Error when reducing the number of classes from 32 to 8

[YasarL]

Hey there,
did someone try to reduce the number of classes? In my case it's about reducing from 32 to 8. Is that possible at all with this implementation?

When I adjust the label_codes, label_names dictionary to 8 entries and also change
from "model = get_small_unet(n_filters = 32)"
to model = get_small_unet(n_filters = 8)

I hope you understand my problem. If more information is required from my side please tell me so I can give it to you! I'm new to posting questions on GitHub ;)

The error I get when training the network:

Epoch 1/2
Found 3312 images belonging to 1 classes.
Found 3312 images belonging to 1 classes.

ValueError Traceback (most recent call last)
in ()
6 #result = model.fit_generator(TrainAugmentGenerator(), steps_per_epoch=18 ,
7 validation_data = ValAugmentGenerator(),
----> 8 validation_steps = validation_steps, epochs=num_epochs, callbacks=callbacks)
9 model.save_weights("camvid_model_150_epochs.h5", overwrite=True)

/net/store/nbp/projects/affordance_prediction/alimberg/tf2/local/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training.pyc in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1295 shuffle=shuffle,
1296 initial_epoch=initial_epoch,
-> 1297 steps_name='steps_per_epoch')
1298
1299 def evaluate_generator(self,

/net/store/nbp/projects/affordance_prediction/alimberg/tf2/local/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_generator.pyc in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
263
264 is_deferred = not model._is_compiled
--> 265 batch_outs = batch_function(*batch_data)
266 if not isinstance(batch_outs, list):
267 batch_outs = [batch_outs]

/net/store/nbp/projects/affordance_prediction/alimberg/tf2/local/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training.pyc in train_on_batch(self, x, y, sample_weight, class_weight, reset_metrics)
971 outputs = training_v2_utils.train_on_batch(
972 self, x, y=y, sample_weight=sample_weight,
--> 973 class_weight=class_weight, reset_metrics=reset_metrics)
974 outputs = (outputs['total_loss'] + outputs['output_losses'] +
975 outputs['metrics'])

/net/store/nbp/projects/affordance_prediction/alimberg/tf2/local/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.pyc in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics)
251 x, y, sample_weights = model._standardize_user_data(
252 x, y, sample_weight=sample_weight, class_weight=class_weight,
--> 253 extract_tensors_from_dataset=True)
254 batch_size = array_ops.shape(nest.flatten(x, expand_composites=True)[0])[0]
255 # If model._distribution_strategy is True, then we are in a replica context

/net/store/nbp/projects/affordance_prediction/alimberg/tf2/local/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training.pyc in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
2536 # Additional checks to avoid users mistakenly using improper loss fns.
2537 training_utils.check_loss_and_target_compatibility(
-> 2538 y, self._feed_loss_fns, feed_output_shapes)
2539
2540 # If sample weight mode has not been set and weights are None for all the

/net/store/nbp/projects/affordance_prediction/alimberg/tf2/local/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_utils.pyc in check_loss_and_target_compatibility(targets, loss_fns, output_shapes)
741 raise ValueError('A target array with shape ' + str(y.shape) +
742 ' was passed for an output of shape ' + str(shape) +
--> 743 ' while using as loss ' + loss_name + '. '
744 'This loss expects targets to have the same shape '
745 'as the output.')

ValueError: A target array with shape (5, 256, 256, 8) was passed for an output of shape (5, 256, 256, 32) while using as loss categorical_crossentropy. This loss expects targets to have the same shape as the output._

[YasarL]

Solved.