Bugs in 3 of the demo_tutorial/NN_uncertainty_analysis

[sylvielsstfr]

System information

• I try to learn astroNN by executing https://github.com/henrysky/astroNN/tree/master/demo_tutorial/NN_uncertainty_analysis noteboos on my Mac or on binder

But these introductory examples are buggy. As a beginner on deeplearning, it is not obvious for me to correct some simple bugs.

Those notebooks are very old and are not working anymore.

• **OS Platform and Distribution MacOSX: Big Sur (but same on binder)

• astroNN (Build or Version): master

• Did you try the latest astroNN commit?: I have done git clone from master

• TensorFlow installed from (source or binary, official build?): pip install

• TensorFlow version: tensorflow 2.12.0

• Python version: Python 3.9.16

• Exact command/script to reproduce (if applicable):

Describe the problem

Describe the problem clearly here. Be sure to describe here why it's a bug in astroNN (instead of Tensorflow's problem) or a feature request.

Among the 4 examples

• Uncertainty_Demo_MNIST.ipynb --> OK
• Uncertainty_Demo_quad.ipynb --> Does not work
• Uncertainty_Demo_x_sinx.ipynb --> Does not work
• Uncertainty_Demo_x_sinx_tfp.ipyn --> Does note work

After minor numpy format correction I have found inUncertainty_Demo_quad.ipynb , the generator generate_train_batch(x, y, y_err) is not accepted by model.fit(), more over the proposed model.fit_generator() is not accepted anymore by Tensorflow.

In the section Third, use a single model to get both epistemic and aleatoric uncertainty with variational inference

I tried to skip the generator by providing directly the data not involving any generator, but the data format was not accepted.

   the_in,the_out =  next(generator) 
  model.fit(the_in,the_out, epochs=20, max_queue_size=20, verbose=0, 
                steps_per_epoch= x.shape[0] // batch_size)

I have no deep knowledge in Tensorflow to understand the data format error.

     TypeError: You are passing KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='Placeholder:0', description="created by layer 'tf.cast_2'"), an intermediate Keras symbolic input/output, to a TF API that does not allow registering custom dispatchers, such as `tf.cond`, `tf.function`, gradient tapes, or `tf.map_fn`. Keras Functional model construction only supports TF API calls that *do* support dispatching, such as `tf.math.add` or `tf.reshape`. Other APIs cannot be called directly on symbolic Kerasinputs/outputs. You can work around this limitation by putting the operation in a custom Keras layer `call` and calling that layer on this symbolic input/output.

I hope you could quickly fix these simple examples such I could start from a simple working example.

Many thanks.

[henrysky]

Thanks for the bug report! I have just pushed a fix for those notebooks, you can check if they are working for you now. Please close this issue if the issue is fixed now.