The loss is Nan about fcn.tensorflow HOT 6 CLOSED

Make sure your class labels are 0 and 1. I'm guessing the reason 151 works is because your label values fit within that range. The model afaik is trying to allocate a position in a one hot vector for each pixel, so if your label is outside the range of that vector then it will go nan.

from fcn.tensorflow.

It's funny. And I also train the code on my dataset. When the NUM_OF_CLASSES is less than 60, the loss is Nan. And when I change the NUM_OF_CLASSES to 61, it works.
After some experiments, I found some results:
First, the direct reason is because the values of conv1_1 is Nan when the NUM_OF_CLASSES is less than 60. But the input image is not Nan. Second, what really matters is the NUM_OF_CLASSES in the third transpose convolution. That's saying it's no matter how you change the NUM_OF_CLASSES in the first transpose convolution.
But unfortunatly, I didn't find the reson and don't know how to fix it.
Do you fix it ?
Thanks,

from fcn.tensorflow.

Agree with @Exuro889 . Just double check you label.

from fcn.tensorflow.

@xtudbxk I have found the problem.I checked my dataset and surprisingly the max value of my label is 2.No wonder that 2 classes didn't work.I strongly suggest you checking your label again to make sure the num of labels is 60.
Meanwhile,thanks you @rayhou0710 @Exuro889

from fcn.tensorflow.

@abc8350712 @Exuro889
Thanks for your help, I just get the loss which is not nan through your ways.

from fcn.tensorflow.

Sorry for the late response. The above comment by @Exuro889 does solve the problem.
Just fyi in the documentation for tf.nn.sparse_softmax_cross_entropy_with_logits you will notice the labels are to be in range [0, num_classes). This is the same conclusion made in the above comments. This is just how the function is implemented.

from fcn.tensorflow.