From 06d7daf0cce4838e1ece07c97572c0906c0d10dd Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Tue, 2 Jan 2024 20:36:04 +0100
Subject: [PATCH] Update README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
---
 pytorch/networks/README.md | 47 ++++++++++++++++++++++++++++++++++++--
 1 file changed, 45 insertions(+), 2 deletions(-)

diff --git a/pytorch/networks/README.md b/pytorch/networks/README.md
index 29e4b85..af3185f 100644
--- a/pytorch/networks/README.md
+++ b/pytorch/networks/README.md
@@ -1088,7 +1088,7 @@ Usually you will see this construct in tutorials:
 
 ```python
 class MyNetworkClass(torch.nn.Module):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
 
         input_number_of_channel: int = 1
@@ -1147,7 +1147,7 @@ class MyNetworkClass(torch.nn.Module):
             bias=True,
         )
 
-    def forward(self, input):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
         out = self.conv1(input)
         out = self.relu1(out)
         out = self.max_pooling_1(out)
@@ -1160,3 +1160,46 @@ class MyNetworkClass(torch.nn.Module):
 
 ```
 
+In the constructor of the class you define the layers as elements of the class. And we write a forward function that connections the flow of information from the input to the output feed through the layers.
+
+Now we can do the following:
+
+```python
+network = MyNetworkClass()
+
+number_of_pattern: int = 111
+input_number_of_channel: int = 1
+input_dim_x: int = 24
+input_dim_y: int = 24
+
+fake_input = torch.rand(
+    (number_of_pattern, input_number_of_channel, input_dim_x, input_dim_y),
+    dtype=torch.float32,
+)
+output = network(fake_input)
+print(fake_input.shape)  # -> torch.Size([111, 1, 24, 24])
+print(output.shape)  # -> torch.Size([111, 10])
+```
+
+For accessing the innards we now need to address them via their variable names: 
+
+```python
+print(network.conv1.__dict__["_parameters"].keys()) # -> odict_keys(['weight', 'bias'])
+print(network.conv2.__dict__["_parameters"].keys()) # -> odict_keys(['weight', 'bias'])
+print(network.fully_connected_1.__dict__["_parameters"].keys()) # -> odict_keys(['weight', 'bias'])
+```
+
+Save is still like this:
+
+```python
+torch.save(network.state_dict(), "torch_network_dict_class.pt")
+```
+
+and load shortens, if it can reuse the class defintion, to:
+
+```python
+network = MyNetworkClass()
+network.load_state_dict(torch.load("torch_network_dict_class.pt"))
+network.eval()
+```
+