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ra25.go
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ra25.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.
// ra25 runs a simple random-associator four-layer leabra network
// that uses the standard supervised learning paradigm to learn
// mappings between 25 random input / output patterns
// defined over 5x5 input / output layers (i.e., 25 units)
package main
import (
"flag"
"fmt"
"log"
"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/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/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"
)
// 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
// 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: "norm and momentum on works better, but wt bal is not better for smaller nets",
Params: params.Params{
"Prjn.Learn.Norm.On": "true",
"Prjn.Learn.Momentum.On": "true",
"Prjn.Learn.WtBal.On": "false",
}},
{Sel: "Layer", Desc: "using default 1.8 inhib for all of network -- can explore",
Params: params.Params{
"Layer.Inhib.Layer.Gi": "1.8",
}},
{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: "#Output", Desc: "output definitely needs lower inhib -- true for smaller layers in general",
Params: params.Params{
"Layer.Inhib.Layer.Gi": "1.4",
}},
},
"Sim": ¶ms.Sheet{ // sim params apply to sim object
{Sel: "Sim", Desc: "best params always finish in this time",
Params: params.Params{
"Sim.MaxEpcs": "50",
}},
},
}},
{Name: "DefaultInhib", Desc: "output uses default inhib instead of lower", Sheets: params.Sheets{
"Network": ¶ms.Sheet{
{Sel: "#Output", Desc: "go back to default",
Params: params.Params{
"Layer.Inhib.Layer.Gi": "1.8",
}},
},
"Sim": ¶ms.Sheet{ // sim params apply to sim object
{Sel: "Sim", Desc: "takes longer -- generally doesn't finish..",
Params: params.Params{
"Sim.MaxEpcs": "100",
}},
},
}},
{Name: "NoMomentum", Desc: "no momentum or normalization", Sheets: params.Sheets{
"Network": ¶ms.Sheet{
{Sel: "Prjn", Desc: "no norm or momentum",
Params: params.Params{
"Prjn.Learn.Norm.On": "false",
"Prjn.Learn.Momentum.On": "false",
}},
},
}},
{Name: "WtBalOn", Desc: "try with weight bal on", Sheets: params.Sheets{
"Network": ¶ms.Sheet{
{Sel: "Prjn", Desc: "weight bal on",
Params: params.Params{
"Prjn.Learn.WtBal.On": "true",
}},
},
}},
}
// 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"`
Pats *etable.Table `view:"no-inline" desc:"the training patterns to use"`
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"`
TstErrLog *etable.Table `view:"no-inline" desc:"log of all test trials where errors were made"`
TstErrStats *etable.Table `view:"no-inline" desc:"stats on test trials where errors were made"`
TstCycLog *etable.Table `view:"no-inline" desc:"testing cycle-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 `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)"`
MaxRuns int `desc:"maximum number of model runs to perform"`
MaxEpcs int `desc:"maximum number of epochs to run per model run"`
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"`
// statistics: note use float64 as that is best for etable.Table
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 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)"`
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"`
// internal state - view:"-"
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"`
CntErr int `view:"-" inactive:"+" desc:"sum of errs 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"`
TstCycPlot *eplot.Plot2D `view:"-" desc:"the test-cycle plot"`
RunPlot *eplot.Plot2D `view:"-" desc:"the run plot"`
TrnEpcFile *os.File `view:"-" desc:"log file"`
RunFile *os.File `view:"-" desc:"log file"`
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"`
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.Pats = &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.Params = ParamSets
ss.RndSeed = 1
ss.ViewOn = true
ss.TrainUpdt = leabra.AlphaCycle
ss.TestUpdt = leabra.Cycle
ss.TestInterval = 5
}
////////////////////////////////////////////////////////////////////////////////////////////
// Configs
// Config configures all the elements using the standard functions
func (ss *Sim) Config() {
//ss.ConfigPats()
ss.OpenPats()
ss.ConfigEnv()
ss.ConfigNet(ss.Net)
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 = 10
}
if ss.MaxEpcs == 0 { // allow user override
ss.MaxEpcs = 50
}
ss.TrainEnv.Nm = "TrainEnv"
ss.TrainEnv.Dsc = "training params and state"
ss.TrainEnv.Table = etable.NewIdxView(ss.Pats)
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.Pats)
ss.TestEnv.Sequential = true
ss.TestEnv.Validate()
// note: to create a train / test split of pats, do this:
// all := etable.NewIdxView(ss.Pats)
// splits, _ := split.Permuted(all, []float64{.8, .2}, []string{"Train", "Test"})
// ss.TrainEnv.Table = splits.Splits[0]
// ss.TestEnv.Table = splits.Splits[1]
ss.TrainEnv.Init(0)
ss.TestEnv.Init(0)
}
func (ss *Sim) ConfigNet(net *leabra.Network) {
net.InitName(net, "RA25")
inLay := net.AddLayer2D("Input", 5, 5, emer.Input)
hid1Lay := net.AddLayer2D("Hidden1", 7, 7, emer.Hidden)
hid2Lay := net.AddLayer4D("Hidden2", 2, 4, 3, 2, emer.Hidden)
outLay := net.AddLayer2D("Output", 5, 5, emer.Target)
// use this to position layers relative to each other
// default is Above, YAlign = Front, XAlign = Center
hid2Lay.SetRelPos(relpos.Rel{Rel: relpos.RightOf, Other: "Hidden1", YAlign: relpos.Front, Space: 2})
net.ConnectLayers(inLay, hid1Lay, prjn.NewFull(), emer.Forward)
net.ConnectLayers(hid1Lay, hid2Lay, prjn.NewFull(), emer.Forward)
net.ConnectLayers(hid2Lay, outLay, prjn.NewFull(), emer.Forward)
// note: see emergent/prjn module for all the options on how to connect
// NewFull returns a new prjn.Full connectivity pattern
net.ConnectLayers(outLay, hid2Lay, prjn.NewFull(), emer.Back)
net.ConnectLayers(hid2Lay, hid1Lay, prjn.NewFull(), emer.Back)
// note: can set these to do parallel threaded computation across multiple cpus
// not worth it for this small of a model, but definitely helps for larger ones
// if Thread {
// hid2Lay.SetThread(1)
// outLay.SetThread(1)
// }
// 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()
}
////////////////////////////////////////////////////////////////////////////////
// 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.ConfigEnv() // re-config env just in case a different set of patterns was
// selected or patterns have been modified etc
ss.StopNow = false
ss.SetParams("", ss.LogSetParams) // 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\tName:\t%v\t\t\t", ss.TrainEnv.Run.Cur, ss.TrainEnv.Epoch.Cur, ss.TrainEnv.Trial.Cur, ss.TrainEnv.TrialName)
} else {
return fmt.Sprintf("Run:\t%d\tEpoch:\t%d\tTrial:\t%d\tName:\t%v\t\t\t", ss.TrainEnv.Run.Cur, ss.TrainEnv.Epoch.Cur, ss.TestEnv.Trial.Cur, ss.TestEnv.TrialName)
}
}
func (ss *Sim) UpdateView(train bool) {
if ss.NetView != nil {
// note: essential to use Go version of update when called from another goroutine
ss.NetView.GoUpdate(ss.Counters(train)) // 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()
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)
}
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 viewUpdt {
case leabra.Quarter:
ss.UpdateView(train)
case leabra.Phase:
if qtr >= 2 {
ss.UpdateView(train)
}
}
}
}
if train {
ss.Net.DWt()
ss.Net.WtFmDWt()
}
if ss.ViewOn && viewUpdt == leabra.AlphaCycle {
ss.UpdateView(train)
}
if !train {
ss.TstCycPlot.GoUpdate() // make sure up-to-date at end
}
}
// 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
inLay := ss.Net.LayerByName("Input").(*leabra.Layer)
outLay := ss.Net.LayerByName("Output").(*leabra.Layer)
inPats := en.State(inLay.Nm)
if inPats != nil {
inLay.ApplyExt(inPats)
}
outPats := en.State(outLay.Nm)
if inPats != nil {
outLay.ApplyExt(outPats)
}
}
// TrainTrial runs one trial of training using TrainEnv
func (ss *Sim) TrainTrial() {
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)
}
if epc%ss.TestInterval == 0 { // note: epc is *next* so won't trigger first time
ss.TestAll()
}
if epc >= ss.MaxEpcs { // done with training..
ss.RunEnd()
if ss.TrainEnv.Run.Incr() { // we are done!
ss.StopNow = true
return
} else {
ss.NewRun()
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)
if ss.SaveWts {
fnm := ss.WeightsFileName()
fmt.Printf("Saving Weights to: %v\n", fnm)
ss.Net.SaveWtsJSON(gi.FileName(fnm))
}
}
// 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.TestEnv.Init(run)
ss.Time.Reset()
ss.Net.InitWts()
ss.InitStats()
ss.TrnEpcLog.SetNumRows(0)
ss.TstEpcLog.SetNumRows(0)
}
// 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.CntErr = 0
ss.FirstZero = -1
// clear rest just to make Sim look initialized
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) {
outLay := ss.Net.LayerByName("Output").(*leabra.Layer)
ss.TrlCosDiff = float64(outLay.CosDiff.Cos)
ss.TrlSSE, ss.TrlAvgSSE = outLay.MSE(0.5) // 0.5 = per-unit tolerance -- right side of .5
if accum {
ss.SumSSE += ss.TrlSSE
ss.SumAvgSSE += ss.TrlAvgSSE
ss.SumCosDiff += ss.TrlCosDiff
if ss.TrlSSE != 0 {
ss.CntErr++
}
}
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() {
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)
return
}
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()
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()
_, _, 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)
if ss.ParamSet != "" && ss.ParamSet != "Base" {
err = ss.SetParamsSet(ss.ParamSet, 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
}
func (ss *Sim) ConfigPats() {
dt := ss.Pats
dt.SetMetaData("name", "TrainPats")
dt.SetMetaData("desc", "Training patterns")
dt.SetFromSchema(etable.Schema{
{"Name", etensor.STRING, nil, nil},
{"Input", etensor.FLOAT32, []int{5, 5}, []string{"Y", "X"}},
{"Output", etensor.FLOAT32, []int{5, 5}, []string{"Y", "X"}},
}, 25)
patgen.PermutedBinaryRows(dt.Cols[1], 6, 1, 0)
patgen.PermutedBinaryRows(dt.Cols[2], 6, 1, 0)
dt.SaveCSV("random_5x5_25_gen.dat", ',', true)
}
func (ss *Sim) OpenPats() {
dt := ss.Pats
dt.SetMetaData("name", "TrainPats")
dt.SetMetaData("desc", "Training patterns")
err := dt.OpenCSV("random_5x5_25.dat", etable.Tab)
if err != nil {
log.Println(err)
}
}
////////////////////////////////////////////////////////////////////////////////////////////
// Logging
// 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"
}
// 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
ss.TrnEpcLog.SetNumRows(row + 1)
hid1Lay := ss.Net.LayerByName("Hidden1").(*leabra.Layer)
hid2Lay := ss.Net.LayerByName("Hidden2").(*leabra.Layer)
outLay := ss.Net.LayerByName("Output").(*leabra.Layer)
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.CntErr) / nt
ss.CntErr = 0
ss.EpcPctCor = 1 - ss.EpcPctErr
ss.EpcCosDiff = ss.SumCosDiff / nt
ss.SumCosDiff = 0
if ss.FirstZero < 0 && ss.EpcPctErr == 0 {
ss.FirstZero = epc
}
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("Hid1 ActAvg", row, float64(hid1Lay.Pools[0].ActAvg.ActPAvgEff))
dt.SetCellFloat("Hid2 ActAvg", row, float64(hid2Lay.Pools[0].ActAvg.ActPAvgEff))
dt.SetCellFloat("Out ActAvg", row, float64(outLay.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, true)
}
}
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))
dt.SetFromSchema(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},
{"Hid1 ActAvg", etensor.FLOAT64, nil, nil},
{"Hid2 ActAvg", etensor.FLOAT64, nil, nil},
{"Out ActAvg", etensor.FLOAT64, nil, nil},
}, 0)
}
func (ss *Sim) ConfigTrnEpcPlot(plt *eplot.Plot2D, dt *etable.Table) *eplot.Plot2D {
plt.Params.Title = "Leabra Random Associator 25 Epoch Plot"
plt.Params.XAxisCol = "Epoch"
plt.SetTable(dt)
// order of params: on, fixMin, min, fixMax, max
plt.SetColParams("Run", false, true, 0, false, 0)
plt.SetColParams("Epoch", false, true, 0, false, 0)
plt.SetColParams("SSE", false, true, 0, false, 0)
plt.SetColParams("AvgSSE", false, true, 0, false, 0)
plt.SetColParams("PctErr", true, true, 0, true, 1) // default plot
plt.SetColParams("PctCor", true, true, 0, true, 1) // default plot
plt.SetColParams("CosDiff", false, true, 0, true, 1)
plt.SetColParams("Hid1 ActAvg", false, true, 0, true, .5)
plt.SetColParams("Hid2 ActAvg", false, true, 0, true, .5)
plt.SetColParams("Out ActAvg", false, true, 0, true, .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) {
inLay := ss.Net.LayerByName("Input").(*leabra.Layer)
hid1Lay := ss.Net.LayerByName("Hidden1").(*leabra.Layer)
hid2Lay := ss.Net.LayerByName("Hidden2").(*leabra.Layer)
outLay := ss.Net.LayerByName("Output").(*leabra.Layer)
epc := ss.TrainEnv.Epoch.Prv // this is triggered by increment so use previous value
trl := ss.TestEnv.Trial.Cur
dt.SetCellFloat("Run", trl, float64(ss.TrainEnv.Run.Cur))
dt.SetCellFloat("Epoch", trl, float64(epc))
dt.SetCellFloat("Trial", trl, float64(trl))
dt.SetCellString("TrialName", trl, ss.TestEnv.TrialName)
dt.SetCellFloat("SSE", trl, ss.TrlSSE)
dt.SetCellFloat("AvgSSE", trl, ss.TrlAvgSSE)
dt.SetCellFloat("CosDiff", trl, ss.TrlCosDiff)
dt.SetCellFloat("Hid1 ActM.Avg", trl, float64(hid1Lay.Pools[0].ActM.Avg))
dt.SetCellFloat("Hid2 ActM.Avg", trl, float64(hid2Lay.Pools[0].ActM.Avg))
dt.SetCellFloat("Out ActM.Avg", trl, float64(outLay.Pools[0].ActM.Avg))
dt.SetCellTensor("InAct", trl, inLay.UnitValsTensor("Act"))
dt.SetCellTensor("OutActM", trl, outLay.UnitValsTensor("ActM"))
dt.SetCellTensor("OutActP", trl, outLay.UnitValsTensor("ActP"))
// note: essential to use Go version of update when called from another goroutine
ss.TstTrlPlot.GoUpdate()
}
func (ss *Sim) ConfigTstTrlLog(dt *etable.Table) {
inLay := ss.Net.LayerByName("Input").(*leabra.Layer)
outLay := ss.Net.LayerByName("Output").(*leabra.Layer)
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
dt.SetFromSchema(etable.Schema{
{"Run", etensor.INT64, nil, nil},
{"Epoch", etensor.INT64, nil, nil},
{"Trial", etensor.INT64, nil, nil},
{"TrialName", etensor.STRING, nil, nil},
{"SSE", etensor.FLOAT64, nil, nil},
{"AvgSSE", etensor.FLOAT64, nil, nil},
{"CosDiff", etensor.FLOAT64, nil, nil},
{"Hid1 ActM.Avg", etensor.FLOAT64, nil, nil},
{"Hid2 ActM.Avg", etensor.FLOAT64, nil, nil},
{"Out ActM.Avg", etensor.FLOAT64, nil, nil},
{"InAct", etensor.FLOAT64, inLay.Shp.Shp, nil},
{"OutActM", etensor.FLOAT64, outLay.Shp.Shp, nil},
{"OutActP", etensor.FLOAT64, outLay.Shp.Shp, nil},
}, nt)
}
func (ss *Sim) ConfigTstTrlPlot(plt *eplot.Plot2D, dt *etable.Table) *eplot.Plot2D {
plt.Params.Title = "Leabra Random Associator 25 Test Trial Plot"
plt.Params.XAxisCol = "Trial"
plt.SetTable(dt)
// order of params: on, fixMin, min, fixMax, max
plt.SetColParams("Run", false, true, 0, false, 0)
plt.SetColParams("Epoch", false, true, 0, false, 0)
plt.SetColParams("Trial", false, true, 0, false, 0)
plt.SetColParams("TrialName", false, true, 0, false, 0)
plt.SetColParams("SSE", false, true, 0, false, 0)
plt.SetColParams("AvgSSE", true, true, 0, false, 0)
plt.SetColParams("CosDiff", true, true, 0, true, 1)
plt.SetColParams("Hid1 ActM.Avg", true, true, 0, true, .5)
plt.SetColParams("Hid2 ActM.Avg", true, true, 0, true, .5)
plt.SetColParams("Out ActM.Avg", true, true, 0, true, .5)
plt.SetColParams("InAct", false, true, 0, true, 1)
plt.SetColParams("OutActM", false, true, 0, true, 1)
plt.SetColParams("OutActP", false, true, 0, true, 1)
return plt
}
//////////////////////////////////////////////
// TstEpcLog
func (ss *Sim) LogTstEpc(dt *etable.Table) {
row := dt.Rows
dt.SetNumRows(row + 1)
trl := ss.TstTrlLog
tix := etable.NewIdxView(trl)
epc := ss.TrainEnv.Epoch.Prv // ?
// note: this shows how to use agg methods to compute summary data from another
// data table, instead of incrementing on the Sim
dt.SetCellFloat("Run", row, float64(ss.TrainEnv.Run.Cur))
dt.SetCellFloat("Epoch", row, float64(epc))
dt.SetCellFloat("SSE", row, agg.Sum(tix, "SSE")[0])
dt.SetCellFloat("AvgSSE", row, agg.Mean(tix, "AvgSSE")[0])
dt.SetCellFloat("PctErr", row, agg.PropIf(tix, "SSE", func(idx int, val float64) bool {
return val > 0
})[0])
dt.SetCellFloat("PctCor", row, agg.PropIf(tix, "SSE", func(idx int, val float64) bool {
return val == 0
})[0])
dt.SetCellFloat("CosDiff", row, agg.Mean(tix, "CosDiff")[0])
trlix := etable.NewIdxView(trl)
trlix.Filter(func(et *etable.Table, row int) bool {
return et.CellFloat("SSE", row) > 0 // include error trials
})
ss.TstErrLog = trlix.NewTable()
allsp := split.All(trlix)
split.Agg(allsp, "SSE", agg.AggSum)
split.Agg(allsp, "AvgSSE", agg.AggMean)
split.Agg(allsp, "InAct", agg.AggMean)
split.Agg(allsp, "OutActM", agg.AggMean)
split.Agg(allsp, "OutActP", agg.AggMean)
ss.TstErrStats = allsp.AggsToTable(false)
// note: essential to use Go version of update when called from another goroutine
ss.TstEpcPlot.GoUpdate()
}
func (ss *Sim) ConfigTstEpcLog(dt *etable.Table) {
dt.SetMetaData("name", "TstEpcLog")
dt.SetMetaData("desc", "Summary stats for testing trials")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", strconv.Itoa(LogPrec))
dt.SetFromSchema(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},
}, 0)
}
func (ss *Sim) ConfigTstEpcPlot(plt *eplot.Plot2D, dt *etable.Table) *eplot.Plot2D {
plt.Params.Title = "Leabra Random Associator 25 Testing Epoch Plot"
plt.Params.XAxisCol = "Epoch"
plt.SetTable(dt)
// order of params: on, fixMin, min, fixMax, max
plt.SetColParams("Run", false, true, 0, false, 0)
plt.SetColParams("Epoch", false, true, 0, false, 0)
plt.SetColParams("SSE", false, true, 0, false, 0)
plt.SetColParams("AvgSSE", false, true, 0, false, 0)
plt.SetColParams("PctErr", true, true, 0, true, 1) // default plot
plt.SetColParams("PctCor", true, true, 0, true, 1) // default plot
plt.SetColParams("CosDiff", false, true, 0, true, 1)
return plt
}
//////////////////////////////////////////////
// TstCycLog
// LogTstCyc adds data from current trial to the TstCycLog table.
// log just has 100 cycles, is overwritten
func (ss *Sim) LogTstCyc(dt *etable.Table, cyc int) {
if dt.Rows <= cyc {
dt.SetNumRows(cyc + 1)
}
hid1Lay := ss.Net.LayerByName("Hidden1").(*leabra.Layer)
hid2Lay := ss.Net.LayerByName("Hidden2").(*leabra.Layer)
outLay := ss.Net.LayerByName("Output").(*leabra.Layer)
dt.SetCellFloat("Cycle", cyc, float64(cyc))
dt.SetCellFloat("Hid1 Ge.Avg", cyc, float64(hid1Lay.Pools[0].Ge.Avg))
dt.SetCellFloat("Hid2 Ge.Avg", cyc, float64(hid2Lay.Pools[0].Ge.Avg))
dt.SetCellFloat("Out Ge.Avg", cyc, float64(outLay.Pools[0].Ge.Avg))
dt.SetCellFloat("Hid1 Act.Avg", cyc, float64(hid1Lay.Pools[0].Act.Avg))
dt.SetCellFloat("Hid2 Act.Avg", cyc, float64(hid2Lay.Pools[0].Act.Avg))
dt.SetCellFloat("Out Act.Avg", cyc, float64(outLay.Pools[0].Act.Avg))
if cyc%10 == 0 { // too slow to do every cyc
// note: essential to use Go version of update when called from another goroutine
ss.TstCycPlot.GoUpdate()
}