soup pictures

code/setups/experiments/Aggregating/log.txt

@@ -1 +0,0 @@
-{'divergent': 43, 'fix_zero': 57, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/experiments/Aggregating_Soup/log.txt

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

code/setups/experiments/FFTN/log.txt

@@ -1 +0,0 @@
-{'divergent': 3, 'fix_zero': 97, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/experiments/Weightwise_Learning/log.txt

@@ -1 +0,0 @@
-{'divergent': 0, 'fix_zero': 100, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/experiments/Weightwise_Soup/log.txt

@@ -1 +0,0 @@
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 10, 'fix_sec': 0, 'other': 0}

code/setups/experiments/exp-FixpointExperiment-_814031087631283-0/log.txt

@@ -1 +0,0 @@
-{'divergent': 0, 'fix_zero': 10, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/experiments/exp-soup-_1552643542.3005245-0/log.txt

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

code/setups/network_trajectorys.py

@@ -26,8 +26,9 @@ if __name__ == '__main__':
                 run_exp(net)
                 K.clear_session()
             exp.log(exp.counters)
+            exp.save(trajectorys=exp.without_particles())
 
-    if True:
+    if False:
         # Aggregating Neural Network
         with FixpointExperiment() as exp:
             for run_id in tqdm(range(10)):
@@ -36,8 +37,9 @@ if __name__ == '__main__':
                 run_exp(net)
                 K.clear_session()
             exp.log(exp.counters)
+            exp.save(trajectorys=exp.without_particles())
 
-    if True:
+    if False:
         #FFT Neural Network
         with FixpointExperiment() as exp:
             for run_id in tqdm(range(10)):
@@ -46,8 +48,9 @@ if __name__ == '__main__':
                 run_exp(net)
                 K.clear_session()
             exp.log(exp.counters)
+            exp.save(trajectorys=exp.without_particles())
 
-    if True:
+    if False:
         # ok so this works quite realiably
         with FixpointExperiment() as exp:
             for i in range(10):
@@ -60,8 +63,9 @@ if __name__ == '__main__':
                     if run_id % 10 == 0:
                         run_exp(net)
                 K.clear_session()
+            exp.save(trajectorys=exp.without_particles())
 
-    if True:
+    if False:
         # ok so this works quite realiably
         with FixpointExperiment() as exp:
             for i in range(10):
@@ -74,6 +78,7 @@ if __name__ == '__main__':
                     if run_id % 10 == 0:
                         run_exp(net)
                 K.clear_session()
+            exp.save(trajectorys=exp.without_particles())
 
     if False:
         # this explodes in our faces completely... NAN everywhere

code/setups/soup_trajectorys.py

@@ -11,15 +11,17 @@ from experiment import *
 if __name__ == '__main__':
     if True:
         with SoupExperiment("soup") as exp:
-            for run_id in range(10):
-                # net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
-                #     .with_keras_params(activation='linear').with_params(epsilon=0.0001)
+            for run_id in range(1):
+                net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
+                     .with_keras_params(activation='linear').with_params(epsilon=0.0001)
                 # net_generator = lambda: TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, 2, 2))\
                 #     .with_keras_params(activation='linear')
-                net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
-                    .with_keras_params(activation='linear')
+                # net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
+                #    .with_keras_params(activation='linear')
                 # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
-                soup = Soup(10, net_generator).with_params(remove_divergent=True, remove_zero=True, train=20)
+                soup = Soup(20, net_generator).with_params(remove_divergent=True, remove_zero=True,
+                                                           train=30,
+                                                           learn_from_rate=-1)
                 soup.seed()
                 for _ in tqdm(range(100)):
                     soup.evolve()