How can I handle TensorFlow sessions to train multiple Keras models at the same time?

Question

I need to train multiple Keras models at the same time. I'm using TensorFlow backend. Problem is, when I try to train, say, two models at the same time, I get Attempting to use uninitialized value.

The error is not really relevant, the main problem seems to be that Keras is forcing the two models to be created in the same session with the same graph so it conflicts.

I am a newbie in TensorFlow but my gut feeling is that the answer is pretty straightforward : you would have to create a different session for each Keras model and train them in their own session. Could someone explain me how it would be done ?

I really hope it is possible to solve this problem while still using Keras and not coding everything in pure TensorFlow. Any workaround would be appreciated too.

Answer 1

You are right, Keras automatically works with the default session. You could use tf.compat.v1.keras.backend.get_session() or tf.compat.v1.keras.backend.set_session(sess) to manually set the global Keras session (see documentation).

For instance:

sess1 = tf.Session()
tf.compat.v1.keras.backend.set_session(sess1)
# Train your first Keras model here ...

sess2 = tf.Session()
tf.compat.v1.keras.backend.set_session(sess2)
# Train your second Keras model here ...