Box and stuff

code/setups/fixpoint-density.py

@@ -28,34 +28,38 @@ def count(counters, net, notable_nets=[]):
         counters['other'] += 1
     return counters, notable_nets
 
-with Experiment('fixpoint-density') as exp:
-    exp.trials = 100
-    exp.epsilon = 1e-4
-    net_generators = []
-    for activation in ['linear', 'sigmoid', 'relu']:
-        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: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
-    all_counters = []
-    all_notable_nets = []
-    all_names = []
-    for net_generator_id, net_generator in enumerate(net_generators):
-        counters = generate_counters()
-        notable_nets = []
-        for _ in tqdm(range(exp.trials)):
-            net = net_generator().with_params(epsilon=exp.epsilon)
-            name = str(net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias='" + str(net.get_keras_params().get('use_bias')) + "'"
-            count(counters, net, notable_nets)
-            keras.backend.clear_session()
-        all_counters += [counters]
-        all_notable_nets += [notable_nets]
-        all_names += [name]
-    exp.save(all_counters=all_counters)
-    exp.save(all_notable_nets=all_notable_nets)
-    exp.save(all_names=all_names)
-    for exp_id, counter in enumerate(all_counters):
-        exp.log(all_names[exp_id])
-        exp.log(all_counters[exp_id])
-        exp.log('\n')
 
-print('Done')
+if __name__ == '__main__':
+    with Experiment('fixpoint-density') as exp:
+        exp.trials = 100
+        exp.epsilon = 1e-4
+        net_generators = []
+        for activation in ['linear', 'sigmoid', 'relu']:
+            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)]
+        all_counters = []
+        all_notable_nets = []
+        all_names = []
+        for net_generator_id, net_generator in enumerate(net_generators):
+            counters = generate_counters()
+            notable_nets = []
+            for _ in tqdm(range(exp.trials)):
+                net = net_generator().with_params(epsilon=exp.epsilon)
+                net = ParticleDecorator(net)
+                name = str(net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias='" + str(net.get_keras_params().get('use_bias')) + "'"
+                count(counters, net, notable_nets)
+                keras.backend.clear_session()
+            all_counters += [counters]
+            all_notable_nets += [notable_nets]
+            all_names += [name]
+        exp.save(all_counters=all_counters)
+        exp.save(all_notable_nets=all_notable_nets)
+        exp.save(all_names=all_names)
+        for exp_id, counter in enumerate(all_counters):
+            exp.log(all_names[exp_id])
+            exp.log(all_counters[exp_id])
+            exp.log('\n')
+
+    print('Done')