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Hi, When I Save Model Using Tf.Keras.Save_Model Or Using Modelcheckpoint Model Is Not Saved As An Artifact. Output Uri Is Set To Google Cloud Bucket. When Reporting With Logger Everything Is Stored Correctly. Do You Maybe Have Any Idea Why This Would Not

Hi, when I save model using tf.keras.save_model or using ModelCheckpoint model is not saved as an artifact. Output uri is set to google cloud bucket. When reporting with logger everything is stored correctly. Do you maybe have any idea why this would not work?

Posted one year ago
Votes Newest

Answers 7

I found out that if I work with one model than that model is saved but if I work with different model that model is not saved, everything else being the same. Both models use Functional API

Edit: I have further pinpointed a problem to a ReLU layer. If i use
layers.ReLU()it does not work, but if I use
layers.Activation("relu")it works.

Posted one year ago

I'm sorry for late response. You could probably replicate it by instead of using activation="relu" you used new ReLU layer after Dense. Or if this does not do, extract part of the model in separate Sequential model, for example
Sequential([ Dense(128), BatchNormalization(), ReLU(), ])

Posted one year ago

OutrageousGiraffe8 so basically replacing to:
self.d1 = ReLU()

Posted one year ago

Hi OutrageousGiraffe8
I was not able to reproduce 😞
Python 3.8 Ubuntu + TF 2.8
I get both metrics and model stored and uploaded
Any idea?

Posted one year ago

self.d1 = Dense(128, dtype=tf.float32) self.d1_a = ReLU()

Posted one year ago

OutrageousGiraffe8 this sounds like a bug, how can we reproduce it?
Maybe a add another layer here?

Posted one year ago

Hi OutrageousGiraffe8

when I save model using tf.keras.save_model

This should create a new Model in the system (not artifact), models have their own entity and UID.
Are you creating the Task with output_uri=" gs://bucket/folder " ?

Posted one year ago
7 Answers
one year ago
8 months ago