pdf merge

code/setups/applying-fixpoints.py

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+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('training_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')