I have the x and y values of solving some kind of differential equation. I am trying to train a neural network on TensorFlow, but the result is unstable and not accurate. What is the reason? It is important to note that the prediction occurs as follows: at the input x at the output x2 at the input x2 at the output x3 at the input x3 at the output x4, etc.

I am trying to implement NBeats model from keras-beats for timeseries prediction forcasting and it shows me valueerror about using float data type. I tried using astype to convert data to float 32 and 64 and it still showed me similar error

This is a follow up to Tensorflow input of varying shapes.

I’m using Tensorflow 2.16.1. I have a list of Tensors I want to use as training data. They are varying shapes. It’s unclear how I can make this work. Here is some example code:

I am interested in developing an LSTM model to make predictions about satellite imagery. I have a workflow in Google Earth Engine that generates TFRecord files in cloud storage. Each example is a series of Int64List or FloatList of the same shape (21 items, corresponding to 21 years of data). All pixels have some number of missing values where the target variable was not surveyed in that year. I know how to serve TFRecord files to a model with TFRecordDataset, and how to do some amount of preprocessing with .filter and .map.

I’m facing challenges in performing inference using a model trained in TensorFlow with the Keras API in a C environment.

Im using jupyter notebook using anaconda and while i am importing keras its giving me optree not found error

I am creating a dataset by generator like this

The following code is from two years ago and throws an error on my current setup. I was wondering if it is related to the change to Keras 3 or what’t is going on here.
I can get it to work

How can I resolve this?