Which seeds have to be set where to realize 100% r

Question

In a general tensorflow setup like

model = construct_model()
with tf.Session() as sess:
    train_model(sess)

Where construct_model() contains the model definition including random initialization of weights (tf.truncated_normal) and train_model(sess) executes the training of the model -

Which seeds do I have to set where to ensure 100% reproducibility between repeated runs of the code snippet above? The tensorflow documentation at

https://www.tensorflow.org/api_docs/python/constant_op/random_tensors#set_random_seed

may be concise, but left me a bit confused. I tried

tf.set_random_seed(1234)
model = construct_model()
    with tf.Session() as sess:
        train_model(sess)

But got different results each time.

Answer 1

One possible reason is that when constructing the model, there are some code using numpy.random module. So maybe you can try to set the seed for numpy, too.