/
hebberr_combo.go
1415 lines (1244 loc) · 48 KB
/
hebberr_combo.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.
/*
hebberr_combo shows how XCal hebbian learning in shallower layers of a network can aid an error driven learning network to generalize to unseen combinations of patterns.
*/
package main
import (
"bytes"
"fmt"
"log"
"math/rand"
"os"
"strconv"
"strings"
"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/split"
"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"
)
// this is the stub main for gogi that calls our actual mainrun function, at end of file
func main() {
gimain.Main(func() {
mainrun()
})
}
// LogPrec is precision for saving float values in logs
const LogPrec = 4
// PatsType is the type of training patterns
type PatsType int32
//go:generate stringer -type=PatsType
var KiT_PatsType = kit.Enums.AddEnum(PatsTypeN, kit.NotBitFlag, nil)
func (ev PatsType) MarshalJSON() ([]byte, error) { return kit.EnumMarshalJSON(ev) }
func (ev *PatsType) UnmarshalJSON(b []byte) error { return kit.EnumUnmarshalJSON(ev, b) }
const (
// Easy patterns can be learned by Hebbian learning
Easy PatsType = iota
// Hard patterns can only be learned with error-driven learning
Hard
// Impossible patterns require error-driven + a hidden layer
Impossible
// Lines patterns
Lines2
PatsTypeN
)
// LearnType is the type of learning to use
type LearnType int32
//go:generate stringer -type=LearnType
var KiT_LearnType = kit.Enums.AddEnum(LearnTypeN, kit.NotBitFlag, nil)
func (ev LearnType) MarshalJSON() ([]byte, error) { return kit.EnumMarshalJSON(ev) }
func (ev *LearnType) UnmarshalJSON(b []byte) error { return kit.EnumUnmarshalJSON(ev, b) }
const (
Hebbian LearnType = iota
ErrorDriven
ErrorHebbIn
LearnTypeN
)
// 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.Norm.On": "false",
"Prjn.Learn.Momentum.On": "false",
"Prjn.Learn.WtBal.On": "false",
}},
{Sel: "Layer", Desc: "needs some special inhibition and learning params",
Params: params.Params{
"Layer.Learn.AvgL.Gain": "3", // this is critical! 2.5 def doesn't work
"Layer.Inhib.Layer.Gi": "1.5",
"Layer.Inhib.ActAvg.Init": "0.2",
"Layer.Inhib.ActAvg.Fixed": "true",
"Layer.Act.Gbar.L": "0.1",
}},
{Sel: ".Back", Desc: "top-down back-projections MUST have lower relative weight scale, otherwise network hallucinates",
Params: params.Params{
"Prjn.WtScale.Rel": "0.3",
}},
},
}},
{Name: "ErrorDriven", Desc: "Error-driven-only learning params", Sheets: params.Sheets{
"Network": ¶ms.Sheet{
{Sel: "Prjn", Desc: "",
Params: params.Params{
"Prjn.Learn.XCal.MLrn": "1",
"Prjn.Learn.XCal.SetLLrn": "true",
"Prjn.Learn.XCal.LLrn": "0",
}},
{Sel: ".Output", Desc: "out inhib",
Params: params.Params{
"Layer.Inhib.Layer.Gi": "1.8",
}},
},
}},
{Name: "Hebbian", Desc: "Hebb-only learning params", Sheets: params.Sheets{
"Network": ¶ms.Sheet{
{Sel: "Prjn", Desc: "",
Params: params.Params{
"Prjn.Learn.XCal.MLrn": "0",
"Prjn.Learn.XCal.SetLLrn": "true",
"Prjn.Learn.XCal.LLrn": "1",
}},
{Sel: ".Output", Desc: "out inhib",
Params: params.Params{
"Layer.Inhib.Layer.Gi": "1.8",
}},
},
}},
{Name: "ErrorHebbIn", Desc: "Error-driven-out Hebb In learning params", Sheets: params.Sheets{
"Network": ¶ms.Sheet{
{Sel: "Prjn", Desc: "",
Params: params.Params{
"Prjn.Learn.XCal.MLrn": "1",
"Prjn.Learn.XCal.SetLLrn": "true",
"Prjn.Learn.XCal.LLrn": "0",
}},
{Sel: ".InputToHidden", Desc: "in hidden self org",
Params: params.Params{
"Prjn.Learn.XCal.MLrn": "0",
"Prjn.Learn.XCal.SetLLrn": "true",
"Prjn.Learn.XCal.LLrn": "1",
"Prjn.Learn.Lrate": "0.02",
"Prjn.Learn.WtBal.On": "true", // note: was on!
}},
{Sel: ".Output", Desc: "out inhib",
Params: params.Params{
"Layer.Inhib.Layer.Gi": "1.8",
}},
},
}},
}
// 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 {
Net *leabra.Network `view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"`
Learn LearnType `desc:"select which type of learning to use"`
Pats PatsType `desc:"select which type of patterns to use"`
Easy *etable.Table `view:"no-inline" desc:"easy training patterns -- can be learned with Hebbian"`
Hard *etable.Table `view:"no-inline" desc:"hard training patterns -- require error-driven"`
Impossible *etable.Table `view:"no-inline" desc:"impossible training patterns -- require error-driven + hidden layer"`
Lines2 *etable.Table `view:"no-inline" desc:"lines training patterns"`
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"`
Params params.Sets `view:"no-inline" desc:"full collection of param sets"`
ParamSet string `view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don't put spaces in ParamSet names!)"`
MaxRuns int `desc:"maximum number of model runs to perform"`
MaxEpcs int `desc:"maximum number of epochs to run per model run"`
NZeroStop int `desc:"if a positive number, training will stop after this many epochs with zero SSE"`
TrainEnv env.FixedTable `desc:"Training environment -- contains everything about iterating over input / output patterns over training"`
TestEnv env.FixedTable `desc:"Testing environment -- manages iterating over testing"`
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"`
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"`
LayStatNms []string `desc:"names of layers to collect more detailed stats on (avg act, etc)"`
// statistics: note use float64 as that is best for etable.Table
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"`
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)"`
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:"-"
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"`
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"`
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"`
}
// 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.Learn = ErrorDriven
ss.Pats = Lines2
ss.Easy = &etable.Table{}
ss.Hard = &etable.Table{}
ss.Impossible = &etable.Table{}
ss.Lines2 = &etable.Table{}
ss.TrnEpcLog = &etable.Table{}
ss.TstEpcLog = &etable.Table{}
ss.TstTrlLog = &etable.Table{}
ss.RunLog = &etable.Table{}
ss.RunStats = &etable.Table{}
ss.Params = ParamSets
ss.RndSeed = 1
ss.ViewOn = true
ss.TrainUpdt = leabra.Quarter
ss.TestUpdt = leabra.Quarter
ss.TestInterval = 5
ss.LayStatNms = []string{"Input", "Output"}
}
////////////////////////////////////////////////////////////////////////////////////////////
// Configs
// Config configures all the elements using the standard functions
func (ss *Sim) Config() {
ss.OpenPats()
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 = 20
}
ss.TrainEnv.Nm = "TrainEnv"
ss.TrainEnv.Dsc = "training params and state"
ss.TrainEnv.Table = etable.NewIdxView(ss.Easy)
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.Easy)
ss.TestEnv.Sequential = true
ss.TestEnv.Validate()
ss.TrainEnv.Init(0)
ss.TestEnv.Init(0)
}
func (ss *Sim) UpdateEnv() {
switch ss.Pats {
case Easy:
ss.TrainEnv.Table = etable.NewIdxView(ss.Easy)
ss.TestEnv.Table = etable.NewIdxView(ss.Easy)
case Hard:
ss.TrainEnv.Table = etable.NewIdxView(ss.Hard)
ss.TestEnv.Table = etable.NewIdxView(ss.Hard)
case Impossible:
ss.TrainEnv.Table = etable.NewIdxView(ss.Impossible)
ss.TestEnv.Table = etable.NewIdxView(ss.Impossible)
case Lines2:
all := etable.NewIdxView(ss.Lines2)
splits, _ := split.Permuted(all, []float64{.9, .1}, []string{"Train", "Test"})
ss.TrainEnv.Table = splits.Splits[0]
ss.TestEnv.Table = splits.Splits[1]
// ss.TrainEnv.Table = splits.Splits[0]//etable.NewIdxView(ss.Lines2)
// ss.TestEnv.Table = splits.Splits[1]//etable.NewIdxView(ss.Lines2)
}
}
func (ss *Sim) ConfigNet(net *leabra.Network) {
net.InitName(net, "PatAssoc")
inp := net.AddLayer2D("Input", 5, 5, emer.Input)
hid := net.AddLayer2D("Hidden", 6, 5, emer.Hidden)
out := net.AddLayer2D("Output", 5, 2, emer.Target)
out.SetClass("Output")
full := prjn.NewFull()
inhid := net.ConnectLayers(inp, hid, full, emer.Forward)
inhid.SetClass("InputToHidden")
net.BidirConnectLayers(hid, out, full)
hid.SetRelPos(relpos.Rel{Rel: relpos.Above, Other: "Input", YAlign: relpos.Front, XAlign: relpos.Left, YOffset: 1})
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.UpdateEnv()
ss.StopNow = false
ss.SetParams("", false) // all sheets
ss.NewRun()
ss.UpdateView(true, -1)
}
// 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%s\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%s\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, cyc int) {
if ss.NetView != nil && ss.NetView.IsVisible() {
ss.NetView.Record(ss.Counters(train), cyc)
// 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
}
ss.Net.AlphaCycInit(train)
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:
if cyc != ss.Time.CycPerQtr-1 { // will be updated by quarter
ss.UpdateView(train, ss.Time.Cycle)
}
case leabra.FastSpike:
if (cyc+1)%10 == 0 {
ss.UpdateView(train, -1)
}
}
}
}
ss.Net.QuarterFinal(&ss.Time)
ss.Time.QuarterInc()
if ss.ViewOn {
switch viewUpdt {
case leabra.Cycle:
ss.UpdateView(train, ss.Time.Cycle)
case leabra.Quarter:
ss.UpdateView(train, -1)
case leabra.Phase:
if qtr >= 2 {
ss.UpdateView(train, -1)
}
}
}
}
if train {
ss.Net.DWt()
if ss.NetView != nil && ss.NetView.IsVisible() {
ss.NetView.RecordSyns()
}
ss.Net.WtFmDWt()
}
if ss.ViewOn && viewUpdt == leabra.AlphaCycle {
ss.UpdateView(train, -1)
}
}
// 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).(leabra.LeabraLayer).AsLeabra()
pats := en.State(ly.Nm)
if pats != nil {
ly.ApplyExt(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 {
ss.LogTrnEpc(ss.TrnEpcLog)
if ss.ViewOn && ss.TrainUpdt > leabra.AlphaCycle {
ss.UpdateView(true, -1)
}
if ss.TestInterval > 0 && epc%ss.TestInterval == 0 { // note: epc is *next* so won't trigger first time
ss.TestAll()
}
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
}
}
}
// note: type must be in place before apply inputs
ss.Net.LayerByName("Output").SetType(emer.Target)
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.UpdateEnv()
ss.TrainEnv.Init(run)
ss.TestEnv.Init(run)
ss.Time.Reset()
ss.Net.InitWts()
ss.InitStats()
ss.TrnEpcLog.SetNumRows(0)
ss.TstEpcLog.SetNumRows(0)
ss.NeedsNewRun = false
ss.TrainEnv.Run.Max = ss.MaxRuns // note: we are not setting epoch max -- do that manually
}
// InitStats initializes all the statistics, especially important for the
// cumulative epoch stats -- called at start of new run
func (ss *Sim) InitStats() {
// accumulators
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.TrlErr = 0
ss.TrlSSE = 0
ss.TrlAvgSSE = 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()
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.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()
if ss.ToolBar != nil {
ss.ToolBar.UpdateActions()
}
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, -1)
}
ss.LogTstEpc(ss.TstEpcLog)
if returnOnChg {
return
}
}
// note: type must be in place before apply inputs
ss.Net.LayerByName("Output").SetType(emer.Compare)
ss.ApplyInputs(&ss.TestEnv)
ss.AlphaCyc(false) // !train
ss.TrialStats(false) // !accumulate
ss.LogTstTrl(ss.TstTrlLog)
}
// TestItem tests given item which is at given index in test item list
func (ss *Sim) TestItem(idx int) {
cur := ss.TestEnv.Trial.Cur
ss.TestEnv.Trial.Cur = idx
ss.TestEnv.SetTrialName()
// note: type must be in place before apply inputs
ss.Net.LayerByName("Output").SetType(emer.Compare)
ss.ApplyInputs(&ss.TestEnv)
ss.AlphaCyc(false) // !train
ss.TrialStats(false) // !accumulate
ss.TestEnv.Trial.Cur = cur
}
// TestAll runs through the full set of testing items
func (ss *Sim) TestAll() {
ss.TestEnv.Init(ss.TrainEnv.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
// 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)
if ss.ParamSet != "" && ss.ParamSet != "Base" {
sps := strings.Fields(ss.ParamSet)
for _, ps := range sps {
err = ss.SetParamsSet(ps, sheet, setMsg)
}
}
switch ss.Learn {
case Hebbian:
ss.SetParamsSet("Hebbian", sheet, setMsg)
case ErrorDriven:
ss.SetParamsSet("ErrorDriven", sheet, setMsg)
case ErrorHebbIn:
ss.SetParamsSet("ErrorHebbIn", sheet, setMsg)
}
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", "TestEnv" etc
return err
}
// OpenPatAsset opens pattern file from embedded assets
func (ss *Sim) OpenPatAsset(dt *etable.Table, fnm, name, desc string) error {
dt.SetMetaData("name", name)
dt.SetMetaData("desc", desc)
ab, err := Asset(fnm)
if err != nil {
log.Println(err)
return err
}
err = dt.ReadCSV(bytes.NewBuffer(ab), etable.Tab)
if err != nil {
log.Println(err)
} else {
for i := 1; i < len(dt.Cols); i++ {
dt.Cols[i].SetMetaData("grid-fill", "0.9")
}
}
return err
}
func (ss *Sim) OpenPats() {
// patgen.ReshapeCppFile(ss.Easy, "easy.dat", "easy.tsv") // one-time reshape
// patgen.ReshapeCppFile(ss.Hard, "hard.dat", "hard.tsv") // one-time reshape
// patgen.ReshapeCppFile(ss.Impossible, "impossible.dat", "impossible.tsv") // one-time reshape
ss.OpenPatAsset(ss.Easy, "easy.tsv", "Easy", "Easy Training patterns")
// err := ss.Easy.OpenCSV("easy.tsv", etable.Tab)
ss.OpenPatAsset(ss.Hard, "hard.tsv", "Hard", "Hard Training patterns")
// err = ss.Hard.OpenCSV("hard.tsv", etable.Tab)
ss.OpenPatAsset(ss.Impossible, "impossible.tsv", "Impossible", "Impossible Training patterns")
// err = ss.Impossible.OpenCSV("impossible.tsv", etable.Tab)
ss.OpenPatAsset(ss.Lines2, "lines2out1.tsv", "Lines2", "Lines2 Training patterns")
// err = ss.Lines2.OpenCSV("lines2out1.tsv", etable.Tab)
}
////////////////////////////////////////////////////////////////////////////////////////////
// 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
}
//////////////////////////////////////////////
// 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.Table.Len()) // number of trials in view
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
}
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)
for _, lnm := range ss.LayStatNms {
ly := ss.Net.LayerByName(lnm).(leabra.LeabraLayer).AsLeabra()
dt.SetCellFloat(ly.Nm+" ActAvg", row, float64(ly.Pools[0].ActAvg.ActPAvgEff))
}
// 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},
}
for _, lnm := range ss.LayStatNms {
sch = append(sch, etable.Column{lnm + " ActAvg", etensor.FLOAT64, nil, nil})
}
dt.SetFromSchema(sch, 0)
}
func (ss *Sim) ConfigTrnEpcPlot(plt *eplot.Plot2D, dt *etable.Table) *eplot.Plot2D {
plt.Params.Title = "Pattern Associator Epoch Plot"
plt.Params.XAxisCol = "Epoch"
plt.SetTable(dt)
// order of params: on, fixMin, min, fixMax, max
plt.SetColParams("Run", eplot.Off, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("Epoch", eplot.Off, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("SSE", eplot.On, eplot.FixMin, 0, eplot.FloatMax, 0) // default plot
plt.SetColParams("AvgSSE", eplot.Off, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("PctErr", eplot.Off, eplot.FixMin, 0, eplot.FixMax, 1)
plt.SetColParams("PctCor", eplot.Off, eplot.FixMin, 0, eplot.FixMax, 1)
plt.SetColParams("CosDiff", eplot.Off, eplot.FixMin, 0, eplot.FixMax, 1)
for _, lnm := range ss.LayStatNms {
plt.SetColParams(lnm+" ActAvg", eplot.Off, eplot.FixMin, 0, eplot.FixMax, 0.5)
}
return plt
}
//////////////////////////////////////////////
// TstTrlLog
// LogTstTrl adds data from current trial to the TstTrlLog table.
// log always contains number of testing items
func (ss *Sim) LogTstTrl(dt *etable.Table) {
epc := ss.TrainEnv.Epoch.Prv // this is triggered by increment so use previous value
inp := ss.Net.LayerByName("Input").(leabra.LeabraLayer).AsLeabra()
out := ss.Net.LayerByName("Output").(leabra.LeabraLayer).AsLeabra()
trl := ss.TestEnv.Trial.Cur
row := trl
if dt.Rows <= row {
dt.SetNumRows(row + 1)
}
dt.SetCellFloat("Run", row, float64(ss.TrainEnv.Run.Cur))
dt.SetCellFloat("Epoch", row, float64(epc))
dt.SetCellFloat("Trial", row, float64(trl))
dt.SetCellString("TrialName", row, ss.TestEnv.TrialName.Cur)
dt.SetCellFloat("Err", row, ss.TrlErr)
dt.SetCellFloat("SSE", row, ss.TrlSSE)
dt.SetCellFloat("AvgSSE", row, ss.TrlAvgSSE)
dt.SetCellFloat("CosDiff", row, ss.TrlCosDiff)
for _, lnm := range ss.LayStatNms {
ly := ss.Net.LayerByName(lnm).(leabra.LeabraLayer).AsLeabra()
dt.SetCellFloat(ly.Nm+" ActM.Avg", row, float64(ly.Pools[0].ActM.Avg))
}
ivt := ss.ValsTsr("Input")
ovt := ss.ValsTsr("Output")
inp.UnitValsTensor(ivt, "Act")
dt.SetCellTensor("InAct", row, ivt)
out.UnitValsTensor(ovt, "ActM")
dt.SetCellTensor("OutActM", row, ovt)
out.UnitValsTensor(ovt, "Targ")
dt.SetCellTensor("OutTarg", row, ovt)
// note: essential to use Go version of update when called from another goroutine
ss.TstTrlPlot.GoUpdate()
}
func (ss *Sim) ConfigTstTrlLog(dt *etable.Table) {
inp := ss.Net.LayerByName("Input").(leabra.LeabraLayer).AsLeabra()
out := ss.Net.LayerByName("Output").(leabra.LeabraLayer).AsLeabra()
dt.SetMetaData("name", "TstTrlLog")
dt.SetMetaData("desc", "Record of testing per input pattern")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", strconv.Itoa(LogPrec))
nt := ss.TestEnv.Table.Len() // number in view
sch := etable.Schema{
{"Run", etensor.INT64, nil, nil},
{"Epoch", etensor.INT64, nil, nil},
{"Trial", etensor.INT64, nil, nil},
{"TrialName", etensor.STRING, nil, nil},
{"Err", etensor.FLOAT64, nil, nil},
{"SSE", etensor.FLOAT64, nil, nil},
{"AvgSSE", etensor.FLOAT64, nil, nil},
{"CosDiff", etensor.FLOAT64, nil, nil},
}
for _, lnm := range ss.LayStatNms {
sch = append(sch, etable.Column{lnm + " ActM.Avg", etensor.FLOAT64, nil, nil})
}
sch = append(sch, etable.Schema{
{"InAct", etensor.FLOAT64, inp.Shp.Shp, nil},
{"OutActM", etensor.FLOAT64, out.Shp.Shp, nil},
{"OutTarg", etensor.FLOAT64, out.Shp.Shp, nil},
}...)
dt.SetFromSchema(sch, nt)
}
func (ss *Sim) ConfigTstTrlPlot(plt *eplot.Plot2D, dt *etable.Table) *eplot.Plot2D {
plt.Params.Title = "Pattern Associator Test Trial Plot"
plt.Params.XAxisCol = "Trial"
plt.SetTable(dt)
// order of params: on, fixMin, min, fixMax, max
plt.SetColParams("Run", eplot.Off, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("Epoch", eplot.Off, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("Trial", eplot.Off, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("TrialName", eplot.Off, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("Err", eplot.Off, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("SSE", eplot.On, eplot.FixMin, 0, eplot.FloatMax, 0) // default plot
plt.SetColParams("AvgSSE", eplot.Off, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("CosDiff", eplot.Off, eplot.FixMin, 0, eplot.FixMax, 1)
for _, lnm := range ss.LayStatNms {
plt.SetColParams(lnm+" ActM.Avg", eplot.Off, eplot.FixMin, 0, eplot.FixMax, 0.5)
}
plt.SetColParams("InAct", eplot.Off, eplot.FixMin, 0, eplot.FixMax, 1)
plt.SetColParams("OutActM", eplot.Off, eplot.FixMin, 0, eplot.FixMax, 1)
plt.SetColParams("OutTarg", eplot.Off, eplot.FixMin, 0, eplot.FixMax, 1)
return plt
}
//////////////////////////////////////////////
// TstEpcLog