MLS-C01 Question 203
Select 3You are training a deep learning model using AWS SageMaker. The model’s training process is taking longer than expected due to poor convergence. You suspect the issue could be related to the initialization of the model’s weights. Which of the following steps can help address this problem?
- A
Use pre-trained weights for initialization rather than random initialization.
- B
Initialize weights using a proven method like Xavier or He initialization.
- C
Increase the learning rate significantly to compensate for poor initialization.
- D
Use zero-initialization for all weights to ensure consistency across layers.
- E
Verify the architecture of your neural network for compatibility with the chosen initialization method.
Show answer and explanation
Correct answers: A, B, E
Explanation
Poor weight initialization can result in slow convergence or unstable training. Using pre-trained weights, employing proven initialization techniques like Xavier or He initialization, and ensuring compatibility between the initialization method and the network architecture are effective strategies to address such issues. Other approaches, like zero-initialization or arbitrarily increasing the learning rate, can cause further problems and are not recommended.
- A. Correct.
Correct. Using pre-trained weights can help the model start training with a better baseline, improving convergence.
- B. Correct.
Correct. Proven initialization methods like Xavier or He are specifically designed to improve convergence during training by addressing issues like vanishing or exploding gradients.
- C. Incorrect.
Incorrect. Increasing the learning rate is not a direct solution to poor weight initialization and can lead to unstable training.
- D. Incorrect.
Incorrect. Zero-initialization for all weights can lead to symmetry issues in the model, where all neurons in a layer learn the same features, hindering convergence.
- E. Correct.
Correct. Ensuring that your network architecture is compatible with the chosen initialization method is critical, as certain initialization methods may be better suited for specific network types.