Serving Tf Model With Variable Length Input

I am trying to serve my TF model with TF Serving. Here's the model's input I have: raw_feature_spec = { 'x': tf.io.VarLenFeature(tf.string), 'y': tf.io.VarLenFeature(tf.s

Solution 1:

You have a placeholder named input_example_tensor in the transformed_features. And also the sparse placeholder of TensorFlow doesn't work.

I solve this problem by representing each sparse tensor with 3 dense tensors in the receiver, namely indices, values, and shape then convert them to sparse tensor in features. For your case, you need to define the serving_input_fn as following:

def serving_input_fn():
    inputs = {
        "x_indices": tf.placeholder(tf.int64, [None, 2]),
        "x_vals": tf.placeholder(tf.string, [None, 2]),
        "x_shape": tf.placeholder(tf.int64, [2]),
        "y_indices": tf.placeholder(tf.int64, [None, 2]),
        "y_vals": tf.placeholder(tf.string, [None, 2]),
        "y_shape": tf.placeholder(tf.int64, [2]),
        "z": tf.placeholder(tf.string, [None, 1])
    }

    fvs = {
        "x": tf.SparseTensor(
                 inputs["x_indices"],
                 inputs["x_vals"],
                 inputs[x_shape]
             ),
        "y": tf.SparseTensor(
                 inputs["y_indices"],
                 inputs["y_vals"],
                 inputs[y_shape]
             ),
        "z": inputs["z"]
    }
    
    return tf.estimator.export.ServingInputReceiver(fvs, inputs)

I also want to ask you a question, how did you manage to train a model that has VarLenFeature with TensorFlow Keras, could you share this part with me? Currently, I'm training this kind of models with TensorFlow Estimator.