Result changes every time I run Neural Network code

Question

I got the results by running the code provided in this link Neural Network – Predicting Values of Multiple Variables. I was able to compute losses accuracy etc. However, every time I run this code, I get a new result. Is it possible to get the same (consistent) result?

Answer 1

The code is full of random.randint() everywhere! Furthermore, the weights are most of the time randomly set aswell, and the batch_size also has an influence (although pretty minor) in the result.

1. Y_train, X_test, X_train are generated randomly
2. Using adam as optimizer, means you'll be performing stochastic gradient descent. With a random beginning point of the iterations in order to converge.
3. A batch_size of 8 means you will run batches consisting of 8 randomly selected samples.

Solution:

1. Set a random seed in your code to have always the random values generated with np.random.seed()
2. Doesn't generate much of an issue although minor deviations
3. Same as 2.

If I find a way to have consistente sampling methods for the batch_size/epoch issue I will edit my answer.