Refactor:

Step 1 - Introduction of Weight object for global weight operations
Step2 - Cleanup
Step 3 - Redone WEightwise network updates in clean numpy code

code/setups/applying-fixpoints.py

@@ -4,7 +4,6 @@ import os
 # Concat top Level dir to system environmental variables
 sys.path += os.path.join('..', '.')
 
-from util import *
 from experiment import *
 from network import *
 

code/setups/fixpoint-density.py

@@ -3,16 +3,18 @@ import os
 # Concat top Level dir to system environmental variables
 sys.path += os.path.join('..', '.')
 
-from util import *
 from experiment import *
 from network import *
 
-import keras.backend
+import tensorflow.python.keras.backend as K
+
 
 def generate_counters():
     return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
 
-def count(counters, net, notable_nets=[]):
+
+def count(counters, net, notable_nets=None):
+    notable_nets = notable_nets or []
     if net.is_diverged():
         counters['divergent'] += 1
     elif net.is_fixpoint():
@@ -52,7 +54,7 @@ if __name__ == '__main__':
                 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()
+                K.clear_session()
             all_counters += [counters]
             # all_notable_nets += [notable_nets]
             all_names += [name]

code/setups/known-fixpoint-variation.py

@@ -5,12 +5,11 @@ import os
 # Concat top Level dir to system environmental variables
 sys.path += os.path.join('..', '.')
 
-from util import *
 from experiment import *
 from network import *
 from soup import prng
 
-import keras.backend
+import tensorflow.python.keras.backend as K
 
 
 from statistics import mean
@@ -85,7 +84,7 @@ if __name__ == '__main__':
                 exp.ys += [time_to_something]
                 # time steps still regarded as sthe initial fix-point
                 exp.zs += [time_as_fixpoint]
-                keras.backend.clear_session()
+                K.backend.clear_session()
             current_scale /= 10.0
         for d in range(exp.depth):
             exp.log('variation 10e-' + str(d))

code/setups/learn_from_soup.py

@@ -6,13 +6,12 @@ sys.path += os.path.join('..', '.')
 
 from typing import Tuple
 
-from util import *
 from experiment import *
 from network import *
 from soup import *
 
 
-import keras.backend
+import tensorflow.python.keras.backend as K
 
 from statistics import mean
 avg = mean
@@ -28,7 +27,7 @@ def generate_counters():
     return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
 
 
-def count(counters, soup, notable_nets=[]):
+def count(counters, soup, notable_nets=None):
     """
     Count the occurences ot the types of weight trajectories.
 
@@ -40,6 +39,7 @@ def count(counters, soup, notable_nets=[]):
     :return:    Both the counter dictionary and the list of interessting nets.
     """
 
+    notable_nets = notable_nets or list()
     for net in soup.particles:
         if net.is_diverged():
             counters['divergent'] += 1

code/setups/mixed-self-fixpoints.py

@@ -6,11 +6,10 @@ 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 *
 
-import keras.backend
+import tensorflow.python.keras.backend as K
 
 
 def generate_counters():
@@ -23,7 +22,7 @@ def generate_counters():
     return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
 
 
-def count(counters, net, notable_nets=[]):
+def count(counters, net, notable_nets=None):
     """
     Count the occurences ot the types of weight trajectories.
 
@@ -34,7 +33,7 @@ def count(counters, net, notable_nets=[]):
     :rtype      Tuple[dict, list]
     :return:    Both the counter dictionary and the list of interessting nets.
     """
-
+    notable_nets = notable_nets or list()
     if net.is_diverged():
         counters['divergent'] += 1
     elif net.is_fixpoint():

code/setups/mixed-soup.py

@@ -6,12 +6,11 @@ sys.path += os.path.join('..', '.')
 
 from typing import Tuple
 
-from util import *
 from experiment import *
 from network import *
 from soup import *
 
-import keras.backend
+import tensorflow.python.keras.backend as K
 
 
 def generate_counters():
@@ -24,7 +23,7 @@ def generate_counters():
     return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
 
 
-def count(counters, soup, notable_nets=[]):
+def count(counters, soup, notable_nets=None):
     """
     Count the occurences ot the types of weight trajectories.
 
@@ -36,6 +35,7 @@ def count(counters, soup, notable_nets=[]):
     :return:    Both the counter dictionary and the list of interessting nets.
     """
 
+    notable_nets = notable_nets or list()
     for net in soup.particles:
         if net.is_diverged():
             counters['divergent'] += 1

code/setups/training-fixpoints.py

@@ -4,16 +4,16 @@ import os
 # Concat top Level dir to system environmental variables
 sys.path += os.path.join('..', '.')
 
-from util import *
 from experiment import *
 from network import *
 
-import keras.backend as K
+import tensorflow.python.keras.backend as K
 
 def generate_counters():
     return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
 
-def count(counters, net, notable_nets=[]):
+def count(counters, net, notable_nets=None):
+    notable_nets = notable_nets or list()
     if net.is_diverged():
         counters['divergent'] += 1
     elif net.is_fixpoint():