tried out some stuff

code/test.py

@@ -0,0 +1,32 @@
+from experiment import *
+from network import *
+from soup import *
+import numpy as np
+
+def vary(e=0.0, f=0.0):
+    return [
+        np.array([[1.0+e, 0.0+f], [0.0+f, 0.0+f], [0.0+f, 0.0+f], [0.0+f, 0.0+f]], dtype=np.float32),
+        np.array([[1.0+e, 0.0+f], [0.0+f, 0.0+f]], dtype=np.float32),
+        np.array([[1.0+e], [0.0+f]], dtype=np.float32)
+    ]
+
+net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='sigmoid')
+if False:
+    net.set_weights([
+        np.array([[1.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], dtype=np.float32),
+        np.array([[1.0, 0.0], [0.0, 0.0]], dtype=np.float32),
+        np.array([[1.0], [0.0]], dtype=np.float32)
+    ])
+    print(net.get_weights())
+    net.self_attack(100)
+    print(net.get_weights())
+    print(net.is_fixpoint())
+if True:
+    net.set_weights(vary(0.01, 0.0))
+    print(net.get_weights())
+    for _ in range(5):
+        net.self_attack()
+        print(net.get_weights())
+    print(net.is_fixpoint())
+    
+