bar plots

code/setups/mixed-soup.py

@@ -52,51 +52,57 @@ def count(counters, soup, notable_nets=[]):
             counters['other'] += 1
     return counters, notable_nets
 
+if __name__ == '__main__':
 
-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)]
+    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 soup_idx 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()
+        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 soup_idx 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]
-        # 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
-        all_data += [{'xs':xs, 'ys':ys, 'zs':zs}]
+                xs += [trains_per_selfattack]
+                ys += [float(counters['fix_zero']) / float(exp.trials)]
+                zs += [float(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 zero-fixpoints found
+            # zs: average amount of non-zero fixpoints
+            all_data += [{'xs': xs, 'ys': ys, 'zs': zs}]
 
-    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')
+        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')