diff --git a/optim_nn.py b/optim_nn.py
index 0031d79..7d50c05 100644
--- a/optim_nn.py
+++ b/optim_nn.py
@@ -230,6 +230,23 @@ class Affine(Layer):
     def dF(self, dY):
         return dY * self.a
 
+class Sigmoid(Layer): # aka Logistic
+    def F(self, X):
+        from scipy.special import expit as sigmoid
+        self.sig = sigmoid(X)
+        return X * self.sig
+
+    def dF(self, dY):
+        return dY * self.sig * (1 - self.sig)
+
+class Tanh(Layer):
+    def F(self, X):
+        self.sig = np.tanh(X)
+        return X * self.sig
+
+    def dF(self, dY):
+        return dY * (1 - self.sig * self.sig)
+
 class Relu(Layer):
     def F(self, X):
         self.cond = X >= 0
@@ -239,7 +256,8 @@ class Relu(Layer):
         return np.where(self.cond, dY, 0)
 
 class GeluApprox(Layer):
-    # refer to https://www.desmos.com/calculator/ydzgtccsld
+    # paper: https://arxiv.org/abs/1606.08415
+    #  plot: https://www.desmos.com/calculator/ydzgtccsld
     def F(self, X):
         from scipy.special import expit as sigmoid
         self.a = 1.704 * X
@@ -385,26 +403,32 @@ if __name__ == '__main__':
     config = DotMap(
         fn = 'ml/cie_mlp_min.h5',
 
-        batch_size = 64,
-
+        # multi-residual network parameters
         res_width = 12,
         res_depth = 3,
-        res_block = 2, # normally 2
-        res_multi = 4, # normally 1
+        res_block = 2, # normally 2 for plain resnet
+        res_multi = 4, # normally 1 for plain resnet
+
+        # style of resnet
+        # only one is implemented so far
+        parallel_style = 'batchless',
         activation = 'gelu',
 
         optim = 'adam',
         nesterov = False, # only used with SGD or Adam
         momentum = 0.33, # only used with SGD
-        epochs = 6, # 6
+
+        # learning parameters: SGD with restarts
         LR = 1e-2,
-        restarts = 3, # 3
+        epochs = 6,
         LR_halve_every = 2,
+        restarts = 3,
         LR_restart_advance = 3,
+
+        # misc
+        batch_size = 64,
         init = 'he_normal',
         loss = 'mse',
-
-        parallel_style = 'batchless',
     )
 
     # toy CIE-2000 data
@@ -429,7 +453,7 @@ if __name__ == '__main__':
     y = x
     last_size = input_samples
 
-    activations = dict(relu=Relu, gelu=GeluApprox)
+    activations = dict(sigmoid=Sigmoid, tanh=Tanh, relu=Relu, gelu=GeluApprox)
     activation = activations[config.activation]
 
     for blah in range(config.res_depth):