Apr 3, 2025 · During training, if the loss function fails to decrease significantly, or if there is erratic behavior in the learning curves, it suggests that the gradients may be vanishing. Additionally, examining the gradients themselves during backpropagation can provide insights.

Dec 6, 2023 · Gradients represent the slope of the loss function with respect to the model’s parameters. When these gradients grow significantly during training, they lead to “exploding gradients.” What is the Exploding Gradient Problem? When Does it Occur? What Causes an Exploding Gradient?

Sep 5, 2019 · Only after 50k steps the loss starts exploding, before that it is remarkably stable. So around the 34th iteration through my train set the loss starts to increase all of a sudden. Why only now? How can it be stable for so long and suddenly increase sharply?

Aug 14, 2019 · Exploding gradients are a problem where large error gradients accumulate and result in very large updates to neural network model weights during training. This has the effect of your model being unstable and unable to learn from your training data.

Aug 28, 2020 · Training neural networks can become unstable, leading to a numerical overflow or underflow referred to as exploding gradients. The training process can be made stable by changing the error gradients either by scaling the vector norm or clipping gradient values to …

Aug 16, 2024 · Neural network models are trained by the optimization algorithm of gradient descent. The input training data helps these models learn, and the loss function gauges how accurate the prediction performance is for each iteration when parameters get updated.

Oct 10, 2020 · Training an RNN is done by defining a loss function (L) that measures the error between the true label and the output, and minimizes it by using forward pass and backward pass. The following...

Oct 5, 2024 · In deep neural networks (DNNs), the vanishing gradient problem is a notorious issue that plagues training, especially when using activation functions like sigmoid and tanh. The problem...

Exploding gradient problem occurs when gradients in neural networks become extremely large, leading to numerical instability. Signs of exploding gradients include unstable model behavior, fluctuating loss, encountering NaN or infinity values, spikes in …

Detecting exploding gradients often involves monitoring the training process: observing sudden spikes in the loss function, checking the magnitude of gradients (gradient norm), or noticing extremely large weight values. Tools like TensorBoard can be helpful for visualizing these metrics.