Building Sublayer 1: Multi-Head Attention
Learn to build sublayer 1: multi-head attention through a step-by-step approach.
We'll cover the following
- Step 1: Represent the input
- Step 2: Initializing the weight matrices
- Step 3: Matrix multiplication to obtain Q, K, and V
- Step 4: Scaled attention scores
- Step 5: Scaled softmax attention scores for each vector
- Step 6: The final attention representations
- Step 7: Summing up the results
- Step 8: Repeat steps 1 to 7 for all the inputs
- Step 9: The output of the heads of the attention sublayer
- Step 10: Concatenation of the output of the heads
We will use basic Python code with only numpy and a softmax function in 10 steps to run the key aspects of the attention mechanism.
Note: Bear in mind that an Industry 4.0 developer will face the challenge of multiple architectures for the same algorithm.
Now let's start building step 1 of our model to represent the input.
Step 1: Represent the input
We will start by only using minimal Python functions to understand the transformer at a low level with the inner workings of an attention head. We will explore the inner workings of the multi-head attention sublayer using basic code:
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