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View Code? Open in Web Editor NEWA library for dimensionality reduction on spatial-temporal PDE
Home Page: https://pswpswpsw.github.io/nif
License: GNU Lesser General Public License v2.1
A library for dimensionality reduction on spatial-temporal PDE
Home Page: https://pswpswpsw.github.io/nif
License: GNU Lesser General Public License v2.1
Hi.
Seemingly tensorflow is missing from the requirements.txt file.
I installed tensorflow==2.10.1 and the importing worked.
Thanks.
Attempting to save a NIFMultiScale model (or its subclasses) using model.save() or tf.saved_model.save() throws an error as below:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/var/folders/dp/zr6n3k1d2lv5b2t917gfqlnm0000gn/T/ipykernel_83723/2345140346.py in <module>
----> 1 model.save('tut_2_model_multiscale')
~/miniconda3/envs/nif/lib/python3.10/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
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
~/miniconda3/envs/nif/lib/python3.10/site-packages/google/protobuf/internal/python_message.py in field_setter(self, new_value)
702 new_value = type_checker.CheckValue(new_value)
703 except TypeError as e:
--> 704 raise TypeError(
705 'Cannot set %s to %.1024r: %s' % (field.full_name, new_value, e))
706 if clear_when_set_to_default and not new_value:
TypeError: Cannot set tensorflow.SavedVariable.trainable to 'float32': 'float32' has type <class 'str'>, but expected one of: (<class 'bool'>, <class 'numbers.Integral'>)
This originates in HyperLinearForSIREN.__init__
where self.w
and self.b
are defined as tf.Variable
s: mixed_policy.variable_dtype
is given for a positional argument that belongs to Variable
's training
argument. This issue is resolved by replacing these lines with dtype=mixed_policy.variable_dtype
. I have a branch that implements this change that I can push if desired.
Although this fixes the problem with tf.saved_model.save()
, using model.save()
still throws other errors once this is resolved. It's unclear if these problems are with NIF or with a Tensorflow bug (this issue).
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