dense | fetching deep in a dense structure

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.

This can be achieved by weight sharing or shared layers. To share layers in different models in keras, you just need to pass the same instance of layer to both of the models.

Example Codes:

I had the same problem, man. I want to transplant tflite to the development board of CEVA. There is no problem in compiling. In the process of running, there is also an error in AddBuiltin(full_connect). At present, the only possible situation I guess is that some devices can not support tflite.

The shape of b is (1,10) and the shape of the expression is (10). It will work if you do

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

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:

One option is to create a logical array and then call Eigen::select on it. Inspired by https://forum.kde.org/viewtopic.php?f=74&t=91378

In this case:

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.