111 lines
4.3 KiB
Python
111 lines
4.3 KiB
Python
import sys
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import os
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# Concat top Level dir to system environmental variables
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sys.path += os.path.join('..', '.')
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from typing import Tuple
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from experiment import *
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from network import *
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from soup import *
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import tensorflow.python.keras.backend as K
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from statistics import mean
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avg = mean
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def generate_counters():
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"""
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Initial build of the counter dict, to store counts.
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:rtype: dict
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:return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
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"""
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return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
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def count(counters, soup, notable_nets=None):
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"""
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Count the occurences ot the types of weight trajectories.
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:param counters: A counter dictionary.
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:param soup: A Soup
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:param notable_nets: A list to store and save intersting candidates
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:rtype Tuple[dict, list]
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:return: Both the counter dictionary and the list of interessting nets.
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"""
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notable_nets = notable_nets or list()
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for net in soup.particles:
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if net.is_diverged():
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counters['divergent'] += 1
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elif net.is_fixpoint():
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if net.is_zero():
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counters['fix_zero'] += 1
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else:
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counters['fix_other'] += 1
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# notable_nets += [net]
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# elif net.is_fixpoint(2):
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# counters['fix_sec'] += 1
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# notable_nets += [net]
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else:
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counters['other'] += 1
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return counters, notable_nets
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if __name__ == '__main__':
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with SoupExperiment('learn-from-soup') as exp:
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exp.soup_size = 10
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exp.soup_life = 100
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exp.trials = 10
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exp.learn_from_severity_values = [10 * i for i in range(11)]
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exp.epsilon = 1e-4
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net_generators = []
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for activation in ['linear']: # ['sigmoid', 'linear', 'relu']:
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for use_bias in [False]:
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net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
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# 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)]
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# net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
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all_names = []
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all_data = []
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for net_generator_id, net_generator in enumerate(net_generators):
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xs = []
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ys = []
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zs = []
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notable_nets = []
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for learn_from_severity in exp.learn_from_severity_values:
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counters = generate_counters()
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results = []
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for _ in tqdm(range(exp.trials)):
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soup = Soup(exp.soup_size, lambda net_generator=net_generator,exp=exp: TrainingNeuralNetworkDecorator(net_generator()).with_params(epsilon=exp.epsilon))
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soup.with_params(attacking_rate=-1, learn_from_rate=0.1, train=0, learn_from_severity=learn_from_severity)
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soup.seed()
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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'))
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for time in range(exp.soup_life):
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soup.evolve()
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count(counters, soup, notable_nets)
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K.clear_session()
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xs += [learn_from_severity]
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ys += [float(counters['fix_zero']) / float(exp.trials)]
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zs += [float(counters['fix_other']) / float(exp.trials)]
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all_names += [name]
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# xs: learn_from_intensity according to exp.learn_from_intensity_values
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# ys: zero-fixpoints after life time
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# zs: non-zero-fixpoints after life time
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all_data += [{'xs':xs, 'ys':ys, 'zs':zs}]
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exp.save(all_names=all_names)
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exp.save(all_data=all_data)
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exp.save(soup=soup.without_particles())
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for exp_id, name in enumerate(all_names):
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exp.log(all_names[exp_id])
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exp.log(all_data[exp_id])
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exp.log('\n')
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