soup trajectory and box plot

code/aggregated_experiments/exp-fixpoint-density-_6512072410481991-0/log.txt

@@ -1,12 +0,0 @@
-ParticleDecorator activiation='linear' use_bias='False'
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
-
-
-ParticleDecorator activiation='sigmoid' use_bias='False'
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
-
-
-ParticleDecorator activiation='relu' use_bias='False'
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
-
-

code/aggregated_experiments/exp-learn-from-soup-_3256036231914285-0/log.txt

@@ -1,4 +0,0 @@
-TrainingNeuralNetworkDecorator activiation='sigmoid' use_bias=False
-{'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'zs': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}
-
-

code/aggregated_experiments/exp-mixed-self-fixpoints-_6512079230926771-0/log.txt

@@ -1,4 +0,0 @@
-ParticleDecorator activiation='linear' use_bias=False
-{'xs': [0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000], 'ys': [0.9, 0.95, 1.0, 0.95, 0.9, 0.95, 0.85, 0.8, 0.85, 0.85, 0.75]}
-
-

code/aggregated_experiments/exp-mixed-self-fixpoints-_814010367758041-0/log.txt

@@ -1,4 +0,0 @@
-TrainingNeuralNetworkDecorator activiation='linear' use_bias=False
-{'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.4, 0.2, 0.3, 0.2, 0.3, 0.3, 0.5, 0.3, 0.9, 0.6, 0.2], 'zs': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}
-
-

code/aggregated_experiments/exp-training_fixpoint-_6512086999889633-0/log.txt

@@ -1,4 +0,0 @@
-ParticleDecorator activiation='linear' use_bias=False
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 20}
-
-

code/box_plots.py

@@ -60,7 +60,16 @@ def plot_box(exp: Experiment, filename='histogram_plot'):
 
     trace_list.extend([vergence_box, fixpoint_box])
 
-    layout = dict(title='{} Histogram Plot'.format('Experiment Name Penis'),
+    layout = dict(title='{}'.format('Known Fixpoint Variation'),
+                  titlefont=dict(size=30),
+                  legend=dict(
+                      orientation="h",
+                      x=.1, y=-0.1,
+                      font=dict(
+                          size=20,
+                          color='black'
+                      ),
+                  ),
                   boxmode='group',
                   boxgap=0,
                   # barmode='group',
@@ -71,9 +80,13 @@ def plot_box(exp: Experiment, filename='histogram_plot'):
                              showticklabels=True),
                   yaxis=dict(
                       title='Occurences',
-                      zeroline=False)
+                      zeroline=False,
+                      titlefont=dict(
+                          size=30
+                      )
+                  ),
                   # height=400, width=400,
-                  # margin=dict(l=20, r=20, t=20, b=20)
+                  margin=dict(t=50)
                   )
 
     fig = go.Figure(data=trace_list, layout=layout)
@@ -94,8 +107,10 @@ def search_and_apply(absolut_file_or_folder, plotting_function, files_to_look_fo
 
             with open(absolut_file_or_folder, 'rb') as in_f:
                 exp = dill.load(in_f)
-
-            plotting_function(exp, filename='{}.html'.format(absolut_file_or_folder[:-5]))
+            try:
+                plotting_function(exp, filename='{}.html'.format(absolut_file_or_folder[:-5]))
+            except AttributeError:
+                pass
 
         else:
             pass

code/network.py

@@ -171,6 +171,10 @@ class ParticleDecorator:
         self.__class__.next_uid += 1
         self.net = net
         self.states = []
+        self.save_state(time=0,
+                        action= 'train_self',
+                        counterpart=None
+        )
 
     def __getattr__(self, name):
         return getattr(self.net, name)

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):

code/visualization.py

@@ -50,7 +50,7 @@ def plot_latent_trajectories(soup_or_experiment, filename='latent_trajectory_plo
     transformer = TSNE()
     for particle_dict in data_dict:
         array = np.asarray([np.hstack([x.flatten() for x in timestamp]).flatten()
-                             for timestamp in particle_dict['trajectory']])
+                            for timestamp in particle_dict['trajectory']])
         particle_dict['trajectory'] = array
         transformer.fit(array)
 
@@ -134,20 +134,20 @@ def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'):
             mode='lines')
 
         line_start = go.Scatter3d(mode='markers', x=[transformed[0, 0]], y=[transformed[0, 1]],
-                                z=np.asarray(particle_dict['time'][0]),
-                                marker=dict(
-                                    color='rgb(255, 0, 0)',
-                                    size=4
-                                ),
-                                showlegend=False
-                                )
+                                  z=np.asarray(particle_dict['time'][0]),
+                                  marker=dict(
+                                      color='rgb(255, 0, 0)',
+                                      size=4
+                                  ),
+                                  showlegend=False
+                                  )
 
         line_end = go.Scatter3d(mode='markers', x=[transformed[-1, 0]], y=[transformed[-1, 1]],
-                              z=np.asarray(particle_dict['time'][-1]),
-                              marker=dict(
-                                  color='rgb(0, 0, 0)',
-                                  size=4
-                              ),
+                                z=np.asarray(particle_dict['time'][-1]),
+                                marker=dict(
+                                    color='rgb(0, 0, 0)',
+                                    size=4
+                                ),
                                 showlegend=False
                                 )
 
@@ -156,14 +156,18 @@ def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'):
     axis_layout = dict(gridcolor='rgb(255, 255, 255)',
                        zerolinecolor='rgb(255, 255, 255)',
                        showbackground=True,
-                       backgroundcolor='rgb(230, 230,230)'
+                       backgroundcolor='rgb(230, 230,230)',
+                       titlefont=dict(
+                           color='black',
+                           size=30
+                           )
                        )
 
     layout = go.Layout(scene=dict(
         # aspectratio=dict(x=2, y=2, z=2),
-        xaxis=dict(tickwidth=1, title='Transformed X', **axis_layout),
-        yaxis=dict(tickwidth=1, title='Transformed Y', **axis_layout),
-        zaxis=dict(tickwidth=1, title='Epoch', **axis_layout)),
+        xaxis=dict(title='Transformed X', **axis_layout),
+        yaxis=dict(title='Transformed Y', **axis_layout),
+        zaxis=dict(title='Epoch', **axis_layout)),
         # title='{} - Latent Trajectory Movement'.format('Soup'),
 
         width=1024, height=1024,
@@ -171,7 +175,7 @@ def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'):
     )
 
     fig = go.Figure(data=data, layout=layout)
-    pl.offline.plot(fig, auto_open=True, filename=filename)
+    pl.offline.plot(fig, auto_open=True, filename=filename, validate=True)
     pass
 
 

code/weightwise_experiments/exp-fixpoint-density-_3256002726938743-0/log.txt

@@ -1,12 +0,0 @@
-ParticleDecorator activiation='linear' use_bias='False'
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
-
-
-ParticleDecorator activiation='sigmoid' use_bias='False'
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
-
-
-ParticleDecorator activiation='relu' use_bias='False'
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 100}
-
-

code/weightwise_experiments/exp-known-fixpoint-variation-_3256002757529753-0/log.txt

@@ -1,30 +0,0 @@
-variation 10e-0
-avg time to vergence 3.65
-avg time as fixpoint 0
-variation 10e-1
-avg time to vergence 5.07
-avg time as fixpoint 0
-variation 10e-2
-avg time to vergence 6.49
-avg time as fixpoint 0
-variation 10e-3
-avg time to vergence 7.97
-avg time as fixpoint 0
-variation 10e-4
-avg time to vergence 9.81
-avg time as fixpoint 0.04
-variation 10e-5
-avg time to vergence 11.4
-avg time as fixpoint 1.51
-variation 10e-6
-avg time to vergence 13.14
-avg time as fixpoint 3.26
-variation 10e-7
-avg time to vergence 14.63
-avg time as fixpoint 4.95
-variation 10e-8
-avg time to vergence 21.35
-avg time as fixpoint 11.47
-variation 10e-9
-avg time to vergence 26.36
-avg time as fixpoint 16.3

code/weightwise_experiments/exp-learn-from-soup-_6512006057392099-0/log.txt

@@ -1,4 +0,0 @@
-TrainingNeuralNetworkDecorator activiation='sigmoid' use_bias=False
-{'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'zs': [0.0, 1.0, 3.4, 7.0, 8.3, 9.3, 9.9, 9.5, 9.7, 9.9, 10.0]}
-
-

code/weightwise_experiments/exp-mixed-self-fixpoints-_3256025691277105-0/log.txt

@@ -1,4 +0,0 @@
-TrainingNeuralNetworkDecorator activiation='linear' use_bias=False
-{'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0], 'zs': [0.0, 0.0, 0.6, 2.2, 3.5, 4.8, 5.6, 7.1, 8.3, 7.5, 9.0]}
-
-

code/weightwise_experiments/exp-mixed-self-fixpoints-_6512040991372981-0/log.txt

@@ -1,4 +0,0 @@
-ParticleDecorator activiation='linear' use_bias=False
-{'xs': [0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000], 'ys': [0.4, 0.45, 0.7, 0.9, 1.0, 0.9, 0.9, 1.0, 0.9, 0.9, 0.9]}
-
-

code/weightwise_experiments/exp-training_fixpoint-_6512069271603393-0/log.txt

@@ -1,4 +0,0 @@
-ParticleDecorator activiation='linear' use_bias=False
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 19, 'fix_sec': 0, 'other': 1}
-
-