How to add SE module in GoogLeNet for classification？

How to add SE module in GoogLeNet for classification？

Answer 1

[Animesh]

Hi Xiao,

To integrate a Squeeze-and-Excitation (SE) module into GoogLeNet for classification in MATLAB, you need to modify the architecture of GoogLeNet by inserting SE blocks at appropriate positions.

Here's a way to approach this:

• Load Pre-trained GoogLeNet:First, load the pre-trained GoogLeNet model. Ensure you have the Deep Learning Toolbox installed.

net = googlenet;

• Define the SE Block:You need to define the SE block as a function or a layer array. An SE block typically consists of global average pooling, a fully connected (FC) layer that reduces dimensionality, a ReLU activation, another FC layer that restores dimensionality, and finally, a sigmoid activation to generate channel-wise weights.Here's a basic function to create an SE block in MATLAB. This function assumes you're adding the SE block to a convolutional layer with C channels:

function layers = seBlock(C, r)

% C is the number of channels in the previous layer

% r is the reduction ratio for the SE block

reducedDims = max(floor(C / r), 1); % Ensure at least one dimension

layers = [

averagePooling2dLayer(1, 'Stride', 1, 'Name', 'se_avg_pool', 'Padding', 'same')

fullyConnectedLayer(reducedDims, 'Name', 'se_fc1')

reluLayer('Name', 'se_relu')

fullyConnectedLayer(C, 'Name', 'se_fc2')

sigmoidLayer('Name', 'se_sigmoid')

];

end

• Integrate SE Blocks into GoogLeNet:To integrate the SE blocks, you need to dissect the GoogLeNet architecture and insert SE blocks at desired points. This can be complex due to GoogLeNet's inception modules.Here's an example of how you might modify one layer, but in practice, you'll need to carefully plan where to insert SE blocks:

lgraph = layerGraph(net);

% Example: Add an SE block after 'inception_3a-output'

seLayers = seBlock(256, 16); % Example parameters

lgraph = addLayers(lgraph, seLayers);

% Then, you need to connect the SE block properly

lgraph = disconnectLayers(lgraph, 'inception_3a-output', 'inception_3b-1x1');

lgraph = connectLayers(lgraph, 'inception_3a-output', 'se_avg_pool');

lgraph = connectLayers(lgraph, 'se_sigmoid', 'inception_3b-1x1');

• Replace and Reconnect Layers:For each point in the network where you add an SE block, you need to adjust connections appropriately, ensuring that the output of the SE block scales the output of the preceding layer before passing it to the next layer.
• Train the Modified Network:Lastly, after modifying the architecture, you can train the network on your dataset. Ensure you have a dataset ready for training, and use “trainNetwork” with appropriate options.

I hope this helps.

Animesh

[shen hedong]

Hello, I feel that your answer is very constructive. May I ask for your help?

I now need to write MATLAB code for Efficient Channel Attention and Convolutional Block Attention Module, or other innovative attention mechanisms such as Light Self Gaussian Attention. I am looking forward to your reply and will continue to follow you closely! thank you!