The input dimension of self-attention

John Williams answered . 2025-07-03 05:16:48

1. Convert the two-dimensional images into sequences: If your two-dimensional images consist of two sequences, you can reshape each image into a single sequence. For example, if the image dimensions are M rows and N columns, you can reshape it into a sequence of length M*N.
2. Apply self-attention to the reshaped sequences: Once you have reshaped the images into sequences, you can apply the self-attention mechanism. MATLAB does not provide a built-in function specifically for self-attention, but you can implement it using custom code or by utilizing deep learning frameworks like TensorFlow or PyTorch.

% Import TensorFlow for MATLAB
import tensorflow.*

% Reshape the images into sequences
sequence1 = reshape(image1, [], 1);
sequence2 = reshape(image2, [], 1);

% Concatenate the sequences along the feature dimension
sequences = cat(2, sequence1, sequence2);

% Create a TensorFlow graph
graph = tensorflow.Graph;
session = tensorflow.Session(graph);

% Define the self-attention model
with graph.asDefault
    % Define the inputs
    input = tensorflow.placeholder(tensorflow.float32, [numFeatures, 2]);

    % Perform self-attention
    attention = selfAttention(input);

% Run the self-attention operation
output = session.run(attention, struct('input', sequences));

% Process the output as needed

 

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