predict | Calibrate your predictions | Data Labeling library

I'd like to run a simple neural network model which uses Keras on a Rasperry microcontroller. I get a problem when I use a layer. The code is defined like this:

I would like to edit the model matrix used by predict.lm() in R to predict main effects but not interactions (but using the coefficients and variance from the full model containing interactions).

I have tried:

I am trying to follow this tutorial here - https://juliasilge.com/blog/xgboost-tune-volleyball/

I am using it on the most recent Tidy Tuesday dataset about great lakes fishing - trying to predict agency based on many other values.

ALL of the code below works except the final row where I get the following error:

I'm trying to learn Flask and use postgresql with it. I'm following this tutorial https://realpython.com/flask-by-example-part-2-postgres-sqlalchemy-and-alembic/, but I keep getting error.

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]))

I'm trying to compute shap values using DeepExplainer, but I get the following error:

keras is no longer supported, please use tf.keras instead

Even though i'm using tf.keras?

I have data in the following format consisting of 80 instances. I need to predict two-parameter latency and accuracy

I would greatly appreciate any feedback you might offer regarding the issue I am having with my Word Prediction Shiny APP Code for the JHU Capstone Project.

My UI code runs correctly and displays the APP. (see image and code below)

Challenge/Issue: My problem is that after entering text into the "Text input" box of the APP, my server.R code does not return the predicted results.

Prediction Function:

When I run this line of code in the RConsole -- predict(corpus_train,"case of") -- the following results are returned: 1 "the" "a" "beer"

When I use this same line of code in my server.r Code, I do not get prediction results.

Any insight suggestions and help would be greatly appreciated.

I have installed minikube cluster and kfserving on a linux desktop. Then I have followed two tutorials
https://github.com/kubeflow/kfserving/tree/master/docs/samples/v1beta1/custom/torchserve
https://github.com/kubeflow/kfserving/tree/master/docs/samples/v1alpha2/custom/kfserving-custom-model

In the second tutorial I have needed to move "name: custom" from "custom:" section to "container:" section in the yaml file.
I expected that serving service was working and responding to serving requests and pods of the service where in kubernetes.
I use the newest stable versions from May 2021.

But I have same bug in both tutorials. Bellow commands are from the first tutorial. When I prepare docker images with models and run