added two more experiments, now we possibly have all of them

code/setups/mixed-soup.py

@@ -0,0 +1,101 @@
+import sys
+import os
+
+sys.path += os.path.join('..', '.')
+
+from typing import Tuple
+
+from util import *
+from experiment import *
+from network import *
+from soup import *
+
+import keras.backend
+
+
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
+
+
+def generate_counters():
+    """
+    Initial build of the counter dict, to store counts.
+
+    :rtype: dict
+    :return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
+    """
+    return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
+
+
+def count(counters, soup, notable_nets=[]):
+    """
+    Count the occurences ot the types of weight trajectories.
+
+    :param counters:      A counter dictionary.
+    :param soup:          A Soup
+    :param notable_nets:  A list to store and save intersting candidates
+
+    :rtype      Tuple[dict, list]
+    :return:    Both the counter dictionary and the list of interessting nets.
+    """
+
+    for net in soup.particles:
+        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('mixed-self-fixpoints') as exp:
+    exp.trials = 10
+    exp.soup_size = 10
+    exp.soup_life = 5
+    exp.trains_per_selfattack_values = [10 * i for i in range(11)]
+    exp.epsilon = 1e-4
+    net_generators = []
+    for activation in ['linear']: #['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 = []
+        zs = []
+        for trains_per_selfattack in exp.trains_per_selfattack_values:
+            counters = generate_counters()
+            notable_nets = []
+            for _ in tqdm(range(exp.trials)):
+                soup = Soup(exp.soup_size, lambda net_generator=net_generator,exp=exp: TrainingNeuralNetworkDecorator(net_generator()).with_params(epsilon=exp.epsilon))
+                soup.with_params(attacking_rate=0.1, learn_from_rate=-1, train=trains_per_selfattack, learn_from_severity=-1)
+                soup.seed()
+                name = str(soup.particles[0].net.__class__.__name__) + " activiation='" + str(soup.particles[0].get_keras_params().get('activation')) + "' use_bias=" + str(soup.particles[0].get_keras_params().get('use_bias'))
+                for _ in range(exp.soup_life):
+                    soup.evolve()
+                count(counters, soup, notable_nets)
+                keras.backend.clear_session()
+            xs += [trains_per_selfattack]
+            ys += [float(counters['fix_zero']) / float(exp.trials)]
+            zs += [float(counters['fix_other']) / float(exp.trials)]
+        all_names += [name]
+        all_data += [{'xs':xs, 'ys':ys, 'zs':zs}] #xs: how many trains per self-attack from exp.trains_per_selfattack_values, ys: average amount of zero-fixpoints found, zs: average amount of non-zero fixpoints
+
+    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')