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code/setups/network_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__':
+    def run_exp(net, prints=False):
+        # 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
+        if prints:
+            print("Fixpoint? " + str(net.is_fixpoint()))
+            print("Loss " + str(loss))
+
+    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)
+
+    if True:
+        # Aggregating Neural Network
+        with FixpointExperiment() as exp:
+            for run_id in tqdm(range(10)):
+                net = ParticleDecorator(AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)
+                                        .with_keras_params(activation='linear'))
+                run_exp(net)
+                K.clear_session()
+            exp.log(exp.counters)
+
+    if True:
+        #FFT Neural Network
+        with FixpointExperiment() as exp:
+            for run_id in tqdm(range(10)):
+                net = ParticleDecorator(FFTNeuralNetwork(aggregates=4, width=2, depth=2)
+                                        .with_keras_params(activation='linear'))
+                run_exp(net)
+                K.clear_session()
+            exp.log(exp.counters)
+
+    if True:
+        # ok so this works quite realiably
+        with FixpointExperiment() as exp:
+            for i in range(10):
+                run_count = 100
+                net = TrainingNeuralNetworkDecorator(ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)))
+                net.with_params(epsilon=0.0001).with_keras_params(activation='linear')
+                for run_id in tqdm(range(run_count+1)):
+                    net.compiled()
+                    loss = net.train(epoch=run_id)
+                    if run_id % 10 == 0:
+                        run_exp(net)
+                K.clear_session()
+
+    if True:
+        # ok so this works quite realiably
+        with FixpointExperiment() as exp:
+            for i in range(10):
+                run_count = 100
+                net = TrainingNeuralNetworkDecorator(ParticleDecorator(AggregatingNeuralNetwork(4, width=2, depth=2)))
+                net.with_params(epsilon=0.0001).with_keras_params(activation='linear')
+                for run_id in tqdm(range(run_count+1)):
+                    net.compiled()
+                    loss = net.train(epoch=run_id)
+                    if run_id % 10 == 0:
+                        run_exp(net)
+                K.clear_session()
+
+    if False:
+        # this explodes in our faces completely... NAN everywhere
+        # TODO: Wtf is happening here?
+        with FixpointExperiment() as exp:
+            run_count = 10000
+            net = TrainingNeuralNetworkDecorator(RecurrentNeuralNetwork(width=2, depth=2))\
+                .with_params(epsilon=0.1e-2).with_keras_params(optimizer='sgd', activation='linear')
+            for run_id in tqdm(range(run_count+1)):
+                loss = net.compiled().train()
+                if run_id % 500 == 0:
+                    net.print_weights()
+                    # print(net.apply_to_network(net))
+                    print("Fixpoint? " + str(net.is_fixpoint()))
+                    print("Loss " + str(loss))
+                    print()
+    if False:
+        # and this gets somewhat interesting... we can still achieve non-trivial fixpoints
+        # over multiple applications when training enough in-between
+        with MixedFixpointExperiment() as exp:
+            for run_id in range(10):
+                net = TrainingNeuralNetworkDecorator(FFTNeuralNetwork(2, width=2, depth=2))\
+                    .with_params(epsilon=0.0001, activation='sigmoid')
+                exp.run_net(net, 500, 10)
+
+                net.print_weights()
+
+                print("Fixpoint? " + str(net.is_fixpoint()))
+            exp.log(exp.counters)