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I was able to use learning_phase but only in symbolic tensor mode (graph) mode:

So, at first we need to disable eager mode (this must be done right after importing tensorflow):

Terraform AWS Provider is upgraded to version 4.0.0 which is published on 10 February 2022.

Major changes in the release include:

• Version 4.0.0 of the AWS Provider introduces significant changes to the aws_s3_bucket resource.
• Version 4.0.0 of the AWS Provider will be the last major version to support EC2-Classic resources as AWS plans to fully retire EC2-Classic Networking. See the AWS News Blog for additional details.
• Version 4.0.0 and 4.x.x versions of the AWS Provider will be the last versions compatible with Terraform 0.12-0.15.

The reason for this change by Terraform is as follows: To help distribute the management of S3 bucket settings via independent resources, various arguments and attributes in the aws_s3_bucket resource have become read-only. Configurations dependent on these arguments should be updated to use the corresponding aws_s3_bucket_* resource. Once updated, new aws_s3_bucket_* resources should be imported into Terraform state.

So, I updated my code accordingly by following the guide here: Terraform AWS Provider Version 4 Upgrade Guide | S3 Bucket Refactor

The new working code looks like this:

This seems to be a bug in Tensorflow 2.7 when using model.save combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format model.save("test", save_format='h5'):

You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01

using OneHotEncoder is not the way to go here, it's better to extract the features from the column time as separate features like year, month, day, hour, minutes etc... and give these columns as input to your model.

Generally, you can try something like this:

From the aws_dynamodb_table docs, stream_arn is only available if stream_enabled is set to true. You might want to add stream_enabled = true to your DynamoDB table definition.

By default stream_enabled is set to false. You can see all the default values here for aws_dynamodb_table.

Since you are using Attributes as Blocks you have to provide values for all options:

The compiler doesn't put the imports into the object file. It throws them away. Import is just a shorthand to the compiler.(Imports are a compile-time feature ).

first step :

use Qdox https://github.com/paul-hammant/qdox to get all the imports in a class :