Multiple sessions and graphs in Tensorflow (in the same process)

Question

I'm training a model where the input vector is the output of another model. This involves restoring the first model from a checkpoint file while initializing the second model from scratch (using tf.initialize_variables()) in the same process.

There is a substantial amount of code and abstraction, so I'm just pasting the relevant sections here.

The following is the restoring code:

self.variables = [var for var in all_vars if var.name.startswith(self.name)]
saver = tf.train.Saver(self.variables, max_to_keep=3)
self.save_path = tf.train.latest_checkpoint(os.path.dirname(self.checkpoint_path))

if should_restore:
    self.saver.restore(self.sess, save_path)
else:
    self.sess.run(tf.initialize_variables(self.variables))

Each model is scoped within its own graph and session, like this:

 self.graph = tf.Graph()
 self.sess = tf.Session(graph=self.graph)

 with self.sess.graph.as_default():
    # Create variables and ops.

All the variables within each model are created within the variable_scope context manager.

The feeding works as follows:

• A background thread calls sess.run(inference_op) on input = scipy.misc.imread(X) and puts the result in a blocking thread-safe queue.
• The main training loop reads from the queue and calls sess.run(train_op) on the second model.

PROBLEM:
I am observing that the loss values, even in the very first iteration of the training (second model) keep changing drastically across runs (and become nan in a few iterations). I confirmed that the output of the first model is exactly the same everytime. Commenting out the sess.run of the first model and replacing it with identical input from a pickled file does not show this behaviour.

This is the train_op:

    loss_op = tf.nn.sparse_softmax_cross_entropy(network.feedforward())
    # Apply gradients.
    with tf.control_dependencies([loss_op]):
        opt = tf.train.GradientDescentOptimizer(lr)
        grads = opt.compute_gradients(loss_op)
        apply_gradient_op = opt.apply_gradients(grads)

    return apply_gradient_op

I know this is vague, but I'm happy to provide more details. Any help is appreciated!

Answer 1

The issue is most certainly happening due to concurrent execution of different session objects. I moved the first model's session from the background thread to the main thread, repeated the controlled experiment several times (running for over 24 hours and reaching convergence) and never observed NaN. On the other hand, concurrent execution diverges the model within a few minutes.

I've restructured my code to use a common session object for all models.