3d-mri-brain-tumor-segmentation-using-autoencoder-regularization
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My input data consists of 2 modalities instead of 4
Hello!
I am Olalla, and I am a bit new in all this DL world. I am trying to implement your algorithm and I need to change the input_shape so that it only considers 2 modalities instead of 4. Also, I only have 1 label for the whole meningioma so the output_channels = 1 instead of 3. I think I have modified the whole code properly but, still, I get this error:
ValueError: Dimensions must be equal, but are 2 and 4 for '{{node loss_VAE/sub}} = Sub[T=DT_FLOAT](IteratorGetNext:2, model_3/Dec_VAE_Output/BiasAdd)' with input shapes: [1,2,80,96,64], [1,4,80,96,64]
Here I attach the model.py file modified:
I hope someone can see what modification it lacks.
Thank you in advance!
Olalla