Journal TEx Text

experiments/soup_exp.py

@@ -63,6 +63,23 @@ class SoupExperiment:
             net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
             self.population.append(net)
 
+    def population_self_train(self):
+        #  Self-training each network in the population
+        for j in range(self.population_size):
+            net = self.population[j]
+
+            for _ in range(self.ST_steps):
+                net.self_train(1, self.log_step_size, self.net_learning_rate)
+
+    def population_attack(self):
+        # A network attacking another network with a given percentage
+        if random.randint(1, 100) <= self.attack_chance:
+            random_net1, random_net2 = random.sample(range(self.population_size), 2)
+            random_net1 = self.population[random_net1]
+            random_net2 = self.population[random_net2]
+            print(f"\n Attack: {random_net1.name} -> {random_net2.name}")
+            random_net1.attack(random_net2)
+
     def evolve(self):
         """ Evolving consists of attacking & self-training. """
 
@@ -71,19 +88,10 @@ class SoupExperiment:
             loop_epochs.set_description("Evolving soup %s" % i)
 
             # A network attacking another network with a given percentage
-            if random.randint(1, 100) <= self.attack_chance:
-                random_net1, random_net2 = random.sample(range(self.population_size), 2)
-                random_net1 = self.population[random_net1]
-                random_net2 = self.population[random_net2]
-                print(f"\n Attack: {random_net1.name} -> {random_net2.name}")
-                random_net1.attack(random_net2)
+            self.population_attack()
 
             #  Self-training each network in the population
-            for j in range(self.population_size):
-                net = self.population[j]
-                
-                for _ in range(self.ST_steps):
-                    net.self_train(1, self.log_step_size, self.net_learning_rate)
+            self.population_self_train()
 
             # Testing for fixpoints after each batch of ST steps to see relevant data
             if i % self.ST_steps == 0:

experiments/soup_melt_exp.py

@@ -0,0 +1,50 @@
+import random
+
+from tqdm import tqdm
+
+from experiments.soup_exp import SoupExperiment
+from functionalities_test import test_for_fixpoints
+
+
+class MeltingSoupExperiment(SoupExperiment):
+
+    def __init__(self, melt_chance, *args, keep_population_size=True, **kwargs):
+        super(MeltingSoupExperiment, self).__init__(*args, **kwargs)
+        self.keep_population_size = keep_population_size
+        self.melt_chance = melt_chance
+
+    def population_melt(self):
+        # A network melting with another network by a given percentage
+        if random.randint(1, 100) <= self.melt_chance:
+            random_net1_idx, random_net2_idx, destroy_idx = random.sample(range(self.population_size), 3)
+            random_net1 = self.population[random_net1_idx]
+            random_net2 = self.population[random_net2_idx]
+            print(f"\n Melt: {random_net1.name} -> {random_net2.name}")
+            melted_network = random_net1.melt(random_net2)
+            if self.keep_population_size:
+                del self.population[destroy_idx]
+            self.population.append(melted_network)
+
+    def evolve(self):
+        """ Evolving consists of attacking, melting & self-training. """
+
+        loop_epochs = tqdm(range(self.epochs))
+        for i in loop_epochs:
+            loop_epochs.set_description("Evolving soup %s" % i)
+
+            self.population_attack()
+
+            self.population_melt()
+
+            self.population_self_train()
+
+            # Testing for fixpoints after each batch of ST steps to see relevant data
+            if i % self.ST_steps == 0:
+                test_for_fixpoints(self.fixpoint_counters, self.population)
+                fixpoints_percentage = round(self.fixpoint_counters["identity_func"] / self.population_size, 1)
+                self.fixpoint_counters_history.append(fixpoints_percentage)
+
+            # Resetting the fixpoint counter. Last iteration not to be reset -
+            #  it is important for the bar_chart_fixpoints().
+            if i < self.epochs:
+                self.reset_fixpoint_counters()