Answers.Body: <p>From your code, I think you are attempting to apply an attention mechanism to your model using the torch framework. the issue you are facing seems to be due to the way you are applying the softmax activation function and weights of the attention layer</p>
  <p>In your code, you apply it like the below</p>
  <pre><code>#y shape (1, 100)
  att_weights = F.softmax(self.attention(y))
  
  att_weights = att_weights / att_weights.sum(dim=1, keepdim=True)
  
  </code></pre>
  <p>The problem here is that the softmax function already normalizes the output to create a probability distribution, so the sum of all the elements will be one, which means the second normalization is not needed and it might cause the problem that all weights are 1. you should remove the second normalization step.
  The line where you normalize the attention weights again is redundant because the softmax already returns a probability distribution.
  In the calculation of <code>attended_y</code>, it is not clear what the shape of <code>att_weights</code> and <code>y</code> are and how they are supposed to align in the multiplication. Since <code>att-weights</code> is a probability distribution over elements of <code>y</code>, you would typically you would typically compute the weighted sum of <code>y</code> by multiplying <code>y</code> element-wise with <code>att_weights</code> and summing the result, or if <code>y</code> and <code>att_weights</code> are 2D, doing matrix multiplication.</p>
  <p>Here below you can find an example of how you can use a self-attention mechanisum using a torch</p>
  <pre><code>def forward(self, y):
      import torch.nn.functional as F
  
      y = y.view(-1, self.input_dim).float()
  
      batch_size = y.size(0)  # set the batch size
  
      # Compute attention scores
      attention_scores = self.attention(y)  # replace this with your score computation
  
      # Compute attention weights
      att_weights = F.softmax(attention_scores, dim=1)
  
      # Apply attention weights to y
      attended_y = torch.sum(att_weights * y, dim=1)
  
      return attended_y
  
  </code></pre>
  <p>for more information you can use these References</p>
  <p><a href="https://stackoverflow.com/questions/76648620/how-do-i-implement-this-attention-layer-in-pytorch">how-do-i-implement-this-attention-layer-in-pytorch</a></p>
  <p><a href="https://spotintelligence.com/2023/01/31/self-attention/" rel="nofollow noreferrer">self-attension example</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-17T19:12:56.350Z
Answers.LastActivityDate: 2023-12-17T19:12:56.350Z
Answers.UserDisplayName: Keval Pandya
Body: <p>I have a deep learning model, and I want to add a attention layer in pytorch. in the forward function, I add the attention as this: however, when I check the weights of the attention, all of the weights are 1! And y before the attention and after the attention is same. Could you please help me with that? Thank you</p>
  <pre><code>def forward(self, y):
      
      import torch.nn.functional as F
      
      y = y.view(-1, self.input_dim).float()
     
      
      batch_size = y.size(0)  # set the batch size
      
      #y shape (1, 100)
      att_weights = F.softmax(self.attention(y))
  
      att_weights = att_weights / att_weights.sum(dim=1, keepdim=True)
  
      attended_y = torch.sum(att_weights.unsqueeze(-1) * y.unsqueeze(1), dim=1)
  
  rest of the code...
  </code></pre>
  
Tags: <pytorch><attention>
Title: add a layer in deep learning model pytorch
UserDisplayName: Sadcow
ThreadText: # add a layer in deep learning model pytorch
  
  (Asked by: username_0 on 2023-12-15)
  
  I have a deep learning model, and I want to add a attention layer in pytorch. in the forward function, I add the attention as this: however, when I check the weights of the attention, all of the weights are 1! And y before the attention and after the attention is same. Could you please help me with that? Thank you
  def forward(self, y):
      
      import torch.nn.functional as F
      
      y = y.view(-1, self.input_dim).float()
     
      
      batch_size = y.size(0)  # set the batch size
      
      #y shape (1, 100)
      att_weights = F.softmax(self.attention(y))
  
      att_weights = att_weights / att_weights.sum(dim=1, keepdim=True)
  
      attended_y = torch.sum(att_weights.unsqueeze(-1) * y.unsqueeze(1), dim=1)
  
  rest of the code...
  
  **Tags:** pytorch, attention
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-12-17. Score: 2
  
  From your code, I think you are attempting to apply an attention mechanism to your model using the torch framework. the issue you are facing seems to be due to the way you are applying the softmax activation function and weights of the attention layer
  In your code, you apply it like the below
  #y shape (1, 100)
  att_weights = F.softmax(self.attention(y))
  
  att_weights = att_weights / att_weights.sum(dim=1, keepdim=True)
  
  
  The problem here is that the softmax function already normalizes the output to create a probability distribution, so the sum of all the elements will be one, which means the second normalization is not needed and it might cause the problem that all weights are 1. you should remove the second normalization step.
  The line where you normalize the attention weights again is redundant because the softmax already returns a probability distribution.
  In the calculation of attended_y, it is not clear what the shape of att_weights and y are and how they are supposed to align in the multiplication. Since att-weights is a probability distribution over elements of y, you would typically you would typically compute the weighted sum of y by multiplying y element-wise with att_weights and summing the result, or if y and att_weights are 2D, doing matrix multiplication.
  Here below you can find an example of how you can use a self-attention mechanisum using a torch
  def forward(self, y):
      import torch.nn.functional as F
  
      y = y.view(-1, self.input_dim).float()
  
      batch_size = y.size(0)  # set the batch size
  
      # Compute attention scores
      attention_scores = self.attention(y)  # replace this with your score computation
  
      # Compute attention weights
      att_weights = F.softmax(attention_scores, dim=1)
  
      # Apply attention weights to y
      attended_y = torch.sum(att_weights * y, dim=1)
  
      return attended_y
  
  
  for more information you can use these References
  how-do-i-implement-this-attention-layer-in-pytorch
  self-attension example
  
  
  ---
  
  