diff --git a/src/vectise/neural_net.py b/src/vectise/neural_net.py
index b2b7d06..6cd3527 100644
--- a/src/vectise/neural_net.py
+++ b/src/vectise/neural_net.py
@@ -10,32 +10,49 @@ class NeuralNet:
         self,
         matrix: np.typing.NDArray | list[np.typing.NDArray] | None = None,
         shape: tuple[int] | None = None,
+        node_colour: str = "blue",
+        node_border: str = "black",
+        edge_colours: list[str] = ["green"],
     ):
         """Initialise the visualisation object.
 
         Parameters
         ----------
-        self :
-            self
         matrix : np.typing.NDArray | None
-            matrix
+            matrix or list of matrices
         shape : tuple[int] | None
             shape
         """
         if matrix is None and shape is None:
             logging.error("supply a matrix or at least its shape!")
         elif matrix is None:
-            self.matrix = np.ones(shape)
+            self.matrix = [
+                np.ones((shape[i], shape[i + 1])) for i in range(len(shape) - 1)
+            ]
             self.shape = shape
         elif shape is None:
-            self.shape = matrix.shape
-            self.matrix = matrix
+            if not isinstance(matrix, list):
+                self.matrix = [matrix]
+            else:
+                self.matrix = matrix
+            shapes = []
+            for m in self.matrix:
+                shapes.extend(m.shape)
+            self.shape = tuple(shapes[:-1:2] + [shapes[-1]])
+        else:
+            logging.error(
+                "not implemented. best to drop the shapes. they will"
+                "get inferred from the matrices."
+            )
+        if len(edge_colours) != len(self.matrix):
+            edge_colours = edge_colours * len(self.matrix)
+
         self.elms: list[svg.Element] = []
         self.r = 12
         self.d_y = 32
         self.d_x = 200
         self.max_card = max(self.shape)
-        self.max_height = self.max_card * self.d_y + 2 * self.d_y
+        self.max_height = (self.max_card - 1) * self.d_y + 2 * self.d_y
 
         node_elms = []
         underlying_rectangles: list[svg.Element] = []
@@ -59,8 +76,8 @@ class NeuralNet:
                         cx=self.d_y + i_layer * self.d_x,
                         cy=offset + self.d_y + self.d_y * i,
                         r=self.r,
-                        stroke="black",
-                        fill="blue",
+                        stroke=node_border,
+                        fill=node_colour,
                         stroke_width=1,
                     )
                 )
@@ -94,7 +111,7 @@ class NeuralNet:
             for (i, p1), (j, p2) in itertools.product(
                 enumerate(node_coords[i_layer]), enumerate(node_coords[i_layer + 1])
             ):
-                if (self.matrix[i, j] == 0).all():
+                if (self.matrix[i_layer][i, j] == 0).all():
                     continue
                 edges.append(
                     svg.Path(
@@ -102,7 +119,7 @@ class NeuralNet:
                             svg.M(p1[0] + self.r, p1[1]),
                             svg.L(p2[0] - self.r, p2[1]),
                         ],
-                        stroke="green",
+                        stroke=edge_colours[i_layer],
                         # marker_end="url(#arrowhead)",
                     )
                 )
@@ -120,7 +137,7 @@ class NeuralNet:
     def svg(self):
         return str(
             svg.SVG(
-                width=(len(self.shape) + 1) * self.d_x,
+                width=(len(self.shape) - 1) * self.d_x + 2 * self.d_y,
                 height=self.max_height,
                 elements=self.elms,
             )