-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathjjb_bptt.wgt
47 lines (41 loc) · 5.18 KB
/
jjb_bptt.wgt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
# This weight file is generated by BPTTwithHebbian
# This file specfies the network size and the weight values
# Note: To train the network you need to load a pattern file
# Note: You can not specify learning parameters from this file
# Note: If you want to continue a learning session that you saved the
# weights from, use Make net.Network from Weight followed by Load Pattern then continue training.
# First matrix is the input(x)->hidden(y) weights(inputToHiddenW)
# Second matrix is the hidden(y)->output(z) weights(hiddenToOutputW)
# Third matrix is the recurrent output(z)->recurrent input(x) weights(recurrentOutputToInputW)
# Fourth matrix is the hebbian->output(z) weights (hebbianToOutputW)
outputdim 3
hiddendim 10
inputdim 7
rindim 5
routdim 5
#input -> hidden weights inputToHiddenW[10][12]
-0.03427901968041098 -0.09529755842395052 -0.7320253203944375 -3.4477797119401 6.600109104648981 -1.6644238956675146 -1.8663688058659134 -0.0677591286970988 -0.11515146191467233 -0.16588717323491606 -0.1443144861013763 -0.08427653092791568
-1.1735978715099495 -3.337339454726752 -1.1382595506527902 -1.7900348326555606 5.101304406262636 -1.308656495308954 -2.278024327746619 -0.7327788728597839 -0.7642574837947017 -0.7375984387245225 -0.8006007586820238 -0.7372552176969601
0.3578330177352192 -7.34296741343637 -5.001647696996164 -4.795911058819856 6.41461872501504 -2.00270892521632 -5.660777144349319 5.688357740537898 5.783410326136696 5.448349346802972 5.594680513625663 5.828379285496762
-3.671986174065918 4.03620341019491 -1.199340873826676 -3.010112691534115 -3.290805514278724 2.776083570203535 3.897001391688727 -5.139430483865068 -5.069973236057634 -5.148417654307117 -5.213181512863575 -5.024217076448035
-8.350596377878842 8.826969608919681 -2.6977769378986936 2.6094710851405045 1.7893887705010398 8.675385570086359 -5.657430897703673 -0.05543662455286978 -0.005951580272746105 -0.07634027241141345 -0.04394443542248156 0.036773482809120954
-0.6943415300953966 3.000259535211327 -1.727838878418276 0.8717760216313649 -4.294192411976369 3.1880273147369533 0.8410237702836442 -4.8976551411241065 -4.902192860703506 -4.99140245886652 -4.958739552797471 -4.9124364995308225
1.265613343677051 -0.9767076306426007 -2.097911120077553 -3.3399330798178086 8.19775938930537 -2.003878089488168 -1.234068821417244 1.1556575740225927 1.1851033554756525 1.0258953809469722 1.0588158310562055 1.20246153466098
-0.5340167075171142 -3.330532418871446 -0.13350713754862845 -3.720521032915264 2.618771947231046 -0.5714877578414892 1.1451861915380184 0.1652484767046714 0.20770358846381895 0.26959215183976565 0.28002740957820793 0.21010742027038604
10.13861806885864 -5.871186423961881 1.372709619948013 0.3818128177765845 3.9368729341016806 -2.3636657940413692 4.060103583519493 5.114903533041558 5.09390580059747 5.116772249091135 5.113664224693944 5.131209045336048
3.561452305514078 -3.3086149116264423 -1.4161594210541946 -7.3824053489592085 0.11451411174678033 -2.483313903513965 4.053815151873499 -4.414298018814771 -4.335573237292352 -4.4449131217307425 -4.420290109463572 -4.283139949744707
#hidden -> output weights hiddenToOutputW[8][10]:
1.262258117062619 0.9162341058938033 -10.907505387007765 -9.97591785611908 7.293385152114619 -3.6392368216599738 -1.7157188029754116 -1.71064379681017 -1.3581032714541401 -5.443322543800454
-1.6742659341715835 -1.6897326689138195 -1.8385006837072628 -2.1045370070464133 -3.7598884656554397 -2.512889647379299 -1.698211788201397 -1.7665782996343327 -3.026589974557455 -2.0548158813890667
-0.9658669192108799 2.9494537303946013 -6.680527466979351 -2.920923145440804 -7.571575929431308 -6.525893392123075 -0.6278385345338066 2.5496112306585053 4.169786200607808 -6.943516824499372
1.546257071556931 1.018510592419447 2.1241729724678584 -0.0027853635846974024 -1.0196947710746538 -2.608147467533101 1.6322367462700342 0.7486297565314832 -3.7162719087909104 1.211218387320415
2.5360144295196263 1.491383647158462 2.4735398910051467 0.5494052532127067 -1.4814776882371423 -2.3251685280788257 2.45186566653354 0.7393454872523069 -4.138188699959096 2.594990301515262
1.1284490468612056 0.9558796630137063 1.4309135444227148 -0.8502271964993217 -0.9784500833304739 -2.6829000638680043 1.180546250355318 0.4568691901520762 -3.2241877605136744 0.029849809058895692
3.733307024195473 1.9612049336457245 -0.255128240873252 -1.6434186478479966 1.4021931505087524 -5.839948631564063 4.602011463931463 1.4818672913704904 -1.7638316528709264 -0.007305686774840797
0.580956120329952 0.7293432417172594 4.772920669686966 -1.1612063165077338 -0.9617461963371375 -4.227405043868575 1.8016620973233093 0.29858883079127085 -5.101899271000711 0.8063726051371506
#recurrent hidden -> recurrent input weights recurrentOutputToInputW[5][5]:
-0.7606940705741759 -1.4554549030355732 -0.19142238962131386 -1.3931088067203465 -2.15680795815028
-0.8345503919877323 -1.420362851339851 -0.2375433963585489 -1.5244075424947694 -2.089010517904615
-0.7371768571876687 -1.5159580838874969 -0.13924941216302728 -1.0210842597215557 -2.295585378921819
-0.7698362628823935 -1.439063879831784 -0.12904444118483321 -1.1945018846290272 -2.271877133439936
-0.8631641460123901 -1.4497960276560926 -0.2819400650403817 -1.6012378282699984 -2.00384846798456