MLS-C01 Modeling Practice Question
A research team is training a deep learning model for image classification using Amazon SageMaker. The model is a convolutional neural network (CNN) with 50 layers. The team uses a single ml.p3.2xlarge instance. After 10 hours of training, the model has not converged and the loss is decreasing very slowly. The team suspects vanishing gradients. They want to diagnose and fix the issue without significant code changes. Which action should they take?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Modify the architecture to include residual connections (skip connections)
(Modify the architecture to include residual connections) directly addresses vanishing gradients by allowing gradients to flow through skip connections. Option A (adding more layers) worsens the problem. Option C (batch normalization) helps but is not as targeted as residual connections. Option D (increase learning rate) may cause divergence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add more convolutional layers to increase model capacity
Why it's wrong here
More layers can worsen vanishing gradients.
- ✓
Modify the architecture to include residual connections (skip connections)
Why this is correct
Residual connections allow gradients to flow directly through the network.
- ✗
Use batch normalization after each convolutional layer
Why it's wrong here
Batch normalization helps but may not fully solve vanishing gradients in deep networks.
- ✗
Increase the learning rate by a factor of 10
Why it's wrong here
Higher learning rate may cause gradients to explode or loss to diverge.
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