#FORMAT python

# % python -i raam.py # ... runs for a bit # ... look at results, get hidden layer acts at end of sentence # >>> raam.setLayerVerification(0) # >>> retval = raam.propagateFrom("hidden", hidden=[0.89, 0.91, 0.03, 0.76, 0.97, 0.99, 0.11, 0.93]) # >>> retvaloutput # [0.00019695890003820294, 0.99139862359118414, 0.00035552614835753892, 0.021935466446129315] MARY # >>> hid = retvaloutcontext # >>> retval = raam.propagateFrom("hidden", hidden=hid) # >>> retvaloutput # [0.038566659128093157, 0.013443895767298429, 0.90200538449592227, 0.023225075823005009] LIKES # >>> hid = retvaloutcontext # >>> retval = raam.propagateFrom("hidden", hidden=hid) # >>> retvaloutput # [0.015620707998835377, 0.0014200232163398988, 0.027158337904633942, 0.96938726876497006] JOHN # >>> raam.setLayerVerification(1)

# An example showing memory in a sRAAM

from pyrobot.brain.conx import *

# Create network:

raam = SRN() raam.setSequenceType("random-segmented") raam.setPatterns({"john" : [0, 0, 0, 1],

"likes" : [0, 0, 1, 0],

"mary" : [0, 1, 0, 0],

"is" : [1, 0, 0, 0],

})

size = len(raam.getPattern("john")) raam.addSRNLayers(size, size * 2, size) raam.add( Layer("outcontext", size * 2) ) raam.connect("hidden", "outcontext")

raam.associate('input', 'output') raam.associate('context', 'outcontext')

raam.setInputs([ [ "john", "likes", "mary" ],

[ "mary", "likes", "john" ],

[ "john", "is", "john" ],

[ "mary", "is", "mary" ],

])

# Network learning parameters:

raam.setLearnDuringSequence(1) raam.setReportRate(10) raam.setEpsilon(0.1) raam.setMomentum(0.0) raam.setBatch(0)

# Ending criteria: raam.setTolerance(0.4) raam.setStopPercent(1.0) raam.setResetEpoch(5000) raam.setResetLimit(0)

# Train: raam.train()

# Test: raam.setLearning(0) raam.setInteractive(1) raam.sweep() raam.saveWeightsToFile("raam.wts")