Hi @George_Soloupis,
Thanks for looking into this. FYI, the regression model is used as a post-processing step to fine tune some predicted values from an upstream model (not shown in the project I provided you with). I have updated the Colab notebook linear_regression.ipynb under Colab Notebooks - Google Drive 2 to include the step I used to convert .h5 into .tflite. As you can see, the model loaded from the h5 file in the notebook is able to process 5 input values and generate 3 output values. Note that I did have to call adapt() from the Normalization layer on the data with the correct number of elements before instantiating the regression model and loading the weights from the h5 file. I uploaded both .h5 and .tflite models into the netron app and saw the following:

models2249×2249 200 KB

The left is the .h5 model and the right is the .tflite model as you saw earlier. So it seems that the model conversion step that I included in the notebook forced the input size to be 1x1.
Question/request:

What changes do I need to make in the conversion to change that to 1x5?
If we can achieve the above, my next step is to verify that I can use this model to generate the same output [ 30.668777, 111.0412  ,  69.47372 ] (after multiplying each by 256) for the given input [0.195312, 0.05913, 0.089844, 0.550781, 0.320312], with only some small variations, if any. I would really appreciate it if you can try this as a quick test.

Thanks,
George