I worked on switching to a Jupyter notebook instead of just a python file, this made it easier since I didn’t have to constantly retrain the model, and also could print out stuff in line. I also added batch normalization, which helps change the range (really the standard deviation) of the values before going into the activation function (tanh) so that the values are more spread out. tanh squishes all values between -1 and 1, so if all of the numbers are greater than 1 or less than -1, then all of the values will essentially be squished to either -1 or 1, which makes the model lose a lot of the data. But if you divide all the numbers so that they are all closer to 0, then the activation function is a lot more spread out and the data is preserved. (I got the photo from google, I think it’s helpful when visualizing it).