added fixpoint density experiment

code/setups/fixpoint-density.py

@@ -0,0 +1,55 @@
+import sys
+
+sys.path += ['../', './']
+
+from util import *
+from experiment import *
+from network import *
+
+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('fixpoint-density') as exp:
+    exp.trials = 1000
+    exp.epsilon = 1e-4
+    net_generators = []
+    for activation in ['linear', 'sigmoid', 'relu']:
+        net_generators += [lambda activation=activation: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
+        net_generators += [lambda activation=activation: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
+        net_generators += [lambda activation=activation: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
+    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 = net_generator().with_params(epsilon=exp.epsilon)
+            name = str(net.__class__.__name__) + " " + str(net.get_keras_params().get('activation'))
+            count(counters, net, notable_nets)
+        all_counters += [counters]
+        all_notable_nets += [notable_nets]
+        all_names += [name]
+    exp.save(all_counters=all_counters)
+    exp.save(all_notable_nets=all_notable_nets)
+    exp.save(all_names=all_names)
+    for exp_id, counter in enumerate(all_counters):
+        exp.log(all_names[exp_id])
+        exp.log(all_counters[exp_id])
+        exp.log('\n')