problem with dimensions when using keras text_dataset_from_directory i get (None,) and can't lode it in to a model

Question

I am using keras.utils.text_dataset_from_directory (see code). when I reach model.fit I get a warning about input dimentions and a error (that I think has something to do with the output). code:

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers import Rescaling
MAX_VAL = 0.5
num_classes = 2

train_ds = keras.utils.text_dataset_from_directory(
    directory='.../training_data/',
    labels='inferred',
    label_mode='categorical',
    class_names=None,
    batch_size=32,
    max_length=None,
    shuffle=True,
    seed=None,
    validation_split=None,
    subset=None,
    follow_links=False)
validation_ds = keras.utils.text_dataset_from_directory(
    directory='.../validation_data/',
    labels='inferred',
    label_mode='categorical',
    class_names=None,
    batch_size=32,
    max_length=None,
    shuffle=True,
    seed=None,
    validation_split=None,
    subset=None,
    follow_links=False)

inputs = keras.Input(shape=(None,))
x = layers.Reshape((-1, 1))(inputs)
x = Rescaling(scale=1.0 / MAX_VAL)(x)
x = layers.Dense(32, activation="softmax")(x)
outputs = layers.Dense(num_classes, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
model.summary()
keras.utils.plot_model(model, "my_first_model.png")

model.compile(optimizer='Adam', loss='categorical_crossentropy')
history = model.fit(train_ds, epochs=10, validation_data=validation_ds)

output:

Epoch 1/10
WARNING:tensorflow:Model was constructed with shape (None, None) for input KerasTensor(type_spec=TensorSpec(shape=(None, None), dtype=tf.float32, name='input_1'), name='input_1', description="created by layer 'input_1'"), but it was called on an input with incompatible shape (None,).
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-6-3656f32a72a9> in <module>
      1 model.compile(optimizer='Adam', loss='categorical_crossentropy')
----> 2 history = model.fit(train_ds, epochs=10, validation_data=validation_ds)

~\AppData\Roaming\Python\Python38\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\func_graph.py in autograph_handler(*args, **kwargs)
   1145           except Exception as e:  # pylint:disable=broad-except
   1146             if hasattr(e, "ag_error_metadata"):
-> 1147               raise e.ag_error_metadata.to_exception(e)
   1148             else:
   1149               raise

ValueError: in user code:

    File "...\Python\Python38\site-packages\keras\engine\training.py", line 1021, in train_function  *
        return step_function(self, iterator)
    File "...\Python\Python38\site-packages\keras\engine\training.py", line 1010, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "...\Python\Python38\site-packages\keras\engine\training.py", line 1000, in run_step  **
        outputs = model.train_step(data)
    File "...\Python\Python38\site-packages\keras\engine\training.py", line 860, in train_step
        loss = self.compute_loss(x, y, y_pred, sample_weight)
    File "...\Python\Python38\site-packages\keras\engine\training.py", line 918, in compute_loss
        return self.compiled_loss(
    File "...\Python\Python38\site-packages\keras\engine\compile_utils.py", line 201, in __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "...\Python\Python38\site-packages\keras\losses.py", line 141, in __call__
        losses = call_fn(y_true, y_pred)
    File "...\Python\Python38\site-packages\keras\losses.py", line 245, in call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "...\Python\Python38\site-packages\keras\losses.py", line 1789, in categorical_crossentropy
        return backend.categorical_crossentropy(
    File "...\Python\Python38\site-packages\keras\backend.py", line 5083, in categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)

    ValueError: Shapes (None, 2) and (None, 1, 2) are incompatible

The inputs to the model are 1d vectors (of length ~27000) saved in .txt files: 0.101471743,0.099917953,0.103334975,0.099364908,0.099035715,...,0.097369999,0.099680934

readin dataset frome dir formated as:

/training_data/
...class_a/
......a_text_1.txt
......a_text_2.txt
...class_b/
......b_text_1.txt
......b_text_2.txt
/validation_data/
...class_a/
......a_text_1.txt
......a_text_2.txt
...class_b/
......b_text_1.txt
......b_text_2.txt

How can I get the dimensions right?

EDIT: I saved the data as a .jpg file and loaded it using image_dataset_from_directory. this fixed the issue. but I would still like to understand why I cant get the data from .txt files properly. (I lose a lot of data transferring from float data to 8bit int in .jpg. and using image_dataset_from_directory requires all img size to be the same length, I wand my data to be different sizes).