How to get stable results with TensorFlow, setting random seed

Setting the current TensorFlow random seed affects the current default graph only. Since you are creating a new graph for your training and setting it as default (with g.as_default():), you must set the random seed within the scope of that with block.

For example, your loop should look like the following:

for i in range(3):  g = tf.Graph()  with g.as_default():    tf.set_random_seed(1)    accuracy_result, average_error = network.train_network(        parameters, inputHeight, inputWidth, inputChannels, outputClasses)

Note that this will use the same random seed for each iteration of the outer for loop. If you want to use a different—but still deterministic—seed in each iteration, you can use tf.set_random_seed(i + 1).

Deterministic behaviour can be obtained either by supplying a graph-level or an operation-level seed. Both worked for me. A graph-level seed can be placed with tf.set_random_seed. An operation-level seed can be placed e.g, in a variable intializer as in:

myvar = tf.Variable(tf.truncated_normal(((10,10)), stddev=0.1, seed=0))

Tensorflow 2.0 Compatible Answer: For Tensorflow version greater than 2.0, if we want to set the Global Random Seed, the Command used is tf.random.set_seed.

If we are migrating from Tensorflow Version 1.x to 2.x, we can use the command, tf.compat.v2.random.set_seed.

Note that tf.function acts like a re-run of a program in this case.

To set the Operation Level Seed (as answered above), we can use the command, tf.random.uniform([1], seed=1).

For more details, refer this Tensorflow Page.