102 lines
4.2 KiB
Python
102 lines
4.2 KiB
Python
import sys
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import os
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from typing import Tuple
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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 util import *
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from experiment import *
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from network import *
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import keras.backend
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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, net, notable_nets=[]):
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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 net: A Neural Network
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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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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 Experiment('mixed-self-fixpoints') as exp:
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exp.trials = 20
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exp.selfattacks = 4
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exp.trains_per_selfattack_values = [50 * 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', '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: FFTNeuralNetwork(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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for trains_per_selfattack in exp.trains_per_selfattack_values:
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counters = generate_counters()
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notable_nets = []
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for _ in tqdm(range(exp.trials)):
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net = ParticleDecorator(net_generator())
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net = TrainingNeuralNetworkDecorator(net).with_params(epsilon=exp.epsilon)
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name = str(net.net.net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias=" + str(net.get_keras_params().get('use_bias'))
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for selfattack_id in range(exp.selfattacks):
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net.self_attack()
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for train_id in range(trains_per_selfattack):
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loss = net.compiled().train(epoch=selfattack_id*trains_per_selfattack+train_id)
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if net.is_diverged() or net.is_fixpoint():
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break
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count(counters, net, notable_nets)
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keras.backend.clear_session()
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xs += [trains_per_selfattack]
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ys += [float(counters['fix_zero'] + counters['fix_other']) / float(exp.trials)]
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all_names += [name]
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# xs: how many trains per self-attack from exp.trains_per_selfattack_values
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# ys: average amount of fixpoints found
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all_data += [{'xs': xs, 'ys': ys}]
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exp.save(all_names=all_names)
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exp.save(all_data=all_data)
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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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