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Bahdanau Attention is often called Additive Attention because of the mathematical formulation used to compute the attention scores. In contrast to Dot-Product (Multiplicative) Attention, Bahdanau Attention relies on addition and a non-linear activation function.

Let's go through the math step-by-step:

Definitions

( h_i ): Hidden state of the encoder for the (i)-th time step in the source sequence.
( s_t ): Hidden state of the decoder for the (t)-th time step in the target sequence.
( W_1 ) and ( W_2 ): Weight matrices.
( b ): Bias term.
( v ): Context vector.

	%Applying bandpass filter to filter out the unwanted signal <4 and >30Hz
	yy1=bandpass(SSS1,[4 30],128);
	%Apply DWT at 5 level of decomposition
	waveletFunction = 'db2';
	[C,L] = wavedec(yy1,5,waveletFunction);
	cD11 = detcoef(C,L,1);
	cD21 = detcoef(C,L,2);
	cD31 = detcoef(C,L,3);
	cD41 = detcoef(C,L,4);
	cD51 = detcoef(C,L,5);

	import cv2
	import numpy as np
	import dlib

	cap = cv2.VideoCapture(0)

	detector = dlib.get_frontal_face_detector()
	predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

	while True:



	for id = 1:32
	sdata = load(strcat('s',num2str(id),'.mat'));
	sdata = sdata.data;
	slabels = load(strcat('s',num2str(id),'.csv'));
	[~,idx] = sort(slabels(:,2));
	sortedlabels = slabels(idx,:);
	sorteddata = sdata(idx,:,:);

	def shape_list(tensor: Union[tf.Tensor, np.ndarray]) -> List[int]:
	"""
	Deal with dynamic shape in tensorflow cleanly.

	Args:
	tensor (`tf.Tensor` or `np.ndarray`): The tensor we want the shape of.

	Returns:
	`List[int]`: The shape of the tensor as a list.
	"""