adam | reference implementation of an LF-Edge API

As you manually use the loss and metrics function in the train_step (not in the .compile) for the training set, you should also do the same for the validation set or by defining the test_step in the custom model in order to get the loss score and metrics score. Add the following function to your custom model.

I'm not familiar with Discord bots but I don't think your endHandler will ever run the else if part the way it is because your code is always creating a new dispatcher when it plays the next song, but never sets up a finish handler for it.

When you delete a record from a database, an object still persists in memory. That's why expect(person).to be_empty() fails.

RSpec has the change matcher. ActiveRecord has the persisted? method. It returns false if a record is not persisted in a database.

In your problem you are trying to use Sequential API to create the Model. There are Limitations to Sequential API, you can just create a layer by layer model. It can't handle multiple inputs/outputs. It can't be used for Branching also.

Below is the text from Keras official website: https://keras.io/guides/functional_api/

The functional API makes it easy to manipulate multiple inputs and outputs. This cannot be handled with the Sequential API.

Also this stack link will be useful for you: Keras' Sequential vs Functional API for Multi-Task Learning Neural Network

Now you can create a Model using Functional API or Model Sub Classing.

In case of functional API Your Model will be

Assuming Output_1 is classification with 17 classes Output_2 is calssification with 2 classes and Output_3 is regression

This will reorder $array2 (into a new variable $final). First it assembles a simple array $people of the names, then it reassembles $array2 by prioritizing based on the $people array. Was this what you wanted to accomplish?

for MDN model , the likelihood for each sample has to be calculated with all the Gaussians pdf , to do that I think you have to reshape your matrices ( y_true and mu) and take advantage of the broadcasting operation by adding 1 as the last dimension . e.g:

If you have a list or a tuple of dictionaries, you can use indexing:

TL;DR

• Add tf.compat.v1.disable_v2_behavior() at the top for TF 2.4+
• calculate shap values on numpy array, not on df

Full reproducible example:

You have very less data, just 2 columns, 80 rows and 2 target variables. All you can do is:

1. Add more data.
2. Normalize your data and then feed it to the neural network.
3. If neural network not giving good accuracy, try Random Forest or XGBoost.

I also want to add one thing that is your neural network architecture is wrong. Dense layer with 2 outputs and a softmax activation isn't going to give you good result here. You have to use TensorFlow's Funtional API and make 1 input 2 output neural network architecture.