steffen/self-replicating-neural-networks
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Refactor:

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Step 6: Experiments
This commit is contained in:
Si11ium
2019-06-14 18:32:38 +02:00
parent 4a81279b58
commit 5dfbfcaa20
15 changed files with 78 additions and 80 deletions
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13
code/setups/applying-fixpoints.py
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@@ -7,12 +7,13 @@ sys.path += os.path.join('..', '.')
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=[]):
def count(counters, net, notable_nets: list=None):
notable_nets = notable_nets or list()
if net.is_diverged():
counters['divergent'] += 1
elif net.is_fixpoint():
@@ -31,7 +32,7 @@ def count(counters, net, notable_nets=[]):
if __name__ == '__main__':
with Experiment('applying_fixpoint') as exp:
with FixpointExperiment(name='applying_fixpoint') as exp:
exp.trials = 50
exp.run_count = 100
exp.epsilon = 1e-4
@@ -40,7 +41,7 @@ if __name__ == '__main__':
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)]
# 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 = []
@@ -50,14 +51,14 @@ if __name__ == '__main__':
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'))
name = str(net.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.reset_model()
exp.save(all_counters=all_counters)
exp.save(trajectorys=exp.without_particles())
# net types reached in the end