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What does NaN loss mean?
The reason for nan , inf or -inf often comes from the fact that division by 0.0 in TensorFlow doesn’t result in a division by zero exception. It could result in a nan , inf or -inf “value”. In your training data you might have 0.0 and thus in your loss function it could happen that you perform a division by 0.0 .
What is training loss in neural network?
Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function. In simple words, the Loss is used to calculate the gradients. And gradients are used to update the weights of the Neural Net. This is how a Neural Net is trained.
Who is a NaN?
child’s word for a grandmother: The kids love staying with their nan at the weekend. [ as form of address ] Happy birthday, Nan. Thesaurus: synonyms, antonyms, and examples. a grandparent.
What is Nan in neural network?
This function re-encodes unknown input values represented by NaN values into numerical values so the network can operate on the values directly. One can remove this function from the default network object to avoid calling it or even add a custom function to pre-process the inputs.
What to do about Nan loss in Python?
Traceback… tensorflow.contrib.learn.python.learn.monitors.NanLossDuringTrainingError: NaN loss during training. I’ve tried adjusting the optimizer, using a zero for learning rate, and using no optimizer. Any insights into network layers, data size, etc is appreciated. There are lots of things I have seen make a model diverge.
Is there a Nan loss error in TensorFlow?
ERROR:tensorflow:Model diverged with loss = NaN. Traceback… tensorflow.contrib.learn.python.learn.monitors.NanLossDuringTrainingError: NaN loss during training.
Is there an overflow in the Nan model?
I tried with a smaller model, i.e. with only one hidden layer, and same issue (it becomes nan at a different point). However, it does work with less features, i.e. if there are only 5 columns, and gives quite good predictions. It seems to be there is some kind of overflow, but I can’t imagine why–the loss is not unreasonably large at all.
Why is loss being outputed as Nan in keras RNN?
A similar problem was reported here: Loss being outputed as nan in keras RNN. In that case, there were exploding gradients due to incorrect normalisation of values. Thanks for contributing an answer to Data Science Stack Exchange! Please be sure to answer the question. Provide details and share your research!