soup trajectory and box plot

code/setups/experiments/FFTNeuralNetwork/log.txt

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

code/setups/experiments/Known-Fixpoint-Variation/log.txt

@@ -0,0 +1,30 @@
+variation 10e-0
+avg time to vergence 3.63
+avg time as fixpoint 0
+variation 10e-1
+avg time to vergence 5.02
+avg time as fixpoint 0
+variation 10e-2
+avg time to vergence 6.46
+avg time as fixpoint 0
+variation 10e-3
+avg time to vergence 8.04
+avg time as fixpoint 0
+variation 10e-4
+avg time to vergence 9.61
+avg time as fixpoint 0.04
+variation 10e-5
+avg time to vergence 11.23
+avg time as fixpoint 1.38
+variation 10e-6
+avg time to vergence 12.99
+avg time as fixpoint 3.23
+variation 10e-7
+avg time to vergence 14.58
+avg time as fixpoint 4.84
+variation 10e-8
+avg time to vergence 21.95
+avg time as fixpoint 11.91
+variation 10e-9
+avg time to vergence 26.45
+avg time as fixpoint 16.47

code/setups/experiments/exp-FixpointExperiment-_1552645208.2084198-0/log.txt

@@ -1 +0,0 @@
-{'divergent': 6, 'fix_zero': 4, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/fixpoint-density.py

@@ -36,11 +36,11 @@ if __name__ == '__main__':
         exp.trials = 100000
         exp.epsilon = 1e-4
         net_generators = []
-        for activation in ['linear', 'sigmoid', 'relu']:
+        for activation in ['linear']:
             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)]
+            # 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 = []

code/setups/mixed-self-fixpoints.py

@@ -60,8 +60,8 @@ if __name__ == '__main__':
         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: 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)]
 

code/setups/network_trajectorys.py

@@ -19,14 +19,15 @@ if __name__ == '__main__':
 
     if True:
         # WeightWise Neural Network
-        with FixpointExperiment() as exp:
-            for run_id in tqdm(range(10)):
-                net = ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)
-                                        .with_keras_params(activation='linear'))
-                run_exp(net)
-                K.clear_session()
-            exp.log(exp.counters)
-            exp.save(trajectorys=exp.without_particles())
+        for _ in range(10):
+            with FixpointExperiment() as exp:
+                for run_id in tqdm(range(20)):
+                    net = ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)
+                                            .with_keras_params(activation='linear'))
+                    run_exp(net)
+                    K.clear_session()
+                exp.log(exp.counters)
+                exp.save(trajectorys=exp.without_particles())
 
     if False:
         # Aggregating Neural Network

code/setups/training-fixpoints.py

@@ -40,8 +40,8 @@ if __name__ == '__main__':
         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)]
+                # 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 = []
@@ -59,7 +59,9 @@ if __name__ == '__main__':
             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_counters=all_counters)
+        exp.save(trajectorys=exp.without_particles())
+        # 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):