Visuals

code/network.py

@@ -3,7 +3,9 @@ import copy
 import numpy as np
 
 from keras.models import Sequential
+from keras.callbacks import Callback
 from keras.layers import SimpleRNN, Dense
+import keras.backend as K
 
 from util import *
 from experiment import *
@@ -12,6 +14,20 @@ from experiment import *
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 
 
+class SaveStateCallback(Callback):
+    def __init__(self, net, epoch=0):
+        super(SaveStateCallback, self).__init__()
+        self.net = net
+        self.init_epoch = epoch
+
+    def on_epoch_end(self, epoch, logs={}):
+        description = dict(time=epoch+self.init_epoch)
+        description['action'] = 'train_self'
+        description['counterpart'] = None
+        self.net.save_state(**description)
+        return
+
+
 class NeuralNetwork(PrintingObject):
 
     @staticmethod
@@ -64,6 +80,7 @@ class NeuralNetwork(PrintingObject):
         self.params = dict(epsilon=0.00000000000001)
         self.params.update(params)
         self.keras_params = dict(activation='linear', use_bias=False)
+        self.states = []
 
     def get_model(self):
         raise NotImplementedError
@@ -147,6 +164,23 @@ class NeuralNetwork(PrintingObject):
     def print_weights(self, weights=None):
         print(self.repr_weights(weights))
 
+    def make_state(self, **kwargs):
+        weights = self.get_weights_flat()
+        state = {'class': self.__class__.__name__, 'weights': weights}
+        if any(np.isinf(weights)):
+            return None
+        state.update(kwargs)
+        return state
+
+    def save_state(self, **kwargs):
+        state = self.make_state(**kwargs)
+        if state is not None:
+            self.states += [state]
+        else:
+            pass
+    def get_states(self):
+        return self.states
+
 
 class WeightwiseNeuralNetwork(NeuralNetwork):
 
@@ -600,10 +634,11 @@ class TrainingNeuralNetworkDecorator():
             self.model_compiled = True
         return self
 
-    def train(self, batchsize=1):
+    def train(self, batchsize=1, store_states=True, epoch=0):
         self.compiled()
         x, y = self.net.compute_samples()
-        history = self.net.model.fit(x=x, y=y, verbose=0, batch_size=batchsize)
+        savestatecallback = SaveStateCallback(net=self.net, epoch=epoch) if store_states else None
+        history = self.net.model.fit(x=x, y=y, verbose=0, batch_size=batchsize, callbacks=[savestatecallback])
         return history.history['loss'][-1]
 
     def train_other(self, other_network, batchsize=1):
@@ -611,6 +646,7 @@ class TrainingNeuralNetworkDecorator():
         other_network.compiled()
         x, y = other_network.net.compute_samples()
         history = self.net.model.fit(x=x, y=y, verbose=0, batch_size=batchsize)
+
         return history.history['loss'][-1]
 
 
@@ -648,17 +684,21 @@ if __name__ == '__main__':
     if True:
         # ok so this works quite realiably
         with FixpointExperiment() as exp:
-            run_count = 1000
-            net = TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(width=2, depth=2))\
-                .with_params(epsilon=0.0001).with_keras_params(optimizer='sgd')
-            for run_id in tqdm(range(run_count+1)):
-                loss = net.compiled().train()
-                if run_id % 100 == 0:
-                    net.print_weights()
-                    # print(net.apply_to_network(net))
-                    print("Fixpoint? " + str(net.is_fixpoint()))
-                    print("Loss " + str(loss))
-                    print()
+            for i in range(10):
+
+                run_count = 1000
+                net = TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(width=2, depth=2))\
+                    .with_params(epsilon=0.0001).with_keras_params(optimizer='sgd')
+                for run_id in tqdm(range(run_count+1)):
+                    loss = net.compiled().train(epoch=run_id)
+                    if run_id % 100 == 0:
+                        net.print_weights()
+                        # print(net.apply_to_network(net))
+                        print("Fixpoint? " + str(net.is_fixpoint()))
+                        print("Loss " + str(loss))
+                        print()
+                exp.historical_particles[i] = net
+                K.clear_session()
     if False:
         # this does not work as the aggregation function screws over the fixpoint computation....
         # TODO: check for fixpoint in aggregated space...