Example #1
0
File: matnn.py Project: werg/gcpp 
    def run_clean(self, times = None):
        """Runs a generator so that output is clean, as if it came from corpus."""
        if times is None:
            times = cf['eval_len']
        for out in self.run_times(self.gen_input(self.input_size), times):
#            import pdb; pdb.set_trace()
            yield helpers.extremify(out)
Example #2
0
File: resnet.py Project: werg/gcpp 
def crosstrain(generator, classifier, learnrate, refdata, times):
	success = [None for i in xrange(times)]
	outs = [None for i in xrange(times)]
	states = [None for i in xrange(times)]

	classchanges = np.zeros_like(classifier.outnet)
	
	for i in xrange(times):
		(outs[i],states[i]) = generator.update_step()
		(success[i], classchange) = classifier.trainstep(helpers.extremify(outs[i]), -1)
		classchanges += learnrate/times * classchange
		
	smoothify_success(success)
	
	genchanges = np.zeros_like(generator.outnet)
	for (out, state, suc) in zip(outs, states, success):
		genchanges += learnrate/times * weightchange(out, helpers.extremify(suc * out), state)

	
	for (i,ref) in zip(xrange(times), refdata):
		(suc, classchange) = classifier.trainstep(ref, 1)
		classchanges += learnrate/times * classchange
		
	return (genchanges, classchanges)
Example #3
0
File: resnet.py Project: werg/gcpp 
def extremeRI(input_size):
	for i in randomInput(input_size):
		yield helpers.extremify(i)
Example #4
0
File: matnn.py Project: werg/gcpp 
 def run_clean(self, times = None):
     """Runs a generator so that output is clean, as if it came from corpus."""
     if times is None:
         times = cf['eval_len']
     for out in run_times(self, self.gen_input, times):
         yield helpers.extremify(out)
Example #5
0
File: matnn.py Project: werg/gcpp 
 def update_step(self, step_input):
     out = HistMatRNN.update_step(self, step_input)
     self.hist = np.vstack((self.hist[self.histentry_size:],helpers.extremify(out)))
     return out