tensorflow serving: confusion on feature_configs data format - tensorflow

Question

I have followed the tensorflow serving tutorial mnist_saved_model.py
and try to train and export a text-cnn-classifier model
The pipeline is
*embedding layer -> cnn -> maxpool -> cnn -> dropout -> output layer
Tensorflow data input :
data_in = tf.placeholder(tf.int32,[None, sequence_length] , name='data_in')
transformed to
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {'x': tf.FixedLenFeature(shape=[sequence_length],
dtype=tf.int64),}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
# use tf.identity() to assign name
data_in = tf.identity(tf_example['x'], name='x')
This works for training phase
but at test time
it tells
AbortionError(code=StatusCode.INVALID_ARGUMENT, details="Expects arg[0] to be int64 but string is provided")
I am confused about the above line
feature_configs = {'x': tf.FixedLenFeature(shape=[sequence_length],
dtype=tf.int64),}
I changed the line to
feature_configs = {'x': tf.FixedLenFeature(shape=[sequence_length],
dtype=tf.string),}
but it gives the following error at training time:
Traceback (most recent call last):
File "/serving/bazel-bin/tensorflow_serving/example/twitter-sentiment-cnn_saved_model.runfiles/tf_serving/tensorflow_serving/example/twitter-sentiment-cnn_saved_model.py", line 222, in <module>
embedded_chars = tf.nn.embedding_lookup(W, data_in)
File "/serving/bazel-bin/tensorflow_serving/example/twitter-sentiment-cnn_saved_model.runfiles/org_tensorflow/tensorflow/python/ops/embedding_ops.py", line 122, in embedding_lookup
return maybe_normalize(_do_gather(params[0], ids, name=name))
File "/serving/bazel-bin/tensorflow_serving/example/twitter-sentiment-cnn_saved_model.runfiles/org_tensorflow/tensorflow/python/ops/embedding_ops.py", line 42, in _do_gather
return array_ops.gather(params, ids, name=name)
File "/serving/bazel-bin/tensorflow_serving/example/twitter-sentiment-cnn_saved_model.runfiles/org_tensorflow/tensorflow/python/ops/gen_array_ops.py", line 1179, in gather
validate_indices=validate_indices, name=name)
File "/serving/bazel-bin/tensorflow_serving/example/twitter-sentiment-cnn_saved_model.runfiles/org_tensorflow/tensorflow/python/framework/op_def_library.py", line 589, in apply_op
param_name=input_name)
File "/serving/bazel-bin/tensorflow_serving/example/twitter-sentiment-cnn_saved_model.runfiles/org_tensorflow/tensorflow/python/framework/op_def_library.py", line 60, in _SatisfiesTypeConstraint
", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
TypeError: Value passed to parameter 'indices' has DataType string not in list of allowed values: int32, int64

Answer 1

Your code is wrong:
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
that means your input is a string, such as sentence's word. Therefore:
feature_configs = {'x': tf.FixedLenFeature(shape=[sequence_length],
dtype=tf.int64),}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
That means nothing, in my opinion, because you do not though vocabulary transfer string to int. You need load your train data's vocab to get word index!