greedyCWS | Source code for an ACL2017 paper | Machine Learning library

Two things going on here:

1. I think this is a bug introduced with eager execution, I've filed https://github.com/tensorflow/tensorflow/issues/18180 for that. I don't think this exists in release 1.6, so perhaps you could try with that in the interim.

2. That said, I noticed that you're defining an Embedding layer object inside your loss function. This means that each invocation of loss is creating a new Embedding, which is probably not what you want. Instead, you'd probably want to restructure your code as:

   emb = tf.keras.layers.Embedding(10000,50) emb2 = tf.keras.layers.Embedding(10000,50)

   def loss(y): y_ = emb(tf.constant(100)) + emb2(tf.constant(100)) return tf.reduce_sum(y - y_)

With eager execution, parameter ownership is more "Pythonic", in that the parameters associated with the Embedding object (emb and emb2) have the lifetime of the object that created them.

Hope that helps.