Update README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>

pytorch/networks/README.md

@@ -24,7 +24,98 @@ CLASS torch.nn.Sequential(*args: Module)
 
 Example: 
 
-```python
+![image0](network_0.png)
 
+We can just chain the layers together:
+
+```python
+import torch
+
+input_number_of_channel: int = 1
+input_dim_x: int = 24
+input_dim_y: int = 24
+
+number_of_output_channels_conv1: int = 32
+number_of_output_channels_conv2: int = 64
+number_of_output_channels_flatten1: int
+number_of_output_channels_full1: int = 1024
+number_of_output_channels_out: int = 10
+
+kernel_size_conv1: tuple[int, int] = (5, 5)
+kernel_size_pool1: tuple[int, int] = (2, 2)
+kernel_size_conv2: tuple[int, int] = (5, 5)
+kernel_size_pool2: tuple[int, int] = (2, 2)
+
+stride_conv1: tuple[int, int] = (1, 1)
+stride_pool1: tuple[int, int] = (2, 2)
+stride_conv2: tuple[int, int] = (1, 1)
+stride_pool2: tuple[int, int] = (2, 2)
+
+padding_conv1: int = 0
+padding_pool1: int = 0
+padding_conv2: int = 0
+padding_pool2: int = 0
+
+number_of_output_channels_flatten1 = 576
+
+network = torch.nn.Sequential(
+    torch.nn.Conv2d(
+        in_channels=input_number_of_channel,
+        out_channels=number_of_output_channels_conv1,
+        kernel_size=kernel_size_conv1,
+        stride=stride_conv1,
+        padding=padding_conv1,
+    ),
+    torch.nn.ReLU(),
+    torch.nn.MaxPool2d(
+        kernel_size=kernel_size_pool1, stride=stride_pool1, padding=padding_pool1
+    ),
+    torch.nn.Conv2d(
+        in_channels=number_of_output_channels_conv1,
+        out_channels=number_of_output_channels_conv2,
+        kernel_size=kernel_size_conv2,
+        stride=stride_conv2,
+        padding=padding_conv2,
+    ),
+    torch.nn.ReLU(),
+    torch.nn.MaxPool2d(
+        kernel_size=kernel_size_pool2, stride=stride_pool2, padding=padding_pool2
+    ),
+    torch.nn.Flatten(
+        start_dim=1,
+    ),
+    torch.nn.Linear(
+        in_features=number_of_output_channels_flatten1,
+        out_features=number_of_output_channels_full1,
+        bias=True,
+    ),
+    torch.nn.ReLU(),
+    torch.nn.Linear(
+        in_features=number_of_output_channels_full1,
+        out_features=number_of_output_channels_out,
+        bias=True,
+    ),
+)
+
+print(network)
 ```
 
+```python
+Sequential(
+  (0): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))
+  (1): ReLU()
+  (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+  (3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
+  (4): ReLU()
+  (5): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+  (6): Flatten(start_dim=1, end_dim=-1)
+  (7): Linear(in_features=576, out_features=1024, bias=True)
+  (8): ReLU()
+  (9): Linear(in_features=1024, out_features=10, bias=True)
+)
+```
+
+Congratulations you now have the network you wanted.
+
+
+