Diagram of a network with self attention mechanism

While I can't create visual diagrams directly, I can describe how to create a diagram of a network with a self-attention mechanism step by step. You can use this description to create your own diagram using drawing tools or software. ### Self-Attention Mechanism Diagram 1. **Input Layer:** - Start with a series of input vectors (e.g., tokens). Represent these as a series of horizontal arrows pointing to the right, labeled as \( X_1, X_2, ..., X_n \). 2. **Embedding Layer:** - Each input token vector is transformed into an embedding. Draw a box around the arrows and label it "Embedding Layer". This step often involves projecting the inputs into a higher-dimensional space. 3. **Linear Transformations:** - Next, show three sets of linear transformations for the input embeddings to obtain the queries (Q), keys (K), and values (V). - From the embedding, draw three separate arrows leading into three boxes labeled as "Linear Transform (Q)", "Linear Transform (K)", and "Linear Transform (V)". 4. **Calculating Attention Scores:** - Show how the attention scores are calculated: - Draw an arrow from the Query output to a box labeled "Dot Product" with the Key output. - In parallel, indicate this operation results in a matrix of attention scores. - Optionally, draw a scaling factor and indicate that the scores are then passed through a softmax function to normalize them. 5. **Weighted Sum of Values:** - Draw arrows from the Box output (softmax scores) and from the Values output to a new box labeled "Weighted Sum". - This reflects how the attention mechanism computes a context vector by combining the value vectors weighted by attention scores. 6. **Output Layer:** - The output of the weighted sum can be represented as an output vector, leading to final processing. - Draw this as an arrow and label it "Output of Self-Attention". 7. **Residual Connection and Layer Normalization (Optional):** - To indicate the optional post-attention processing, draw a line from the input (embedding layer) to the output of the weighted sum, and then show a box titled "Add & Norm" to indicate a residual connection and layer normalization. 8. **Final Output:** - Finally, show the output from the "Add & Norm" box leading to the next layer or to a classification or regression output, as per your architecture. ### Putting It All Together - Organize these elements on a canvas from left to right: - Input Tokens → Embedding Layer → Linear Transformations for Q, K, V → Attention Score Calculation → Weighted Sum of Values → Output - Incorporate optional components (residual connections and layer normalization) throughout the flow. ### Tools to Use You can use diagramming tools such as: - Microsoft PowerPoint - Google Slides - Lucidchart - Draw.io - Any vector graphic editor This description should help you create a clear and concise diagram of a network with a self-attention mechanism!