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code/setups/soup_trajectorys.py

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+import sys
+import os
+
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
+
+from soup import *
+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)
+                # 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: 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.seed()
+                for _ in tqdm(range(100)):
+                    soup.evolve()
+                exp.log(soup.count())
+                # you can access soup.historical_particles[particle_uid].states[time_step]['loss']
+                #             or soup.historical_particles[particle_uid].states[time_step]['weights']
+                # from soup.dill
+                exp.save(soup=soup.without_particles())