From a5c21ee51235abbf9cf11ff71eda92d14e9c1035 Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Tue, 2 Jan 2024 20:40:35 +0100
Subject: [PATCH] Update README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
---
 pytorch/networks/README.md | 77 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)

diff --git a/pytorch/networks/README.md b/pytorch/networks/README.md
index af3185f..dfef358 100644
--- a/pytorch/networks/README.md
+++ b/pytorch/networks/README.md
@@ -1203,3 +1203,80 @@ network.load_state_dict(torch.load("torch_network_dict_class.pt"))
 network.eval()
 ```
 
+## But how do I get the activities?
+
+
+* Option 0: Use sequential as explained above. I think this is the best way. 
+* Option 1: Rewrite the forward function and save the data as e.g. self.activation_conv1
+* Option 2: Install forward hooks (see [Forward and Backward Function Hooks](https://pytorch.org/tutorials/beginner/former_torchies/nnft_tutorial.html#forward-and-backward-function-hooks))
+  
+First we need a function that we can hook in:
+
+```python
+activation = {}
+
+
+def get_activation(name):
+    def hook(model, input, output):
+        activation[name] = output.detach()
+
+    return hook
+```
+
+Next we need to register the hooks:
+
+```python
+network = MyNetworkClass()
+network.conv1.register_forward_hook(get_activation("Conv1"))
+network.relu1.register_forward_hook(get_activation("ReLU1"))
+network.max_pooling_1.register_forward_hook(get_activation("MaxPooling1"))
+network.conv2.register_forward_hook(get_activation("Conv2"))
+network.relu2.register_forward_hook(get_activation("ReLU2"))
+network.max_pooling_2.register_forward_hook(get_activation("MaxPooling2"))
+network.flatten1.register_forward_hook(get_activation("Flatten1"))
+network.fully_connected_1.register_forward_hook(get_activation("FullyConnected1"))
+```
+
+Then we can run the input through the network:
+
+```python
+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)
+```
+
+And we will find the activations in the variable activation.
+
+```python
+for name, value in activation.items():
+    print(f"Hook name: {name}")
+    print(value.shape)
+```
+
+Output:
+
+```python
+Hook name: Conv1
+torch.Size([111, 32, 20, 20])
+Hook name: ReLU1
+torch.Size([111, 32, 20, 20])
+Hook name: MaxPooling1
+torch.Size([111, 32, 10, 10])
+Hook name: Conv2
+torch.Size([111, 64, 6, 6])
+Hook name: ReLU2
+torch.Size([111, 64, 6, 6])
+Hook name: MaxPooling2
+torch.Size([111, 64, 3, 3])
+Hook name: Flatten1
+torch.Size([111, 576])
+Hook name: FullyConnected1
+torch.Size([111, 10])
+```