bar plots

code/setups/fixpoint-density.py

@@ -1,6 +1,7 @@
 import sys
-
-sys.path += ['../', './']
+import os
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
 
 from util import *
 from experiment import *
@@ -26,7 +27,7 @@ def count(counters, net, notable_nets=[]):
     return counters, notable_nets
 
 with Experiment('fixpoint-density') as exp:
-    exp.trials = 1000
+    exp.trials = 100
     exp.epsilon = 1e-4
     net_generators = []
     for activation in ['linear', 'sigmoid', 'relu']:
@@ -43,6 +44,7 @@ with Experiment('fixpoint-density') as exp:
             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)
+            K.clear_session()
         all_counters += [counters]
         all_notable_nets += [notable_nets]
         all_names += [name]
@@ -52,4 +54,6 @@ with Experiment('fixpoint-density') as exp:
     for exp_id, counter in enumerate(all_counters):
         exp.log(all_names[exp_id])
         exp.log(all_counters[exp_id])
-        exp.log('\n')
+        exp.log('\n')
+
+print('Done')

code/setups/known-fixpoint-variation.py

@@ -1,6 +1,10 @@
 import sys
 
-sys.path += ['../', './']
+import os
+
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
+
 
 from util import *
 from experiment import *

code/setups/mixed-self-fixpoints.py

@@ -1,6 +1,7 @@
 import sys
+import os
 
-sys.path += ['../', './']
+from typing import Tuple
 
 from util import *
 from experiment import *
@@ -9,10 +10,32 @@ 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.
+
+    :rtype: dict
+    :return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
+    """
     return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
 
+
 def count(counters, net, notable_nets=[]):
+    """
+    Count the occurences ot the types of weight trajectories.
+
+    :param counters:      A counter dictionary.
+    :param net:           A Neural Network
+    :param notable_nets:  A list to store and save intersting candidates
+
+    :rtype      Tuple[dict, list]
+    :return:    Both the counter dictionary and the list of interessting nets.
+    """
+
     if net.is_diverged():
         counters['divergent'] += 1
     elif net.is_fixpoint():
@@ -28,6 +51,7 @@ def count(counters, net, notable_nets=[]):
         counters['other'] += 1
     return counters, notable_nets
 
+
 with Experiment('training_fixpoint') as exp:
     exp.trials = 20
     exp.selfattacks = 4
@@ -69,4 +93,4 @@ with Experiment('training_fixpoint') as exp:
     for exp_id, name in enumerate(all_names):
         exp.log(all_names[exp_id])
         exp.log(all_data[exp_id])
-        exp.log('\n')
+        exp.log('\n')

code/setups/training-fixpoints.py

@@ -1,6 +1,8 @@
 import sys
+import os
 
-sys.path += ['../', './']
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
 
 from util import *
 from experiment import *