foundations

code/setups/experiments/exp-mixed-self-fixpoints-_3255826099701415-0/log.txt

@@ -0,0 +1,8 @@
+ParticleDecorator activiation='linear' use_bias=False
+{'xs': [0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000], 'ys': [0.45, 0.4, 0.6, 0.8, 0.95, 0.85, 0.95, 0.85, 0.9, 1.0, 0.8]}
+
+
+ParticleDecorator activiation='linear' use_bias=False
+{'xs': [0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000], 'ys': [0.95, 0.9, 0.9, 0.9, 0.95, 0.8, 0.9, 0.9, 0.85, 0.85, 0.9]}
+
+

code/setups/fixpoint-density.py

@@ -37,7 +37,7 @@ if __name__ == '__main__':
         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: 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 = []
@@ -52,7 +52,7 @@ if __name__ == '__main__':
                 count(counters, net, notable_nets)
                 keras.backend.clear_session()
             all_counters += [counters]
-            all_notable_nets += [notable_nets]
+            # all_notable_nets += [notable_nets]
             all_names += [name]
         exp.save(all_counters=all_counters)
         exp.save(all_notable_nets=all_notable_nets)

code/setups/learn_from_soup.py

@@ -61,15 +61,15 @@ def count(counters, soup, notable_nets=[]):
 
 with SoupExperiment('learn-from-soup') as exp:
     exp.soup_size = 10
-    exp.soup_life = 100
-    exp.trials = 10
+    exp.soup_life = 1000
+    exp.trials = 20
     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: 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 = []
@@ -95,7 +95,10 @@ with SoupExperiment('learn-from-soup') as exp:
             ys += [float(counters['fix_zero']) / float(exp.trials)]
             zs += [float(counters['fix_other']) / float(exp.trials)]        
         all_names += [name]
-        all_data += [{'xs':xs, 'ys':ys, 'zs':zs}] #xs: learn_from_intensity according to exp.learn_from_intensity_values, ys: zero-fixpoints after life time, zs: non-zero-fixpoints after life time
+        # 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)

code/setups/mixed-soup.py

@@ -1,6 +1,7 @@
 import sys
 import os
 
+# Concat top Level dir to system environmental variables
 sys.path += os.path.join('..', '.')
 
 from typing import Tuple
@@ -13,10 +14,6 @@ from soup 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.
@@ -57,7 +54,7 @@ def count(counters, soup, notable_nets=[]):
 
 
 with Experiment('mixed-self-fixpoints') as exp:
-    exp.trials = 10
+    exp.trials = 100
     exp.soup_size = 10
     exp.soup_life = 5
     exp.trains_per_selfattack_values = [10 * i for i in range(11)]
@@ -91,7 +88,10 @@ with Experiment('mixed-self-fixpoints') as exp:
             ys += [float(counters['fix_zero']) / float(exp.trials)]
             zs += [float(counters['fix_other']) / float(exp.trials)]
         all_names += [name]
-        all_data += [{'xs':xs, 'ys':ys, 'zs':zs}] #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
+        # 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)