diff --git a/src/nn/network.nim b/src/nn/network.nim
index e88da14..c5643aa 100644
--- a/src/nn/network.nim
+++ b/src/nn/network.nim
@@ -277,10 +277,13 @@ proc mse(a, b: Matrix[float]): float =
 # Derivative of MSE
 func dxMSE(x, y: Matrix[float]): Matrix[float] = 2.0 * (x - y)
 
+
+func sigmoid(x: float): float = 1 / (1 + exp(-x))
+
 # A bunch of vectorized activation functions
 
 func sigmoid(input: Matrix[float]): Matrix[float] = 
-    result = input.apply(proc (x: float): float = 1 / (1 + exp(-x)) , axis = -1)
+    result = input.apply(sigmoid, axis = -1)
 
 func sigmoidDerivative(input: Matrix[float]): Matrix[float] = sigmoid(input) * (1.0 - sigmoid(input))
 
@@ -299,25 +302,27 @@ func softmaxDerivative(input: Matrix[float]): Matrix[float] =
     # I _love_ stealing functions from numpy!
     result = input.diagflat() - input.dot(input.transpose())
 
-func relu(input: Matrix[float]): Matrix[float] {.used.} = input.apply(proc (x: float): float = max(0.0, x), axis = -1)
+func relu(input: Matrix[float]): Matrix[float] = input.apply(proc (x: float): float = max(0.0, x), axis = -1)
 func dxRelu(input: Matrix[float]): Matrix[float] = input.where(input > 0.0, 0.0)
 
-# TODO: Add derivatives for this stuff
-func step(input: Matrix[float]): Matrix[float] {.used.} = input.apply(proc (x: float): float = (if x < 0.0: 0.0 else: x), axis = -1)
-func silu(input: Matrix[float]): Matrix[float] {.used.} = input.apply(proc (x: float): float = 1 / (1 + exp(-x)), axis= -1)
+func silu(input: Matrix[float]): Matrix[float] = input.apply(proc (x: float): float = x * sigmoid(x), axis= -1)
+func dSilu(input: Matrix[float]): Matrix[float] = input.apply(proc (x: float): float = sigmoid(x) * (1 + x * (1 - sigmoid(x))), axis = -1)
 
-
-func htan(input: Matrix[float]): Matrix[float] {.used.} = 
+func htan(input: Matrix[float]): Matrix[float] = 
     let f = proc (x: float): float = 
         let temp = exp(2 * x)
         result = (temp - 1) / (temp + 1)
     input.apply(f, axis = -1)
 
+func htanDx(input: Matrix[float]): Matrix[float] = input.apply(proc (x: float): float = 1 - (pow(tanh(x), 2)), axis = -1)
+
 {.push.}
 {.hints: off.}   # So nim doesn't complain about the naming
 var Sigmoid* = newActivation(sigmoid, sigmoidDerivative)
 var Softmax* = newActivation(softmax, softmaxDerivative)
 var ReLU* = newActivation(relu, dxRelu)
+var SiLU* = newActivation(silu, dSilu)
+var HTan* = newActivation(htan, htanDx)
 var MSE* = newLoss(mse, dxMSE)
 {.pop.}