Pyro SRN Experiments

This section will explore a series of experiments originally designed and run by Jeffery Elman.

First, we will generate a corpus of data of the form:

dog walks .
dog who dogs who hit chase feeds .
John chases .
girl who dogs who feed live walks .
Mary lives .
boy sees .
Mary hits .
John hits .
cat sees .
girls who hear walk .
boy who hits feeds .
cat lives .
girls who hear hit .

Results

Experiment #1: The network was trained on a corpus of sentences, and the it failed miserably. It learned some, but overall very poorly. Maybe Gold was right?

Experiment #2: The network was trained according to a strict schedule, 5 epochs each:

Phase 1	10,000 simple sentences	0 complex sentences
Phase 2	7,500 simple sentences	2,500 complex sentences
Phase 3	5,000 simple sentences	5,000 complex sentences
Phase 4	2,500 simple sentences	7,500 complex sentences
Phase 5	0 simple sentences	10,000 complex sentences

This worked! However, it was very ad hoc and probably took some fiddling by a graduate student to get it to work correctly. In addition, the environment was manipulated in a manner very much unlike the way that children learn language; they are exposed to it in all its complexity from early on. Could it be learned without such a strict manipulative schedule?

Experiment #3: The context bank of the network was randomly wiped out every 2 or 3 words with random patterns. The length between wipe-outs was slowly increased, until no wipe-outs were made.

This worked!

from pyrobot.brain.conx import SRN
import pyrobot.system.debug

#class mySRN(SRN):
#    def preStep(self):
#        print "pre step..."

network = SRN()
network.setSequenceType("random-continuous")

network.addLayer("input", 26)
network.addContextLayer("context", 70, "hidden")
network.addLayer("hid1", 10)
network.addLayer("hidden", 70)
network.addLayer("hid2", 10)
network.addLayer("output", 26)

network.connect("input", "hid1")
network.connect("hid1", "hidden")
network.connect("context", "hidden")
network.connect("hidden", "hid2")
network.connect("hid2", "output")

def makePattern(n):
    retval = [0.0] * 26
    retval[-n] = 1.0
    return retval

network.predict("input", "output")

network.setPatterns( {".": makePattern(1),
                      "boy": makePattern(2),
                      "girl": makePattern(3),
                      "cat": makePattern(4),
                      "dog": makePattern(5),
                      "chase": makePattern(6),
                      "feed": makePattern(7),
                      "see": makePattern(8),
                      "hear": makePattern(9),
                      "walk": makePattern(10),
                      "live": makePattern(11),
                      "boys": makePattern(12),
                      "girls": makePattern(13),
                      "cats": makePattern(14),
                      "dogs": makePattern(15),
                      "chases": makePattern(16),
                      "feeds": makePattern(17),
                      "sees": makePattern(18),
                      "hears": makePattern(19),
                      "walks": makePattern(20),
                      "lives": makePattern(21),
                      "John": makePattern(22),
                      "Mary": makePattern(23),
                      "hit": makePattern(24),
                      "hits": makePattern(25),
                      "who": makePattern(26) } )

network.loadInputPatternsFromFile("elman100.dat", checkEven=0)
#network.setInteractive(1)
network.setReportRate(1)
network.train()

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