68 lines
2.8 KiB
Python
68 lines
2.8 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 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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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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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('fixpoint-density') as exp:
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#NOTE: settings could/should stay this way
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#FFT doesn't work though
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exp.trials = 100000
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exp.epsilon = 1e-4
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net_generators = []
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for activation in ['linear']:
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net_generators += [lambda activation=activation: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
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net_generators += [lambda activation=activation: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
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# net_generators += [lambda activation=activation: FFTNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
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# net_generators += [lambda activation=activation: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
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all_counters = []
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all_notable_nets = []
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all_names = []
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for net_generator_id, net_generator in enumerate(net_generators):
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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 = net_generator().with_params(epsilon=exp.epsilon)
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net = ParticleDecorator(net)
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name = str(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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count(counters, net, notable_nets)
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keras.backend.clear_session()
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all_counters += [counters]
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# all_notable_nets += [notable_nets]
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all_names += [name]
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exp.save(all_counters=all_counters)
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exp.save(all_notable_nets=all_notable_nets)
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exp.save(all_names=all_names)
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for exp_id, counter in enumerate(all_counters):
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exp.log(all_names[exp_id])
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exp.log(all_counters[exp_id])
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exp.log('\n')
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print('Done')
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