109 lines
4.3 KiB
Python
109 lines
4.3 KiB
Python
import sys
|
|
import os
|
|
|
|
# Concat top Level dir to system environmental variables
|
|
sys.path += os.path.join('..', '.')
|
|
|
|
from typing import Tuple
|
|
|
|
from util import *
|
|
from experiment import *
|
|
from network import *
|
|
from soup import *
|
|
|
|
import keras.backend
|
|
|
|
|
|
def generate_counters():
|
|
"""
|
|
Initial build of the counter dict, to store counts.
|
|
|
|
:rtype: dict
|
|
:return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
|
|
"""
|
|
return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
|
|
|
|
|
|
def count(counters, soup, notable_nets=[]):
|
|
"""
|
|
Count the occurences ot the types of weight trajectories.
|
|
|
|
:param counters: A counter dictionary.
|
|
:param soup: A Soup
|
|
:param notable_nets: A list to store and save intersting candidates
|
|
|
|
:rtype Tuple[dict, list]
|
|
:return: Both the counter dictionary and the list of interessting nets.
|
|
"""
|
|
|
|
for net in soup.particles:
|
|
if net.is_diverged():
|
|
counters['divergent'] += 1
|
|
elif net.is_fixpoint():
|
|
if net.is_zero():
|
|
counters['fix_zero'] += 1
|
|
else:
|
|
counters['fix_other'] += 1
|
|
# notable_nets += [net]
|
|
# elif net.is_fixpoint(2):
|
|
# counters['fix_sec'] += 1
|
|
# notable_nets += [net]
|
|
else:
|
|
counters['other'] += 1
|
|
return counters, notable_nets
|
|
|
|
if __name__ == '__main__':
|
|
|
|
with Experiment('mixed-soup') as exp:
|
|
exp.trials = 10
|
|
exp.soup_size = 10
|
|
exp.soup_life = 5
|
|
exp.trains_per_selfattack_values = [10 * i for i in range(11)]
|
|
exp.epsilon = 1e-4
|
|
net_generators = []
|
|
for activation in ['linear']: # ['linear', 'sigmoid', 'relu']:
|
|
for use_bias in [False]:
|
|
net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
|
|
net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
|
|
# net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
|
|
|
|
all_names = []
|
|
all_data = []
|
|
for net_generator_id, net_generator in enumerate(net_generators):
|
|
xs = []
|
|
ys = []
|
|
zs = []
|
|
for trains_per_selfattack in exp.trains_per_selfattack_values:
|
|
counters = generate_counters()
|
|
notable_nets = []
|
|
for soup_idx in tqdm(range(exp.trials)):
|
|
soup = Soup(exp.soup_size,
|
|
lambda net_generator=net_generator, exp=exp: TrainingNeuralNetworkDecorator(
|
|
net_generator()).with_params(epsilon=exp.epsilon))
|
|
soup.with_params(attacking_rate=0.1, learn_from_rate=-1, train=trains_per_selfattack,
|
|
learn_from_severity=-1)
|
|
soup.seed()
|
|
name = str(soup.particles[0].net.__class__.__name__) + " activiation='" + str(
|
|
soup.particles[0].get_keras_params().get('activation')) + "' use_bias=" + str(
|
|
soup.particles[0].get_keras_params().get('use_bias'))
|
|
for _ in range(exp.soup_life):
|
|
soup.evolve()
|
|
count(counters, soup, notable_nets)
|
|
keras.backend.clear_session()
|
|
|
|
xs += [trains_per_selfattack]
|
|
ys += [float(counters['fix_zero']) / float(exp.trials)]
|
|
zs += [float(counters['fix_other']) / float(exp.trials)]
|
|
all_names += [name]
|
|
# xs: how many trains per self-attack from exp.trains_per_selfattack_values
|
|
# ys: average amount of zero-fixpoints found
|
|
# zs: average amount of non-zero fixpoints
|
|
all_data += [{'xs': xs, 'ys': ys, 'zs': zs}]
|
|
|
|
exp.save(all_names=all_names)
|
|
exp.save(all_data=all_data)
|
|
for exp_id, name in enumerate(all_names):
|
|
exp.log(all_names[exp_id])
|
|
exp.log(all_data[exp_id])
|
|
exp.log('\n')
|