Box and stuff

code/setups/experiments/exp-fixpoint-density-_6511547300443771-0/log.txt

@@ -0,0 +1,36 @@
+ParticleDecorator activiation='linear' use_bias='False'
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
+
+
+ParticleDecorator activiation='linear' use_bias='False'
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
+
+
+ParticleDecorator activiation='linear' use_bias='False'
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
+
+
+ParticleDecorator activiation='sigmoid' use_bias='False'
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
+
+
+ParticleDecorator activiation='sigmoid' use_bias='False'
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
+
+
+ParticleDecorator activiation='sigmoid' use_bias='False'
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
+
+
+ParticleDecorator activiation='relu' use_bias='False'
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
+
+
+ParticleDecorator activiation='relu' use_bias='False'
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
+
+
+ParticleDecorator activiation='relu' use_bias='False'
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
+
+

code/setups/experiments/exp-known-fixpoint-variation-_813943796847257-0/log.txt

@@ -0,0 +1,30 @@
+variation 10e-0
+avg time to vergence 3.72
+avg time as fixpoint 0
+variation 10e-1
+avg time to vergence 5.13
+avg time as fixpoint 0
+variation 10e-2
+avg time to vergence 6.53
+avg time as fixpoint 0
+variation 10e-3
+avg time to vergence 8.09
+avg time as fixpoint 0
+variation 10e-4
+avg time to vergence 9.81
+avg time as fixpoint 0.06
+variation 10e-5
+avg time to vergence 11.43
+avg time as fixpoint 1.51
+variation 10e-6
+avg time to vergence 13.15
+avg time as fixpoint 3.34
+variation 10e-7
+avg time to vergence 14.57
+avg time as fixpoint 4.79
+variation 10e-8
+avg time to vergence 22.41
+avg time as fixpoint 12.37
+variation 10e-9
+avg time to vergence 26.17
+avg time as fixpoint 16.11

code/setups/experiments/exp-training_fixpoint-_813946210831437-0/log.txt

@@ -0,0 +1,8 @@
+ParticleDecorator activiation='linear' use_bias=False
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 19, 'fix_sec': 0, 'other': 1}
+
+
+ParticleDecorator activiation='linear' use_bias=False
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 20}
+
+

code/setups/fixpoint-density.py

@@ -28,34 +28,38 @@ def count(counters, net, notable_nets=[]):
         counters['other'] += 1
     return counters, notable_nets
 
-with Experiment('fixpoint-density') as exp:
-    exp.trials = 100
-    exp.epsilon = 1e-4
-    net_generators = []
-    for activation in ['linear', 'sigmoid', 'relu']:
-        net_generators += [lambda activation=activation: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
-        net_generators += [lambda activation=activation: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
-        net_generators += [lambda activation=activation: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
-    all_counters = []
-    all_notable_nets = []
-    all_names = []
-    for net_generator_id, net_generator in enumerate(net_generators):
-        counters = generate_counters()
-        notable_nets = []
-        for _ in tqdm(range(exp.trials)):
-            net = net_generator().with_params(epsilon=exp.epsilon)
-            name = str(net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias='" + str(net.get_keras_params().get('use_bias')) + "'"
-            count(counters, net, notable_nets)
-            keras.backend.clear_session()
-        all_counters += [counters]
-        all_notable_nets += [notable_nets]
-        all_names += [name]
-    exp.save(all_counters=all_counters)
-    exp.save(all_notable_nets=all_notable_nets)
-    exp.save(all_names=all_names)
-    for exp_id, counter in enumerate(all_counters):
-        exp.log(all_names[exp_id])
-        exp.log(all_counters[exp_id])
-        exp.log('\n')
 
-print('Done')
+if __name__ == '__main__':
+    with Experiment('fixpoint-density') as exp:
+        exp.trials = 100
+        exp.epsilon = 1e-4
+        net_generators = []
+        for activation in ['linear', 'sigmoid', 'relu']:
+            net_generators += [lambda activation=activation: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
+            net_generators += [lambda activation=activation: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
+            net_generators += [lambda activation=activation: FFTNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
+            # net_generators += [lambda activation=activation: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
+        all_counters = []
+        all_notable_nets = []
+        all_names = []
+        for net_generator_id, net_generator in enumerate(net_generators):
+            counters = generate_counters()
+            notable_nets = []
+            for _ in tqdm(range(exp.trials)):
+                net = net_generator().with_params(epsilon=exp.epsilon)
+                net = ParticleDecorator(net)
+                name = str(net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias='" + str(net.get_keras_params().get('use_bias')) + "'"
+                count(counters, net, notable_nets)
+                keras.backend.clear_session()
+            all_counters += [counters]
+            all_notable_nets += [notable_nets]
+            all_names += [name]
+        exp.save(all_counters=all_counters)
+        exp.save(all_notable_nets=all_notable_nets)
+        exp.save(all_names=all_names)
+        for exp_id, counter in enumerate(all_counters):
+            exp.log(all_names[exp_id])
+            exp.log(all_counters[exp_id])
+            exp.log('\n')
+
+    print('Done')

code/setups/known-fixpoint-variation.py

@@ -5,7 +5,6 @@ import os
 # Concat top Level dir to system environmental variables
 sys.path += os.path.join('..', '.')
 
-
 from util import *
 from experiment import *
 from network import *
@@ -16,19 +15,22 @@ import keras.backend
 
 from statistics import mean
 avg = mean
-    
+
+
 def generate_fixpoint_weights():
     return [
         np.array([[1.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], dtype=np.float32),
         np.array([[1.0, 0.0], [0.0, 0.0]], dtype=np.float32),
         np.array([[1.0], [0.0]], dtype=np.float32)
     ]
-    
+
+
 def generate_fixpoint_net():
     net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='sigmoid')
     net.set_weights(generate_fixpoint_weights())
     return net
 
+
 def vary(old_weights, e=1.0):
     new_weights = copy.deepcopy(old_weights)
     for layer_id, layer in enumerate(new_weights):
@@ -40,45 +42,49 @@ def vary(old_weights, e=1.0):
                     new_weights[layer_id][cell_id][weight_id] = weight - prng() * e
     return new_weights
 
-with Experiment('known-fixpoint-variation') as exp:
-    exp.depth = 10
-    exp.trials = 100
-    exp.max_steps = 100
-    exp.epsilon = 1e-4
-    exp.xs = []
-    exp.ys = []
-    exp.zs = []
-    exp.notable_nets = []
-    current_scale = 1.0
-    for _ in range(exp.depth):
-        print('variation scale ' + str(current_scale))
-        for _ in tqdm(range(exp.trials)):
-            net = generate_fixpoint_net().with_params(epsilon=exp.epsilon)
-            net.set_weights(vary(net.get_weights(), current_scale))
-            time_to_something = 0
-            time_as_fixpoint = 0
-            still_fixpoint = True
-            for _ in range(exp.max_steps):
-                net.self_attack()
-                if net.is_zero() or net.is_diverged():
-                    break
-                if net.is_fixpoint():
-                    if still_fixpoint:
-                        time_as_fixpoint += 1
+
+if __name__ == '__main__':
+    with Experiment('known-fixpoint-variation') as exp:
+        exp.depth = 10
+        exp.trials = 100
+        exp.max_steps = 100
+        exp.epsilon = 1e-4
+        exp.xs = []
+        exp.ys = []
+        exp.zs = []
+        exp.notable_nets = []
+        current_scale = 1.0
+        for _ in range(exp.depth):
+            print('variation scale ' + str(current_scale))
+            for _ in tqdm(range(exp.trials)):
+                net = generate_fixpoint_net().with_params(epsilon=exp.epsilon)
+                net = ParticleDecorator(net)
+                net.set_weights(vary(net.get_weights(), current_scale))
+                time_to_something = 0
+                time_as_fixpoint = 0
+                still_fixpoint = True
+                for _ in range(exp.max_steps):
+                    net.self_attack()
+                    if net.is_zero() or net.is_diverged():
+                        break
+                    if net.is_fixpoint():
+                        if still_fixpoint:
+                            time_as_fixpoint += 1
+                        else:
+                            print('remarkable')
+                            exp.notable_nets += [net.get_weights()]
+                            still_fixpoint = True
                     else:
-                        print('remarkable')
-                        exp.notable_nets += [net.get_weights()]
-                        still_fixpoint = True
-                else:
-                    still_fixpoint = False
-                time_to_something += 1
-            exp.xs += [current_scale]
-            exp.ys += [time_to_something] #time steps taken to reach divergence or zero (reaching another fix-point is basically never happening)
-            exp.zs += [time_as_fixpoint] #time steps still regarded as sthe initial fix-point
-            keras.backend.clear_session()
-        current_scale /= 10.0
-    for d in range(exp.depth):
-        exp.log('variation 10e-' + str(d))
-        exp.log('avg time to vergence ' + str(avg(exp.ys[d*exp.trials:(d+1)*exp.trials])))
-        exp.log('avg time as fixpoint ' + str(avg(exp.zs[d*exp.trials:(d+1)*exp.trials])))
-        
+                        still_fixpoint = False
+                    time_to_something += 1
+                exp.xs += [current_scale]
+                # time steps taken to reach divergence or zero (reaching another fix-point is basically never happening)
+                exp.ys += [time_to_something]
+                # time steps still regarded as sthe initial fix-point
+                exp.zs += [time_as_fixpoint]
+                keras.backend.clear_session()
+            current_scale /= 10.0
+        for d in range(exp.depth):
+            exp.log('variation 10e-' + str(d))
+            exp.log('avg time to vergence ' + str(avg(exp.ys[d*exp.trials:(d+1) * exp.trials])))
+            exp.log('avg time as fixpoint ' + str(avg(exp.zs[d*exp.trials:(d+1) * exp.trials])))

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')

code/setups/training-fixpoints.py

@@ -8,7 +8,7 @@ from util import *
 from experiment import *
 from network import *
 
-import keras.backend
+import keras.backend as K
 
 def generate_counters():
     return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
@@ -29,36 +29,40 @@ def count(counters, net, notable_nets=[]):
         counters['other'] += 1
     return counters, notable_nets
 
-with Experiment('training_fixpoint') as exp:
-    exp.trials = 5
-    exp.run_count = 500
-    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)]
-    all_counters = []
-    all_notable_nets = []
-    all_names = []
-    for net_generator_id, net_generator in enumerate(net_generators):
-        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 run_id in range(exp.run_count):
-                loss = net.compiled().train(epoch=run_id+1)
-            count(counters, net, notable_nets)
-            keras.backend.clear_session()
-        all_counters += [counters]
-        all_notable_nets += [notable_nets]
-        all_names += [name]
-    exp.save(all_counters=all_counters) #net types reached in the end
-    exp.save(all_notable_nets=all_notable_nets)
-    exp.save(all_names=all_names) #experiment setups
-    for exp_id, counter in enumerate(all_counters):
-        exp.log(all_names[exp_id])
-        exp.log(all_counters[exp_id])
-        exp.log('\n')
+
+if __name__ == '__main__':
+
+    with Experiment('training_fixpoint') as exp:
+        exp.trials = 20
+        exp.run_count = 500
+        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)]
+        all_counters = []
+        all_notable_nets = []
+        all_names = []
+        for net_generator_id, net_generator in enumerate(net_generators):
+            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 run_id in range(exp.run_count):
+                    loss = net.compiled().train(epoch=run_id+1)
+                count(counters, net, notable_nets)
+            all_counters += [counters]
+            all_notable_nets += [notable_nets]
+            all_names += [name]
+            K.clear_session()
+        exp.save(all_counters=all_counters) #net types reached in the end
+        # exp.save(all_notable_nets=all_notable_nets)
+        exp.save(all_names=all_names) #experiment setups
+        for exp_id, counter in enumerate(all_counters):
+            exp.log(all_names[exp_id])
+            exp.log(all_counters[exp_id])
+            exp.log('\n')