Thanks. I tried adding an optimizer and a loss function before posting this, but I received an ambiguous error instead.
fx.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer='sgd')

    ValueError: Shapes (None, 1) and (None, 9, 128) are incompatible

I’m not sure where these shapes come from, as none of my data lookes like this. However, it is triggered by the fit function. Right before the fit function everything is shaped as such:
>>>print(test_labels.shape, test.shape, train_labels.shape, train.shape)
(4503,) (4503, 100, 2) (13508,) (13508, 100, 2)