Minor updates

src/nn/network.nim

@@ -156,6 +156,7 @@ proc mse(a, b: Matrix[float]): float =
 func dxMSE(x, y: Matrix[float]): Matrix[float] = 2.0 * (x - y)
 
 # 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)
 
@@ -166,12 +167,12 @@ func softmax(input: Matrix[float]): Matrix[float] =
     var input = input - input.max()
     result = input.apply(math.exp, axis = -1) / input.apply(math.exp, axis = -1).sum()
 
-
 func softmaxDerivative(input: Matrix[float]): Matrix[float] =
     var input = input.reshape(input.shape.cols, 1)
     result = input.diagflat() - input.dot(input.transpose())
 
 
+# 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 relu(input: Matrix[float]): Matrix[float] {.used.} = input.apply(proc (x: float): float = max(0.0, x), axis = -1)