TypeError: Failed to convert elements of <keras.losses.SparseCategoricalCrossentropy object to tensor - tensorflow

Question

I'm trying to train a CNN on my own images. I've set class_mode to sparse, so I figured I would use SparseCategoricalCrossentropy. However, when I do this (or even CategoricalCrossentropy), I get this error about the elements not being of a supported type.
I've tried a few different loss functions with the same error persisting. I think there must be something wrong with the formatting of my data. I've attached my data_gen and NN code along with the full traceback.
train_datagen = ImageDataGenerator(
rescale=1./255,
width_shift_range=.05,
height_shift_range=.05,
zoom_range=0.05,
validation_split=0.3,
horizontal_flip=True,
vertical_flip=True,
fill_mode="constant",
cval=0)
test_datagen = ImageDataGenerator(
rescale=1./255)
#data augmentation
train_ds = train_datagen.flow_from_directory(
train_data_dir,
shuffle=True,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='sparse',
subset='training');
cnn = Sequential()
cnn.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(img_width,img_height,3)))
cnn.add(BatchNormalization())
cnn.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
cnn.add(BatchNormalization())
cnn.add(MaxPooling2D(pool_size=(2, 2)))
cnn.add(Dropout(0.2))
cnn.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
cnn.add(BatchNormalization())
cnn.add(Flatten())
cnn.add(Dense(64, activation='relu'))
cnn.add(Dense(22, activation='softmax'))
cnn.compile(loss=keras.losses.SparseCategoricalCrossentropy,
optimizer="Adam",
metrics=['accuracy'])
--------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In [24], line 111
106 cnn.compile(loss=keras.losses.SparseCategoricalCrossentropy,
107 optimizer="Adam",
108 metrics=['accuracy'])
110 epochs=20
--> 111 history = cnn.fit(
112 train_ds,
113 validation_data=valid_ds,
114 steps_per_epoch=math.ceil(len(train_ds) / batch_size),
115 epochs=epochs)
117 plt.plot(history.history['loss'], label='train')
118 plt.plot(history.history['val_loss'], label='validation')
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File ~\AppData\Local\Temp\__autograph_generated_fileswqeb6ya.py:15, in outer_factory.<locals>.inner_factory.<locals>.tf__train_function(iterator)
13 try:
14 do_return = True
---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
16 except:
17 do_return = False
TypeError: in user code:
File "C:\Users\maiaz\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py", line 1160, in train_function *
return step_function(self, iterator)
File "C:\Users\maiaz\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py", line 1146, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\maiaz\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py", line 1135, in run_step **
outputs = model.train_step(data)
File "C:\Users\maiaz\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py", line 994, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "C:\Users\maiaz\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\training.py", line 1052, in compute_loss
return self.compiled_loss(
File "C:\Users\maiaz\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\compile_utils.py", line 265, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "C:\Users\maiaz\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\losses.py", line 158, in __call__
return losses_utils.compute_weighted_loss(
File "C:\Users\maiaz\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\utils\losses_utils.py", line 328, in compute_weighted_loss
losses = tf.convert_to_tensor(losses)
TypeError: Failed to convert elements of <keras.losses.SparseCategoricalCrossentropy object at 0x0000017523557AC0> to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes.