Body: <p>I'm training a deep learning model in PyTorch for a classification problem, and I’ve noticed that the validation accuracy is consistently higher than the training accuracy throughout the training process. This behavior persists even after tuning the model. Here’s a quick summary of my problem and setup:</p>
  <p>Dataset:
  The dataset is imbalanced, particularly for two of the classes. This imbalance might be affecting the model’s performance.</p>
  <p><strong>The architecture includes (HAR problem):</strong></p>
  <ul>
  <li><p>A CNN feature extractor with two convolutional layers: First layer:
  64 filters, kernel size = 10, stride = 10. Second layer: 32 filters,
  kernel size = 5, stride = 2.</p>
  </li>
  <li><p>A dense layer for a compact latent space
  creates a shared feature representation between different branches.</p>
  </li>
  <li><p>LSTM layer for sequence modeling. A final dense layer for
  classification.</p>
  </li>
  </ul>
  <p>Regularization: I’ve experimented with different dropout rates (e.g., 0.3, 0.5, 0.6), primarily in the LSTM layer and last CNN, and it does impact the stability of the training process.</p>
  <p><strong>Training Details:</strong></p>
  <p>Loss Function: Cross-entropy loss. I modified the cross-entropy loss to account for each branch's predictions in the latent space representation
  Optimizer: Adam with a learning rate of 0.0005.
  Batch size: 32.
  Training for 10 epochs.</p>
  <p><strong>What I’ve Tried:</strong></p>
  <p>Dropout Tuning: Tried dropout values between 0.2 and 0.6 in the LSTM and fully connected layers.
  Reducing Model Complexity: Lowered the number of neurons in the LSTM, which stabilized validation accuracy but didn’t resolve the discrepancy.
  Class Imbalance Considerations: Since the dataset is imbalanced, I suspect this might be causing problems.</p>
  <p>Is it normal for validation accuracy to be consistently higher than training accuracy in such a scenario?
  Could the class imbalance or dropout be the primary reason for this behavior?
  Is this model overfitting?</p>
  <p><a href="https://i.sstatic.net/UiYDptED.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/UiYDptED.png" alt="Validation Accuracy Higher than V.Training" /></a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-28T00:01:11.810Z
Comments.Text: dropout usually causes val-acc to be higher than train-acc since is disabled in validation
Comments.UserDisplayName: Alberto
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-28T09:44:34.173Z
Comments.Text: I have removed the dropout and the behavior is similar... is this a bad sign?
Comments.UserDisplayName: Kat27
Tags: <overfitting><accuracy><generalization><dropout><validation-loss>
Title: Is It Normal for Validation Accuracy to be Higher than Training Accuracy?
UserDisplayName: Kat27
ThreadText: # Is It Normal for Validation Accuracy to be Higher than Training Accuracy?
  
  (Asked by: username_0 on 2024-11-27)
  
  I'm training a deep learning model in PyTorch for a classification problem, and I’ve noticed that the validation accuracy is consistently higher than the training accuracy throughout the training process. This behavior persists even after tuning the model. Here’s a quick summary of my problem and setup:
  
  Dataset:
  The dataset is imbalanced, particularly for two of the classes. This imbalance might be affecting the model’s performance.
  
  **The architecture includes (HAR problem):**
  
  * A CNN feature extractor with two convolutional layers: First layer:
    64 filters, kernel size = 10, stride = 10. Second layer: 32 filters,
    kernel size = 5, stride = 2.
  * A dense layer for a compact latent space
    creates a shared feature representation between different branches.
  * LSTM layer for sequence modeling. A final dense layer for
    classification.
  
  Regularization: I’ve experimented with different dropout rates (e.g., 0.3, 0.5, 0.6), primarily in the LSTM layer and last CNN, and it does impact the stability of the training process.
  
  **Training Details:**
  
  Loss Function: Cross-entropy loss. I modified the cross-entropy loss to account for each branch's predictions in the latent space representation
  Optimizer: Adam with a learning rate of 0.0005.
  Batch size: 32.
  Training for 10 epochs.
  
  **What I’ve Tried:**
  
  Dropout Tuning: Tried dropout values between 0.2 and 0.6 in the LSTM and fully connected layers.
  Reducing Model Complexity: Lowered the number of neurons in the LSTM, which stabilized validation accuracy but didn’t resolve the discrepancy.
  Class Imbalance Considerations: Since the dataset is imbalanced, I suspect this might be causing problems.
  
  Is it normal for validation accuracy to be consistently higher than training accuracy in such a scenario?
  Could the class imbalance or dropout be the primary reason for this behavior?
  Is this model overfitting?
  
  [![Validation Accuracy Higher than V.Training](https://i.sstatic.net/UiYDptED.png)](https://i.sstatic.net/UiYDptED.png)
  
  **Tags:** overfitting, accuracy, generalization, dropout, validation-loss
  > **Comments:**
  > **username_1**: dropout usually causes val-acc to be higher than train-acc since is disabled in validation  
  > **username_0**: I have removed the dropout and the behavior is similar... is this a bad sign?  
  
  ---
  
  ## 0 answers
  
  