Comments (2)
@shachi01: I've also noticed this behavior with tf.keras.layers
, due to how Keras layers interact with variable scoping. The best fix for now would be to use tf.layers
equivalents instead, since they behave correctly.
We'll try to add the correct support for Keras layers in a future AdaNet release.
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Thanks fir your response @cweill .
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