71 lines
2.8 KiB
Python
71 lines
2.8 KiB
Python
import sys
|
|
import os
|
|
|
|
# Concat top Level dir to system environmental variables
|
|
sys.path += os.path.join('..', '.')
|
|
|
|
from util import *
|
|
from experiment import *
|
|
from network import *
|
|
|
|
import keras.backend as K
|
|
|
|
def generate_counters():
|
|
return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
|
|
|
|
def count(counters, net, notable_nets=[]):
|
|
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('applying_fixpoint') as exp:
|
|
exp.trials = 50
|
|
exp.run_count = 100
|
|
exp.epsilon = 1e-4
|
|
net_generators = []
|
|
for activation in ['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_counters = []
|
|
all_notable_nets = []
|
|
all_names = []
|
|
for net_generator_id, net_generator in enumerate(net_generators):
|
|
counters = generate_counters()
|
|
notable_nets = []
|
|
for _ in tqdm(range(exp.trials)):
|
|
net = ParticleDecorator(net_generator())
|
|
net.with_params(epsilon=exp.epsilon)
|
|
name = str(net.net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias=" + str(net.get_keras_params().get('use_bias'))
|
|
for run_id in range(exp.run_count):
|
|
loss = net.self_attack()
|
|
count(counters, net, notable_nets)
|
|
all_counters += [counters]
|
|
all_notable_nets += [notable_nets]
|
|
all_names += [name]
|
|
K.clear_session()
|
|
exp.save(all_counters=all_counters)
|
|
exp.save(trajectorys=exp.without_particles())
|
|
# net types reached in the end
|
|
# exp.save(all_notable_nets=all_notable_nets)
|
|
exp.save(all_names=all_names) #experiment setups
|
|
for exp_id, counter in enumerate(all_counters):
|
|
exp.log(all_names[exp_id])
|
|
exp.log(all_counters[exp_id])
|
|
exp.log('\n')
|