Comments (1)
Hi @cevheck,
thank you for reporting this. However, I do not think that this is in Bug. The reason of this behavior are the BatchNormalization Layers. These layers behave differently during training and inference. Thus, the result printed via the training differ compared to the results calculated afterwards.
You might take a further look into:
- https://keras.io/api/layers/normalization_layers/batch_normalization/
- keras-team/keras#6977
- If you clone sktime you can test it locally also by commenting out all the BatchNormalisation Layers.
from sktime.
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