categorical_crossentropy

[avincanbaris]

I use google colab and when I run the train code I get this error

---

ValueError Traceback (most recent call last)
in ()
4 model_trainer.setModelTypeAsResNet()
5 model_trainer.setDataDirectory("/content/idenprof/v_data")
----> 6 model_trainer.trainModel(num_objects=10, num_experiments=200, enhance_data=True, batch_size=32, show_network_summary=True)

5 frames
/usr/local/lib/python3.6/dist-packages/imageai/Prediction/Custom/init.py in trainModel(self, num_objects, num_experiments, enhance_data, batch_size, initial_learning_rate, show_network_summary, training_image_size, continue_from_model, transfer_from_model, transfer_with_full_training, initial_num_objects, save_full_model)
340 model.fit_generator(train_generator, steps_per_epoch=int(num_train / batch_size), epochs=self.__num_epochs,
341 validation_data=test_generator,
--> 342 validation_steps=int(num_test / batch_size), callbacks=[checkpoint, lr_scheduler, tensorboard])
343
344

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py 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)
1294 shuffle=shuffle,
1295 initial_epoch=initial_epoch,
-> 1296 steps_name='steps_per_epoch')
1297
1298 def evaluate_generator(self,

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_generator.py 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]

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight, reset_metrics)
989 x, y, sample_weights = self._standardize_user_data(
990 x, y, sample_weight=sample_weight, class_weight=class_weight,
--> 991 extract_tensors_from_dataset=True)
992
993 # If self._distribution_strategy is True, then we are in a replica context

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py 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)
2535 # Additional checks to avoid users mistakenly using improper loss fns.
2536 training_utils.check_loss_and_target_compatibility(
-> 2537 y, self._feed_loss_fns, feed_output_shapes)
2538
2539 # If sample weight mode has not been set and weights are None for all the

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_utils.py in check_loss_and_target_compatibility(targets, loss_fns, output_shapes)
739 raise ValueError('A target array with shape ' + str(y.shape) +
740 ' was passed for an output of shape ' + str(shape) +
--> 741 ' while using as loss ' + loss_name + '. '
742 'This loss expects targets to have the same shape '
743 'as the output.')

ValueError: A target array with shape (32, 11) was passed for an output of shape (None, 10) while using as loss categorical_crossentropy. This loss expects targets to have the same shape as the output.