Viz updates

code/experiment.py

@@ -18,13 +18,12 @@ class Experiment:
         self.base_dir = self.experiment_name
         self.next_iteration = 0
         self.log_messages = []
-        self.historical_particles = dict()
+        self.historical_particles = {}
     
     def __enter__(self):
-        self.dir = os.path.join(self.base_dir, 'experiments', 'exp-{name}-{id}-{it}'.format(
+        self.dir = os.path.join('experiments', 'exp-{name}-{id}-{it}'.format(
             name=self.experiment_name, id=self.experiment_id, it=self.next_iteration)
                                 )
-
         os.makedirs(self.dir)
         print("** created {dir} **".format(dir=self.dir))
         return self
@@ -60,13 +59,9 @@ class Experiment:
             with open(os.path.join(self.dir, "{name}.dill".format(name=name)), "wb") as dill_file:
                 dill.dump(value, dill_file)
 
-    def add_trajectory_segment(self, run_id, trajectory):
-        self.historical_particles[run_id].append(trajectory)
-        return
-
 
 class FixpointExperiment(Experiment):
-    
+
     def __init__(self):
         super().__init__(name=self.__class__.__name__)
         self.counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
@@ -78,8 +73,7 @@ class FixpointExperiment(Experiment):
             net.self_attack()
             i += 1
             if run_id:
-                weights = net.get_weights_flat()
+                net.save_state(time=run_id)
-                self.add_trajectory_segment(run_id, weights)
         self.count(net)
 
     def count(self, net):

code/network.py

@@ -1,5 +1,3 @@
-import math
-import copy
 import numpy as np
 
 from keras.models import Sequential
@@ -47,9 +45,9 @@ class NeuralNetwork(PrintingObject):
         for layer_id, layer in enumerate(network_weights):
             for cell_id, cell in enumerate(layer):
                 for weight_id, weight in enumerate(cell):
-                    if math.isnan(weight):
+                    if np.isnan(weight):
                         return True
-                    if math.isinf(weight):
+                    if np.isinf(weight):
                         return True
         return False
 
@@ -100,13 +98,13 @@ class NeuralNetwork(PrintingObject):
         return self
 
     def get_weights(self):
-        return self.get_model().get_weights()
+        return self.model.get_weights()
 
     def get_weights_flat(self):
         return np.hstack([weight.flatten() for weight in self.get_weights()])
 
     def set_weights(self, new_weights):
-        return self.get_model().set_weights(new_weights)
+        return self.model.set_weights(new_weights)
 
     def apply_to_weights(self, old_weights):
         raise NotImplementedError
@@ -133,11 +131,11 @@ class NeuralNetwork(PrintingObject):
         return self.attack(new_other_network)
 
     def is_diverged(self):
-        return NeuralNetwork.are_weights_diverged(self.get_weights())
+        return self.are_weights_diverged(self.get_weights())
 
     def is_zero(self, epsilon=None):
         epsilon = epsilon or self.get_params().get('epsilon')
-        return NeuralNetwork.are_weights_within(self.get_weights(), -epsilon, epsilon)
+        return self.are_weights_within(self.get_weights(), -epsilon, epsilon)
 
     def is_fixpoint(self, degree=1, epsilon=None):
         assert degree >= 1, "degree must be >= 1"
@@ -167,7 +165,7 @@ class NeuralNetwork(PrintingObject):
     def make_state(self, **kwargs):
         weights = self.get_weights_flat()
         state = {'class': self.__class__.__name__, 'weights': weights}
-        if any(np.isinf(weights)):
+        if any(np.isnan(weights)) or any(np.isinf(weights)):
             return None
         state.update(kwargs)
         return state
@@ -593,7 +591,8 @@ class LearningNeuralNetwork(NeuralNetwork):
             for epoch in range(epochs):
                 old_weights = self.get_weights()
                 x = reduction(old_weights, self.features)
-                history = self.model.fit(x=x, y=x, verbose=0, batch_size=batchsize)
+                savestateCallback = SaveStateCallback(self, epoch=epoch)
+                history = self.model.fit(x=x, y=x, verbose=0, batch_size=batchsize, callbacks=savestateCallback)
                 bar.postfix[1]["value"] = history.history['loss'][-1]
                 bar.update()
 
@@ -654,18 +653,20 @@ if __name__ == '__main__':
     if False:
         with FixpointExperiment() as exp:
             for run_id in tqdm(range(100)):
-                net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
+                # net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
                 # net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)\
-                # net = FFTNeuralNetwork(aggregates=4, width=2, depth=2) \
+                net = FFTNeuralNetwork(aggregates=4, width=2, depth=2) \
-                #     .with_params(print_all_weight_updates=False, use_bias=False)
+                    .with_params(print_all_weight_updates=False, use_bias=False)
                 # net = RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')\
                 # .with_params(print_all_weight_updates=True)
                 # net.print_weights()
 
                 # INFO Run_ID needs to be more than 0, so that exp stores the trajectories!
                 exp.run_net(net, 100, run_id=run_id+1)
+                exp.historical_particles[run_id] = net
             exp.log(exp.counters)
     if False:
+        # TODO SI: Ich muss noch apply to weights implementieren
         # is_fixpoint was wrong because it trivially returned the old weights
         with IdentLearningExperiment() as exp:
             net = LearningNeuralNetwork(width=2, depth=2, features=2, )\
@@ -674,17 +675,13 @@ if __name__ == '__main__':
             net.print_weights()
             time.sleep(0.1)
             print(net.is_fixpoint(epsilon=0.1e-6))
-            print()
             net.learn(1, reduction=LearningNeuralNetwork.fft_reduction)
-            import time
-            time.sleep(0.1)
-            net.print_weights()
-            time.sleep(0.1)
             print(net.is_fixpoint(epsilon=0.1e-6))
-    if True:
+
+    if False:
         # ok so this works quite realiably
         with FixpointExperiment() as exp:
-            for i in range(10):
+            for i in range(1):
 
                 run_count = 1000
                 net = TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(width=2, depth=2))\

code/soup.py

@@ -59,7 +59,7 @@ class Soup:
                     particle.attack(other_particle)
                     description['action'] = 'attacking'
                     description['counterpart'] = other_particle.get_uid()
-                if prng() < self.params.get('train_other_rate'):
+                if prng() < self.params.get('train_other_rate') and hasattr(self, 'train_other'):
                     other_particle_id = int(prng() * len(self.particles))
                     other_particle = self.particles[other_particle_id]
                     particle.train_other(other_particle)
@@ -150,7 +150,7 @@ class ParticleDecorator:
         
 
 if __name__ == '__main__':
-    if False:
+    if True:
         with SoupExperiment() as exp:
             for run_id in range(1):
                 net_generator = lambda: WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
@@ -159,11 +159,11 @@ if __name__ == '__main__':
                 # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
                 soup = Soup(100, net_generator).with_params(remove_divergent=True, remove_zero=True)
                 soup.seed()
-                for _ in tqdm(range(100)):
+                for _ in tqdm(range(1000)):
                     soup.evolve()
                 exp.log(soup.count())
 
-    if True:
+    if False:
         with SoupExperiment("soup") as exp:
             for run_id in range(1):
                 net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)).with_keras_params(

code/visualization.py

@@ -14,7 +14,7 @@ import colorlover as cl
 
 import dill
 
-from sklearn.manifold.t_sne import TSNE
+from sklearn.manifold.t_sne import TSNE, PCA
 
 
 def build_args():
@@ -31,8 +31,8 @@ def build_from_soup_or_exp(soup):
         particle_dict = dict(
             trajectory=[event['weights'] for event in particle],
             time=[event['time'] for event in particle],
-            action=[event['action'] for event in particle],
+            action=[event.get('action', None) for event in particle],
-            counterpart=[event['counterpart'] for event in particle]
+            counterpart=[event.get('counterpart', None) for event in particle]
         )
         particle_list.append(particle_dict)
     return particle_list
@@ -101,15 +101,18 @@ def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'):
     scale = cl.interp(bupu, len(data_list)+1)  # Map color scale to N bins
 
     # Fit the embedding space
-    transformer = TSNE()
+    transformer = PCA(n_components=2)
+
+    array = []
     for particle_dict in data_list:
-        array = np.asarray(particle_dict['trajectory'])
+        array.append(particle_dict['trajectory'])
-        transformer.fit(array)
+
+    transformer.fit(np.vstack(array))
 
     # Transform data accordingly and plot it
     data = []
     for p_id, particle_dict in enumerate(data_list):
-        transformed = transformer._fit(particle_dict['trajectory'])
+        transformed = transformer.transform(particle_dict['trajectory'])
         line_trace = go.Scatter3d(
             x=transformed[:, 0],
             y=transformed[:, 1],