def trainSequence(self, xs, cs, update=1, key=None):
"Train with an integer sequence of codes."
assert xs.shape[1] == self.Ni, "wrong image height"
# forward step
self.outputs = array(self.lstm.forward(xs))
# CTC alignment
self.targets = array(make_target(cs, self.No))
self.aligned = array(
ctc_align_targets(self.outputs,
self.targets,
debug=self.debug_align))
# propagate the deltas back
deltas = self.aligned - self.outputs
self.lstm.backward(deltas)
if update: self.lstm.update()
# translate back into a sequence
result = translate_back(self.outputs)
# compute least square error
self.error = sum(deltas**2)
self.error_log.append(self.error**.5 / len(cs))
# compute class error
self.cerror = levenshtein(cs, result)
self.cerror_log.append((self.cerror, len(cs)))
# training keys
self.key_log.append(key)
return result
def testLevenshtein(a, b, should):
if edist.levenshtein(a, b) == should:
print 'ok - levenshtein(%s, %s) == %s' % (a, b, should)
return 0
else:
print 'not ok - levenshtein(%s, %s) == %s' % (a, b, should)
return 1
def trainSequence(self,xs,cs,update=1,key=None):
"Train with an integer sequence of codes."
assert xs.shape[1]==self.Ni,"wrong image height"
# forward step
self.outputs = array(self.lstm.forward(xs))
# CTC alignment
self.targets = array(make_target(cs,self.No))
self.aligned = array(ctc_align_targets(self.outputs,self.targets,debug=self.debug_align))
# propagate the deltas back
deltas = self.aligned-self.outputs
self.lstm.backward(deltas)
if update: self.lstm.update()
# translate back into a sequence
result = translate_back(self.outputs)
# compute least square error
self.error = sum(deltas**2)
self.error_log.append(self.error**.5/len(cs))
# compute class error
self.cerror = edist.levenshtein(cs,result)
self.cerror_log.append((self.cerror,len(cs)))
# training keys
self.key_log.append(key)
return result