Using hub.text_embedding_column with tf.contrib.estimator.RNNClassifier - tensorflow

Question

I'm trying to use a module off Tensorflow Hub (a word embedding module) with tf.contrib.estimator.RNNClassifier.
My desired model
embedded_text_feature_column = hub.text_embedding_column(
key="description",
module_spec="https://tfhub.dev/google/nnlm-en-dim128/1")
estimator = tf.contrib.estimator.RNNClassifier(
sequence_feature_columns=[embedded_text_feature_column],
num_units=[32, 16])
Running that returns the following error:
ValueError: All feature_columns must be of type _SequenceDenseColumn.
You can wrap a sequence_categorical_column with an embedding_column or indicator_column.
Given (
type <class 'tensorflow_hub.feature_column._TextEmbeddingColumn'>):
_TextEmbeddingColumn(key='title_description', module_spec=<tensorflow_hub.native_module._ModuleSpec object at 0x7fb0102a5a90>, trainable=False
)
A working model
Using the TF Hub module works fine with:
estimator = tf.estimator.DNNClassifier(
hidden_units=[32, 16],
feature_columns=[embedded_text_feature_column])
Is it possible to use the nnlm module with RNNClassifier?

Answer 1

The Code corresponding to your Desired Model seems to be working without error in Google Colab with Tensorflow Version 1.15.
Please find the working code below:
!pip install tensorflow==1.15
import tensorflow as tf
import tensorflow_hub as hub
embedded_text_feature_column = hub.text_embedding_column(
key="description",
module_spec="https://tfhub.dev/google/nnlm-en-dim128/1")
estimator = tf.contrib.estimator.RNNClassifier(
sequence_feature_columns=[embedded_text_feature_column],
num_units=[32, 16])
Here is the Link for Github Colab Gist.