How can you detect and mitigate vanishing gradients in a deep Transformer network?

Vanishing gradients in deep Transformer networks can be detected by monitoring the magnitude of the gradients during training. If the gradients in the earlier layers of the network are significantly smaller than the gradients in the later layers, it indicates that the gradients are vanishing. This can be monitored using tools like TensorBoard or by logging the gradient norms during training. Another way to detect vanishing gradients is to observe the learning rate adaptation. If the adaptive learning rate methods, like Adam, result in very large learning rates for the initial layers it also indicates that the gradients are small. Several techniques can be used to mitigate vanishing gradients. Residual connections, as used in the Transformer architecture, provide a direct path for gradients to flow from later layers to earlier layers, bypassing the intervening layers and preventing the gradients from being attenuated. Layer normalization also helps to stabilize training and to prevent vanishing gradients by normalizing the activations within each layer. Careful initialization of the weights can also help to prevent vanishing gradients. For example, using the Xavier or He initialization schemes can ensure that the weights are initialized in a way that preserves the variance of the activations. Using ReLU (Rectified Linear Unit) or other activation functions that do not saturate can also help to prevent vanishing gradients. Saturated activation functions, like sigmoid or tanh, can cause the gradients to become very small when the activations are in the saturated region. Gradient clipping, which involves limiting the magnitude of the gradients, can also help to prevent exploding gradients, which can sometimes be associated with vanishing gradients. By implementing these techniques, it is possible to effectively mitigate vanishing gradients and to train deep Transformer networks successfully.