diff --git a/draw_kernels_v2.py b/draw_kernels_v2.py
index 62a1a65..a6721b2 100644
--- a/draw_kernels_v2.py
+++ b/draw_kernels_v2.py
@@ -3,8 +3,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.patches as patch
 import matplotlib as mpl
-import torch
 from cycler import cycler
+from functions.analyse_network import analyse_network
 
 mpl.rcParams["text.usetex"] = True
 mpl.rcParams["font.family"] = "serif"
@@ -85,108 +85,6 @@ def draw_kernel(
     plt.show(block=True)
 
 
-def unfold(
-    layer: torch.nn.Conv2d | torch.nn.MaxPool2d | torch.nn.AvgPool2d, size: int
-) -> torch.Tensor:
-    if isinstance(layer.kernel_size, tuple):
-        assert layer.kernel_size[0] == layer.kernel_size[1]
-        kernel_size: int = int(layer.kernel_size[0])
-    else:
-        kernel_size = int(layer.kernel_size)
-
-    if isinstance(layer.dilation, tuple):
-        assert layer.dilation[0] == layer.dilation[1]
-        dilation: int = int(layer.dilation[0])
-    else:
-        dilation = int(layer.dilation)  # type: ignore
-
-    if isinstance(layer.padding, tuple):
-        assert layer.padding[0] == layer.padding[1]
-        padding: int = int(layer.padding[0])
-    else:
-        padding = int(layer.padding)
-
-    if isinstance(layer.stride, tuple):
-        assert layer.stride[0] == layer.stride[1]
-        stride: int = int(layer.stride[0])
-    else:
-        stride = int(layer.stride)
-
-    out = (
-        torch.nn.functional.unfold(
-            torch.arange(0, size, dtype=torch.float32)
-            .unsqueeze(0)
-            .unsqueeze(0)
-            .unsqueeze(-1),
-            kernel_size=(kernel_size, 1),
-            dilation=(dilation, 1),
-            padding=(padding, 0),
-            stride=(stride, 1),
-        )
-        .squeeze(0)
-        .type(torch.int64)
-    )
-
-    return out
-
-
-def analyse_network(
-    model: torch.nn.Sequential, input_shape: int
-) -> tuple[list, list, list]:
-    combined_list: list = []
-    coordinate_list: list = []
-    layer_type_list: list = []
-    pixel_used: list[int] = []
-
-    size: int = int(input_shape)
-
-    for layer_id in range(0, len(model)):
-        if isinstance(
-            model[layer_id], (torch.nn.Conv2d, torch.nn.MaxPool2d, torch.nn.AvgPool2d)
-        ):
-            out = unfold(layer=model[layer_id], size=size)
-            coordinate_list.append(out)
-            layer_type_list.append(
-                str(type(model[layer_id])).split(".")[-1].split("'")[0]
-            )
-            size = int(out.shape[-1])
-        else:
-            coordinate_list.append(None)
-            layer_type_list.append(None)
-
-    assert coordinate_list[0] is not None
-    combined_list.append(coordinate_list[0])
-
-    for i in range(1, len(coordinate_list)):
-        if coordinate_list[i] is None:
-            combined_list.append(combined_list[i - 1])
-        else:
-            for pos in range(0, coordinate_list[i].shape[-1]):
-                idx_shape: int | None = None
-
-                idx = torch.unique(
-                    torch.flatten(combined_list[i - 1][:, coordinate_list[i][:, pos]])
-                )
-                if idx_shape is None:
-                    idx_shape = idx.shape[0]
-                assert idx_shape == idx.shape[0]
-
-            assert idx_shape is not None
-
-            temp = torch.zeros((idx_shape, coordinate_list[i].shape[-1]))
-            for pos in range(0, coordinate_list[i].shape[-1]):
-                idx = torch.unique(
-                    torch.flatten(combined_list[i - 1][:, coordinate_list[i][:, pos]])
-                )
-                temp[:, pos] = idx
-            combined_list.append(temp)
-
-    for i in range(0, len(combined_list)):
-        pixel_used.append(int(torch.unique(torch.flatten(combined_list[i])).shape[0]))
-
-    return combined_list, layer_type_list, pixel_used
-
-
 # %%
 if __name__ == "__main__":
     import os