added a fourth experiment

code/setups/mixed-self-fixpoints.py

@@ -0,0 +1,72 @@
+import sys
+
+sys.path += ['../', './']
+
+from util import *
+from experiment import *
+from network import *
+
+import keras.backend
+
+
+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
+
+with Experiment('training_fixpoint') as exp:
+    exp.trials = 20
+    exp.selfattacks = 4
+    exp.trains_per_selfattack_values = [100 * 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: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
+
+    all_names = []
+    all_data = []
+    for net_generator_id, net_generator in enumerate(net_generators):
+        xs = []
+        ys = []
+        for trains_per_selfattack in exp.trains_per_selfattack_values:
+            counters = generate_counters()
+            notable_nets = []
+            for _ in tqdm(range(exp.trials)):
+                net = TrainingNeuralNetworkDecorator(net_generator()).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 selfattack_id in range(exp.selfattacks):
+                    net.self_attack()
+                    for train_id in range(trains_per_selfattack):
+                        loss = net.compiled().train(epoch=selfattack_id*trains_per_selfattack+train_id)
+                    if net.is_diverged() or net.is_fixpoint():
+                        break
+                count(counters, net, notable_nets)
+                keras.backend.clear_session()
+            xs += [trains_per_selfattack]
+            ys += [float(counters['fix_zero'] + counters['fix_other']) / float(exp.trials)]
+        all_names += [name]
+        all_data += [{'xs':xs, 'ys':ys}]
+
+    exp.save(all_names=all_names)
+    exp.save(all_data=all_data)
+    for exp_id, name in enumerate(all_names):
+        exp.log(all_names[exp_id])
+        exp.log(all_data[exp_id])
+        exp.log('\n')