71 lines
2.9 KiB
Python
71 lines
2.9 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 as K
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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('training_fixpoint') as exp:
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exp.trials = 50
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exp.run_count = 1000
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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: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
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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 = 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 run_id in range(exp.run_count):
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loss = net.compiled().train(epoch=run_id+1)
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count(counters, net, notable_nets)
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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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K.clear_session()
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exp.save(all_counters=all_counters)
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exp.save(trajectorys=exp.without_particles())
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# net types reached in the end
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# exp.save(all_notable_nets=all_notable_nets)
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exp.save(all_names=all_names) #experiment setups
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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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