Does high learning rate produces NaN?

Does high learning rate produces NaN? - PyTorch Forums

It does seem like the learning rate is responsible for this. The reason this occurs is because of the way gradient descent works.

Deep-Learning Nan loss reasons - python - Stack Overflow

There are lots of things I have seen make a model diverge. Too high of a learning rate. You can often tell if this is the case if the loss ...

A high learning rate may cause a nan or an inf loss with tf.keras ...

@gdhy9064 High learning rate is usually the root cause for many NAN problems. You can try with a lower value, or with another adaptive ...

Nan when trying different learning rates - DeepLearning.AI

The short answer is that with a higher learning rate you are forcing the model's gradient, and by implication the weights and the cost, into “ ...

Common Causes of NaNs During Training - Baeldung

In cases where NaNs result from the learning rate, extensive hyperparameter tuning can help set the optimal learning rate for the model's ...

Why is learning rate causing my neural network's weights to ...

If your values are close to 0, the gradient is going to be small than if it further, but if your learning rate is big then, instead of getting ...

Why model return 'NaN' values - Medium

Adjust Learning Rate: A too high learning rate can lead to gradient explosion, causing NaN values. Consider reducing the learning rate or using ...

What would cause loss to steadily decrease then suddenly become ...

Usually this is preceded by the loss suddenly spiking, then becoming nan though, which I didn't observe here. My learning rate also started at ...

Cost function turning into nan after a certain number of iterations

Suppose you are training a deep learning neural network. The implementation details are not relevant for my question. I know very well that if ...

Why my deep learning network is producing NaN outputs?

When a deep CNN network produces NaN outputs during training, it typically indicates that there is an issue with the numerical stability of the network.

Common Causes of NANs During Training | The Truth of Sisyphus

Gradient blow up · Bad learning rate policy and params · Faulty Loss function · Faulty input · Stride larger than kernel size in "Pooling" layer.

The weight of the convolution kernel become NaN after training ...

Weights going to NaN is typically due to overflow. The most common cause I know for this issue is a too high learning rate and no gradient clipping.

Getting nan values for training and validation loss #620 - GitHub

It looks like the learning rate may just be on the high side. On the other had, the behavior shouldn't be too different from old versions. The ...

Nan loss in machine learning model: what to do? - Reddit

Exploding gradients: your loss becomes too large to fit the numeric constraint so it becomes Nan. If this happens, you might want to check the ...

What can be the reason of loss=nan and accuracy = 0 in an ML ...

Whenever your loss comes as Nan, keep learning rate reducing to check whether NaN is going away or not. I would also suggest scaling ( ...

Why do l get NaN values when l train my neural network ... - Quora

This simple 1D toy model exhibits same NaN behavior if we knock off the sigmoid layer, and just increase the number of nodes in single layer to ...

Why my deep learning network is producing NaN outputs?

When a deep CNN network produces NaN outputs during training, it typically indicates that there is an issue with the numerical stability of the ...

SVI returns NaN loss - Misc. - Pyro Discussion Forum

The first thing to try is to reduce your learning rate. Try 0.001, and if that doesn't NAN, try 0.01.

Policy returning NaN weights and NaN biases. In addition, Policy ...

@MrDracoG From what you mention the NaNs in your weights might stem from very high losses/gradients. Did you observe any spikes in your losses?

Shorter Training=Smaller Chances of NaNs; A hypothesis by an ...

But why is it my model was failing at the lowest learning rates, yet when I raised the learning rate and gave it a shorter training cycle there ...