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What Causes Exploding Gradients and Leads to Uncontrollable Model Parameters?
Understand the critical issue of exploding gradients in neural network training. Learn how excessively large gradients cause drastic weight updates, leading to numerical instability (NaN values), uncontrollable parameter growth, and complete model divergence, and see how techniques like gradient clipping solve it.
Question
What happens if gradients explode during training?
A. The model will always achieve higher accuracy.
B. Training becomes slower but stable.
C. The learning rate automatically decreases.
D. The model parameters may grow uncontrollably, causing divergence.
Answer
D. The model parameters may grow uncontrollably, causing divergence.
Explanation
Exploding gradients lead to unstable training. This phenomenon occurs when gradients become excessively large, leading to massive updates to the network’s weights and making the training process unstable.
During the training of a neural network via backpropagation, the gradients of the loss function with respect to the weights are calculated. The optimizer then uses these gradients to update the weights in the direction that minimizes the loss. The update rule is conceptually new_weight = old_weight – learning_rate * gradient.
Exploding gradients occur when the gradients accumulate during backpropagation and become exceedingly large. This is often a problem in deep networks or recurrent neural networks where the chain rule of differentiation involves many repeated multiplications. If these derivative values are consistently greater than 1, their product can grow exponentially.
When the gradient value is enormous, the weight update step becomes a huge leap. Instead of taking a small step towards the minimum of the loss function, the update drastically overshoots the target, sending the weights to extreme values. This can cause the weights to become so large that they are represented as inf (infinity) or NaN (Not a Number), a state from which the model cannot recover. This breakdown of the training process, where the loss shoots to infinity or NaN, is known as divergence.
A common technique to mitigate this is gradient clipping, where the gradient values are capped at a certain threshold before the weights are updated.
Analysis of Incorrect Options
A. The model will always achieve higher accuracy: This is incorrect. Exploding gradients cause the training to fail completely. The loss function diverges, and the model’s performance collapses, leading to terrible accuracy.
B. Training becomes slower but stable: This describes the opposite problem: vanishing gradients. When gradients are extremely small, weight updates are minuscule, causing the training process to become incredibly slow or stall, but it remains numerically stable. Exploding gradients create instability, not slowness.
C. The learning rate automatically decreases: This is false. A standard training loop does not automatically adjust the learning rate in response to exploding gradients. While techniques like learning rate scheduling exist, they are pre-configured strategies, not an automatic consequence of this problem. In fact, a learning rate that is too high is a common cause of exploding gradients, not a solution that happens automatically.
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