Update Attention Maths.md

Author: purvasingh96

Chapter-wise code/Code - PyTorch/7. Attention Models/2. Neural Text Summarization/1. Transformer Models/Attention Maths.md

@@ -16,22 +16,21 @@ Lk: no. of keys
 3. dim[K] = [Lk, D];
 4. dim[V] = [Lk, D]
 
-<img src="../images/15.step - 1.png"></img> <br><br>
+<br>
+<img src="../images/15. step - 1.png" width="50%"></img> <br><br>
 
 4. A query Q, will assign each key K, a probability  that key K is a match for Q. Similarity is measured by taking dot
-product of vectors. So Q and K are similar iff `Q dot K` is large. 
-<img src="../images/16.step - 2.png"></img> <br><br>
+product of vectors. So Q and K are similar iff `Q dot K` is large. <br>
+<img src="../images/16. step - 2.png" width="50%"></img> <br><br>
 
 5. To make attention more focused on best matching keys, use softmax `(softmax(Q.KTranspose))`. Hence, we now calculate a matrix of Q-K probabailities
-often called *attention weights*. The shape of this matrix is `[Lq, Lk]`.
+often called *attention weights*. The shape of this matrix is `[Lq, Lk]`.<br>
 
-6. In the final step, we take values and get weighted sum of values, weighting each value Vi by the probability that the key Ki matches the query.
+6. In the final step, we take values and get weighted sum of values, weighting each value Vi by the probability that the key Ki matches the query.<br>
+<img src="../images/17. step - 3.png" width="50%"></img> <br><br>
 
-7. Finally the attention mechanism calculates the dynamic or alignment weights representing the relative importance of the inputs in this sequence.
-<img src="../images/17.step - 3.png"></img> <br><br>
+7. Finally the attention mechanism calculates the dynamic or alignment weights representing the relative importance of the inputs in this sequence.<br>
+<img src="../images/18. step - 4.png" width="50%"></img> <br><br>
 
-8. Multiplying alignment weights with input sequence (values), will then weight the sequence.
-<img src="../images/18.step - 4.png"></img> <br><br>
-
-9. A single context vector can then be calculated using the sum of weighted vectors.
-<img src="../images/19.step - 5.png"></img> <br><br>
+8. Multiplying alignment weights with input sequence (values), will then weight the sequence. A single context vector can then be calculated using the sum of weighted vectors.<br>
+<img src="../images/19. step - 5.png" width="50%"></img> <br><br>