Personal image recognition exercise, loss/error rate to large

[rmwkwok]

@Kristjan_Antunovic

So I’m trying to figure out two things now:

1. Why did the proposed scaling not work and X / 255 work so well? Is this normal practice basically trying out different feature scaling options or did I create an issue using that approach to scale values.
2. What I’m trying to understand is what is happening inside a Dense layer. Say on the first layer Dense(units=400, activation='relu'), we are taking 400 features, calculating gradient descent for each unit a new feature but what is this feature? What does setting it to 400 different units actually do won’t the gradient descent actually comes to the somewhat same values for each run ? Or is each unit in a dense layer attempting a say higher order polynomial function ?

I think my above reply answered your Q1. For your Q2, please refer to this post and the link therein.