def __init__(self):
super(Vis, self).__init__()
self.ClipToFit = True
self.SetTags(
"ClipToFit",
'desc:"if true, and input image is larger than target image size, central region is clipped out as the input -- otherwise image is sized to target size"'
)
self.DoG = dog.Filter()
self.SetTags("DoG", 'desc:"LGN DoG filter parameters"')
self.Geom = vfilter.Geom()
self.SetTags(
"Geom",
'inactive:"+" view:"inline" desc:"geometry of input, output"')
self.ImgSize = image.Point()
self.SetTags(
"ImgSize",
'desc:"target image size to use -- images will be rescaled to this size"'
)
self.DoGTsr = etensor.Float32()
self.SetTags("DoGTsr", 'view:"no-inline" desc:"DoG filter tensor"')
self.Img = image.Image()
self.SetTags("Img", 'view:"-" desc:"current input image"')
self.ImgTsr = etensor.Float32()
self.SetTags("ImgTsr", 'view:"no-inline" desc:"input image as tensor"')
self.OutTsr = etensor.Float32()
self.SetTags("OutTsr",
'view:"no-inline" desc:"DoG filter output tensor"')
def __init__(self):
super(ImgEnv, self).__init__()
self.Nm = str()
self.SetTags("Nm", 'desc:"name of this environment"')
self.Dsc = str()
self.SetTags("Dsc", 'desc:"description of this environment"')
self.ImageFiles = []
self.SetTags("ImageFiles", 'desc:"paths to images"')
self.Images = []
self.SetTags("Images", 'desc:"images (preload for speed)"')
self.ImageIdx = env.CurPrvInt()
self.SetTags("ImageIdx", 'desc:"current image index"')
self.Vis = Vis()
self.SetTags("Vis", 'desc:"visual processing params"')
self.XFormRand = vxform.Rand()
self.SetTags("XFormRand", 'desc:"random transform parameters"')
self.XForm = vxform.XForm()
self.SetTags("XForm", 'desc:"current -- prev transforms"')
self.Run = env.Ctr()
self.SetTags(
"Run",
'view:"inline" desc:"current run of model as provided during Init"'
)
self.Epoch = env.Ctr()
self.SetTags(
"Epoch",
'view:"inline" desc:"number of times through Seq.Max number of sequences"'
)
self.Trial = env.Ctr()
self.SetTags(
"Trial",
'view:"inline" desc:"trial is the step counter within epoch"')
self.OrigImg = etensor.Float32()
self.SetTags("OrigImg",
'desc:"original image prior to random transforms"')
def __init__(self):
super(ProbeEnv, self).__init__()
self.Nm = str()
self.SetTags("Nm", 'desc:"name of this environment"')
self.Dsc = str()
self.SetTags("Dsc", 'desc:"description of this environment"')
self.Words = go.Slice_string()
self.SetTags(
"Words",
'desc:"list of words used for activating state units according to index"'
)
self.WordState = etensor.Float32()
self.SetTags("WordState", 'desc:"current sentence activation state"')
self.Run = env.Ctr()
self.SetTags(
"Run",
'view:"inline" desc:"current run of model as provided during Init"'
)
self.Epoch = env.Ctr()
self.SetTags(
"Epoch",
'view:"inline" desc:"number of times through Seq.Max number of sequences"'
)
self.Trial = env.Ctr()
self.SetTags(
"Trial",
'view:"inline" desc:"trial is the step counter within sequence - how many steps taken within current sequence -- it resets to 0 at start of each sequence"'
)
def __init__(self):
super(LEDEnv, self).__init__()
self.Nm = str()
self.SetTags("Nm", 'desc:"name of this environment"')
self.Dsc = str()
self.SetTags("Dsc", 'desc:"description of this environment"')
self.Draw = LEDraw()
self.SetTags("Draw", 'desc:"draws LEDs onto image"')
self.Vis = Vis()
self.SetTags("Vis", 'desc:"visual processing params"')
self.MinLED = int()
self.SetTags(
"MinLED",
'min:"0" max:"19" desc:"minimum LED number to draw (0-19)"')
self.MaxLED = int()
self.SetTags(
"MaxLED",
'min:"0" max:"19" desc:"maximum LED number to draw (0-19)"')
self.CurLED = int()
self.SetTags("CurLED",
'inactive:"+" desc:"current LED number that was drawn"')
self.PrvLED = int()
self.SetTags("PrvLED",
'inactive:"+" desc:"previous LED number that was drawn"')
self.XFormRand = vxform.Rand()
self.SetTags("XFormRand", 'desc:"random transform parameters"')
self.XForm = vxform.XForm()
self.SetTags("XForm", 'desc:"current -- prev transforms"')
self.Run = env.Ctr()
self.SetTags(
"Run",
'view:"inline" desc:"current run of model as provided during Init"'
)
self.Epoch = env.Ctr()
self.SetTags(
"Epoch",
'view:"inline" desc:"number of times through Seq.Max number of sequences"'
)
self.Trial = env.Ctr()
self.SetTags(
"Trial",
'view:"inline" desc:"trial is the step counter within epoch"')
self.OrigImg = etensor.Float32()
self.SetTags("OrigImg",
'desc:"original image prior to random transforms"')
self.Output = etensor.Float32()
self.SetTags("Output", 'desc:"CurLED one-hot output tensor"')
def ValsTsr(ss, name):
"""
ValsTsr gets value tensor of given name, creating if not yet made
"""
if name in ss.ValsTsrs:
return ss.ValsTsrs[name]
tsr = etensor.Float32()
ss.ValsTsrs[name] = tsr
return tsr
def PrjnPlot(ss):
ss.TestAll()
rvec0 = ss.ValsTsr("rvec0")
rvec1 = ss.ValsTsr("rvec1")
rvec0.SetShape(go.Slice_int([256]), go.nil, go.nil)
rvec1.SetShape(go.Slice_int([256]), go.nil, go.nil)
for i in range(256):
rvec0.Values[i] = .15 * (2*rand.Float32() - 1)
rvec1.Values[i] = .15 * (2*rand.Float32() - 1)
tst = ss.TstTrlLog
nr = tst.Rows
dt = ss.PrjnTable
ss.ConfigPrjnTable(dt)
for r in range(nr):
emote = 0.5*tst.CellTensorFloat1D("Emotion", r, 0) + -0.5*tst.CellTensorFloat1D("Emotion", r, 1)
emote += .1 * (2*rand.Float64() - 1)
gend = 0.5*tst.CellTensorFloat1D("Gender", r, 0) + -0.5*tst.CellTensorFloat1D("Gender", r, 1)
gend += .1 * (2*rand.Float64() - 1) # some jitter so labels are readable
input = etensor.Float32(tst.CellTensor("Input", r))
rprjn0 = metric.InnerProduct32(rvec0.Values, input.Values)
rprjn1 = metric.InnerProduct32(rvec1.Values, input.Values)
dt.SetCellFloat("Trial", r, tst.CellFloat("Trial", r))
dt.SetCellString("TrialName", r, tst.CellString("TrialName", r))
dt.SetCellFloat("GendPrjn", r, gend)
dt.SetCellFloat("EmotePrjn", r, emote)
dt.SetCellFloat("RndPrjn0", r, float(rprjn0))
dt.SetCellFloat("RndPrjn1", r, float(rprjn1))
plt = ss.PrjnRandom
plt.InitName(plt, "PrjnRandom")
plt.Params.Title = "Face Random Prjn Plot"
plt.Params.XAxisCol = "RndPrjn0"
plt.SetTable(dt)
plt.Params.Lines = False
plt.Params.Points = True
# order of params: on, fixMin, min, fixMax, max
plt.SetColParams("TrialName", eplot.On, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("RndPrjn0", eplot.Off, eplot.FixMin, -1, eplot.FixMax, 1)
plt.SetColParams("RndPrjn1", eplot.On, eplot.FixMin, -1, eplot.FixMax, 1)
plt = ss.PrjnEmoteGend
plt.InitName(plt, "PrjnEmoteGend")
plt.Params.Title = "Face Emotion / Gender Prjn Plot"
plt.Params.XAxisCol = "GendPrjn"
plt.SetTable(dt)
plt.Params.Lines = False
plt.Params.Points = True
# order of params: on, fixMin, min, fixMax, max
plt.SetColParams("TrialName", eplot.On, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("GendPrjn", eplot.Off, eplot.FixMin, -1, eplot.FixMax, 1)
plt.SetColParams("EmotePrjn", eplot.On, eplot.FixMin, -1, eplot.FixMax, 1)
def MtxInput(ss, dt):
col = etensor.Float32(dt)
vals = col.Values
inp = leabra.Layer(ss.Net.LayerByName("Input"))
isz = inp.Shape().Len()
hid = leabra.Layer(ss.Net.LayerByName("MatrixGo"))
ysz = hid.Shape().Dim(2)
xsz = hid.Shape().Dim(3)
for y in range(ysz):
for x in range(xsz):
ui = (y * xsz + x)
ust = ui * isz
vls = vals[ust:ust + isz]
inp.SendPrjnVals(vls, "Wt", hid, ui, "")
def __init__(self):
super(SemEnv, self).__init__()
self.Nm = str()
self.SetTags("Nm", 'desc:"name of this environment"')
self.Dsc = str()
self.SetTags("Dsc", 'desc:"description of this environment"')
self.Sequential = False
self.SetTags(
"Sequential",
'desc:"if true, go sequentially through paragraphs -- else permuted"'
)
self.Order = go.Slice_int()
self.SetTags(
"Order",
'desc:"permuted order of paras to present if not sequential -- updated every time through the list"'
)
self.TextFiles = []
self.SetTags("TextFiles", 'desc:"paths to text files"')
self.Words = go.Slice_string()
self.SetTags("Words", 'desc:"list of words, in alpha order"')
self.WordMap = {}
self.SetTags("WordMap",
'view:"-" desc:"map of words onto index in Words list"')
self.CurParaState = etensor.Float32()
self.SetTags("CurParaState", 'desc:"current para activation state"')
self.Paras = []
self.SetTags("Paras", 'view:"-" desc:"paragraphs"')
self.ParaLabels = []
self.SetTags(
"ParaLabels",
'view:"-" desc:"special labels for each paragraph (provided in first word of para)"'
)
self.Run = env.Ctr()
self.SetTags(
"Run",
'view:"inline" desc:"current run of model as provided during Init"'
)
self.Epoch = env.Ctr()
self.SetTags(
"Epoch",
'view:"inline" desc:"number of times through Seq.Max number of sequences"'
)
self.Trial = env.Ctr()
self.SetTags(
"Trial",
'view:"inline" desc:"trial is the step counter within epoch -- this is the index into Paras"'
)
def Numpy(ss):
"""
test conversions to / from numpy
"""
dt = ss.Pats
print("\n\n##############################")
print("to / from numpy")
etf = etensor.Float32(dt.Cols[1])
npf = pyet.etensor_to_numpy(etf)
print(npf)
ctf = pyet.numpy_to_etensor(npf)
print(ctf)
etu32 = etensor.NewUint32(go.Slice_int([3,4,5]), go.nil, go.nil)
sz = etf.Len()
for i in range(sz):
etu32.Values[i] = int(etf.Values[i])
print(etu32)
npu32 = pyet.etensor_to_numpy(etu32)
print(npu32)
ctu32 = pyet.numpy_to_etensor(npu32)
print(ctu32)
pyet.copy_etensor_to_numpy(npu32, etu32)
pyet.copy_numpy_to_etensor(etu32, npu32)
ets = etensor.String(dt.Cols[0])
nps = pyet.etensor_to_numpy(ets)
print(nps)
cts = pyet.numpy_to_etensor(nps)
print(cts)
pyet.copy_etensor_to_numpy(nps, ets)
pyet.copy_numpy_to_etensor(ets, nps)
etb = etensor.NewBits(go.Slice_int([3,4,5]), go.nil, go.nil)
sz = etb.Len()
for i in range(sz):
etb.Set1D(i, erand.BoolProb(.2, -1))
print(etb)
npb = pyet.etensor_to_numpy(etb)
print(npb)
ctb = pyet.numpy_to_etensor(npb)
print(ctb)
pyet.copy_etensor_to_numpy(npb, etb)
pyet.copy_numpy_to_etensor(etb, npb)
def etensor_to_numpy(et):
"""
returns a numpy ndarray constructed from the given etensor.Tensor.
data is copied into the numpy ndarray -- it is not a view.
"""
nar = 0
if et.DataType() == etensor.UINT8:
nar = np.array(etensor.Uint8(et).Values, dtype=np.uint8)
elif et.DataType() == etensor.INT8:
nar = np.array(etensor.Int8(et).Values, dtype=np.int8)
elif et.DataType() == etensor.UINT16:
nar = np.array(etensor.Uint16(et).Values, dtype=np.uint16)
elif et.DataType() == etensor.INT16:
nar = np.array(etensor.Int16(et).Values, dtype=np.int16)
elif et.DataType() == etensor.UINT32:
nar = np.array(etensor.Uint32(et).Values, dtype=np.uint32)
elif et.DataType() == etensor.INT32:
nar = np.array(etensor.Int32(et).Values, dtype=np.int32)
elif et.DataType() == etensor.UINT64:
nar = np.array(etensor.Uint64(et).Values, dtype=np.uint64)
elif et.DataType() == etensor.INT64:
nar = np.array(etensor.Int64(et).Values, dtype=np.int64)
elif et.DataType() == etensor.FLOAT32:
nar = np.array(etensor.Float32(et).Values, dtype=np.float32)
elif et.DataType() == etensor.FLOAT64:
nar = np.array(etensor.Float64(et).Values, dtype=np.float64)
elif et.DataType() == etensor.STRING:
nar = np.array(etensor.String(et).Values)
elif et.DataType() == etensor.INT:
nar = np.array(etensor.Int(et).Values, dtype=np.intc)
elif et.DataType() == etensor.BOOL:
etb = etensor.Bits(et)
sz = etb.Len()
nar = np.zeros(sz, dtype=np.bool_)
for i in range(sz):
nar[i] = etb.Value1D(i)
else:
raise TypeError("tensor with type %s cannot be converted" %
(et.DataType().String()))
return 0
# there does not appear to be a way to set the shape at the same time as initializing
return nar.reshape(et.Shapes())
def copy_numpy_to_etensor(et, nar):
"""
copies data from numpy ndarray (nar, source) to existing etensor.Tensor (et, dest)
"""
narf = np.reshape(nar, -1)
etv = et
if et.DataType() == etensor.UINT8:
etv = etensor.Uint8(et).Values
elif et.DataType() == etensor.INT8:
etv = etensor.Int8(et).Values
elif et.DataType() == etensor.UINT16:
etv = etensor.Uint16(et).Values
elif et.DataType() == etensor.INT16:
etv = etensor.Int16(et).Values
elif et.DataType() == etensor.UINT32:
etv = etensor.Uint32(et).Values
elif et.DataType() == etensor.INT32:
etv = etensor.Int32(et).Values
elif et.DataType() == etensor.UINT64:
etv = etensor.Uint64(et).Values
elif et.DataType() == etensor.INT64:
etv = etensor.Int64(et).Values
elif et.DataType() == etensor.FLOAT32:
etv = etensor.Float32(et).Values
elif et.DataType() == etensor.FLOAT64:
etv = etensor.Float64(et).Values
elif et.DataType() == etensor.STRING:
etv = etensor.String(et).Values
elif et.DataType() == etensor.INT:
etv = etensor.Int(et).Values
elif et.DataType() == etensor.BOOL:
etb = etensor.Bits(et)
sz = min(etb.Len(), len(narf))
for i in range(sz):
narf[i] = etb.Value1D(i)
return
else:
raise TypeError("tensor with type %s cannot be copied" %
(et.DataType().String()))
return 0
etv.copy(narf) # go slice copy, not python copy = clone
def __init__(self):
super(Sim, self).__init__()
self.ExcitLateralScale = float(0.2)
self.SetTags(
"ExcitLateralScale",
'def:"0.2" desc:"excitatory lateral (recurrent) WtScale.Rel value"'
)
self.InhibLateralScale = float(0.2)
self.SetTags(
"InhibLateralScale",
'def:"0.2" desc:"inhibitory lateral (recurrent) WtScale.Abs value"'
)
self.ExcitLateralLearn = True
self.SetTags(
"ExcitLateralLearn",
'def:"true" desc:"do excitatory lateral (recurrent) connections learn?"'
)
self.Net = leabra.Network()
self.SetTags(
"Net",
'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"'
)
self.Probes = etable.Table()
self.SetTags("Probes", 'view:"no-inline" desc:"probe inputs"')
self.TrnEpcLog = etable.Table()
self.SetTags("TrnEpcLog",
'view:"no-inline" desc:"training epoch-level log data"')
self.TstEpcLog = etable.Table()
self.SetTags("TstEpcLog",
'view:"no-inline" desc:"testing epoch-level log data"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog",
'view:"no-inline" desc:"testing trial-level log data"')
self.RunLog = etable.Table()
self.SetTags("RunLog",
'view:"no-inline" desc:"summary log of each run"')
self.RunStats = etable.Table()
self.SetTags("RunStats",
'view:"no-inline" desc:"aggregate stats on all runs"')
self.Params = params.Sets()
self.SetTags("Params",
'view:"no-inline" desc:"full collection of param sets"')
self.ParamSet = str()
self.SetTags(
"ParamSet",
'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!)"'
)
self.V1onWts = etensor.Float32()
self.SetTags("V1onWts",
'view:"-" desc:"weights from input to V1 layer"')
self.V1offWts = etensor.Float32()
self.SetTags("V1offWts",
'view:"-" desc:"weights from input to V1 layer"')
self.V1Wts = etensor.Float32()
self.SetTags(
"V1Wts",
'view:"no-inline" desc:"net on - off weights from input to V1 layer"'
)
self.MaxRuns = int(1)
self.SetTags("MaxRuns",
'desc:"maximum number of model runs to perform"')
self.MaxEpcs = int(100)
self.SetTags("MaxEpcs",
'desc:"maximum number of epochs to run per model run"')
self.MaxTrls = int(100)
self.SetTags("MaxTrls",
'desc:"maximum number of training trials per epoch"')
self.NZeroStop = int(-1)
self.SetTags(
"NZeroStop",
'desc:"if a positive number, training will stop after this many epochs with zero SSE"'
)
self.TrainEnv = ImgEnv()
self.SetTags("TrainEnv",
'desc:"Training environment -- visual images"')
self.TestEnv = env.FixedTable()
self.SetTags(
"TestEnv",
'desc:"Testing environment -- manages iterating over testing"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewOn = True
self.SetTags(
"ViewOn",
'desc:"whether to update the network view while running"')
self.TrainUpdt = leabra.TimeScales.AlphaCycle
self.SetTags(
"TrainUpdt",
'desc:"at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model"'
)
self.TestUpdt = leabra.TimeScales.Cycle
self.SetTags(
"TestUpdt",
'desc:"at what time scale to update the display during testing? Anything longer than Epoch updates at Epoch in this model"'
)
self.LayStatNms = go.Slice_string(["V1"])
self.SetTags(
"LayStatNms",
'desc:"names of layers to collect more detailed stats on (avg act, etc)"'
)
# statistics: note use float64 as that is best for etable.Table
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.CurImgGrid = 0
self.SetTags("CurImgGrid",
'view:"-" desc:"the current image grid view"')
self.WtsGrid = 0
self.SetTags("WtsGrid", 'view:"-" desc:"the weights grid view"')
self.TrnEpcPlot = 0
self.SetTags("TrnEpcPlot", 'view:"-" desc:"the training epoch plot"')
self.TstEpcPlot = 0
self.SetTags("TstEpcPlot", 'view:"-" desc:"the testing epoch plot"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.RunPlot = 0
self.SetTags("RunPlot", 'view:"-" desc:"the run plot"')
self.TrnEpcFile = 0
self.SetTags("TrnEpcFile", 'view:"-" desc:"log file"')
self.RunFile = 0
self.SetTags("RunFile", 'view:"-" desc:"log file"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.NeedsNewRun = False
self.SetTags(
"NeedsNewRun",
'view:"-" desc:"flag to initialize NewRun if last one finished"')
self.RndSeed = int(1)
self.SetTags("RndSeed", 'view:"-" desc:"the current random seed"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.BurstDaGain = float(1)
self.SetTags(
"BurstDaGain",
'min:"0" step:"0.1" desc:"strength of dopamine bursts: 1 default -- reduce for PD OFF, increase for PD ON"'
)
self.DipDaGain = float(1)
self.SetTags(
"DipDaGain",
'min:"0" step:"0.1" desc:"strength of dopamine dips: 1 default -- reduce to siulate D2 agonists"'
)
self.Net = pbwm.Network()
self.SetTags(
"Net",
'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"'
)
self.TrnEpcLog = etable.Table()
self.SetTags("TrnEpcLog",
'view:"no-inline" desc:"training epoch-level log data"')
self.TstEpcLog = etable.Table()
self.SetTags("TstEpcLog",
'view:"no-inline" desc:"testing epoch-level log data"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog",
'view:"no-inline" desc:"testing trial-level log data"')
self.MtxInputWts = etensor.Float32()
self.SetTags(
"MtxInputWts",
'view:"no-inline" desc:"weights from input to hidden layer"')
self.RunLog = etable.Table()
self.SetTags("RunLog",
'view:"no-inline" desc:"summary log of each run"')
self.RunStats = etable.Table()
self.SetTags("RunStats",
'view:"no-inline" desc:"aggregate stats on all runs"')
self.Params = params.Sets()
self.SetTags("Params",
'view:"no-inline" desc:"full collection of param sets"')
self.ParamSet = str()
self.SetTags(
"ParamSet",
'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!)"'
)
self.MaxRuns = int(1)
self.SetTags("MaxRuns",
'desc:"maximum number of model runs to perform"')
self.MaxEpcs = int(30)
self.SetTags("MaxEpcs",
'desc:"maximum number of epochs to run per model run"')
self.MaxTrls = int(100)
self.SetTags("MaxTrls",
'desc:"maximum number of training trials per epoch"')
self.TrainEnv = BanditEnv()
self.SetTags("TrainEnv",
'desc:"Training environment -- bandit environment"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewOn = True
self.SetTags(
"ViewOn",
'desc:"whether to update the network view while running"')
self.TrainUpdt = leabra.TimeScales.AlphaCycle
self.SetTags(
"TrainUpdt",
'desc:"at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model"'
)
self.TestUpdt = leabra.TimeScales.AlphaCycle
self.SetTags(
"TestUpdt",
'desc:"at what time scale to update the display during testing? Anything longer than Epoch updates at Epoch in this model"'
)
self.TstRecLays = go.Slice_string(["MatrixGo", "MatrixNoGo"])
self.SetTags(
"TstRecLays",
'desc:"names of layers to record activations etc of during testing"'
)
# internal state - view:"-"
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.WtsGrid = 0
self.SetTags("WtsGrid", 'view:"-" desc:"the weights grid view"')
self.TrnEpcPlot = 0
self.SetTags("TrnEpcPlot", 'view:"-" desc:"the training epoch plot"')
self.TstEpcPlot = 0
self.SetTags("TstEpcPlot", 'view:"-" desc:"the testing epoch plot"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.RunPlot = 0
self.SetTags("RunPlot", 'view:"-" desc:"the run plot"')
self.TrnEpcFile = 0
self.SetTags("TrnEpcFile", 'view:"-" desc:"log file"')
self.RunFile = 0
self.SetTags("RunFile", 'view:"-" desc:"log file"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.NeedsNewRun = False
self.SetTags(
"NeedsNewRun",
'view:"-" desc:"flag to initialize NewRun if last one finished"')
self.RndSeed = int(1)
self.SetTags("RndSeed", 'view:"-" desc:"the current random seed"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(SentGenEnv, self).__init__()
self.Nm = str()
self.SetTags("Nm", 'desc:"name of this environment"')
self.Dsc = str()
self.SetTags("Dsc", 'desc:"description of this environment"')
self.Rules = esg.Rules()
self.SetTags(
"Rules",
'desc:"core sent-gen rules -- loaded from a grammar / rules file -- Gen() here generates one sentence"'
)
self.PPassive = float()
self.SetTags(
"PPassive",
'desc:"probability of generating passive sentence forms"')
self.WordTrans = {}
self.SetTags(
"WordTrans",
'desc:"translate unambiguous words into ambiguous words"')
self.Words = go.Slice_string()
self.SetTags(
"Words",
'desc:"list of words used for activating state units according to index"'
)
self.WordMap = {}
self.SetTags("WordMap", 'desc:"map of words onto index in Words list"')
self.Roles = []
self.SetTags(
"Roles",
'desc:"list of roles used for activating state units according to index"'
)
self.RoleMap = {}
self.SetTags("RoleMap", 'desc:"map of roles onto index in Roles list"')
self.Fillers = []
self.SetTags(
"Fillers",
'desc:"list of filler concepts used for activating state units according to index"'
)
self.FillerMap = {}
self.SetTags("FillerMap",
'desc:"map of roles onto index in Words list"')
self.AmbigVerbs = []
self.SetTags("AmbigVerbs", 'desc:"ambiguous verbs"')
self.AmbigNouns = []
self.SetTags("AmbigNouns", 'desc:"ambiguous nouns"')
self.AmbigVerbsMap = {}
self.SetTags("AmbigVerbsMap", 'desc:"map of ambiguous verbs"')
self.AmbigNounsMap = {}
self.SetTags("AmbigNounsMap", 'desc:"map of ambiguous nouns"')
self.CurSentOrig = []
self.SetTags(
"CurSentOrig",
'desc:"original current sentence as generated from Rules"')
self.CurSent = []
self.SetTags(
"CurSent",
'desc:"current sentence, potentially transformed to passive form"')
self.NAmbigNouns = int()
self.SetTags("NAmbigNouns", 'desc:"number of ambiguous nouns"')
self.NAmbigVerbs = int()
self.SetTags("NAmbigVerbs",
'desc:"number of ambiguous verbs (0 or 1)"')
self.SentInputs = []
self.SetTags(
"SentInputs",
'desc:"generated sequence of sentence inputs including role-filler queries"'
)
self.SentIdx = env.CurPrvInt()
self.SetTags("SentIdx", 'desc:"current index within sentence inputs"')
self.QType = str()
self.SetTags(
"QType",
'desc:"current question type -- from 4th value of SentInputs"')
self.WordState = etensor.Float32()
self.SetTags("WordState", 'desc:"current sentence activation state"')
self.RoleState = etensor.Float32()
self.SetTags("RoleState", 'desc:"current role query activation state"')
self.FillerState = etensor.Float32()
self.SetTags("FillerState",
'desc:"current filler query activation state"')
self.Run = env.Ctr()
self.SetTags(
"Run",
'view:"inline" desc:"current run of model as provided during Init"'
)
self.Epoch = env.Ctr()
self.SetTags(
"Epoch",
'view:"inline" desc:"number of times through Seq.Max number of sequences"'
)
self.Seq = env.Ctr()
self.SetTags("Seq",
'view:"inline" desc:"sequence counter within epoch"')
self.Tick = env.Ctr()
self.SetTags("Tick",
'view:"inline" desc:"tick counter within sequence"')
self.Trial = env.Ctr()
self.SetTags(
"Trial",
'view:"inline" desc:"trial is the step counter within sequence - how many steps taken within current sequence -- it resets to 0 at start of each sequence"'
)
def __init__(self):
super(Sim, self).__init__()
self.AvgLGain = float(2.5)
self.SetTags("AvgLGain", 'min:"0.1" step:"0.5" def:"2.5" desc:"key BCM hebbian learning parameter, that determines how high the floating threshold goes -- higher = more homeostatic pressure against rich-get-richer feedback loops"')
self.InputNoise = float(0)
self.SetTags("InputNoise", 'min:"0" def:"0" desc:"variance on gaussian noise to add to inputs"')
self.TrainGi = float(1.8)
self.SetTags("TrainGi", 'min:"0" step:"0.1" def:"1.8" desc:"strength of inhibition during training with two lines present in input"')
self.TestGi = float(2.5)
self.SetTags("TestGi", 'min:"0" step:"0.1" def:"2.5" desc:"strength of inhibition during testing with one line present in input -- higher because fewer neurons should be active"')
self.Net = leabra.Network()
self.SetTags("Net", 'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"')
self.Lines2 = etable.Table()
self.SetTags("Lines2", 'view:"no-inline" desc:"easy training patterns -- can be learned with Hebbian"')
self.Lines1 = etable.Table()
self.SetTags("Lines1", 'view:"no-inline" desc:"hard training patterns -- require error-driven"')
self.TrnEpcLog = etable.Table()
self.SetTags("TrnEpcLog", 'view:"no-inline" desc:"training epoch-level log data"')
self.TstEpcLog = etable.Table()
self.SetTags("TstEpcLog", 'view:"no-inline" desc:"testing epoch-level log data"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog", 'view:"no-inline" desc:"testing trial-level log data"')
self.HidFmInputWts = etensor.Float32()
self.SetTags("HidFmInputWts", 'view:"no-inline" desc:"weights from input to hidden layer"')
self.RunLog = etable.Table()
self.SetTags("RunLog", 'view:"no-inline" desc:"summary log of each run"')
self.RunStats = etable.Table()
self.SetTags("RunStats", 'view:"no-inline" desc:"aggregate stats on all runs"')
self.SimMat = simat.SimMat()
self.SetTags("SimMat", 'view:"no-inline" desc:"similarity matrix"')
self.Params = params.Sets()
self.SetTags("Params", 'view:"no-inline" desc:"full collection of param sets"')
self.ParamSet = str()
self.SetTags("ParamSet", '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!)"')
self.MaxRuns = int(8)
self.SetTags("MaxRuns", 'desc:"maximum number of model runs to perform"')
self.MaxEpcs = int(30)
self.SetTags("MaxEpcs", 'desc:"maximum number of epochs to run per model run"')
self.TrainEnv = env.FixedTable()
self.SetTags("TrainEnv", 'desc:"Training environment -- contains everything about iterating over input / output patterns over training"')
self.TestEnv = env.FixedTable()
self.SetTags("TestEnv", 'desc:"Testing environment -- manages iterating over testing"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewOn = True
self.SetTags("ViewOn", 'desc:"whether to update the network view while running"')
self.TrainUpdt = leabra.TimeScales.AlphaCycle
self.SetTags("TrainUpdt", 'desc:"at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model"')
self.TestUpdt = leabra.TimeScales.Cycle
self.SetTags("TestUpdt", 'desc:"at what time scale to update the display during testing? Anything longer than Epoch updates at Epoch in this model"')
self.TestInterval = int(1)
self.SetTags("TestInterval", 'desc:"how often to run through all the test patterns, in terms of training epochs"')
self.TstRecLays = go.Slice_string(["Input", "Hidden"])
self.SetTags("TstRecLays", 'desc:"names of layers to record activations etc of during testing"')
self.UniqPats = float(0)
self.SetTags("UniqPats", 'inactive:"+" desc:"number of uniquely-coded line patterns, computed during testing -- maximum 10, higher is better"')
# internal state - view:"-"
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.WtsGrid = 0
self.SetTags("WtsGrid", 'view:"-" desc:"the weights grid view"')
self.TrnEpcPlot = 0
self.SetTags("TrnEpcPlot", 'view:"-" desc:"the training epoch plot"')
self.TstEpcPlot = 0
self.SetTags("TstEpcPlot", 'view:"-" desc:"the testing epoch plot"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.RunPlot = 0
self.SetTags("RunPlot", 'view:"-" desc:"the run plot"')
self.TrnEpcFile = 0
self.SetTags("TrnEpcFile", 'view:"-" desc:"log file"')
self.RunFile = 0
self.SetTags("RunFile", 'view:"-" desc:"log file"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.NeedsNewRun = False
self.SetTags("NeedsNewRun", 'view:"-" desc:"flag to initialize NewRun if last one finished"')
self.RndSeed = int(1)
self.SetTags("RndSeed", 'view:"-" desc:"the current random seed"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Vis, self).__init__()
self.V1sGabor = gabor.Filter()
self.SetTags("V1sGabor", 'desc:"V1 simple gabor filter parameters"')
self.V1sGeom = vfilter.Geom()
self.SetTags(
"V1sGeom",
'inactive:"+" view:"inline" desc:"geometry of input, output for V1 simple-cell processing"'
)
self.V1sNeighInhib = kwta.NeighInhib()
self.SetTags(
"V1sNeighInhib",
'desc:"neighborhood inhibition for V1s -- each unit gets inhibition from same feature in nearest orthogonal neighbors -- reduces redundancy of feature code"'
)
self.V1sKWTA = kwta.KWTA()
self.SetTags("V1sKWTA", 'desc:"kwta parameters for V1s"')
self.ImgSize = image.Point()
self.SetTags(
"ImgSize",
'desc:"target image size to use -- images will be rescaled to this size"'
)
self.V1sGaborTsr = etensor.Float32()
self.SetTags("V1sGaborTsr",
'view:"no-inline" desc:"V1 simple gabor filter tensor"')
self.ImgTsr = etensor.Float32()
self.SetTags("ImgTsr", 'view:"no-inline" desc:"input image as tensor"')
self.Img = image.Image()
self.SetTags("Img", 'view:"-" desc:"current input image"')
self.V1sTsr = etensor.Float32()
self.SetTags(
"V1sTsr",
'view:"no-inline" desc:"V1 simple gabor filter output tensor"')
self.V1sExtGiTsr = etensor.Float32()
self.SetTags(
"V1sExtGiTsr",
'view:"no-inline" desc:"V1 simple extra Gi from neighbor inhibition tensor"'
)
self.V1sKwtaTsr = etensor.Float32()
self.SetTags(
"V1sKwtaTsr",
'view:"no-inline" desc:"V1 simple gabor filter output, kwta output tensor"'
)
self.V1sPoolTsr = etensor.Float32()
self.SetTags(
"V1sPoolTsr",
'view:"no-inline" desc:"V1 simple gabor filter output, max-pooled 2x2 of V1sKwta tensor"'
)
self.V1sUnPoolTsr = etensor.Float32()
self.SetTags(
"V1sUnPoolTsr",
'view:"no-inline" desc:"V1 simple gabor filter output, un-max-pooled 2x2 of V1sPool tensor"'
)
self.V1sAngOnlyTsr = etensor.Float32()
self.SetTags(
"V1sAngOnlyTsr",
'view:"no-inline" desc:"V1 simple gabor filter output, angle-only features tensor"'
)
self.V1sAngPoolTsr = etensor.Float32()
self.SetTags(
"V1sAngPoolTsr",
'view:"no-inline" desc:"V1 simple gabor filter output, max-pooled 2x2 of AngOnly tensor"'
)
self.V1cLenSumTsr = etensor.Float32()
self.SetTags(
"V1cLenSumTsr",
'view:"no-inline" desc:"V1 complex length sum filter output tensor"'
)
self.V1cEndStopTsr = etensor.Float32()
self.SetTags(
"V1cEndStopTsr",
'view:"no-inline" desc:"V1 complex end stop filter output tensor"')
self.V1AllTsr = etensor.Float32()
self.SetTags(
"V1AllTsr",
'view:"no-inline" desc:"Combined V1 output tensor with V1s simple as first two rows, then length sum, then end stops = 5 rows total"'
)
self.V1sInhibs = fffb.Inhibs()
self.SetTags("V1sInhibs",
'view:"no-inline" desc:"inhibition values for V1s KWTA"')
