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Si11ium
2019-03-15 18:59:50 +01:00
parent 809f178e2f
commit 869833da3b
39 changed files with 275 additions and 120 deletions
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8
code/setups/mixed-self-fixpoints.py
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@@ -55,15 +55,15 @@ if __name__ == '__main__':
with Experiment('mixed-self-fixpoints') as exp:
exp.trials = 20
exp.selfattacks = 4
exp.trains_per_selfattack_values = [100 * i for i in range(11)]
exp.trains_per_selfattack_values = [50 * i for i in range(11)]
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: 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: FFTNeuralNetwork(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_names = []
all_data = []
@@ -77,7 +77,7 @@ if __name__ == '__main__':
for _ in tqdm(range(exp.trials)):
net = ParticleDecorator(net_generator())
net = TrainingNeuralNetworkDecorator(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.net.net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias=" + str(net.get_keras_params().get('use_bias'))
for selfattack_id in range(exp.selfattacks):
net.self_attack()
for train_id in range(trains_per_selfattack):