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)
.