<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>

CC BY-SA 4.0
2024-11-28T00:01:11.810Z
dropout usually causes val-acc to be higher than train-acc since is disabled in validation
Alberto
CC BY-SA 4.0
2024-11-28T09:44:34.173Z
I have removed the dropout and the behavior is similar... is this a bad sign?
Kat27
<overfitting><accuracy><generalization><dropout><validation-loss>
Is It Normal for Validation Accuracy to be Higher than Training Accuracy?
Kat27
# 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?  

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## 0 answers

