diff --git a/architectures/utils/arbitrary_act.py b/architectures/utils/arbitrary_act.py
new file mode 100644
index 0000000..6f6834f
--- /dev/null
+++ b/architectures/utils/arbitrary_act.py
@@ -0,0 +1,63 @@
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.python.framework import ops
+
+a=6.12
+
+def HCAL_act(x):
+    
+    return np.divide(a,np.add(1,np.exp(np.add(-x,a))))
+    
+    
+np_HCAL_act = np.vectorize(HCAL_act)
+
+def d_HCAL_act(x):
+    num = np.multiply(a,np.exp(np.add(a,-x)))
+    den = np.square(np.add(1., np.exp(np.add(a,-x))))
+    return num/den
+
+np_d_HCAL_act = np.vectorize(d_HCAL_act)
+
+np_d_HCAL_act_32 = lambda x: np_d_HCAL_act(x).astype(np.float32)
+
+def tf_d_HCAL_act(x,name=None):
+    with tf.name_scope(name, "d_HCAL_act", [x]) as name:
+        y = tf.py_func(np_d_HCAL_act_32,
+                        [x],
+                        [tf.float32],
+                        name=name,
+                        stateful=False)
+        return y[0]
+
+def py_func(func, inp, Tout, stateful=True, name=None, grad=None):
+
+    # Need to generate a unique name to avoid duplicates:
+    rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
+
+    tf.RegisterGradient(rnd_name)(grad)  # see _MySquareGrad for grad example
+    g = tf.get_default_graph()
+    with g.gradient_override_map({"PyFunc": rnd_name}):
+        return tf.py_func(func, inp, Tout, stateful=stateful, name=name)
+
+def HCAL_grad(op, grad):
+    x = op.inputs[0]
+
+    n_gr = tf_d_HCAL_act(x)
+    return grad * n_gr 
+
+
+np_HCAL_act_32 = lambda x: np_HCAL_act(x).astype(np.float32)
+
+def tf_HCAL_act(x, name=None):
+
+    with tf.name_scope(name, "HCAL_act", [x]) as name:
+        y = py_func(np_HCAL_act_32,
+                        [x],
+                        [tf.float32],
+                        name=name,
+                        grad=HCAL_grad)  # <-- here's the call to the gradient
+        return y[0]
+
+
+    
\ No newline at end of file