bar plots

code/setups/experiments/Soup/log.txt

@@ -1 +0,0 @@
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 13, 'fix_sec': 0, 'other': 7}

code/setups/experiments/exp-learn-from-soup-_1552658566.5572753-0/log.txt

@@ -0,0 +1,4 @@
+TrainingNeuralNetworkDecorator activiation='linear' use_bias=False
+{'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'zs': [0.0, 1.2, 5.2, 7.4, 8.1, 9.1, 9.6, 9.8, 10.0, 9.9, 9.9]}
+
+

code/setups/experiments/exp-mixed-self-fixpoints-_1552658653.1189044-0/log.txt

@@ -0,0 +1,4 @@
+TrainingNeuralNetworkDecorator activiation='linear' use_bias=False
+{'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'zs': [0.0, 0.0, 0.6, 1.8, 2.7, 5.1, 5.8, 7.8, 8.5, 9.0, 8.8]}
+
+

code/setups/experiments/exp-mixed-self-fixpoints-_1552666977.5858653-0/log.txt

@@ -0,0 +1,12 @@
+WeightwiseNeuralNetwork activiation='linear' use_bias=False
+{'xs': [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500], 'ys': [0.2, 0.3, 0.15, 0.55, 0.7, 0.85, 0.8, 0.95, 0.9, 1.0, 1.0]}
+
+
+AggregatingNeuralNetwork activiation='linear' use_bias=False
+{'xs': [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500], 'ys': [1.0, 0.95, 1.0, 1.0, 0.95, 0.9, 0.8, 1.0, 0.85, 1.0, 0.9]}
+
+
+RecurrentNeuralNetwork activiation='linear' use_bias=False
+{'xs': [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500], 'ys': [0.05, 0.0, 0.05, 0.0, 0.0, 0.1, 0.1, 0.05, 0.1, 0.0, 0.0]}
+
+

code/setups/experiments/exp-training_fixpoint-_1552658296.0913951-0/log.txt

@@ -0,0 +1,12 @@
+WeightwiseNeuralNetwork activiation='linear' use_bias=False
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 50, 'fix_sec': 0, 'other': 0}
+
+
+AggregatingNeuralNetwork activiation='linear' use_bias=False
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 50}
+
+
+RecurrentNeuralNetwork activiation='linear' use_bias=False
+{'divergent': 38, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 12}
+
+

code/setups/experiments/exp-weightwise_self_application-_1552664922.4501734-0/log.txt

@@ -0,0 +1 @@
+{'divergent': 11, 'fix_zero': 9, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/learn_from_soup.py

@@ -57,52 +57,54 @@ def count(counters, soup, notable_nets=[]):
     return counters, notable_nets
 
 
-with SoupExperiment('learn-from-soup') as exp:
-    exp.soup_size = 10
-    exp.soup_life = 100
-    exp.trials = 10
-    exp.learn_from_severity_values = [10 * i for i in range(11)]
-    exp.epsilon = 1e-4
-    net_generators = []
-    for activation in ['sigmoid']: #['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)]
+if __name__ == '__main__':
 
-    all_names = []
-    all_data = []
-    for net_generator_id, net_generator in enumerate(net_generators):
-        xs = []
-        ys = []
-        zs = []
-        notable_nets = []
-        for learn_from_severity in exp.learn_from_severity_values:
-            counters = generate_counters()
-            results = []
-            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=-1, learn_from_rate=0.1, train=0, learn_from_severity=learn_from_severity)
-                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 time in range(exp.soup_life):
-                    soup.evolve()
-                count(counters, soup, notable_nets)
-                keras.backend.clear_session()
+    with SoupExperiment('learn-from-soup') as exp:
+        exp.soup_size = 10
+        exp.soup_life = 100
+        exp.trials = 10
+        exp.learn_from_severity_values = [10 * i for i in range(11)]
+        exp.epsilon = 1e-4
+        net_generators = []
+        for activation in ['linear']:  # ['sigmoid', 'linear', '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)]
 
-            xs += [learn_from_severity]
-            ys += [float(counters['fix_zero']) / float(exp.trials)]
-            zs += [float(counters['fix_other']) / float(exp.trials)]        
-        all_names += [name]
-        # xs: learn_from_intensity according to exp.learn_from_intensity_values
-        # ys: zero-fixpoints after life time
-        # zs: non-zero-fixpoints after life time
-        all_data += [{'xs':xs, 'ys':ys, 'zs':zs}]
+        all_names = []
+        all_data = []
+        for net_generator_id, net_generator in enumerate(net_generators):
+            xs = []
+            ys = []
+            zs = []
+            notable_nets = []
+            for learn_from_severity in exp.learn_from_severity_values:
+                counters = generate_counters()
+                results = []
+                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=-1, learn_from_rate=0.1, train=0, learn_from_severity=learn_from_severity)
+                    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 time in range(exp.soup_life):
+                        soup.evolve()
+                    count(counters, soup, notable_nets)
+                    keras.backend.clear_session()
 
-    exp.save(all_names=all_names)
-    exp.save(all_data=all_data)
-    exp.save(soup=soup.without_particles())
-    for exp_id, name in enumerate(all_names):
-        exp.log(all_names[exp_id])
-        exp.log(all_data[exp_id])
-        exp.log('\n')
+                xs += [learn_from_severity]
+                ys += [float(counters['fix_zero']) / float(exp.trials)]
+                zs += [float(counters['fix_other']) / float(exp.trials)]
+            all_names += [name]
+            # xs: learn_from_intensity according to exp.learn_from_intensity_values
+            # ys: zero-fixpoints after life time
+            # zs: non-zero-fixpoints after life time
+            all_data += [{'xs':xs, 'ys':ys, 'zs':zs}]
+
+        exp.save(all_names=all_names)
+        exp.save(all_data=all_data)
+        exp.save(soup=soup.without_particles())
+        for exp_id, name in enumerate(all_names):
+            exp.log(all_names[exp_id])
+            exp.log(all_data[exp_id])
+            exp.log('\n')

code/setups/mixed-self-fixpoints.py

@@ -55,15 +55,15 @@ 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.trains_per_selfattack_values = [50 * 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: 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)]
+                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 = []
@@ -77,7 +77,7 @@ if __name__ == '__main__':
                 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'))
+                    name = str(net.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):

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')

code/setups/network_trajectorys.py

@@ -19,19 +19,18 @@ if __name__ == '__main__':
 
     if True:
         # WeightWise Neural Network
-        for _ in range(10):
-            with FixpointExperiment() as exp:
-                for run_id in tqdm(range(20)):
-                    net = ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)
-                                            .with_keras_params(activation='linear'))
-                    run_exp(net)
-                    K.clear_session()
-                exp.log(exp.counters)
-                exp.save(trajectorys=exp.without_particles())
+        with FixpointExperiment(name="weightwise_self_application") as exp:
+            for run_id in tqdm(range(20)):
+                net = ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)
+                                        .with_keras_params(activation='linear'))
+                run_exp(net)
+                K.clear_session()
+            exp.log(exp.counters)
+            exp.save(trajectorys=exp.without_particles())
 
     if False:
         # Aggregating Neural Network
-        with FixpointExperiment() as exp:
+        with FixpointExperiment(name="aggregating_self_application") as exp:
             for run_id in tqdm(range(10)):
                 net = ParticleDecorator(AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)
                                         .with_keras_params(activation='linear'))
@@ -53,31 +52,33 @@ if __name__ == '__main__':
 
     if False:
         # ok so this works quite realiably
-        with FixpointExperiment() as exp:
+        with FixpointExperiment(name="weightwise_learning") as exp:
             for i in range(10):
                 run_count = 100
                 net = TrainingNeuralNetworkDecorator(ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)))
                 net.with_params(epsilon=0.0001).with_keras_params(activation='linear')
+                exp.historical_particles[net.get_uid()] = net
                 for run_id in tqdm(range(run_count+1)):
                     net.compiled()
                     loss = net.train(epoch=run_id)
-                    if run_id % 10 == 0:
-                        run_exp(net)
+                    # run_exp(net)
+                    # net.save_state(time=run_id)
                 K.clear_session()
             exp.save(trajectorys=exp.without_particles())
 
     if False:
         # ok so this works quite realiably
-        with FixpointExperiment() as exp:
+        with FixpointExperiment(name="aggregating_learning") as exp:
             for i in range(10):
                 run_count = 100
                 net = TrainingNeuralNetworkDecorator(ParticleDecorator(AggregatingNeuralNetwork(4, width=2, depth=2)))
                 net.with_params(epsilon=0.0001).with_keras_params(activation='linear')
+                exp.historical_particles[net.get_uid()] = net
                 for run_id in tqdm(range(run_count+1)):
                     net.compiled()
                     loss = net.train(epoch=run_id)
-                    if run_id % 10 == 0:
-                        run_exp(net)
+                    # run_exp(net)
+                    # net.save_state(time=run_id)
                 K.clear_session()
             exp.save(trajectorys=exp.without_particles())
 

code/setups/training-fixpoints.py

@@ -40,8 +40,8 @@ if __name__ == '__main__':
         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)]
+                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_counters = []
         all_notable_nets = []
         all_names = []
@@ -51,7 +51,7 @@ if __name__ == '__main__':
             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'))
+                name = str(net.net.net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias=" + str(net.get_keras_params().get('use_bias'))
                 for run_id in range(exp.run_count):
                     loss = net.compiled().train(epoch=run_id+1)
                 count(counters, net, notable_nets)