neural-net | Basic implementation of a feedforward neural network | Machine Learning library

That's because holding all elements of your dataset in the buffer is expensive. Unless you absolutely need perfect randomness, you should use a smaller buffer_size. All elements will eventually be taken, but in a more deterministic manner.

This is what's going to happen with a smaller buffer_size, say 3. The buffer is the brackets, and Tensorflow samples a random value in this bracket. The one randomly picked is ^

I have managed to solve the error related to the mlUpdate task, the issue was that I was referencing the .mlmodel instead of the compiled version, which is .mlmodelc . When building the iOS app from Xcode this file is automatically generated.

I now get the following error:

You probably have to check every point against every other point whether the distance is less than your threshold. So, create a table with all these points, the vector between them and plot them with vectors. The following example creates some random points with random sizes and random colors.

Code:

This is because you are appending the same node to two different divs. If you look at the documentation to appendChild here, you can see this:

This has nothing to do with Python or requests.get and little to do with URL formats. It's the service that decides how to parse that URL and how to map its elements.

• In the first case https://jsonplaceholder.typicode.com/users/1 is treated as a single resource identifier (which it is) so the service returns a single object serialized as JSON. In JavaScript objects are dictionaries. What you posted is a single object, not nested dictionaries

• In the second case, https://jsonplaceholder.typicode.com/users?id=1it treats the URL as a query on the Users resource with a filter id=1 so it returns an array of objects, even though there's only a single matching object.

JSONPlaceholder's docs explain that this syntax is used for filtering:

Filtering resources

Basic filtering is supported through query parameters.

Another service could decide that since id is a key, it should return a single object instead of an array. Or it could use a different query string, using eg filter to explicitly specify filter parameters

There's no standard for either case. While it's a common convention especially in REST-like APIs to include an object's ID in the URL path, there's no single standard or even convention for queries. Each service uses its own format.

GraphQL (from Facebook) and OData (from Microsoft) are two common query and manipulation protocols. They're a lot more complex than the very simple filtering used by JSONPlaceholder though

Technically the input will be 1D, but that doesn't matter.

The internal architecture of your neural network will take care of recognizing the different words. You could for example have a convolution with a stride equal to the embedding size.

You can flatten a 2D input to become 1D and it will work fine. This is the way you'd normally do it with word embeddings.

If I understand your question properly, you want to get output feature maps of each layer of a model. Normally, as we mentioned in the comment box, a model with one (or multiple) inputs and one (or multiple) outputs. But in order to inspect the activation feature maps of inside layers, we can adopt some strategies. Some possible scenarios: (1). Want to get output feature maps of each layer in run-time or training time. (2). Want to get output feature maps of each layer in the inference time or after training. And as you quoted:

I am now running an image through the trained network and want to get the network output (feature maps etc.) at every stage.

That goes to number 2, get the feature maps in inference time. Below is a simple possible workaround to do this. First, we build a model, and then after training, we will modify the trained model to get feature maps of each layer within it (technically creating the same model with some modification).

RTX 3060 cards are based on the Ampere architecture for which compatible CUDA version start with 11.x.

Your issue can be resolved once you upgrade tensorflow version to 2.4.0, CUDA to 11.0 and cuDNN to 8.0.

For more details you can refer here.

Linq join at rescue.

Let's suppose this classes close to yours: