Tensorflow Transfer Learning (VGG16) Error: ValueError: Shapes (None, 1) and (None, 4) are incompatible

Hello! So I am trying to create a multiclass classifier using VGG16 in transfer learning to classify users’I emotions. The data is sorted into 4 classes, which have their proper directories so I can use the ‘image_dataset_from_directory’ function.

def dataset_creator(directory=""): from keras.preprocessing.image import ImageDataGenerator data = image_dataset_from_directory(directory=directory,labels='inferred') return data train_ds = dataset_creator(directory=traindir) val_set = dataset_creator(directory="~/Documents/CC/visSystems/val_set/") print(type(train_ds)) num_classes = 4 base_model = VGG16(weights="imagenet", include_top=False, input_shape=(256,256,3),classes=4) base_model.trainable = False normalization_layer = layers.Rescaling(scale=1./127.5, offset=-1) flatten_layer = layers.Flatten() dense_layer_0 = layers.Dense(520, activation='relu') dense_layer_1 = layers.Dense(260, activation='relu') dense_layer_2 = layers.Dense(160, activation='relu') dense_layer_3 = layers.Dense(80, activation='relu') prediction_layer = layers.Dense(4, activation='softmax') model = models.Sequential([ base_model, normalization_layer, flatten_layer, dense_layer_1, dense_layer_2, dense_layer_3, prediction_layer ]) from tensorflow.keras.callbacks import EarlyStopping model.compile( optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'], ) es = EarlyStopping(monitor='val_accuracy', mode='max', patience=3, restore_best_weights=True) model.fit(train_ds,validation_data=val_set, epochs=10, callbacks=[es]) model.save("~/Documents/CC/visSystems/affect2model/saved_model") 

My code correctly identifies X number of images to 4 classes, but when I try to execute model.fit() it returns this error:

ValueError: in user code: File "/home/blabs/.local/lib/python3.9/site-packages/keras/engine/training.py", line 878, in train_function * return step_function(self, iterator) File "/home/blabs/.local/lib/python3.9/site-packages/keras/engine/training.py", line 867, in step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) File "/home/blabs/.local/lib/python3.9/site-packages/keras/engine/training.py", line 860, in run_step ** outputs = model.train_step(data) File "/home/blabs/.local/lib/python3.9/site-packages/keras/engine/training.py", line 809, in train_step loss = self.compiled_loss( File "/home/blabs/.local/lib/python3.9/site-packages/keras/engine/compile_utils.py", line 201, in __call__ loss_value = loss_obj(y_t, y_p, sample_weight=sw) File "/home/blabs/.local/lib/python3.9/site-packages/keras/losses.py", line 141, in __call__ losses = call_fn(y_true, y_pred) File "/home/blabs/.local/lib/python3.9/site-packages/keras/losses.py", line 245, in call ** return ag_fn(y_true, y_pred, **self._fn_kwargs) File "/home/blabs/.local/lib/python3.9/site-packages/keras/losses.py", line 1664, in categorical_crossentropy return backend.categorical_crossentropy( File "/home/blabs/.local/lib/python3.9/site-packages/keras/backend.py", line 4994, in categorical_crossentropy target.shape.assert_is_compatible_with(output.shape) ValueError: Shapes (None, 1) and (None, 4) are incompatible 

How can I approach solving this issue? Thank you for your help.