Mixture | powerful node-based tool | Graphics library

I am trying to make a next-word prediction model with LSTM + Mixture Density Network Based on this implementation(https://www.katnoria.com/mdn/).

Input: 300-dimensional word vectors*window size(5) and 21-dimensional array(c) representing topic distribution of the document, used to train hidden initial states.

Output: mixing coefficient*num_gaussians, variance*num_gaussians, mean*num_gaussians*300(vector size)

x.shape, y.shape, c.shape with an experimental 161 obserbations gives me such:

(TensorShape([161, 5, 300]), TensorShape([161, 300]), TensorShape([161, 21]))

Let's say I have something like this

• Controller uses Service.
• Service has History, Source and HttpClient.
• Source has SourceRepository and id.
  • Source is only useful for other objects after fetching information from SourceRepository.
• History has HistoryRepository and Source.

Below is a mixture of PHP + pseudocode (for the sake of simplicity) to ilustrate this scenario.

In Python, I do some SymPy calculations that yield an array full of polynomials such as:

a*(a*(a*(a + b) + b*(a + b)) + b*(a*(a + b) + b*(a + b))) + b*(a*(a*(a + b) + b*(a + b)) + b*(a*(a + b) + b*(a + b)))

Note that this example happens to simplify to (a+b)**4, but this won't always be the case obviously. So how do I convert this expression to the following form:

c_1*a**4 + c_2*a**3*b + ... + c_n*b**4

And once I have such an expression, how would I extract the exponents c_1, ..., c_n? All I have is the .exp command, but it only works on expressions of the form a**n (i.e. no mixture of a and b and a coefficient of 1).

Any help would be majorly appreciated.

I'm writing a program to take a json formatted file and create a proxy PAC file. One of the challenges I've encountered is that the json file contains a mixture of data which is not neatly organized. I would like to summarize the data like so:

Input data:

I try to train two DNN jointly, The model is trained and goes to the validation phase after every 5 epochs, the problem is after the 5 epochs it is okay and no problem with memory, but after 10 epochs the model complains about Cuda memory. Any help to solve the memory issue.

I set myself this task to help learn variadic templates. The function add_and_cat() should take first a pair, then a variable number of ints or strings or a mixture. As it encounters these recursively, it should subtract each int from pair.first, and concatenate each string onto pair.second. The function pp just prints the pair.

It seems to work in many cases, but in others I get a compile error claiming no matching function (at the bottom) ... even though there seems to be only one perfectly obvious candidate as far as I can tell :/ I've played around and tried to reason it out but ... no luck yet. What am I missing?

-------------------------- Code: ----------------------------

I was preparing for Java Certification Exam and there are few scenarios which gets very complicated sometimes when to comes to a mixture of polymorphism, Inheritance, overloading, overriding, Generics as well as Casting.

I am stuck with understanding these examples mentioned below :

As part of a new project I'm trying to integrate authentication with a React app and a .NET 5 API using Azure AD B2C. I'm almost there, I think, but when making the request I get back a 401 message with "Bearer error="invalid_token", error_description="The signature is invalid".

• I've registered the API and React app in Azure AD B2C.
• I've given permission to the API from the React app.
• I've created a test scope to start
• I can sign in to my React app and I'm given a bearer token.
• If I inspect the token I can see the audience is my API with the client Id.
• If I make the request, it's passing the token to the API as it should I believe.

In my Startup.cs, I have it defined like so:

I'm doing animal studies and using automated operant boxes that output massive csv files, I've got multiple animals running multiple sessions per day and so in order to make sense of my data I loop through multiple csv files and extract relevant data to a separate excel file. I managed to make almost all of the the code dynamic except for one crucial bit: I have to assign subject IDs to multiple ranges of rows that correspond to that subject's data.

The way I'm currently doing it is first extracting a list of [ID]'s of 16 subjects in the order the they were run on that day and then creating a new session order column in my dataframe that tells which session the data is for. Then I've made a blank 'ID' column to which I then manually assign each range of rows from session one to the first element in the [ID] list, then the session 2 to second element and so on, here is the code example: