Contents
Why am I getting NaN loss in keras?
Most of the time losses you log will be just some regular values but sometimes you might get nans when working with Keras loss functions. Use of the wrong optimizer function, Large (exploding) gradients that result in a large update to network weights during training.
What does a loss of NaN 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 .
Why is loss function NaN?
3 Answers. Well, if you get NaN values in your cost function, it means that the input is outside of the function domain. E.g. the logarithm of 0. Or it could be in the domain analytically, but due to numerical errors we get the same problem (e.g. a small value gets rounded to 0).
How do you solve Nan loss?
Here are some things you could potentially try:
- Normalize your outputs by quantile normalizing or z scoring.
- Add regularization, either by increasing the dropout rate or adding L1 and L2 penalties to the weights.
- If these still don’t help, reduce the size of your network.
- Increase the batch size from 32 to 128.
How do I fix loss NaN?
How do you solve NaN loss?
Why is the NaN value in loss in keras?
The shape your logits is [None, 1], so keras assumes that the number of your classes is 1, but your are feeding more than one classes ( 0 or 1) and that is causing the error. The right activation function here is sigmoid (tanh also could work by altering the dataset target to be either -1 or 1).
How to increase batch size in keras RNN?
Try to increase the batch size (e.g. 32 to 64 or 128) to increase the stability of your optimization. Check the size of your last batch which may be different from the batch size. A similar problem was reported here: Loss being outputed as nan in keras RNN.
How does the sequential model in keras work?
I’m implementing a neural network with Keras, but the Sequential model returns nan as loss value. I have sigmoid activation function in the output layer to squeeze output between 0 and 1, but maybe doesn’t work properly.
How to track NaN values in loss model?
Here are few steps to track down the cause, 1) If an input is outside of the function domain, then determine what those inputs are. Track the progression of input values to your cost function. 2) Check if there are any null or nan values in input data set.