Using trainable word embedding layer with LSTM and dynamic RNN: AdamOptimizer expected float_ref instead of float

Question

I'm using an RNN on sequences of word embeddings to classify sentences. At first I was feeding pre-trained word embeddings and everything worked fine. I made the embeddings matrix a tf.placeholder with dimension (Vocab size, Embedding size) and fed some pre-trained embeddings from GloVe. I also use tf.nn.embedding_lookup to translate my inputs (which are sequences of word IDs) into sequences of embeddings.

Then I wanted to allow the model to train the embeddings as well, so I made the embedding matrix a tf.Variable instead of a placeholder. Now TensorFlow gives me this error -- apparently the AdamOptimizer can't handle the embedding lookup. Any idea what's up or how to fix this?

tensorflow.python.framework.errors_impl.InvalidArgumentError: Input 0 of node 
Adam/update_embeddings/AssignSub was passed float from _recv_embeddings_0:0 
incompatible with expected float_ref.

Answer 1

You cannot feed a value to a variable and optimize it at the same time. Instead, you must first run a tf.assign on that variable to initialize it to the fed value, and then run the optimier. Or, more easily, you can just pass the glove vectors as the initializer of the variable and run tf.global_variables_initializer.