0.36813 -0.17193 -0.15732 -0.091941 -0.31362 0.16376 0.19695 -0.054758 -0.048513 0.25853 -0.082019 -0.22646 0.26641 -0.17032 0.052413 0.091387 0.037523 0.046991 0.16638 -0.17341 0.031912 0.039461 -0.16813 0.049594 -0.055904 0.17197 0.063157 0.011592 -0.10973 -0.16039 0.14447 0.37026 0.055234 0.12431 -0.32994 0.16109 0.24207 -0.3082 0.10437 -0.031148 0.30501 0.17281 0.074012 -0.21251 0.31639 0.12154 0.27019 0.035283 0.010634 0.14596 -0.11356 0.22376 -0.005687 0.2354 0.18467 0.30154 -0.15345 -0.12211 -0.31571 0.16496 0.081898 0.23165 -0.19046 0.24053 0.33129 0.0056417 0.12159 0.10587 0.11307 -0.11543 0.26196 0.23725 -0.1914 0.044061 0.027817 -0.23344 0.1431 -0.29592 0.09374 -0.079591 0.014051 0.090615 0.29193 0.23053 0.19635 0.61701 0.1951 -0.09174 0.17461 0.27697 0.13351 -0.23349 0.41543 0.35445 '

In [336]:

    

word_vectors = {}
for lin in word_vectors_twitter:
    line = lin.split()
#     print(line)
    try:
        word_vectors[line[0]] = np.array(line[1:])
    except:
        print(line, lin)


    

    




[] 

In [337]:

    

len(list(word_vectors.keys()))


    

    
Out[337]:





113975

In [341]:

    

word_vectors['to']


    

    
Out[341]:





array([['0.28784'],
       ['-0.16593'],
       ['0.21775'],
       ['-0.03476'],
       ['0.17177'],
       ['-0.24388'],
       ['-0.36813'],
       ['-0.17193'],
       ['-0.15732'],
       ['-0.091941'],
       ['-0.31362'],
       ['0.16376'],
       ['0.19695'],
       ['-0.054758'],
       ['-0.048513'],
       ['0.25853'],
       ['-0.082019'],
       ['-0.22646'],
       ['0.26641'],
       ['-0.17032'],
       ['0.052413'],
       ['0.091387'],
       ['0.037523'],
       ['0.046991'],
       ['0.16638'],
       ['-0.17341'],
       ['0.031912'],
       ['0.039461'],
       ['-0.16813'],
       ['0.049594'],
       ['-0.055904'],
       ['0.17197'],
       ['0.063157'],
       ['0.011592'],
       ['-0.10973'],
       ['-0.16039'],
       ['0.14447'],
       ['0.37026'],
       ['0.055234'],
       ['0.12431'],
       ['-0.32994'],
       ['0.16109'],
       ['0.24207'],
       ['-0.3082'],
       ['0.10437'],
       ['-0.031148'],
       ['0.30501'],
       ['0.17281'],
       ['0.074012'],
       ['-0.21251'],
       ['0.31639'],
       ['0.12154'],
       ['0.27019'],
       ['0.035283'],
       ['0.010634'],
       ['0.14596'],
       ['-0.11356'],
       ['0.22376'],
       ['-0.005687'],
       ['0.2354'],
       ['0.18467'],
       ['0.30154'],
       ['-0.15345'],
       ['-0.12211'],
       ['-0.31571'],
       ['0.16496'],
       ['0.081898'],
       ['0.23165'],
       ['-0.19046'],
       ['0.24053'],
       ['0.33129'],
       ['0.0056417'],
       ['0.12159'],
       ['0.10587'],
       ['0.11307'],
       ['-0.11543'],
       ['0.26196'],
       ['0.23725'],
       ['-0.1914'],
       ['0.044061'],
       ['0.027817'],
       ['-0.23344'],
       ['0.1431'],
       ['-0.29592'],
       ['0.09374'],
       ['-0.079591'],
       ['0.014051'],
       ['0.090615'],
       ['0.29193'],
       ['0.23053'],
       ['0.19635'],
       ['0.61701'],
       ['0.1951'],
       ['-0.09174'],
       ['0.17461'],
       ['0.27697'],
       ['0.13351'],
       ['-0.23349'],
       ['0.41543'],
       ['0.35445']],
      dtype='<U9')

In [343]:

    

from sklearn.metrics.pairwise import cosine_similarity


    

In [344]:

    

cosine_similarity([[1, 0, -1]], [[-1,-1, 0]])


    

    
Out[344]:





array([[-0.5]])

In [355]:

    

def nn(word):
    nearest = 0
    max = (0, 'max')
    for w in word_vectors.keys():
        try:
            if cosine_similarity([word_vectors[word]], [word_vectors[w]])[0][0] > max[0] and w != word:
                max = (cosine_similarity([word_vectors[word]], [word_vectors[w]])[0][0], w)
        except:
            pass
#             print(w, word_vectors[word], word_vectors[w])
    print(max[1])


    

In [356]:

    

nn('hate')


    

    




ihate

In [ ]: