/
run_configs.go
1329 lines (1324 loc) · 43.8 KB
/
run_configs.go
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// Copyright (c) 2020, The Emergent Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
package obsolete
//func (ss *Sim) RunConfigs() *etable.Table {
// allConfigs := AllRunConfigs()
// sch := etable.Schema{
// {"ConfigId", etensor.STRING, nil, nil}, // identifier for this type of configuration
// {"Desc", etensor.STRING, nil, nil}, // description of this configuration
// {"Users", etensor.STRING, nil, nil}, // users of this particular config_id can add name if they wish to be informed/involved in changes etc. so others know who to coordinate changes with, ask questions of, etc.; main user(s)/quasi-owner(s) should be listed first KEY: TH = Thomas Hazy;
// {"ConfigSeqList", etensor.STRING, nil, nil}, // to run multiple other configurations in order, list them here by name, with space between, and leave env_params_table empty
// {"EnvParamsNm", etensor.STRING, nil, nil}, // Which table of parameters from the EnvParams group to use in generating environmental events to be presented to the network. Environental events can be statically or dynamically generated and, if the latter, can involve environmental "reactions" to the network's last output.
// {"FixedProb", etensor.BOOl, nil, nil}, // general purpose bool-like param to use when one wants to use exact probabilities; comes in handy for de-bugging, etc.
// {"StartupProg", etensor.STRING, nil}, nil}, // program for handling startup args specific to this environment
// {"InitProg", etensor.STRING, nil, nil}, // program to run to initialize at start of training
// {"RunProg", etensor.STRING, nil, nil}, // program controlling each step of running
// {"EnvProg", etensor.STRING, nil, nil}, // program to set up environment for this configuration
// {"TrainEpochs", etensor.INT, nil, nil}, // number of total epochs to train for
// {"UseTrialGp", etensor.BOOl, nil, nil}, // If true uses eco_trials_per_epoch as master; ELSE uses trials_per_epoch - avoids confusion since these two are logically interdependent. network.group member can be used to index and count the number of eco trials.
// {"TrialGpsPerEpoch", etensor.INT, nil, nil}, // If used (use_eco == true), assigns the number of ecologically-defined trials per epoch. Thus, the eco_trial becomes the dominant "trial" of reference. An eco trial is defined as two or more conventional Leabra-like trials in sequence. This concept is meant to capture the temporally-extended nature of most learning episodes under ecological conditions. Eco trials can be tracked by the network variable network.group.
// {"PermuteTrialGps", etensor.BOOl, nil, nil}, // permute list of TrialInstance-level trials after generation
// {"TrialsPerEpoch", etensor.INT, nil, nil}, // number of trials per epoch
// {"SaveFinalWts", etensor.BOOl, nil, nil}, // save final weights after training
// {"SaveWtsInterval", etensor.INT, nil, nil}, // how frequently to save weights during training (in epochs)
// {"TestInterval", etensor.INT, nil, nil}, // how frequently to test performance without training -- only applicable to training cases
// {"LogTrials", etensor.BOOl, nil, nil}, // whether to log trial-level data or not
// {"LoadWeights", etensor.BOOl, nil, nil}, // whether to load weights at start -- for sequences of configs, this loads weights from previous step in sequence instead of using named file
// {"WeightsFile", etensor.STRING, nil, nil}, // full relative path (from project) to a weights file to load at start of training
// {"LoadStEpc", etensor.INT, nil, nil}, // if loading weights, what epoch does it start at?
// {"LrsStepEpochs", etensor.INT, nil, nil}, // how many epochs per given step in the lrate schedule: each step drops the lrate roughly in half (log scale)
// }
//
// dt := etable.New(sch, len(*allConfigs))
//
// for i, config := range *allConfigs {
// for j, key := range dt.ColNames {
// //tens := etensor.NewInt(shape: []int{1}, strides: nil, names: []string{key}, &config[key])
// dt.SetCellFloat()
// }
// }
//}
type RunConfig struct {
ConfigId string `desc:"Identifier for this type of configuration"`
Desc string `desc:"Description of this configuration"`
Users string `desc:"Users of this particular config_id can add name if they wish to be informed/involved in changes etc. so others know who to coordinate changes with, ask questions of, etc.; main user(s)/quasi-owner(s) should be listed first KEY: TH = Thomas Hazy;"`
ConfigSeqList string `desc:"To run multiple other configurations in order, list them here by name, with space between, and leave env_params_table empty"`
EnvParamsTable string `desc:"Which table of parameters from the EnvParams group to use in generating environmental events to be presented to the network. Environmental events can be statically or dynamically generated and, if the latter, can involve environmental 'reactions' to the network's last output."`
FixedProb bool `desc:"General purpose bool-like param to use when one wants to use exact probabilities; comes in handy for de-bugging, etc."`
StartupProg string `desc:"Grogram for handling startup args specific to this environment"`
InitProg string `desc:"Program to run to initialize at start of training"`
RunProg string `desc:"Program controlling each step of running"`
EnvProg string `desc:"Program to set up environment for this configuration"`
TrainEpochs int `desc:"Number of total epochs to train for"`
UseTrialGp bool `desc:"If true uses eco_trials_per_epoch as master; ELSE uses trials_per_epoch - avoids confusion since these two are logically interdependent. network.group member can be used to index and count the number of eco trials."`
TrialGpsPerEpoch int `desc:"If used (use_eco == true), assigns the number of ecologically-defined trials per epoch. Thus, the eco_trial becomes the dominant 'trial' of reference. An eco trial is defined as two or more conventional Leabra-like trials in sequence. This concept is meant to capture the temporally-extended nature of most learning episodes under ecological conditions. Eco trials can be tracked by the network variable network.group."`
PermuteTrialGps bool `desc:"Permute list of Trial-level trials after generation"`
TrialsPerEpoch int `desc:"Number of trials per epoch"`
SaveFinalWts bool `desc:"Save final weights after training"`
SaveWtsInterval int `desc:"How frequently to save weights during training (in epochs)"`
TestInterval int `desc:"How frequently to test performance without training -- only applicable to training cases"`
LogTrials bool `desc:"Whether to log trial-level data or not"`
LoadWeights bool `desc:"Whether to load weights at start -- for sequences of configs, this loads weights from previous step in sequence instead of using named file"`
WeightsFile string `desc:"Full relative path (from project) to a weights file to load at start of training"`
LoadStEpc int `desc:"If loading weights, what epoch does it start at?"`
LrsStepEpochs int `desc:"how many epochs per given step in the lrate schedule: each step drops the lrate roughly in half (log scale)"`
}
type RunConfigsMap map[string]RunConfig
// imported from PVLV->
func AllRunConfigs() RunConfigsMap {
// each row (key: ConfigId) defines a distinct training and/or testing regimen
sets := RunConfigsMap{
"PosAcq": {
ConfigId: "PosAcq",
Desc: "Pavlovian conditioning w/ positively-valenced USType: A_Rf_POS",
Users: "TH, JM",
ConfigSeqList: "",
EnvParamsTable: "PosAcq",
FixedProb: true,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 50,
UseTrialGp: true,
TrialGpsPerEpoch: 8,
PermuteTrialGps: true,
TrialsPerEpoch: 10,
SaveFinalWts: false,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"PosCondInhib": {
ConfigId: "PosCondInhib",
Desc: "conditioned inhibition training: AX_NRf_POS, A_Rf_POS interleaved",
Users: "JM, TH",
ConfigSeqList: "",
EnvParamsTable: "PosCondInhib",
FixedProb: false,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 100,
UseTrialGp: true,
TrialGpsPerEpoch: 4,
PermuteTrialGps: true,
TrialsPerEpoch: 4,
SaveFinalWts: false,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "wts/bvPVLVNet_cel_AB_POS_trn.00_0050.wts.gz",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"PosExtinct": {
ConfigId: "PosExtinct",
Desc: "Pavlovian extinction: A_NRf_POS",
Users: "JM, TH",
ConfigSeqList: "",
EnvParamsTable: "PosExtinct",
FixedProb: false,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 50,
UseTrialGp: true,
TrialGpsPerEpoch: 8,
PermuteTrialGps: true,
TrialsPerEpoch: 10,
SaveFinalWts: false,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "wts/bvPVLVNet_cel_AB_POS_trn.00_0050.wts.gz",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"PosCondInhib_test": {
ConfigId: "PosCondInhib_test",
Desc: "Testing session: A_NRf_POS, AX_NRf_POS, and X_NRf_POS cases",
Users: "JM",
ConfigSeqList: "",
EnvParamsTable: "PosCondInhib_test",
FixedProb: false,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 5,
UseTrialGp: true,
TrialGpsPerEpoch: 3,
PermuteTrialGps: false,
TrialsPerEpoch: 10,
SaveFinalWts: false,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "wts/bvPVLVNet_cel_AB_POS_cond_inhib_trn.00_0050.wts.gz",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"NegAcq": {
ConfigId: "NegAcq",
Desc: "Pavlovian conditioning w/ negatively-valenced USType: A_Rf_NEG",
Users: "JM",
ConfigSeqList: "",
EnvParamsTable: "NegAcq",
FixedProb: false,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 150,
UseTrialGp: true,
TrialGpsPerEpoch: 10,
PermuteTrialGps: true,
TrialsPerEpoch: 10,
SaveFinalWts: false,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"CondExp": {
ConfigId: "CondExp",
Desc: "",
Users: "JM",
ConfigSeqList: "",
EnvParamsTable: "CondExp",
FixedProb: false,
StartupProg: "",
InitProg: "CondInit_exp",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 1,
UseTrialGp: true,
TrialGpsPerEpoch: 296,
PermuteTrialGps: false,
TrialsPerEpoch: 296,
SaveFinalWts: false,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"PosAcqOmit": {
ConfigId: "PosAcqOmit",
Desc: "Pavlovian conditioning w/ positively-valenced USType: A_Rf_POS, A_Rf_POS_omit trials, interleaved",
Users: "JM",
ConfigSeqList: "",
EnvParamsTable: "PosAcqOmit",
FixedProb: false,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 10,
UseTrialGp: false,
TrialGpsPerEpoch: 0,
PermuteTrialGps: false,
TrialsPerEpoch: 10,
SaveFinalWts: true,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"NegCondInh": {
ConfigId: "NegCondInh",
Desc: "condition inhibition w/ negatively-valenced USType: CZ_NRf_NEG, C_Rf_NEG interleaved; i.e., Z = security signal",
Users: "JM",
ConfigSeqList: "",
EnvParamsTable: "NegCondInhib",
FixedProb: false,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 150,
UseTrialGp: true,
TrialGpsPerEpoch: 10,
PermuteTrialGps: true,
TrialsPerEpoch: 10,
SaveFinalWts: false,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "wts/bvPVLVNet_DE_NEG_trn.00_0150.wts.gz",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"NegCondInh_test": {
ConfigId: "NegCondInh_test",
Desc: "condition inhibition w/ negatively-valenced USType: CZ_NRf_NEG, C_Rf_NEG interleaved; i.e., Z = security signal",
Users: "JM",
ConfigSeqList: "",
EnvParamsTable: "NegCondInhib_test",
FixedProb: false,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 5,
UseTrialGp: true,
TrialGpsPerEpoch: 3,
PermuteTrialGps: false,
TrialsPerEpoch: 10,
SaveFinalWts: false,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "wts/bvPVLVNet_DU_NEG_trn.00_0150.wts.gz",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"NegExtinct": {
ConfigId: "NegExtinct",
Desc: "Pavlovian conditioning w/ negatively-valenced USType: A_Rf_NEG",
Users: "JM, TH",
ConfigSeqList: "",
EnvParamsTable: "NegExtinct",
FixedProb: false,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 75,
UseTrialGp: true,
TrialGpsPerEpoch: 2,
PermuteTrialGps: true,
TrialsPerEpoch: 10,
SaveFinalWts: false,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "wts/bvPVLVNet_DE_NEG_trn.00_0150.wts.gz",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"PosAcq_contextA": {
ConfigId: "PosAcq_contextA",
Desc: "Pavlovian conditioning w/ positively-valenced USType: A_Rf_POS, A_Rf_POS_omit trials, interleaved",
Users: "JM",
ConfigSeqList: "",
EnvParamsTable: "PosAcq_contextA",
FixedProb: false,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 50,
UseTrialGp: true,
TrialGpsPerEpoch: 10,
PermuteTrialGps: false,
TrialsPerEpoch: 10,
SaveFinalWts: false,
SaveWtsInterval: 200,
TestInterval: 0,
LogTrials: false,
LoadWeights: false,
WeightsFile: "",
LoadStEpc: 0,
LrsStepEpochs: 0,
},
"PosExtinct_contextB": {
ConfigId: "PosExtinct_contextB",
Desc: "Pavlovian conditioning w/ positively-valenced USType: A_Rf_POS, A_Rf_POS_omit trials, interleaved",
Users: "JM",
ConfigSeqList: "",
EnvParamsTable: "PosExtinct_contextB",
FixedProb: false,
StartupProg: "",
InitProg: "BVPVLVInit",
RunProg: "BVPVLVRun",
EnvProg: "BVPVLVEnv",
TrainEpochs: 25,
UseTrialGp: true,
TrialGpsPerEpoch: 10,
PermuteTrialGps: false,
TrialsPerEpoch: 10,
SaveFinalWts: false,
SaveWtsInterval: 200,
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