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sir2.go
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sir2.go
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// Copyright (c) 2019, 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.
/*
sir illustrates the dynamic gating of information into PFC active maintenance, by the basal ganglia (BG). It uses a simple Store-Ignore-Recall (SIR) task, where the BG system learns via phasic dopamine signals and trial-and-error exploration, discovering what needs to be stored, ignored, and recalled as a function of reinforcement of correct behavior, and learned reinforcement of useful working memory representations.
*/
package main
import (
"flag"
"fmt"
"log"
"math"
"math/rand"
"os"
"strconv"
"time"
"github.com/emer/emergent/emer"
"github.com/emer/emergent/env"
"github.com/emer/emergent/netview"
"github.com/emer/emergent/params"
"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/simat"
"github.com/emer/etable/split"
"github.com/dhairyyas/leabra-sleep/deep"
"github.com/dhairyyas/leabra-sleep/leabra"
"github.com/dhairyyas/leabra-sleep/pbwm"
"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
// ParamSets is the default set of parameters -- Base is always applied, and others can be optionally
// selected to apply on top of that
var ParamSets = params.Sets{
{Name: "Base", Desc: "these are the best params", Sheets: params.Sheets{
"Network": ¶ms.Sheet{
{Sel: "Prjn", Desc: "no extra learning factors",
Params: params.Params{
"Prjn.Learn.Lrate": "0.02", // slower overall is key
"Prjn.Learn.Norm.On": "false",
"Prjn.Learn.Momentum.On": "false",
"Prjn.Learn.WtBal.On": "false",
}},
{Sel: "Layer", Desc: "no special params",
Params: params.Params{}},
{Sel: ".Back", Desc: "top-down back-projections MUST have lower relative weight scale, otherwise network hallucinates",
Params: params.Params{
"Prjn.WtScale.Rel": "0.2",
}},
{Sel: ".BgFixed", Desc: "BG Matrix -> GP wiring",
Params: params.Params{
"Prjn.Learn.Learn": "false",
"Prjn.WtInit.Mean": "0.8",
"Prjn.WtInit.Var": "0",
"Prjn.WtInit.Sym": "false",
}},
{Sel: "RWPrjn", Desc: "Reward prediction -- into PVi",
Params: params.Params{
"Prjn.Learn.Lrate": "0.02",
"Prjn.WtInit.Mean": "0",
"Prjn.WtInit.Var": "0",
"Prjn.WtInit.Sym": "false",
}},
{Sel: "#RWPredToSNc", Desc: "Fixed strong",
Params: params.Params{
"Prjn.Learn.Learn": "false",
"Prjn.WtInit.Mean": "1",
"Prjn.WtInit.Var": "0",
"Prjn.WtInit.Sym": "false",
}},
{Sel: "#Rew", Desc: "Reward layer -- no clamp limits",
Params: params.Params{
"Layer.Act.Clamp.Range.Min": "-1",
"Layer.Act.Clamp.Range.Max": "1",
}},
{Sel: ".PFCToDeep", Desc: "PFC -> Deep consistent wt",
Params: params.Params{
"Prjn.WtInit.Mean": "0.8",
"Prjn.WtInit.Var": "0",
"Prjn.WtInit.Sym": "false",
}},
{Sel: ".PFCFmDeep", Desc: "PFC Deep -> PFC fixed",
Params: params.Params{
"Prjn.Learn.Learn": "false",
"Prjn.WtInit.Mean": "0.8",
"Prjn.WtInit.Var": "0",
"Prjn.WtInit.Sym": "false",
}},
{Sel: ".PFCMntDToOut", Desc: "PFC MntD -> PFC Out fixed",
Params: params.Params{
"Prjn.Learn.Learn": "false",
"Prjn.WtInit.Mean": "0.8",
"Prjn.WtInit.Var": "0",
"Prjn.WtInit.Sym": "false",
}},
{Sel: ".FmPFCOutD", Desc: "PFC OutD needs to be strong b/c avg act says weak",
Params: params.Params{
"Prjn.WtScale.Abs": "4",
}},
{Sel: ".PFCFixed", Desc: "Input -> PFC",
Params: params.Params{
"Prjn.Learn.Learn": "false",
"Prjn.WtInit.Mean": "0.8",
"Prjn.WtInit.Var": "0",
"Prjn.WtInit.Sym": "false",
}},
{Sel: ".MatrixPrjn", Desc: "Matrix learning",
Params: params.Params{
"Prjn.Learn.Lrate": "0.04",
"Prjn.WtInit.Var": "0.1",
"Prjn.Trace.GateNoGoPosLR": "1.0", // .1 default -- 1.0 seems potentially even better
"Prjn.Trace.NotGatedLR": "0.7", // 0.7 default
"Prjn.Trace.Decay": "1.0", // 1 default
}},
{Sel: "MatrixLayer", Desc: "exploring these options",
Params: params.Params{
"Layer.Act.XX1.Gain": "100",
"Layer.Inhib.Layer.Gi": "1.9",
"Layer.Inhib.Layer.FB": "0.5",
"Layer.Inhib.Pool.On": "true",
"Layer.Inhib.Pool.Gi": "2.1", // def 1.9
"Layer.Inhib.Pool.FB": "0",
"Layer.Inhib.Self.On": "true",
"Layer.Inhib.Self.Gi": "0.4", // def 0.3
"Layer.Inhib.ActAvg.Init": "0.05",
"Layer.Inhib.ActAvg.Fixed": "true",
}},
{Sel: "#GPiThal", Desc: "defaults also set automatically by layer but included here just to be sure",
Params: params.Params{
"Layer.Inhib.Layer.Gi": "1.8",
"Layer.Inhib.Layer.FB": "0.5",
"Layer.Inhib.Pool.On": "false",
"Layer.Inhib.ActAvg.Init": ".2",
"Layer.Inhib.ActAvg.Fixed": "true",
"Layer.Act.Dt.GTau": "3",
"Layer.Gate.NoGo": "1",
"Layer.Gate.Thr": "0.2",
}},
{Sel: "#GPeNoGo", Desc: "GPe is a regular layer -- needs special params",
Params: params.Params{
"Layer.Inhib.Layer.Gi": "2.2",
"Layer.Inhib.Layer.FB": "0.5",
"Layer.Inhib.Layer.FBTau": "3", // otherwise a bit jumpy
"Layer.Inhib.Pool.On": "false",
"Layer.Inhib.ActAvg.Init": ".2",
"Layer.Inhib.ActAvg.Fixed": "true",
}},
{Sel: ".PFC", Desc: "pfc defaults",
Params: params.Params{
"Layer.Inhib.Layer.On": "false",
"Layer.Inhib.Pool.On": "true",
"Layer.Inhib.Pool.Gi": "1.8",
"Layer.Inhib.Pool.FB": "1",
"Layer.Inhib.ActAvg.Init": "0.2",
"Layer.Inhib.ActAvg.Fixed": "true",
}},
{Sel: "#Input", Desc: "Basic params",
Params: params.Params{
"Layer.Inhib.ActAvg.Init": "0.25",
"Layer.Inhib.ActAvg.Fixed": "true",
}},
{Sel: "#Output", Desc: "Basic params",
Params: params.Params{
"Layer.Inhib.Layer.Gi": "2",
"Layer.Inhib.Layer.FB": "0.5",
"Layer.Inhib.ActAvg.Init": "0.25",
"Layer.Inhib.ActAvg.Fixed": "true",
}},
{Sel: "#InputToOutput", Desc: "weaker",
Params: params.Params{
"Prjn.WtScale.Rel": "0.5",
}},
{Sel: "#Hidden", Desc: "Basic params",
Params: params.Params{
"Layer.Inhib.Layer.Gi": "2",
"Layer.Inhib.Layer.FB": "0.5",
}},
{Sel: "#SNc", Desc: "allow negative",
Params: params.Params{
"Layer.Act.Clamp.Range.Min": "-1",
"Layer.Act.Clamp.Range.Max": "1",
}},
},
}},
}
// 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 {
BurstDaGain float32 `desc:"strength of dopamine bursts: 1 default -- reduce for PD OFF, increase for PD ON"`
DipDaGain float32 `desc:"strength of dopamine dips: 1 default -- reduce to siulate D2 agonists"`
Net *pbwm.Network `view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"`
TrnEpcLog *etable.Table `view:"no-inline" desc:"training epoch-level log data"`
TstEpcLog *etable.Table `view:"no-inline" desc:"testing epoch-level log data"`
TstTrlLog *etable.Table `view:"no-inline" desc:"testing trial-level log data"`
RunLog *etable.Table `view:"no-inline" desc:"summary log of each run"`
RunStats *etable.Table `view:"no-inline" desc:"aggregate stats on all runs"`
SimMat *simat.SimMat `view:"no-inline" desc:"similarity matrix"`
Params params.Sets `view:"no-inline" desc:"full collection of param sets"`
ParamSet string `desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set"`
Tag string `desc:"extra tag string to add to any file names output from sim (e.g., weights files, log files, params for run)"`
MaxRuns int `desc:"maximum number of model runs to perform"`
MaxEpcs int `desc:"maximum number of epochs to run per model run"`
MaxTrls int `desc:"maximum number of training trials per epoch"`
NZeroStop int `desc:"if a positive number, training will stop after this many epochs with zero SSE"`
TrainEnv SIREnv `desc:"Training environment -- SIR environment"`
TestEnv SIREnv `desc:"Testing nvironment -- SIR environment"`
Time leabra.Time `desc:"leabra timing parameters and state"`
ViewOn bool `desc:"whether to update the network view while running"`
TrainUpdt leabra.TimeScales `desc:"at what time scale to update the display during training? 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"`
TstRecLays []string `desc:"names of layers to record activations etc of during testing"`
TrlDA float64 `inactive:"+" desc:"dopamine level on this trial"`
TrlAbsDA float64 `inactive:"+" desc:"absolute value of dopamine on this trial"`
TrlRewPred float64 `inactive:"+" desc:"reward prediction level on this trial"`
TrlErr float64 `inactive:"+" desc:"1 if trial was error, 0 if correct -- based on SSE = 0 (subject to .5 unit-wise tolerance)"`
TrlSSE float64 `inactive:"+" desc:"current trial's sum squared error"`
TrlAvgSSE float64 `inactive:"+" desc:"current trial's average sum squared error"`
TrlCosDiff float64 `inactive:"+" desc:"current trial's cosine difference"`
EpcDA float64 `inactive:"+" desc:"last epoch's average dopamine"`
EpcAbsDA float64 `inactive:"+" desc:"last epoch's avg abs dopamine"`
EpcRewPred float64 `inactive:"+" desc:"last epoch's avg rew pred dopamine"`
EpcSSE float64 `inactive:"+" desc:"last epoch's total sum squared error"`
EpcAvgSSE float64 `inactive:"+" desc:"last epoch's average sum squared error (average over trials, and over units within layer)"`
EpcPctErr float64 `inactive:"+" desc:"last epoch's average TrlErr"`
EpcPctCor float64 `inactive:"+" desc:"1 - last epoch's average TrlErr"`
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)"`
EpcPerTrlMSec float64 `inactive:"+" desc:"how long did the epoch take per trial in wall-clock milliseconds"`
FirstZero int `inactive:"+" desc:"epoch at when SSE first went to zero"`
NZero int `inactive:"+" desc:"number of epochs in a row with zero SSE"`
// internal state - view:"-"
SumDA float64 `view:"-" inactive:"+" desc:"sum to increment as we go through epoch"`
SumAbsDA float64 `view:"-" inactive:"+" desc:"sum to increment as we go through epoch"`
SumRewPred float64 `view:"-" inactive:"+" desc:"sum to increment as we go through epoch"`
SumErr float64 `view:"-" inactive:"+" desc:"sum to increment as we go through epoch"`
SumSSE float64 `view:"-" inactive:"+" desc:"sum to increment as we go through epoch"`
SumAvgSSE float64 `view:"-" inactive:"+" desc:"sum to increment as we go through epoch"`
SumCosDiff float64 `view:"-" inactive:"+" desc:"sum to increment as we go through epoch"`
Win *gi.Window `view:"-" desc:"main GUI window"`
NetView *netview.NetView `view:"-" desc:"the network viewer"`
ToolBar *gi.ToolBar `view:"-" desc:"the master toolbar"`
WtsGrid *etview.TensorGrid `view:"-" desc:"the weights grid view"`
TrnEpcPlot *eplot.Plot2D `view:"-" desc:"the training epoch plot"`
TstEpcPlot *eplot.Plot2D `view:"-" desc:"the testing epoch plot"`
TstTrlPlot *eplot.Plot2D `view:"-" desc:"the test-trial plot"`
RunPlot *eplot.Plot2D `view:"-" desc:"the run plot"`
TrnEpcFile *os.File `view:"-" desc:"log file"`
RunFile *os.File `view:"-" desc:"log file"`
ValsTsrs map[string]*etensor.Float32 `view:"-" desc:"for holding layer values"`
SaveWts bool `view:"-" desc:"for command-line run only, auto-save final weights after each run"`
NoGui bool `view:"-" desc:"if true, runing in no GUI mode"`
LogSetParams bool `view:"-" desc:"if true, print message for all params that are set"`
IsRunning bool `view:"-" desc:"true if sim is running"`
StopNow bool `view:"-" desc:"flag to stop running"`
NeedsNewRun bool `view:"-" desc:"flag to initialize NewRun if last one finished"`
RndSeed int64 `view:"-" desc:"the current random seed"`
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 = &pbwm.Network{}
ss.TrnEpcLog = &etable.Table{}
ss.TstEpcLog = &etable.Table{}
ss.TstTrlLog = &etable.Table{}
ss.RunLog = &etable.Table{}
ss.RunStats = &etable.Table{}
ss.SimMat = &simat.SimMat{}
ss.Params = ParamSets
ss.RndSeed = 1
ss.ViewOn = true
ss.TrainUpdt = leabra.AlphaCycle //leabra.AlphaCycle
ss.TestUpdt = leabra.AlphaCycle
ss.TstRecLays = []string{"Input", "Output", "GPiThal", "PFCmntD", "PFCoutD"}
ss.Defaults()
}
func (ss *Sim) Defaults() {
ss.BurstDaGain = 1
ss.DipDaGain = 1
}
////////////////////////////////////////////////////////////////////////////////////////////
// Configs
// Config configures all the elements using the standard functions
func (ss *Sim) Config() {
ss.ConfigEnv()
ss.ConfigNet(ss.Net)
ss.ConfigTrnEpcLog(ss.TrnEpcLog)
ss.ConfigTstEpcLog(ss.TstEpcLog)
ss.ConfigTstTrlLog(ss.TstTrlLog)
ss.ConfigRunLog(ss.RunLog)
}
func (ss *Sim) ConfigEnv() {
if ss.MaxRuns == 0 { // allow user override
ss.MaxRuns = 10
}
if ss.MaxEpcs == 0 { // allow user override
ss.MaxEpcs = 100
ss.NZeroStop = 5
}
if ss.MaxTrls == 0 { // allow user override
ss.MaxTrls = 100
}
ss.TrainEnv.Nm = "TrainEnv"
ss.TrainEnv.Dsc = "training params and state"
ss.TrainEnv.SetNStim(4)
ss.TrainEnv.RewVal = 1
ss.TrainEnv.NoRewVal = 0
ss.TrainEnv.Validate()
ss.TrainEnv.Run.Max = ss.MaxRuns // note: we are not setting epoch max -- do that manually
ss.TrainEnv.Trial.Max = ss.MaxTrls
ss.TestEnv.Nm = "TestEnv"
ss.TestEnv.Dsc = "testing params and state"
ss.TestEnv.SetNStim(4)
ss.TestEnv.RewVal = 1
ss.TestEnv.NoRewVal = 0
ss.TestEnv.Validate()
ss.TestEnv.Run.Max = ss.MaxRuns // note: we are not setting epoch max -- do that manually
ss.TestEnv.Trial.Max = 20 // good amount for testing
ss.TrainEnv.Init(0)
ss.TestEnv.Init(0)
}
func (ss *Sim) ConfigNet(net *pbwm.Network) {
net.InitName(net, "SIR")
rew, rp, da := net.AddRWLayers("", relpos.Behind, 2)
snc := da.(*pbwm.RWDaLayer)
snc.SetName("SNc")
inp := net.AddLayer2D("Input", 1, 7, emer.Input)
ctrl := net.AddLayer2D("CtrlInput", 1, 3, emer.Input)
out := net.AddLayer2D("Output", 1, 4, emer.Target)
hid := net.AddLayer2D("Hidden", 7, 7, emer.Hidden)
inp.SetRelPos(relpos.Rel{Rel: relpos.Above, Other: rew.Name(), YAlign: relpos.Front, XAlign: relpos.Left})
out.SetRelPos(relpos.Rel{Rel: relpos.RightOf, Other: "Input", YAlign: relpos.Front, Space: 1})
ctrl.SetRelPos(relpos.Rel{Rel: relpos.Behind, Other: "Input", XAlign: relpos.Left, Space: 2})
hid.SetRelPos(relpos.Rel{Rel: relpos.Behind, Other: "CtrlInput", XAlign: relpos.Left, Space: 2})
// args: nY, nMaint, nOut, nNeurBgY, nNeurBgX, nNeurPfcY, nNeurPfcX
mtxGo, mtxNoGo, gpe, gpi, pfcMnt, pfcMntD, pfcOut, pfcOutD := net.AddPBWM("", 2, 2, 2, 1, 3, 1, 7)
_ = gpe
_ = gpi
_ = pfcMnt
_ = pfcMntD
_ = pfcOut
mtxGo.SetRelPos(relpos.Rel{Rel: relpos.RightOf, Other: "Rew", YAlign: relpos.Front, Space: 14})
full := prjn.NewFull()
fmin := prjn.NewRect()
fmin.Size.Set(1, 1)
fmin.Scale.Set(1, 1)
fmin.Wrap = true
net.ConnectLayersPrjn(inp, rp, full, emer.Forward, &pbwm.RWPrjn{})
net.ConnectLayersPrjn(pfcMntD, rp, full, emer.Forward, &pbwm.RWPrjn{})
pj := net.ConnectLayersPrjn(ctrl, mtxGo, fmin, emer.Forward, &pbwm.MatrixTracePrjn{})
pj.SetClass("MatrixPrjn")
pj = net.ConnectLayersPrjn(ctrl, mtxNoGo, fmin, emer.Forward, &pbwm.MatrixTracePrjn{})
pj.SetClass("MatrixPrjn")
pj = net.ConnectLayers(inp, pfcMnt, fmin, emer.Forward)
pj.SetClass("PFCFixed")
net.ConnectLayers(inp, hid, full, emer.Forward)
net.BidirConnectLayers(hid, out, full)
pj = net.ConnectLayers(pfcOutD, hid, full, emer.Forward)
pj.SetClass("FmPFCOutD")
pj = net.ConnectLayers(pfcOutD, out, full, emer.Forward)
pj.SetClass("FmPFCOutD")
net.ConnectLayers(inp, out, full, emer.Forward)
snc.SendToAllBut(nil) // send dopamine to all layers..
net.Defaults()
ss.SetParams("Network", false) // only set Network params
err := net.Build()
if err != nil {
log.Println(err)
return
}
net.InitWts()
}
////////////////////////////////////////////////////////////////////////////////
// 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.StopNow = false
ss.SetParams("", false) // all sheets
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.String())
} 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.String())
}
}
func (ss *Sim) UpdateView(train bool) {
if ss.NetView != nil && ss.NetView.IsVisible() {
ss.NetView.Record(ss.Counters(train))
// 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.
if train {
ss.Net.WtFmDWt()
}
ss.Net.AlphaCycInit()
ss.Time.AlphaCycStart()
for qtr := 0; qtr < 4; qtr++ {
for cyc := 0; cyc < ss.Time.CycPerQtr; cyc++ {
ss.Net.Cycle(&ss.Time)
ss.Time.CycleInc()
if ss.ViewOn {
switch viewUpdt {
case leabra.Cycle:
ss.UpdateView(train)
case leabra.FastSpike:
if (cyc+1)%10 == 0 {
ss.UpdateView(train)
}
}
}
}
ss.Net.QuarterFinal(&ss.Time)
ss.Time.QuarterInc()
if ss.ViewOn {
switch {
case viewUpdt <= leabra.Quarter:
ss.UpdateView(train)
case viewUpdt == leabra.Phase:
if qtr >= 2 {
ss.UpdateView(train)
}
}
}
if qtr == 2 {
ss.ApplyReward(train)
}
}
if train {
ss.Net.DWt()
}
if ss.ViewOn && viewUpdt == leabra.AlphaCycle {
ss.UpdateView(train)
}
}
// ApplyInputs applies input patterns from given envirbonment.
// It is good practice to have this be a separate method with appropriate
// args so that it can be used for various different contexts
// (training, testing, etc).
func (ss *Sim) ApplyInputs(en env.Env) {
ss.Net.InitExt() // clear any existing inputs -- not strictly necessary if always
// going to the same layers, but good practice and cheap anyway
lays := []string{"Input", "Output"}
for _, lnm := range lays {
ly := ss.Net.LayerByName(lnm).(deep.DeepLayer).AsDeep()
pats := en.State(ly.Nm)
if pats == nil {
continue
}
ly.ApplyExt(pats)
if lnm == "Input" {
ly = ss.Net.LayerByName("CtrlInput").(deep.DeepLayer).AsDeep()
ly.ApplyExt(pats)
}
}
}
// ApplyReward computes reward based on network output and applies it -- call
// at start of 3rd quarter (plus phase)
func (ss *Sim) ApplyReward(train bool) {
var en *SIREnv
if train {
en = &ss.TrainEnv
} else {
en = &ss.TestEnv
}
if en.Act != Recall { // only reward on recall trials!
return
}
out := ss.Net.LayerByName("Output").(deep.DeepLayer).AsDeep()
mxi := out.Pools[0].Inhib.Act.MaxIdx
en.SetReward(mxi)
pats := en.State("Reward")
ly := ss.Net.LayerByName("Rew").(deep.DeepLayer).AsDeep()
ly.ApplyExt1DTsr(pats)
}
// TrainTrial runs one trial of training using TrainEnv
func (ss *Sim) TrainTrial() {
if ss.NeedsNewRun {
ss.NewRun()
}
ss.TrainEnv.Step() // the Env encapsulates and manages all counter state
// Key to query counters FIRST because current state is in NEXT epoch
// if epoch counter has changed
epc, _, chg := ss.TrainEnv.Counter(env.Epoch)
if chg {
if ss.ViewOn && ss.TrainUpdt > leabra.AlphaCycle {
ss.UpdateView(true)
}
ss.LogTrnEpc(ss.TrnEpcLog)
if ss.ViewOn && ss.TrainUpdt > leabra.AlphaCycle {
ss.UpdateView(true)
}
if epc >= ss.MaxEpcs || (ss.NZeroStop > 0 && ss.NZero >= ss.NZeroStop) {
// done with training..
ss.RunEnd()
if ss.TrainEnv.Run.Incr() { // we are done!
ss.StopNow = true
return
} else {
ss.NeedsNewRun = true
return
}
}
}
ss.ApplyInputs(&ss.TrainEnv)
ss.AlphaCyc(true) // train
ss.TrialStats(true) // accumulate
}
// RunEnd is called at the end of a run -- save weights, record final log, etc here
func (ss *Sim) RunEnd() {
ss.LogRun(ss.RunLog)
}
// NewRun intializes a new run of the model, using the TrainEnv.Run counter
// for the new run value
func (ss *Sim) NewRun() {
run := ss.TrainEnv.Run.Cur
ss.TrainEnv.Init(run)
ss.Time.Reset()
ss.Net.InitWts()
ss.InitStats()
ss.TrnEpcLog.SetNumRows(0)
ss.TstEpcLog.SetNumRows(0)
ss.NeedsNewRun = false
}
// InitStats initializes all the statistics, especially important for the
// cumulative epoch stats -- called at start of new run
func (ss *Sim) InitStats() {
// accumulators
ss.SumDA = 0
ss.SumAbsDA = 0
ss.SumRewPred = 0
ss.SumSSE = 0
ss.SumAvgSSE = 0
ss.SumCosDiff = 0
ss.SumErr = 0
ss.FirstZero = -1
ss.NZero = 0
// clear rest just to make Sim look initialized
ss.TrlDA = 0
ss.TrlAbsDA = 0
ss.TrlRewPred = 0
ss.TrlErr = 0
ss.TrlSSE = 0
ss.TrlAvgSSE = 0
ss.EpcDA = 0
ss.EpcAbsDA = 0
ss.EpcRewPred = 0
ss.EpcSSE = 0
ss.EpcAvgSSE = 0
ss.EpcPctErr = 0
ss.EpcCosDiff = 0
}
// TrialStats computes the trial-level statistics and adds them to the epoch accumulators if
// accum is true. Note that we're accumulating stats here on the Sim side so the
// core algorithm side remains as simple as possible, and doesn't need to worry about
// different time-scales over which stats could be accumulated etc.
// You can also aggregate directly from log data, as is done for testing stats
func (ss *Sim) TrialStats(accum bool) (sse, avgsse, cosdiff float64) {
out := ss.Net.LayerByName("Output").(leabra.LeabraLayer).AsLeabra()
snc := ss.Net.LayerByName("SNc").(leabra.LeabraLayer).AsLeabra()
rp := ss.Net.LayerByName("RWPred").(leabra.LeabraLayer).AsLeabra()
ss.TrlDA = float64(snc.Neurons[0].Act)
ss.TrlAbsDA = math.Abs(ss.TrlDA)
ss.TrlRewPred = float64(rp.Neurons[0].Act)
ss.TrlCosDiff = float64(out.CosDiff.Cos)
ss.TrlSSE, ss.TrlAvgSSE = out.MSE(0.5) // 0.5 = per-unit tolerance -- right side of .5
if ss.TrlSSE > 0 {
ss.TrlErr = 1
} else {
ss.TrlErr = 0
}
if accum {
ss.SumDA += ss.TrlDA
ss.SumAbsDA += ss.TrlAbsDA
ss.SumRewPred += ss.TrlRewPred
ss.SumErr += ss.TrlErr
ss.SumSSE += ss.TrlSSE
ss.SumAvgSSE += ss.TrlAvgSSE
ss.SumCosDiff += ss.TrlCosDiff
}
return
}
// TrainEpoch runs training trials for remainder of this epoch
func (ss *Sim) TrainEpoch() {
ss.StopNow = false
curEpc := ss.TrainEnv.Epoch.Cur
for {
ss.TrainTrial()
if ss.StopNow || ss.TrainEnv.Epoch.Cur != curEpc {
break
}
}
ss.Stopped()
}
// TrainRun runs training trials for remainder of run
func (ss *Sim) TrainRun() {
ss.StopNow = false
curRun := ss.TrainEnv.Run.Cur
for {
ss.TrainTrial()
if ss.StopNow || ss.TrainEnv.Run.Cur != curRun {
break
}
}
ss.Stopped()
}
// Train runs the full training from this point onward
func (ss *Sim) Train() {
ss.StopNow = false
for {
ss.TrainTrial()
if ss.StopNow {
break
}
}
ss.Stopped()
}
// Stop tells the sim to stop running
func (ss *Sim) Stop() {
ss.StopNow = true
}
// Stopped is called when a run method stops running -- updates the IsRunning flag and toolbar
func (ss *Sim) Stopped() {
ss.IsRunning = false
if ss.Win != nil {
vp := ss.Win.WinViewport2D()
vp.BlockUpdates()
if ss.ToolBar != nil {
ss.ToolBar.UpdateActions()
}
vp.UnblockUpdates()
vp.SetNeedsFullRender()
}
}
// SaveWeights saves the network weights -- when called with giv.CallMethod
// it will auto-prompt for filename
func (ss *Sim) SaveWeights(filename gi.FileName) {
ss.Net.SaveWtsJSON(filename)
}
////////////////////////////////////////////////////////////////////////////////////////////
// Testing
// TestTrial runs one trial of testing -- always sequentially presented inputs
func (ss *Sim) TestTrial(returnOnChg bool) {
ss.TestEnv.Step()
// Query counters FIRST
_, _, chg := ss.TestEnv.Counter(env.Epoch)
if chg {
if ss.ViewOn && ss.TestUpdt > leabra.AlphaCycle {
ss.UpdateView(false)
}
ss.LogTstEpc(ss.TstEpcLog)
if returnOnChg {
return
}
}
ss.ApplyInputs(&ss.TestEnv)
ss.AlphaCyc(false) // !train
ss.TrialStats(false) // !accumulate
ss.LogTstTrl(ss.TstTrlLog)
}
// TestAll runs through the full set of testing items
func (ss *Sim) TestAll() {
ss.TestEnv.Init(ss.TestEnv.Run.Cur)
for {
ss.TestTrial(true) // return on chg, don't present
_, _, chg := ss.TestEnv.Counter(env.Epoch)
if chg || ss.StopNow {
break
}
}
}
// RunTestAll runs through the full set of testing items, has stop running = false at end -- for gui
func (ss *Sim) RunTestAll() {
ss.StopNow = false
ss.TestAll()
ss.Stopped()
}
/////////////////////////////////////////////////////////////////////////
// Params setting
// ParamsName returns name of current set of parameters
func (ss *Sim) ParamsName() string {
if ss.ParamSet == "" {
return "Base"
}
return ss.ParamSet
}
// SetParams sets the params for "Base" and then current ParamSet.
// If sheet is empty, then it applies all avail sheets (e.g., Network, Sim)
// otherwise just the named sheet
// if setMsg = true then we output a message for each param that was set.
func (ss *Sim) SetParams(sheet string, setMsg bool) error {
if sheet == "" {
// this is important for catching typos and ensuring that all sheets can be used
ss.Params.ValidateSheets([]string{"Network", "Sim"})
}
err := ss.SetParamsSet("Base", sheet, setMsg)
// gpi := ss.Net.LayerByName("GPiThal").(*pbwm.GPiThalLayer)
// gpi.Gate.Thr = 0.5 // todo: these are not taking in params
// gpi.Gate.NoGo = 0.4
//
matg := ss.Net.LayerByName("MatrixGo").(*pbwm.MatrixLayer)
matn := ss.Net.LayerByName("MatrixNoGo").(*pbwm.MatrixLayer)
matg.Matrix.BurstGain = ss.BurstDaGain
matg.Matrix.DipGain = ss.DipDaGain
matn.Matrix.BurstGain = ss.BurstDaGain
matn.Matrix.DipGain = ss.DipDaGain
return err
}
// SetParamsSet sets the params for given params.Set name.
// If sheet is empty, then it applies all avail sheets (e.g., Network, Sim)
// otherwise just the named sheet
// if setMsg = true then we output a message for each param that was set.
func (ss *Sim) SetParamsSet(setNm string, sheet string, setMsg bool) error {
pset, err := ss.Params.SetByNameTry(setNm)
if err != nil {
return err
}
if sheet == "" || sheet == "Network" {
netp, ok := pset.Sheets["Network"]
if ok {
ss.Net.ApplyParams(netp, setMsg)
}
}
if sheet == "" || sheet == "Sim" {
simp, ok := pset.Sheets["Sim"]
if ok {
simp.Apply(ss, setMsg)
}
}
// note: if you have more complex environments with parameters, definitely add
// sheets for them, e.g., "TrainEnv", "TrainEnv" etc
return err
}
////////////////////////////////////////////////////////////////////////////////////////////
// Logging
// ValsTsr gets value tensor of given name, creating if not yet made
func (ss *Sim) ValsTsr(name string) *etensor.Float32 {
if ss.ValsTsrs == nil {
ss.ValsTsrs = make(map[string]*etensor.Float32)
}
tsr, ok := ss.ValsTsrs[name]
if !ok {
tsr = &etensor.Float32{}
ss.ValsTsrs[name] = tsr
}
return tsr
}
// RunName returns a name for this run that combines Tag and Params -- add this to
// any file names that are saved.
func (ss *Sim) RunName() string {
if ss.Tag != "" {
return ss.Tag + "_" + ss.ParamsName()
} else {
return ss.ParamsName()
}
}
// RunEpochName returns a string with the run and epoch numbers with leading zeros, suitable
// for using in weights file names. Uses 3, 5 digits for each.
func (ss *Sim) RunEpochName(run, epc int) string {
return fmt.Sprintf("%03d_%05d", run, epc)
}
// WeightsFileName returns default current weights file name
func (ss *Sim) WeightsFileName() string {
return ss.Net.Nm + "_" + ss.RunName() + "_" + ss.RunEpochName(ss.TrainEnv.Run.Cur, ss.TrainEnv.Epoch.Cur) + ".wts.gz"
}
// LogFileName returns default log file name
func (ss *Sim) LogFileName(lognm string) string {
return ss.Net.Nm + "_" + ss.RunName() + "_" + lognm + ".csv"
}
//////////////////////////////////////////////
// TrnEpcLog
// LogTrnEpc adds data from current epoch to the TrnEpcLog table.
// computes epoch averages prior to logging.
func (ss *Sim) LogTrnEpc(dt *etable.Table) {
row := dt.Rows
dt.SetNumRows(row + 1)
epc := ss.TrainEnv.Epoch.Prv // this is triggered by increment so use previous value
nt := float64(ss.TrainEnv.Trial.Max) // number of trials in view
ss.EpcDA = ss.SumDA / nt
ss.SumDA = 0
ss.EpcAbsDA = ss.SumAbsDA / nt
ss.SumAbsDA = 0
ss.EpcRewPred = ss.SumRewPred / nt
ss.SumRewPred = 0
ss.EpcSSE = ss.SumSSE / nt
ss.SumSSE = 0
ss.EpcAvgSSE = ss.SumAvgSSE / nt
ss.SumAvgSSE = 0
ss.EpcPctErr = float64(ss.SumErr) / nt
ss.SumErr = 0
ss.EpcPctCor = 1 - ss.EpcPctErr
ss.EpcCosDiff = ss.SumCosDiff / nt
ss.SumCosDiff = 0
if ss.FirstZero < 0 && ss.EpcPctErr == 0 {
ss.FirstZero = epc
}
if ss.EpcPctErr == 0 {
ss.NZero++
} else {
ss.NZero = 0
}
if ss.LastEpcTime.IsZero() {
ss.EpcPerTrlMSec = 0
} else {
iv := time.Now().Sub(ss.LastEpcTime)
ss.EpcPerTrlMSec = float64(iv) / (nt * float64(time.Millisecond))
}
ss.LastEpcTime = time.Now()
dt.SetCellFloat("Run", row, float64(ss.TrainEnv.Run.Cur))
dt.SetCellFloat("Epoch", row, float64(epc))
dt.SetCellFloat("SSE", row, ss.EpcSSE)
dt.SetCellFloat("AvgSSE", row, ss.EpcAvgSSE)
dt.SetCellFloat("PctErr", row, ss.EpcPctErr)
dt.SetCellFloat("PctCor", row, ss.EpcPctCor)
dt.SetCellFloat("CosDiff", row, ss.EpcCosDiff)
dt.SetCellFloat("DA", row, ss.EpcDA)
dt.SetCellFloat("AbsDA", row, ss.EpcAbsDA)
dt.SetCellFloat("RewPred", row, ss.EpcRewPred)
dt.SetCellFloat("PerTrlMSec", row, ss.EpcPerTrlMSec)
// note: essential to use Go version of update when called from another goroutine
ss.TrnEpcPlot.GoUpdate()
if ss.TrnEpcFile != nil {
if ss.TrainEnv.Run.Cur == 0 && epc == 0 {
dt.WriteCSVHeaders(ss.TrnEpcFile, etable.Tab)
}
dt.WriteCSVRow(ss.TrnEpcFile, row, etable.Tab)
}
}
func (ss *Sim) ConfigTrnEpcLog(dt *etable.Table) {
dt.SetMetaData("name", "TrnEpcLog")
dt.SetMetaData("desc", "Record of performance over epochs of training")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", strconv.Itoa(LogPrec))
sch := etable.Schema{
{"Run", etensor.INT64, nil, nil},
{"Epoch", etensor.INT64, nil, nil},
{"SSE", etensor.FLOAT64, nil, nil},
{"AvgSSE", etensor.FLOAT64, nil, nil},
{"PctErr", etensor.FLOAT64, nil, nil},
{"PctCor", etensor.FLOAT64, nil, nil},
{"CosDiff", etensor.FLOAT64, nil, nil},
{"DA", etensor.FLOAT64, nil, nil},
{"AbsDA", etensor.FLOAT64, nil, nil},
{"RewPred", etensor.FLOAT64, nil, nil},
{"PerTrlMSec", etensor.FLOAT64, nil, nil},
}
dt.SetFromSchema(sch, 0)
}
func (ss *Sim) ConfigTrnEpcPlot(plt *eplot.Plot2D, dt *etable.Table) *eplot.Plot2D {
plt.Params.Title = "SIR Epoch Plot"
plt.Params.XAxisCol = "Epoch"