Exploding Gradients is a phenomenon that occurs during the training of deep learning models, particularly those with many layers, such as recurrent neural networks (RNNs). It refers to the situation where the gradients (the values used to update the model’s parameters) become excessively large. This can cause the model’s weights to update in a way that leads to instability, resulting in model divergence rather than convergence.
In mathematical terms, gradients are computed during the backpropagation phase of training. If the gradients grow too large, they can produce extremely large updates to the model’s parameters. This can manifest as NaN (Not a Number) values in the model weights, or the model may produce nonsensical outputs. This issue is particularly prevalent in deep networks where the gradients can accumulate exponentially through multiple layers.
Several strategies are employed to mitigate the problem of exploding gradients. One common method is gradient clipping, which involves setting a threshold value for gradients. If the computed gradients exceed this threshold, they are scaled down to prevent excessive updates. Other approaches include using more stable activation functions, adjusting the model architecture, or employing different optimization algorithms.
Understanding and addressing exploding gradients is crucial for effectively training deep learning models, as it allows for more stable convergence and improved performance.