def countex(epochs=5000,nbits=15,ncases=500,lrate=0.5,showint=500,mbs=20,vfrac=0.1,tfrac=0.1,vint=200,sm=True,bestk=1):
case_generator = (lambda: TFT.gen_vector_count_cases(ncases,nbits))
cman = Caseman(cfunc=case_generator, vfrac=vfrac, tfrac=tfrac)
ann = Gann(dims=[nbits, nbits*3, nbits+1], cman=cman, lrate=lrate, showint=showint, mbs=mbs, vint=vint, softmax=sm)
ann.run(epochs,bestk=bestk)
TFT.fireup_tensorboard('probeview')
return ann
def autoex(epochs=300,nbits=4,lrate=0.03,showint=100,mbs=None,vfrac=0.1,tfrac=0.1,vint=100,sm=False,bestk=None):
size = 2**nbits
mbs = mbs if mbs else size
case_generator = (lambda : TFT.gen_all_one_hot_cases(2**nbits))
cman = Caseman(cfunc=case_generator,vfrac=vfrac,tfrac=tfrac)
ann = Gann(dims=[size,nbits,size],cman=cman,lrate=lrate,showint=showint,mbs=mbs,vint=vint,softmax=sm)
ann.gen_probe(0,'wgt',('hist','avg')) # Plot a histogram and avg of the incoming weights to module 0.
ann.gen_probe(1,'out',('avg','max')) # Plot average and max value of module 1's output vector
ann.add_grabvar(0,'wgt') # Add a grabvar (to be displayed in its own matplotlib window).
ann.run(epochs,bestk=bestk)
ann.runmore(epochs*2,bestk=bestk)
TFT.fireup_tensorboard('probeview')
return ann
def main():
parser = argument_parser.argument_parser()
parser.parse()
parser.organize()
# (self, cases, vfrac, tfrac, casefrac, mapsep)
caseman = gann_base.Caseman(parser.data_set_v, parser.vfrac_v,
parser.tfrac_v, parser.casefrac_v,
parser.mapbs_v)
# (self, dims, cman, afunc, ofunc, cfunc, optimizer, lrate, wrange, vint, mbs, usevsi, showint=None):
ann = gann_base.Gann(parser.dims_v,
caseman,
parser.afunc_v,
parser.ofunc_v,
parser.cfunc_v,
parser.optimizer_v,
parser.lrate_v,
parser.wrange_v,
parser.vint_v,
parser.mbs_v,
parser.usevsi_v,
showint=parser.steps_v - 1)
for layer in parser.dispw_v:
ann.add_grabvar(layer, type='wgt')
ann.gen_probe(layer, 'wgt', 'hist')
for layer in parser.dispb_v:
ann.add_grabvar(layer, type='bias')
ann.gen_probe(layer, 'bias', 'hist')
# run, then map
ann.run(steps=parser.steps_v,
sess=None,
continued=False,
bestk=parser.best1_v)
ann.remove_grabvars()
for layer in parser.maplayers_v:
if layer == 0:
ann.add_grabvar(layer, type='in', add_figure=False)
else:
ann.add_grabvar(layer - 1, type='out', add_figure=False)
res, labs = ann.do_mapping()
results = []
for i in range(len(res[0])):
l = np.array([r[i] for r in res])
l = l.reshape(l.shape[0], l.shape[2])
TFT.hinton_plot(l,
title="mapping test output of layer " +
str(parser.maplayers_v[i]))
results.append(l)
for i, r in enumerate(results):
# DENDOGRAM
# if parser.maplayers_v[i] in parser.mapdend_v:
if parser.best1_v:
TFT.dendrogram(r,
list(map(TFT.one_hot_to_int, labs)),
title="Dendrogram " + str(parser.maplayers_v[i]))
gann_base.PLT.show()
TFT.fireup_tensorboard('probeview')