Update dependencies for v1.2 release#796
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Rebased and force-pushed to tidy commit history and drop upgrade to scipy 1.13 for now (because not supporting Python 3.9, which it should support?). Reminder to self: Scipy 1.13 needs these changes. |
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I built Dockerimage from this branch and tried to deploy to OpenShift for the ai.dev.finto.fi instance. NN ensemble models fail to load: |
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This seems like a problem with Keras modules that have been reorganized, so older imports (from inside the saved model) no longer work. Here were two suggestions from GPT-4 on how that might be fixed: Use Custom Object ScopesIf you cannot change the TensorFlow version due to other dependencies, try using custom object scopes to map the missing or changed classes and functions: from tensorflow.keras.models import load_model
from tensorflow.keras.layers import InputLayer, Flatten, Dropout, Dense
from tensorflow.keras import layers
# Assuming `MeanLayer` is a custom layer you have the code for
from your_custom_layers import MeanLayer
custom_objects = {
'Functional': layers.Functional, # Adjust according to the actual required class
'MeanLayer': MeanLayer
}
model = load_model('path_to_your_model.keras', custom_objects=custom_objects)Adjust the Model Loading CodeSometimes, simply adjusting the import statements or model loading code can resolve compatibility issues: from tensorflow import keras
model = keras.models.load_model('path_to_your_model.keras', compile=False) |
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The second suggestion by GPT-4 did not work, and for the first I don't know what layer/class here should be: There is a Keras issue about the incompatibility of models by versions 2.15.0 and 3: keras-team/keras#20083 The |
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Updates the outdated dependencies.
Depedencies, which are not upgraded to the newest releases:
Also resolves two DeprecationWarnings by Schemathesis.