An exploding gradient is a common problem in deep neural networks where gradients become excessively large during training, leading to unstable updates and divergence. To address this, several strategies can be employed:
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Gradient Clipping: This is the most direct method. It involves setting a threshold for the gradients. If the norm of the gradient exceeds this threshold, it is scaled down to match the threshold. This prevents large updates from destabilizing the model.
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Weight Initialization: Proper weight initialization, such as Xavier/Glorot or He initialization, can help keep the variance of activations and gradients more stable, reducing the likelihood of them exploding.
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Lower Learning Rate: A smaller learning rate can slow down the training process, making it less susceptible to large, destabilizing updates caused by exploding gradients.
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Batch Normalization: By normalizing the inputs to each layer, batch normalization can help stabilize the training process and mitigate the vanishing or exploding gradient problem.
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Network Architecture: Sometimes, the architecture itself can contribute. Using architectures with skip connections (like ResNets) or recurrent architectures designed to handle gradients better (like LSTMs or GRUs for RNNs) can be beneficial.
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Weight Regularization: Techniques like L1 or L2 regularization can penalize large weights, indirectly helping to control gradient magnitudes.