Box and stuff

code/setups/mixed-self-fixpoints.py

@@ -3,6 +3,9 @@ import os
 
 from typing import Tuple
 
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
+
 from util import *
 from experiment import *
 from network import *
@@ -10,10 +13,6 @@ from network 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.
@@ -51,46 +50,52 @@ def count(counters, net, notable_nets=[]):
         counters['other'] += 1
     return counters, notable_nets
 
+if __name__ == '__main__':
 
-with Experiment('mixed-self-fixpoints') 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)]
+    with Experiment('mixed-self-fixpoints') 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: FFTNeuralNetwork(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}] #xs: how many trains per self-attack from exp.trains_per_selfattack_values, ys: average amount of fixpoints found
+        all_names = []
+        all_data = []
 
-    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')
+        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 = ParticleDecorator(net_generator())
+                    net = TrainingNeuralNetworkDecorator(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 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]
+            # xs: how many trains per self-attack from exp.trains_per_selfattack_values
+            # ys: average amount of fixpoints found
+            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')