tf | TensorFlow samples | Machine Learning library

As you manually use the loss and metrics function in the train_step (not in the .compile) for the training set, you should also do the same for the validation set or by defining the test_step in the custom model in order to get the loss score and metrics score. Add the following function to your custom model.

I fixed my issue.

Here are the steps of solving that issue.

1 ) Download CUDA from https://developer.nvidia.com/cuda-downloads

2 ) Download CUDNN from https://developer.nvidia.com/rdp/cudnn-download

3 ) Copy bin,include and lastly lib from CUDNN zip file and paste it C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA{version}

4 ) Then run the .py code in PyCharm and it perceives GPU at last.

Multiple things could cause this behaviour, because the pipeline runs fine with less Data, analysing what has changed could lead us to a resolution.

The third line of the logs you provide might indicate that you're processing unclean data in your bigger pipeline mmap(NULL, could mean that | "Get Content" >> beam.Map(lambda x: x.read_utf8()) is trying to read a null value.

Is there an empty file somewhere ? Are your files utf8 encoded ?

I'm guessing using the fileio.ReadMatches() will try to load into memory the whole file, if your file is bigger than your memory, this could lead to errors. Can you split your data into smaller files ?

If files are too big for your current machine with a DirectRunner you could try to use an on-demand infrastructure using another runner on the Cloud such as DataflowRunner

There are multiple potential places to measure execution time, first might be defining very specifically what is the intended measurement.

1. Measuring the training time of each client as proposed is a great way to get a sense of the variability among clients. This could help identify whether rounds frequently have stragglers. Using tf.timestamp() at the beginning and end of the client_update function seems reasonable. The question correctly notes that this happens in parallel, summing all of these times would be akin to CPU time.

2. Measuring the time it takes to complete all client training in a round would generally be the maximum of the values above. This might not be true when simulating FL in TFF, as TFF maybe decided to run some number of clients sequentially due to system resources constraints. In practice all of these clients would run in parallel.

3. Measuring the time it takes to complete a full round (the maximum time it takes to run a client, plus the time it takes for the server to update) could be done by moving the tf.timestamp calls to the outer training loop. This would be wrapping the call to trainer.next() in the snippet on https://www.tensorflow.org/federated. This would be most similar to elapsed real time (wall clock time).

The documentation for the azurerm_web_application_firewall_policy resource is out of date but http_listener_ids and path_based_rule_ids are read only now (as of v2.55.0) so you can't set them and can only read them as an attribute of the resource.

In your problem you are trying to use Sequential API to create the Model. There are Limitations to Sequential API, you can just create a layer by layer model. It can't handle multiple inputs/outputs. It can't be used for Branching also.

Below is the text from Keras official website: https://keras.io/guides/functional_api/

The functional API makes it easy to manipulate multiple inputs and outputs. This cannot be handled with the Sequential API.

Also this stack link will be useful for you: Keras' Sequential vs Functional API for Multi-Task Learning Neural Network

Now you can create a Model using Functional API or Model Sub Classing.

In case of functional API Your Model will be

Assuming Output_1 is classification with 17 classes Output_2 is calssification with 2 classes and Output_3 is regression

The hclwrite tool currently has no facility to automatically generate arbitrary expressions. Its helper functions are limited only to generating plain references and literal values. SetAttributeValue is the one for literal values, and so that's why the library correctly escaped the ${ sequence in your string, to ensure that it will be interpreted literally.

If you want to construct a more elaborate expression then you'll need to do so manually by assembling the tokens that form the expression and then calling SetAttributeRaw instead.

In the case of your example, it looks like you'd need to generate the following eight tokens:

• TokenOQuote with the bytes "
• TokenQuotedLit with the bytes hello
• TokenTemplateInterp with the bytes ${
• TokenIdent with the bytes var
• TokenDot with the bytes .
• TokenIdent with the bytes stage
• TokenTemplateSeqEnd with the bytes }
• TokenCQuote with the bytes "

The SetAttributeValue function is automating the simpler case of generating three tokens: TokenOQuote, TokenQuotedLit, TokenCQuote.

You can potentially automate the creation of tokens for the var.stage portion of this expression by using TokensForTraversal, which is what SetAttributeTraversal does internally. However, unless you already have a parsed hcl.Traversal representing var.stage, or unless you need things to be more dynamic than you've shown in practice, I expect that it would take more code to construct that input traversal than to just write out the three tokens literally as I showed above.

for MDN model , the likelihood for each sample has to be calculated with all the Gaussians pdf , to do that I think you have to reshape your matrices ( y_true and mu) and take advantage of the broadcasting operation by adding 1 as the last dimension . e.g:

TL;DR

• Add tf.compat.v1.disable_v2_behavior() at the top for TF 2.4+
• calculate shap values on numpy array, not on df

Full reproducible example: