HOT | Hierarchical Optimization Time Integration | Machine Learning library

This bug has been fixed as of version 17.1 of Visual Studio 2022.

This feature is currently unsupported when using the debugger in WebAssembly apps. According to Microsoft:

*In Visual Studio 2022 GA release Hot Reload support for Blazor WebAssembly when using the Visual Studio debugger isn’t enabled yet. You can still get Hot Reload If you start your app through Visual Studio without the debugger, and we are working to resolve this in the next Visual Studio update.

According to Microsoft, this will be fixed in the 17.1 release of VS 2022.

The fix will be included in the 17.1 release.

The latest preview version of Visual Studio 2022 (17.1.0 Preview 2 released Jan 5, 2022) contains the fix for this. I tested this personally and verified it's working. Note that you will still need to wait for 17.1 if you don't want to use the preview channel.

You need to update your vue-loader

A cold stream does not start producing values until one starts to collect them. A hot stream on the other hand starts producing values immediately.

I would recommend to read below to understand hot and cold steams with usage:

https://balwindersinghrajput.medium.com/complete-guide-to-livedata-and-flow-answering-why-where-when-and-which-6b31496ba7f3

https://developer.android.com/kotlin/flow/stateflow-and-sharedflow

I had this exact same issue. I solved it by adding "firebase": "^8.9.1" to package.json, running yarn install and changing the import import firebase from "firebase" (remove all the other imports you have). Apparently selective imports have a bug in v8, but at least it works well :)

For your issue, currently Blazor WebAssembly only supports hot reload when not debugging. This is kind of documented here: https://docs.microsoft.com/en-us/aspnet/core/test/hot-reload?view=aspnetcore-6.0

In Visual Studio 2022 GA (17.0), Hot Reload is only supported when running without the debugger.

I found your Q while trying to diagnose why my own existing Blazor WebAssembly ASP.Net Core hosted app wouldn't hot reload and after 4-5 hours of trying all sorts of things I finally found that there was a project reference to a class library that still targeted .NET 5. In my case this reference was no longer required, removing it fixed my issues and my Hot Reload output once again showed.

I fixed it. I did 2 things:

• Updated npm to latest
• Updated react-scripts to latest

Not sure which one fixed it.

Old x86-64 CPUs don't support lzcnt, so rustc/llvm won't emit it by default. (They would execute it as bsr but the behavior is not identical.)

Use -C target-feature=+lzcnt to enable it. Try.

More generally, you may wish to use -C target-cpu=XXX to enable all the features of a specific CPU model. Use rustc --print target-cpus for a list.

In particular, -C target-cpu=native will generate code for the CPU that rustc itself is running on, e.g. if you will run the code on the same machine where you are compiling it.

If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

Some further ideas:

• If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
• If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.

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.