Replicating models in Keras and Tensorflow for a multi-threaded setting

Question

I am trying to implement the asynchronous version of actor-critic in Keras and TensorFlow. I am using Keras just as a front-end for building my network layers (I am updating the parameters directly with tensorflow). I have a `global_model` and one main tensorflow session. But inside each thread I am creating a `local_model` which copies parameters from the `global_model`. My code looks something like this ``` def main(args): config=tf.ConfigProto(log_device_placement=False,allow_soft_placement=True) sess = tf.Session(config=config) K.set_session(sess) # K is keras backend global_model = ConvNetA3C(84,84,4,num_actions=3) threads = [threading.Thread(target=a3c_thread, args=(i, sess, global_model)) for i in range(NUM_THREADS)] for t in threads: t.start() def a3c_thread(i, sess, global_model): K.set_session(sess) # registering a session for each thread (don't know if it matters) local_model = ConvNetA3C(84,84,4,num_actions=3) sync = local_model.get_from(global_model) # I get the error her…

Accepted Answer

The error comes from Keras because `tf.get_default_graph() is sess.graph` is returning `False`. From the TF docs, I see that `tf.get_default_graph()` is returning the default graph for the current thread. The moment I start a new thread and create a graph, it is built as a separate graph specific to that thread. I can solve this issue by doing the following,

```
with sess.graph.as_default():
   local_model = ConvNetA3C(84,84,4,3)
```

Replicating models in Keras and Tensorflow for a multi-threaded setting

Solution

Problem