model only predict one label after training

[pissw2016]

Hi, Golbstein
With weight provided by you, the IOU and prediction is all good.(I suppose the generator is right?) But with the training weight by me , the model just predict 0 all the time.
And in training the accuracy stucked in a very low value in first epoch and never change. All accuracy being difference refers to different batch size.
np.unique(model1.predict(x))return only one value 0.042with my training weight
in generator:
np.unique(self.Y[0,:]) I got array([ 0., 1., 15., 21.], dtype=float32)
np.unique(self.SW[0,:]) I got array([0. , 0.37763503, 4.679469 , 7.2337375 ], dtype=float32) they match like{0: 0.377, 1:7.23, 15:4.67, 21:0.}
I visualize the self.X, self.y, self.SW SW seems like label and image match the label.dtype all float(including self.X).
I cant figure out what is the problem. There is only the generator need to be change, right?

[pissw2016]

I found out it is the problem on loss function, I make generator produce onehot label directly and then use 'categorical_crossentropy' as loss function in compile. It is fine.

[tiagojc]

@pissw2016 did you solve the problem? I'm facing same issue here