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hip_bench_te.go
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hip_bench_te.go
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// Copyright (c) 2020, The Emergent Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
// hip_bench runs a hippocampus model for testing parameters and new learning ideas
package main
import (
"bytes"
"flag"
"fmt"
"log"
"math/rand"
"os"
"strconv"
"strings"
"time"
"github.com/emer/etable/simat"
"github.com/emer/etable/metric"
"github.com/emer/emergent/emer"
"github.com/emer/emergent/env"
"github.com/emer/emergent/evec"
"github.com/emer/emergent/netview"
"github.com/emer/emergent/params"
"github.com/emer/emergent/patgen"
"github.com/emer/emergent/prjn"
"github.com/emer/emergent/relpos"
"github.com/emer/etable/agg"
"github.com/emer/etable/eplot"
"github.com/emer/etable/etable"
"github.com/emer/etable/etensor"
_ "github.com/emer/etable/etview" // include to get gui views
"github.com/emer/etable/split"
"github.com/emer/leabra/hip"
"github.com/emer/leabra/leabra"
"github.com/goki/gi/gi"
"github.com/goki/gi/gimain"
"github.com/goki/gi/giv"
"github.com/goki/ki/ki"
"github.com/goki/ki/kit"
"github.com/goki/mat32"
)
func main() {
TheSim.New()
TheSim.Config()
if len(os.Args) > 1 {
TheSim.CmdArgs() // simple assumption is that any args = no gui -- could add explicit arg if you want
} else {
gimain.Main(func() { // this starts gui -- requires valid OpenGL display connection (e.g., X11)
guirun()
})
}
}
func guirun() {
TheSim.Init()
win := TheSim.ConfigGui()
win.StartEventLoop()
}
// LogPrec is precision for saving float values in logs
const LogPrec = 4
// see def_params.go for the default params, and params.go for user-saved versions
// from the gui.
// see bottom of file for multi-factor testing params
// HipParams have the hippocampus size and connectivity parameters
type HipParams struct {
// size of EC in terms of overall pools (outer dimension)
ECSize evec.Vec2i `desc:"size of EC in terms of overall pools (outer dimension)"`
// size of one EC pool
ECPool evec.Vec2i `desc:"size of one EC pool"`
// size of one CA1 pool
CA1Pool evec.Vec2i `desc:"size of one CA1 pool"`
// size of CA3
CA3Size evec.Vec2i `desc:"size of CA3"`
// size of DG / CA3
DGRatio float32 `desc:"size of DG / CA3"`
// size of DG
DGSize evec.Vec2i `inactive:"+" desc:"size of DG"`
// percent connectivity into DG
DGPCon float32 `desc:"percent connectivity into DG"`
// percent connectivity into CA3
CA3PCon float32 `desc:"percent connectivity into CA3"`
// percent connectivity into CA3 from DG
MossyPCon float32 `desc:"percent connectivity into CA3 from DG"`
// percent activation in EC pool
ECPctAct float32 `desc:"percent activation in EC pool"`
// delta in mossy effective strength between minus and plus phase
MossyDel float32 `desc:"delta in mossy effective strength between minus and plus phase"`
// delta in mossy strength for testing (relative to base param)
MossyDelTest float32 `desc:"delta in mossy strength for testing (relative to base param)"`
}
func (hp *HipParams) Update() {
hp.DGSize.X = int(float32(hp.CA3Size.X) * hp.DGRatio)
hp.DGSize.Y = int(float32(hp.CA3Size.Y) * hp.DGRatio)
}
// PatParams have the pattern parameters
type PatParams struct {
// number of A-B, A-C patterns each
ListSize int `desc:"number of A-B, A-C patterns each"`
// minimum difference between item random patterns, as a proportion (0-1) of total active
MinDiffPct float32 `desc:"minimum difference between item random patterns, as a proportion (0-1) of total active"`
// use drifting context representations -- otherwise does bit flips from prototype
DriftCtxt bool `desc:"use drifting context representations -- otherwise does bit flips from prototype"`
// proportion (0-1) of active bits to flip for each context pattern, relative to a prototype, for non-drifting
CtxtFlipPct float32 `desc:"proportion (0-1) of active bits to flip for each context pattern, relative to a prototype, for non-drifting"`
}
// TEParams = testing effect params
type TEParams struct {
// whether RP is EDL
EDL bool `desc:"whether RP is EDL"`
// whether RP or RS
IsRP bool `desc:"whether RP or RS"`
}
// Sim encapsulates the entire simulation model, and we define all the
// functionality as methods on this struct. This structure keeps all relevant
// state information organized and available without having to pass everything around
// as arguments to methods, and provides the core GUI interface (note the view tags
// for the fields which provide hints to how things should be displayed).
type Sim struct {
// [view: no-inline]
Net *leabra.Network `view:"no-inline"`
// hippocampus sizing parameters
Hip HipParams `desc:"hippocampus sizing parameters"`
// parameters for the input patterns
Pat PatParams `desc:"parameters for the input patterns"`
// parameters for the testing effect
TE TEParams `desc:"parameters for the testing effect"`
// [view: no-inline] pool patterns vocabulary
PoolVocab patgen.Vocab `view:"no-inline" desc:"pool patterns vocabulary"`
// [view: no-inline] AB training patterns to use
TrainAB *etable.Table `view:"no-inline" desc:"AB training patterns to use"`
// [view: no-inline] AC training patterns to use
TrainRP *etable.Table `view:"no-inline" desc:"AC training patterns to use"`
// [view: no-inline] AC training patterns to use
TrainRestudy *etable.Table `view:"no-inline" desc:"AC training patterns to use"`
// [view: no-inline] AB testing patterns to use
TestAB *etable.Table `view:"no-inline" desc:"AB testing patterns to use"`
// [view: no-inline] AB testing patterns to use
TestLong *etable.Table `view:"no-inline" desc:"AB testing patterns to use"`
// [view: no-inline] AC testing patterns to use
TestAC *etable.Table `view:"no-inline" desc:"AC testing patterns to use"`
// [view: no-inline] all training patterns -- for pretrain
TrainAll *etable.Table `view:"no-inline" desc:"all training patterns -- for pretrain"`
// [view: no-inline] training trial-level log data for pattern similarity
TrnCycPatSimLog *etable.Table `view:"no-inline" desc:"training trial-level log data for pattern similarity"`
// [view: no-inline] training trial-level log data
TrnTrlLog *etable.Table `view:"no-inline" desc:"training trial-level log data"`
// [view: no-inline] training epoch-level log data
TrnEpcLog *etable.Table `view:"no-inline" desc:"training epoch-level log data"`
// [view: no-inline] testing epoch-level log data
TstEpcLog *etable.Table `view:"no-inline" desc:"testing epoch-level log data"`
// [view: no-inline] testing trial-level log data
TstTrlLog *etable.Table `view:"no-inline" desc:"testing trial-level log data"`
// [view: no-inline] testing cycle-level log data
TstCycLog *etable.Table `view:"no-inline" desc:"testing cycle-level log data"`
// [view: no-inline] summary log of each run
RunLog *etable.Table `view:"no-inline" desc:"summary log of each run"`
// [view: no-inline] aggregate stats on all runs
RunStats *etable.Table `view:"no-inline" desc:"aggregate stats on all runs"`
// [view: no-inline] testing stats
TstStats *etable.Table `view:"no-inline" desc:"testing stats"`
// [view: no-inline] similarity matrix results for layers
SimMats map[string]*simat.SimMat `view:"no-inline" desc:"similarity matrix results for layers"`
// [view: no-inline] full collection of param sets
Params params.Sets `view:"no-inline" desc:"full collection of param sets"`
// which set of *additional* parameters to use -- always applies Base and optionaly this next if set
ParamSet string `desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set"`
// extra tag string to add to any file names output from sim (e.g., weights files, log files, params)
Tag string `desc:"extra tag string to add to any file names output from sim (e.g., weights files, log files, params)"`
// current batch run number, for generating different seed
BatchRun int `desc:"current batch run number, for generating different seed"`
// maximum number of model runs to perform
MaxRuns int `desc:"maximum number of model runs to perform"`
// maximum number of epochs to run per model run
MaxEpcs int `desc:"maximum number of epochs to run per model run"`
// number of epochs to run for pretraining
PreTrainEpcs int `desc:"number of epochs to run for pretraining"`
// if a positive number, training will stop after this many epochs with zero mem errors
NZeroStop int `desc:"if a positive number, training will stop after this many epochs with zero mem errors"`
// Training environment -- contains everything about iterating over input / output patterns over training
TrainEnv env.FixedTable `desc:"Training environment -- contains everything about iterating over input / output patterns over training"`
// Testing environment -- manages iterating over testing
TestEnv env.FixedTable `desc:"Testing environment -- manages iterating over testing"`
// leabra timing parameters and state
Time leabra.Time `desc:"leabra timing parameters and state"`
// whether to update the network view while running
ViewOn bool `desc:"whether to update the network view while running"`
// at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model
TrainUpdt leabra.TimeScales `desc:"at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model"`
// at what time scale to update the display during testing? Anything longer than Epoch updates at Epoch in this model
TestUpdt leabra.TimeScales `desc:"at what time scale to update the display during testing? Anything longer than Epoch updates at Epoch in this model"`
// how often to run through all the test patterns, in terms of training epochs -- can use 0 or -1 for no testing
TestInterval int `desc:"how often to run through all the test patterns, in terms of training epochs -- can use 0 or -1 for no testing"`
// threshold to use for memory test -- if error proportion is below this number, it is scored as a correct trial
MemThr float64 `desc:"threshold to use for memory test -- if error proportion is below this number, it is scored as a correct trial"`
// slice of slice for logging DG patterns every trial
dgCycPats [100][]float32 `desc:"slice of slice for logging DG patterns every trial"`
// slice of slice for logging CA3 patterns every trial
ca3CycPats [100][]float32 `desc:"slice of slice for logging CA3 patterns every trial"`
// slice of slice for logging CA1 patterns every trial
ca1CycPats [100][]float32 `desc:"slice of slice for logging CA1 patterns every trial"`
// what set of patterns are we currently testing
TestNm string `inactive:"+" desc:"what set of patterns are we currently testing"`
// whether current trial's ECout met memory criterion
Mem float64 `inactive:"+" desc:"whether current trial's ECout met memory criterion"`
// current trial's proportion of bits where target = on but ECout was off ( < 0.5), for all bits
TrgOnWasOffAll float64 `inactive:"+" desc:"current trial's proportion of bits where target = on but ECout was off ( < 0.5), for all bits"`
// current trial's proportion of bits where target = on but ECout was off ( < 0.5), for only completion bits that were not active in ECin
TrgOnWasOffCmp float64 `inactive:"+" desc:"current trial's proportion of bits where target = on but ECout was off ( < 0.5), for only completion bits that were not active in ECin"`
// current trial's proportion of bits where target = off but ECout was on ( > 0.5)
TrgOffWasOn float64 `inactive:"+" desc:"current trial's proportion of bits where target = off but ECout was on ( > 0.5)"`
// current trial's sum squared error
TrlSSE float64 `inactive:"+" desc:"current trial's sum squared error"`
// current trial's average sum squared error
TrlAvgSSE float64 `inactive:"+" desc:"current trial's average sum squared error"`
// current trial's cosine difference
TrlCosDiff float64 `inactive:"+" desc:"current trial's cosine difference"`
// last epoch's total sum squared error
EpcSSE float64 `inactive:"+" desc:"last epoch's total sum squared error"`
// last epoch's average sum squared error (average over trials, and over units within layer)
EpcAvgSSE float64 `inactive:"+" desc:"last epoch's average sum squared error (average over trials, and over units within layer)"`
// last epoch's percent of trials that had SSE > 0 (subject to .5 unit-wise tolerance)
EpcPctErr float64 `inactive:"+" desc:"last epoch's percent of trials that had SSE > 0 (subject to .5 unit-wise tolerance)"`
// last epoch's percent of trials that had SSE == 0 (subject to .5 unit-wise tolerance)
EpcPctCor float64 `inactive:"+" desc:"last epoch's percent of trials that had SSE == 0 (subject to .5 unit-wise tolerance)"`
// last epoch's average cosine difference for output layer (a normalized error measure, maximum of 1 when the minus phase exactly matches the plus)
EpcCosDiff float64 `inactive:"+" desc:"last epoch's average cosine difference for output layer (a normalized error measure, maximum of 1 when the minus phase exactly matches the plus)"`
// how long did the epoch take per trial in wall-clock milliseconds
EpcPerTrlMSec float64 `inactive:"+" desc:"how long did the epoch take per trial in wall-clock milliseconds"`
// epoch at when Mem err first went to zero
FirstZero int `inactive:"+" desc:"epoch at when Mem err first went to zero"`
// number of epochs in a row with zero Mem err
NZero int `inactive:"+" desc:"number of epochs in a row with zero Mem err"`
// [view: -] sum to increment as we go through epoch
SumSSE float64 `view:"-" inactive:"+" desc:"sum to increment as we go through epoch"`
// [view: -] sum to increment as we go through epoch
SumAvgSSE float64 `view:"-" inactive:"+" desc:"sum to increment as we go through epoch"`
// [view: -] sum to increment as we go through epoch
SumCosDiff float64 `view:"-" inactive:"+" desc:"sum to increment as we go through epoch"`
// [view: -] sum of errs to increment as we go through epoch
CntErr int `view:"-" inactive:"+" desc:"sum of errs to increment as we go through epoch"`
// [view: -] main GUI window
Win *gi.Window `view:"-" desc:"main GUI window"`
// [view: -] the network viewer
NetView *netview.NetView `view:"-" desc:"the network viewer"`
// [view: -] the master toolbar
ToolBar *gi.ToolBar `view:"-" desc:"the master toolbar"`
// [view: -] the training trial plot
TrnTrlPlot *eplot.Plot2D `view:"-" desc:"the training trial plot"`
// [view: -] the training epoch plot
TrnEpcPlot *eplot.Plot2D `view:"-" desc:"the training epoch plot"`
// [view: -] the testing epoch plot
TstEpcPlot *eplot.Plot2D `view:"-" desc:"the testing epoch plot"`
// [view: -] the test-trial plot
TstTrlPlot *eplot.Plot2D `view:"-" desc:"the test-trial plot"`
// [view: -] the test-cycle plot
TstCycPlot *eplot.Plot2D `view:"-" desc:"the test-cycle plot"`
// [view: -] the run plot
RunPlot *eplot.Plot2D `view:"-" desc:"the run plot"`
// [view: -] the run stats plot - ABmem
RunStatsPlot1 *eplot.Plot2D `view:"-" desc:"the run stats plot - ABmem"`
// [view: -] the run stats plot - learning time
RunStatsPlot2 *eplot.Plot2D `view:"-" desc:"the run stats plot - learning time"`
// [view: -] log file
TrnCycPatSimFile *os.File `view:"-" desc:"log file"`
// [view: -] headers written
TrnCycPatSimHdrs bool `view:"-" desc:"headers written"`
// [view: -] log file
TstEpcFile *os.File `view:"-" desc:"log file"`
// [view: -] log file
TstTrialFile *os.File `view:"-" desc:"log file"`
// [view: -] headers written
TstEpcHdrs bool `view:"-" desc:"headers written"`
// [view: -] log file
RunFile *os.File `view:"-" desc:"log file"`
// [view: -] headers written
RunHdrs bool `view:"-" desc:"headers written"`
// [view: -] temp slice for holding values -- prevent mem allocs
TmpVals []float32 `view:"-" desc:"temp slice for holding values -- prevent mem allocs"`
// [view: -] names of layers to collect more detailed stats on (avg act, etc)
LayStatNms []string `view:"-" desc:"names of layers to collect more detailed stats on (avg act, etc)"`
// [view: -] names of test tables
TstNms []string `view:"-" desc:"names of test tables"`
// [view: -] names of sim mat stats
SimMatStats []string `view:"-" desc:"names of sim mat stats"`
// [view: -] names of test stats
TstStatNms []string `view:"-" desc:"names of test stats"`
// [view: -] for holding layer values
ValsTsrs map[string]*etensor.Float32 `view:"-" desc:"for holding layer values"`
// [view: -] for command-line run only, auto-save final weights after each run
SaveWts bool `view:"-" desc:"for command-line run only, auto-save final weights after each run"`
// [view: -] pretrained weights file
PreTrainWts []byte `view:"-" desc:"pretrained weights file"`
// [view: -] if true, pretraining is done
PretrainDone bool `view:"-" desc:"if true, pretraining is done"`
// [view: -] if true, runing in no GUI mode
NoGui bool `view:"-" desc:"if true, runing in no GUI mode"`
// [view: -] if true, print message for all params that are set
LogSetParams bool `view:"-" desc:"if true, print message for all params that are set"`
// [view: -] true if sim is running
IsRunning bool `view:"-" desc:"true if sim is running"`
// [view: -] flag to stop running
StopNow bool `view:"-" desc:"flag to stop running"`
// [view: -] flag to initialize NewRun if last one finished
NeedsNewRun bool `view:"-" desc:"flag to initialize NewRun if last one finished"`
// [view: -] the current random seed
RndSeed int64 `view:"-" desc:"the current random seed"`
// [view: -] timer for last epoch
LastEpcTime time.Time `view:"-" desc:"timer for last epoch"`
}
// this registers this Sim Type and gives it properties that e.g.,
// prompt for filename for save methods.
var KiT_Sim = kit.Types.AddType(&Sim{}, SimProps)
// TheSim is the overall state for this simulation
var TheSim Sim
// New creates new blank elements and initializes defaults
func (ss *Sim) New() {
ss.Net = &leabra.Network{}
ss.PoolVocab = patgen.Vocab{}
ss.TrainAB = &etable.Table{}
ss.TrainRP = &etable.Table{}
ss.TrainRestudy = &etable.Table{}
ss.TestAB = &etable.Table{}
ss.TestAC = &etable.Table{}
ss.TrainAll = &etable.Table{}
ss.TrnCycPatSimLog = &etable.Table{}
ss.TrnTrlLog = &etable.Table{}
ss.TrnEpcLog = &etable.Table{}
ss.TstEpcLog = &etable.Table{}
ss.TstTrlLog = &etable.Table{}
ss.TstCycLog = &etable.Table{}
ss.RunLog = &etable.Table{}
ss.RunStats = &etable.Table{}
ss.SimMats = make(map[string]*simat.SimMat)
ss.Params = ParamSets // in def_params -- current best params, zycyc test
//ss.Params = OrigParamSets // original, previous model
//ss.Params = SavedParamsSets // current user-saved gui params
ss.RndSeed = 2
ss.ViewOn = true
ss.TrainUpdt = leabra.AlphaCycle
ss.TestUpdt = leabra.Cycle
ss.TestInterval = 1
ss.LogSetParams = false
ss.MemThr = 0.34
ss.LayStatNms = []string{"ECin", "DG", "CA3", "CA1"}
ss.TstNms = []string{"AB"}
ss.TstStatNms = []string{"Mem", "TrgOnWasOff", "TrgOffWasOn"}
ss.SimMatStats = []string{"Within"} // zycyc bug source
ss.Defaults()
}
func (te *TEParams) Defaults() {
te.EDL = true
te.IsRP = true
}
func (pp *PatParams) Defaults() {
pp.ListSize = 100 // 10 is too small to see issues..
pp.MinDiffPct = 0.5
pp.CtxtFlipPct = .25
}
func (hp *HipParams) Defaults() {
// size
hp.ECSize.Set(2, 3)
hp.ECPool.Set(7, 7)
hp.CA1Pool.Set(15, 15) // using MedHip now
hp.CA3Size.Set(30, 30) // using MedHip now
hp.DGRatio = 2.236 // c.f. Ketz et al., 2013
// ratio
hp.DGPCon = 0.25 // .35 is sig worse, .2 learns faster but AB recall is worse
hp.CA3PCon = 0.25
hp.MossyPCon = 0.02 // .02 > .05 > .01 (for small net)
hp.ECPctAct = 0.2
hp.MossyDel = 4 // 4 > 2 -- best is 4 del on 4 rel baseline
hp.MossyDelTest = 3 // for rel = 4: 3 > 2 > 0 > 4 -- 4 is very bad -- need a small amount..
}
func (ss *Sim) Defaults() {
ss.TE.Defaults()
ss.Hip.Defaults()
ss.Pat.Defaults()
ss.BatchRun = 0 // for initializing envs if using Gui
ss.Time.CycPerQtr = 25 // note: key param - 25 seems like it is actually fine?
ss.Update()
}
func (ss *Sim) Update() {
ss.Hip.Update()
}
////////////////////////////////////////////////////////////////////////////////////////////
// Configs
// Config configures all the elements using the standard functions
func (ss *Sim) Config() {
ss.ConfigPats()
ss.ConfigEnv()
ss.ConfigNet(ss.Net)
ss.ConfigTrnCycPatSimLog(ss.TrnCycPatSimLog)
ss.ConfigTrnTrlLog(ss.TrnTrlLog)
ss.ConfigTrnEpcLog(ss.TrnEpcLog)
ss.ConfigTstEpcLog(ss.TstEpcLog)
ss.ConfigTstTrlLog(ss.TstTrlLog)
ss.ConfigTstCycLog(ss.TstCycLog)
ss.ConfigRunLog(ss.RunLog)
}
func (ss *Sim) ConfigEnv() {
if ss.MaxRuns == 0 { // allow user override
ss.MaxRuns = 30
}
if ss.MaxEpcs == 0 { // allow user override
ss.MaxEpcs = 1
ss.NZeroStop = 1
ss.PreTrainEpcs = 5 // seems sufficient? increase?
}
ss.TrainEnv.Nm = "TrainEnv"
ss.TrainEnv.Dsc = "training params and state"
ss.TrainEnv.Table = etable.NewIdxView(ss.TrainAB)
ss.TrainEnv.Validate()
ss.TrainEnv.Run.Max = ss.MaxRuns // note: we are not setting epoch max -- do that manually
ss.TestEnv.Nm = "TestEnv"
ss.TestEnv.Dsc = "testing params and state"
ss.TestEnv.Table = etable.NewIdxView(ss.TestAB)
ss.TestEnv.Sequential = true
ss.TestEnv.Validate()
ss.TrainEnv.Init(ss.BatchRun)
ss.TestEnv.Init(ss.BatchRun)
}
// SetEnv select which set of patterns to train on: AB or AC
func (ss *Sim) SetEnv(trainRP bool) {
if trainRP {
ss.TrainEnv.Table = etable.NewIdxView(ss.TrainRP)
} else {
ss.TrainEnv.Table = etable.NewIdxView(ss.TrainAB)
}
ss.TrainEnv.Init(ss.BatchRun)
}
func (ss *Sim) ConfigNet(net *leabra.Network) {
net.InitName(net, "Hip_bench")
hp := &ss.Hip
in := net.AddLayer4D("Input", hp.ECSize.Y, hp.ECSize.X, hp.ECPool.Y, hp.ECPool.X, emer.Input)
ecin := net.AddLayer4D("ECin", hp.ECSize.Y, hp.ECSize.X, hp.ECPool.Y, hp.ECPool.X, emer.Hidden)
ecout := net.AddLayer4D("ECout", hp.ECSize.Y, hp.ECSize.X, hp.ECPool.Y, hp.ECPool.X, emer.Target) // clamped in plus phase
ca1 := net.AddLayer4D("CA1", hp.ECSize.Y, hp.ECSize.X, hp.CA1Pool.Y, hp.CA1Pool.X, emer.Hidden)
dg := net.AddLayer2D("DG", hp.DGSize.Y, hp.DGSize.X, emer.Hidden)
ca3 := net.AddLayer2D("CA3", hp.CA3Size.Y, hp.CA3Size.X, emer.Hidden)
ecin.SetClass("EC")
ecout.SetClass("EC")
ecin.SetRelPos(relpos.Rel{Rel: relpos.Above, Other: "Input", YAlign: relpos.Front, XAlign: relpos.Right, Space: 4})
ecout.SetRelPos(relpos.Rel{Rel: relpos.RightOf, Other: "ECin", YAlign: relpos.Front, Space: 2})
dg.SetRelPos(relpos.Rel{Rel: relpos.Above, Other: "ECin", YAlign: relpos.Front, XAlign: relpos.Left, Space: 2})
ca3.SetRelPos(relpos.Rel{Rel: relpos.Above, Other: "DG", YAlign: relpos.Front, XAlign: relpos.Left, Space: 0})
ca1.SetRelPos(relpos.Rel{Rel: relpos.RightOf, Other: "CA3", YAlign: relpos.Front, Space: 2})
onetoone := prjn.NewOneToOne()
pool1to1 := prjn.NewPoolOneToOne()
full := prjn.NewFull()
net.ConnectLayers(in, ecin, onetoone, emer.Forward)
net.ConnectLayers(ecout, ecin, onetoone, emer.Back)
// EC <-> CA1 encoder pathways
pj := net.ConnectLayersPrjn(ecin, ca1, pool1to1, emer.Forward, &hip.EcCa1Prjn{})
pj.SetClass("EcCa1Prjn")
pj = net.ConnectLayersPrjn(ca1, ecout, pool1to1, emer.Forward, &hip.EcCa1Prjn{})
pj.SetClass("EcCa1Prjn")
pj = net.ConnectLayersPrjn(ecout, ca1, pool1to1, emer.Back, &hip.EcCa1Prjn{})
pj.SetClass("EcCa1Prjn")
// Perforant pathway
ppathDG := prjn.NewUnifRnd()
ppathDG.PCon = hp.DGPCon
ppathCA3 := prjn.NewUnifRnd()
ppathCA3.PCon = hp.CA3PCon
pj = net.ConnectLayersPrjn(ecin, dg, ppathDG, emer.Forward, &hip.CHLPrjn{})
pj.SetClass("HippoCHL")
if true { // toggle for bcm vs. ppath, zycyc: must use false for orig_param, true for def_param
pj = net.ConnectLayersPrjn(ecin, ca3, ppathCA3, emer.Forward, &hip.EcCa1Prjn{})
pj.SetClass("PPath")
pj = net.ConnectLayersPrjn(ca3, ca3, full, emer.Lateral, &hip.EcCa1Prjn{})
pj.SetClass("PPath")
} else {
// so far, this is sig worse, even with error-driven MinusQ1 case (which is better than off)
pj = net.ConnectLayersPrjn(ecin, ca3, ppathCA3, emer.Forward, &hip.CHLPrjn{})
pj.SetClass("HippoCHL")
pj = net.ConnectLayersPrjn(ca3, ca3, full, emer.Lateral, &hip.CHLPrjn{})
pj.SetClass("HippoCHL")
}
// always use this for now:
if true {
pj = net.ConnectLayersPrjn(ca3, ca1, full, emer.Forward, &hip.CHLPrjn{})
pj.SetClass("HippoCHL")
} else {
// note: this requires lrate = 1.0 or maybe 1.2, doesn't work *nearly* as well
pj = net.ConnectLayers(ca3, ca1, full, emer.Forward) // default con
// pj.SetClass("HippoCHL")
}
// Mossy fibers
mossy := prjn.NewUnifRnd()
mossy.PCon = hp.MossyPCon
pj = net.ConnectLayersPrjn(dg, ca3, mossy, emer.Forward, &hip.CHLPrjn{}) // no learning
pj.SetClass("HippoCHL")
// using 4 threads total (rest on 0)
dg.(leabra.LeabraLayer).SetThread(1)
ca3.(leabra.LeabraLayer).SetThread(2)
ca1.(leabra.LeabraLayer).SetThread(3) // this has the most
// note: if you wanted to change a layer type from e.g., Target to Compare, do this:
// outLay.SetType(emer.Compare)
// that would mean that the output layer doesn't reflect target values in plus phase
// and thus removes error-driven learning -- but stats are still computed.
net.Defaults()
ss.SetParams("Network", ss.LogSetParams) // only set Network params
err := net.Build()
if err != nil {
log.Println(err)
return
}
net.InitWts()
}
func (ss *Sim) ReConfigNet() {
ss.Update()
ss.ConfigPats()
ss.Net = &leabra.Network{} // start over with new network
ss.ConfigNet(ss.Net)
if ss.NetView != nil {
ss.NetView.SetNet(ss.Net)
ss.NetView.Update() // issue #41 closed
}
}
////////////////////////////////////////////////////////////////////////////////
// Init, utils
// Init restarts the run, and initializes everything, including network weights
// and resets the epoch log table
func (ss *Sim) Init() {
rand.Seed(ss.RndSeed)
ss.SetParams("", ss.LogSetParams) // all sheets
ss.ReConfigNet()
ss.ConfigEnv() // re-config env just in case a different set of patterns was
// selected or patterns have been modified etc
ss.StopNow = false
ss.NewRun()
ss.UpdateView(true)
}
// NewRndSeed gets a new random seed based on current time -- otherwise uses
// the same random seed for every run
func (ss *Sim) NewRndSeed() {
ss.RndSeed = time.Now().UnixNano()
}
// Counters returns a string of the current counter state
// use tabs to achieve a reasonable formatting overall
// and add a few tabs at the end to allow for expansion..
func (ss *Sim) Counters(train bool) string {
if train {
return fmt.Sprintf("Run:\t%d\tEpoch:\t%d\tTrial:\t%d\tCycle:\t%d\tName:\t%v\t\t\t", ss.TrainEnv.Run.Cur, ss.TrainEnv.Epoch.Cur, ss.TrainEnv.Trial.Cur, ss.Time.Cycle, ss.TrainEnv.TrialName.Cur)
} else {
return fmt.Sprintf("Run:\t%d\tEpoch:\t%d\tTrial:\t%d\tCycle:\t%d\tName:\t%v\t\t\t", ss.TrainEnv.Run.Cur, ss.TrainEnv.Epoch.Cur, ss.TestEnv.Trial.Cur, ss.Time.Cycle, ss.TestEnv.TrialName.Cur)
}
}
func (ss *Sim) UpdateView(train bool) {
if ss.NetView != nil && ss.NetView.IsVisible() {
ss.NetView.Record(ss.Counters(train), -1)
// note: essential to use Go version of update when called from another goroutine
ss.NetView.GoUpdate() // note: using counters is significantly slower..
}
}
////////////////////////////////////////////////////////////////////////////////
// Running the Network, starting bottom-up..
// AlphaCyc runs one alpha-cycle (100 msec, 4 quarters) of processing.
// External inputs must have already been applied prior to calling,
// using ApplyExt method on relevant layers (see TrainTrial, TestTrial).
// If train is true, then learning DWt or WtFmDWt calls are made.
// Handles netview updating within scope of AlphaCycle
func (ss *Sim) AlphaCyc(train bool) {
// ss.Win.PollEvents() // this can be used instead of running in a separate goroutine
viewUpdt := ss.TrainUpdt
if !train {
viewUpdt = ss.TestUpdt
}
// update prior weight changes at start, so any DWt values remain visible at end
// you might want to do this less frequently to achieve a mini-batch update
// in which case, move it out to the TrainTrial method where the relevant
// counters are being dealt with.
dg := ss.Net.LayerByName("DG").(leabra.LeabraLayer).AsLeabra()
ca1 := ss.Net.LayerByName("CA1").(leabra.LeabraLayer).AsLeabra()
ca3 := ss.Net.LayerByName("CA3").(leabra.LeabraLayer).AsLeabra()
input := ss.Net.LayerByName("Input").(leabra.LeabraLayer).AsLeabra()
ecin := ss.Net.LayerByName("ECin").(leabra.LeabraLayer).AsLeabra()
ecout := ss.Net.LayerByName("ECout").(leabra.LeabraLayer).AsLeabra()
ca1FmECin := ca1.SendName("ECin").(leabra.LeabraPrjn).AsLeabra()
ca1FmCa3 := ca1.SendName("CA3").(leabra.LeabraPrjn).AsLeabra()
ca3FmDg := ca3.SendName("DG").(leabra.LeabraPrjn).AsLeabra()
_ = ecin
_ = input
// First Quarter: CA1 is driven by ECin, not by CA3 recall
// (which is not really active yet anyway)
ca1FmECin.WtScale.Abs = 1
ca1FmCa3.WtScale.Abs = 0
dgwtscale := ca3FmDg.WtScale.Rel
// train same edl, test separates
if train { // zycyc: assuming same day1 learning, all with EDL ??
ca3FmDg.WtScale.Rel = dgwtscale - ss.Hip.MossyDel
} else { // not important but keep it consistent with RP
if ss.TE.EDL == true {
ca3FmDg.WtScale.Rel = dgwtscale - ss.Hip.MossyDel
} else {
ca3FmDg.WtScale.Rel = dgwtscale - ss.Hip.MossyDelTest
}
}
if train {
ecout.SetType(emer.Target) // clamp a plus phase during testing
} else {
ecout.SetType(emer.Compare) // don't clamp
}
ecout.UpdateExtFlags() // call this after updating type
ss.Net.AlphaCycInit(true)
ss.Time.AlphaCycStart()
for qtr := 0; qtr < 4; qtr++ {
for cyc := 0; cyc < ss.Time.CycPerQtr; cyc++ {
ss.Net.Cycle(&ss.Time)
if !train {
ss.LogTstCyc(ss.TstCycLog, ss.Time.Cycle)
} else if ss.PretrainDone { // zycyc Pat Sim log
var dgCycPat []float32
var ca3CycPat []float32
var ca1CycPat []float32
dg.UnitVals(&dgCycPat, "Act")
ca3.UnitVals(&ca3CycPat, "Act")
ca1.UnitVals(&ca1CycPat, "Act")
ss.dgCycPats[cyc+qtr*25] = dgCycPat
ss.ca3CycPats[cyc+qtr*25] = ca3CycPat
ss.ca1CycPats[cyc+qtr*25] = ca1CycPat
}
ss.Time.CycleInc()
if ss.ViewOn {
switch viewUpdt {
case leabra.Cycle:
if cyc != ss.Time.CycPerQtr-1 { // will be updated by quarter
ss.UpdateView(train)
}
case leabra.FastSpike:
if (cyc+1)%10 == 0 {
ss.UpdateView(train)
}
}
}
}
switch qtr + 1 {
case 1: // Second, Third Quarters: CA1 is driven by CA3 recall
ca1FmECin.WtScale.Abs = 0
ca1FmCa3.WtScale.Abs = 1
if train {
ca3FmDg.WtScale.Rel = dgwtscale
} else {
ca3FmDg.WtScale.Rel = dgwtscale - ss.Hip.MossyDelTest // testing
}
ss.Net.GScaleFmAvgAct() // update computed scaling factors
ss.Net.InitGInc() // scaling params change, so need to recompute all netins
case 3: // Fourth Quarter: CA1 back to ECin drive only
ca1FmECin.WtScale.Abs = 1
ca1FmCa3.WtScale.Abs = 0
ss.Net.GScaleFmAvgAct() // update computed scaling factors
ss.Net.InitGInc() // scaling params change, so need to recompute all netins
if train { // clamp ECout from ECin
ecin.UnitVals(&ss.TmpVals, "Act") // note: could use input instead -- not much diff
ecout.ApplyExt1D32(ss.TmpVals)
}
}
ss.Net.QuarterFinal(&ss.Time)
if qtr+1 == 3 {
ss.MemStats(train) // must come after QuarterFinal
}
ss.Time.QuarterInc()
if ss.ViewOn {
switch {
case viewUpdt <= leabra.Quarter:
ss.UpdateView(train)
case viewUpdt == leabra.Phase:
if qtr >= 2 {
ss.UpdateView(train)
}
}
}
}
ca3FmDg.WtScale.Rel = dgwtscale // restore
ca1FmCa3.WtScale.Abs = 1
if train {
ss.Net.DWt()
if len(os.Args) <= 1 {
ss.NetView.RecordSyns()
}
ss.Net.WtFmDWt() // so testing is based on updated weights
}
if ss.ViewOn && viewUpdt == leabra.AlphaCycle {
ss.UpdateView(train)
}
if !train {
if ss.TstCycPlot != nil {
ss.TstCycPlot.GoUpdate()
} // make sure up-to-date at end
}
}
func (ss *Sim) AlphaCycRestudy(train bool) {
// ss.Win.PollEvents() // this can be used instead of running in a separate goroutine
viewUpdt := ss.TrainUpdt
if !train {
viewUpdt = ss.TestUpdt
}
// update prior weight changes at start, so any DWt values remain visible at end
// you might want to do this less frequently to achieve a mini-batch update
// in which case, move it out to the TrainTrial method where the relevant
// counters are being dealt with.
if train {
ss.Net.WtFmDWt()
}
dg := ss.Net.LayerByName("DG").(leabra.LeabraLayer).AsLeabra()
ca1 := ss.Net.LayerByName("CA1").(leabra.LeabraLayer).AsLeabra()
ca3 := ss.Net.LayerByName("CA3").(leabra.LeabraLayer).AsLeabra()
input := ss.Net.LayerByName("Input").(leabra.LeabraLayer).AsLeabra()
ecin := ss.Net.LayerByName("ECin").(leabra.LeabraLayer).AsLeabra()
ecout := ss.Net.LayerByName("ECout").(leabra.LeabraLayer).AsLeabra()
ca1FmECin := ca1.SendName("ECin").(leabra.LeabraPrjn).AsLeabra()
ca1FmCa3 := ca1.SendName("CA3").(leabra.LeabraPrjn).AsLeabra()
ca3FmDg := ca3.SendName("DG").(leabra.LeabraPrjn).AsLeabra()
_ = ecin
_ = input
// First Quarter: CA1 is driven by ECin, not by CA3 recall
// (which is not really active yet anyway)
ca1FmECin.WtScale.Abs = 1
ca1FmCa3.WtScale.Abs = 0
dgwtscale := ca3FmDg.WtScale.Rel
if ss.TE.EDL == true {
ca3FmDg.WtScale.Rel = dgwtscale - ss.Hip.MossyDel
} else {
ca3FmDg.WtScale.Rel = dgwtscale
}
if train {
ecout.SetType(emer.Target) // clamp a plus phase during testing
} else {
ecout.SetType(emer.Compare) // don't clamp
}
ecout.UpdateExtFlags() // call this after updating type
ss.Net.AlphaCycInit(true)
ss.Time.AlphaCycStart()
for qtr := 0; qtr < 4; qtr++ {
for cyc := 0; cyc < ss.Time.CycPerQtr; cyc++ {
ss.Net.Cycle(&ss.Time)
if !train {
ss.LogTstCyc(ss.TstCycLog, ss.Time.Cycle)
} else if ss.PretrainDone { // zycyc Pat Sim log
var dgCycPat []float32
var ca3CycPat []float32
var ca1CycPat []float32
dg.UnitVals(&dgCycPat, "Act")
ca3.UnitVals(&ca3CycPat, "Act")
ca1.UnitVals(&ca1CycPat, "Act")
ss.dgCycPats[cyc+qtr*25] = dgCycPat
ss.ca3CycPats[cyc+qtr*25] = ca3CycPat
ss.ca1CycPats[cyc+qtr*25] = ca1CycPat
}
ss.Time.CycleInc()
if ss.ViewOn {
switch viewUpdt {
case leabra.Cycle:
if cyc != ss.Time.CycPerQtr-1 { // will be updated by quarter
ss.UpdateView(train)
}
case leabra.FastSpike:
if (cyc+1)%10 == 0 {
ss.UpdateView(train)
}
}
}
}
switch qtr + 1 {
case 1: // Second, Third Quarters: CA1 is driven by CA3 recall
ca1FmECin.WtScale.Abs = 0
ca1FmCa3.WtScale.Abs = 1
if train {
ca3FmDg.WtScale.Rel = dgwtscale
} else {
ca3FmDg.WtScale.Rel = dgwtscale - ss.Hip.MossyDelTest // testing
}
ss.Net.GScaleFmAvgAct() // update computed scaling factors
ss.Net.InitGInc() // scaling params change, so need to recompute all netins
case 3: // Fourth Quarter: CA1 back to ECin drive only
ca1FmECin.WtScale.Abs = 1
ca1FmCa3.WtScale.Abs = 0
ss.Net.GScaleFmAvgAct() // update computed scaling factors
ss.Net.InitGInc() // scaling params change, so need to recompute all netins
if train { // clamp ECout from input
input.UnitVals(&ss.TmpVals, "Act") // note: could use input instead -- not much diff
ecout.ApplyExt1D32(ss.TmpVals)
}
}
ss.Net.QuarterFinal(&ss.Time)
if qtr+1 == 3 {
ss.MemStats(train) // must come after QuarterFinal
}
ss.Time.QuarterInc()
if ss.ViewOn {
switch {
case viewUpdt <= leabra.Quarter:
ss.UpdateView(train)
case viewUpdt == leabra.Phase:
if qtr >= 2 {
ss.UpdateView(train)
}
}
}
}
ca3FmDg.WtScale.Rel = dgwtscale // restore
ca1FmCa3.WtScale.Abs = 1
if train {
ss.Net.DWt()
}
if ss.ViewOn && viewUpdt == leabra.AlphaCycle {
ss.UpdateView(train)
}
if !train {
if ss.TstCycPlot != nil {
ss.TstCycPlot.GoUpdate()
} // make sure up-to-date at end
}
}
func (ss *Sim) AlphaCycRP(train bool) {
// ss.Win.PollEvents() // this can be used instead of running in a separate goroutine
viewUpdt := ss.TrainUpdt