Input 0 of layer "dense_3" is incompatible with the layer

[sparta-2024]

Describe the bug
A clear and concise description of what the bug is.
tf_results = model.predict(
^^^^^^^^^^^^^^
File "d:\Users\anaconda3\Lib\site-packages\tf_keras\src\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\dd\AppData\Local\Temp_autograph_generated_filezo42g_5q.py", line 15, in tf__predict_function
retval = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
^^^^^
ValueError: in user code:

File "d:\Users\anaconda3\Lib\site-packages\tf_keras\src\engine\training.py", line 2436, in predict_function  *
    return step_function(self, iterator)
File "d:\Users\anaconda3\Lib\site-packages\tf_keras\src\engine\training.py", line 2421, in step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
File "d:\Users\anaconda3\Lib\site-packages\tf_keras\src\engine\training.py", line 2409, in run_step  **
    outputs = model.predict_step(data)
File "d:\Users\anaconda3\Lib\site-packages\tf_keras\src\engine\training.py", line 2377, in predict_step
    return self(x, training=False)
File "d:\Users\anaconda3\Lib\site-packages\tf_keras\src\utils\traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
File "d:\Users\anaconda3\Lib\site-packages\tf_keras\src\engine\input_spec.py", line 280, in assert_input_compatibility
    raise ValueError(

ValueError: Exception encountered when calling layer 'pilot_net' (type Functional).

Input 0 of layer "dense_3" is incompatible with the layer: expected axis -1 of input shape to have value 101, but received input with shape (None, 100)

Call arguments received by layer 'pilot_net' (type Functional):
  • inputs=('tf.Tensor(shape=(None, 96, 256, 3), dtype=float32)', 'tf.Tensor(shape=<unknown>, dtype=float32)')
  • training=False
  • mask=None

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Expected behavior
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[isha382]

hi @sparta-2024, the issue is of the incompatibility with the version you can try with 2.9.0 version of tensorflow which is preferred version and can refer following documentation policy for more information.