datagen | Multi-process test data files generator | Runtime Evironment library

after setting droput rate 0.2, it started working

After weeks I stopped trying it with tensorboard. I reduced the number of features in the output layer to 256, 128, 64 and I previously reduced the features with PCA and Truncated SDV but nothing changed.

Now I use sklearn.manifold.TSNE and visualize the output with plotly. This is also easy, works fine and I can see appropriate patterns while t-SNE in tensorboard still produces a random distribution. So I guess for the algorithm in tensorboard it´s too many classes. Or I made a mistake when preparing the data and didn´t notice that (but then why does PCA work?)

If anyone knows what the problem was, I´m still curious. But in case someone else is facing the same problem, I´d recommend trying it with sklearn.

This is because you set the loss to None, no gradient is provided from the loss function back to your model. Modify

You want to use the IsVisible field. It is listed here: https://swharden.com/scottplot/cookbooks/4.1.13-beta/api/plottable/scatterplot/#isvisible

The documentation isn't there, but IsVisible is what you think it is.

Your code becomes this:

You're using dev.onyxstudios.cca, whatever that might be, and it is using reflection to get at a field named type of some unspecified class.

It is either trying to get at the field named type of one of JDK's own classes, in which case the fix is to uninstall whatever JDK you installed and install AdoptOpenJDK11: You're on a too-new version of java and these most recent versions have been breaking apps left and right by disabling aspects of the reflective API.

Or, it is trying to get to a field named type in one of the classes of the FABRIC project, perhaps, whatever that might be, based on the content of this error message. In which case, the problem is a version incompatibility between these two plugins. Look up the project pages of these 2 plugins and install 2 versions whose release dates are close together. This usually involves downgrading the more recently updated one.

To investigate your model layer-by-layer please see example how to show summary of the model and also how to save the model:

I shamelessly copy Dian Fu's reply in http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Flink-Not-able-to-sink-a-stream-into-csv-td43105.html.

You need to set the rolling policy for filesystem. You could refer to the Rolling Policy section [1] for more details.

Actually there are output and you could execute command ls -la /tmp/output/, then you will see several files named “.part-xxx”.

For your job, you need to set the execution.checkpointing.interval in the configuration and sink.rolling-policy.rollover-interval in the property of Filesystem connector.

The job will look like the following:

As of April 19. 2020 it is not possible with the .NET Wrapper as documented in this issue on the GitHub page for Keras.NET

If you really want to avoid putting that into preprocessing (which would appear to me to be the easiest way - adding a preprocessing_function to the ImageDataGenerator) you can start your network with a Multiply and an Add layer, to scale (by 0.5, because your Std is now 1) and to shift (by 0.5, because your mean is now 0) - please be aware that both Multiply and Add layers require the arguments to be tensors of the same size, so see the doc how to create arbitrary tensors.