Merge branch 'master' of gitlab.lrz.de:mobile-ifi/bannana-networks
This commit is contained in:
Si11ium
@ -674,8 +674,6 @@ if __name__ == '__main__':
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run_exp(net)
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if False:
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# this does not work as the aggregation function screws over the fixpoint computation....
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# TODO: check for fixpoint in aggregated space...
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with FixpointExperiment() as exp:
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run_count = 1000
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net = TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, width=2, depth=2)).with_params(epsilon=0.1e-6)
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55
code/setups/fixpoint-density.py
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55
code/setups/fixpoint-density.py
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@ -0,0 +1,55 @@
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import sys
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sys.path += ['../', './']
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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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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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with Experiment('fixpoint-density') as exp:
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exp.trials = 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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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: 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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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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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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76
code/setups/known-fixpoint-variation.py
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76
code/setups/known-fixpoint-variation.py
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@ -0,0 +1,76 @@
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import sys
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sys.path += ['../', './']
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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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from soup import prng
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from statistics import mean
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avg = mean
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def generate_fixpoint_weights():
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return [
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np.array([[1.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], dtype=np.float32),
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np.array([[1.0, 0.0], [0.0, 0.0]], dtype=np.float32),
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np.array([[1.0], [0.0]], dtype=np.float32)
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]
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def generate_fixpoint_net():
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net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='sigmoid')
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net.set_weights(generate_fixpoint_weights())
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return net
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def vary(old_weights, e=1.0):
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new_weights = copy.deepcopy(old_weights)
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for layer_id, layer in enumerate(new_weights):
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for cell_id, cell in enumerate(layer):
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for weight_id, weight in enumerate(cell):
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if prng() < 0.5:
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new_weights[layer_id][cell_id][weight_id] = weight + prng() * e
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else:
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new_weights[layer_id][cell_id][weight_id] = weight - prng() * e
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return new_weights
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with Experiment('known-fixpoint-variation') as exp:
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exp.depth = 10
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exp.trials = 100
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exp.max_steps = 100
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exp.epsilon = 1e-4
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exp.xs = []
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exp.ys = []
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exp.zs = []
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exp.notable_nets = []
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current_scale = 1.0
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for _ in range(exp.depth):
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print('variation scale ' + str(current_scale))
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for _ in tqdm(range(exp.trials)):
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net = generate_fixpoint_net().with_params(epsilon=exp.epsilon)
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net.set_weights(vary(net.get_weights(), current_scale))
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time_to_something = 0
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time_as_fixpoint = 0
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still_fixpoint = True
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for _ in range(exp.max_steps):
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net.self_attack()
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if net.is_zero() or net.is_diverged():
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break
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if net.is_fixpoint():
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if still_fixpoint:
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time_as_fixpoint += 1
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else:
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print('remarkable')
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exp.notable_nets += [net.get_weights()]
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still_fixpoint = True
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else:
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still_fixpoint = False
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time_to_something += 1
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exp.xs += [current_scale]
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exp.ys += [time_to_something]
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exp.zs += [time_as_fixpoint]
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current_scale /= 10.0
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for d in range(exp.depth):
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exp.log('variation 10e-' + str(d))
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exp.log('avg time to vergence ' + str(avg(exp.ys[d*exp.trials:(d+1)*exp.trials])))
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exp.log('avg time as fixpoint ' + str(avg(exp.zs[d*exp.trials:(d+1)*exp.trials])))
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72
code/setups/mixed-self-fixpoints.py
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72
code/setups/mixed-self-fixpoints.py
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@ -0,0 +1,72 @@
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import sys
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sys.path += ['../', './']
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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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with Experiment('training_fixpoint') 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 = [100 * 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: 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 = TrainingNeuralNetworkDecorator(net_generator()).with_params(epsilon=exp.epsilon)
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name = str(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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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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59
code/setups/training-fixpoints.py
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59
code/setups/training-fixpoints.py
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@ -0,0 +1,59 @@
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import sys
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sys.path += ['../', './']
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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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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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with Experiment('training_fixpoint') as exp:
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exp.trials = 5
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exp.run_count = 500
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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 = TrainingNeuralNetworkDecorator(net_generator()).with_params(epsilon=exp.epsilon)
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name = str(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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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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