title: TypeError: Cannot convert value dtype to a TensorFlow DType
body: <p>I am fairly new to tensorflow. I am loading a model from a .h5 file and trying to make a prediction using the model.</p>
  
  <pre><code>import tensorflow as tf
  import pandas as pd
  import numpy as np
  
  file_path = '/path/to/file'
  model = tf.keras.models.load_model(file_path)
  
  query = np.array(('cat1', 69.0, 'cat2', 9.2, 'cat3', 0, 0, 1, 0, 3, 0, 43, 1200, 4380), dtype='U32, float32, U32, float32, U32, uint16, uint16, uint16, uint16, uint16, uint16, uint16, float32, float32')
  result = model.predict(query)
  </code></pre>
  
  <p>I tried to format the query as pandas.Series but couldn't get it working with mixed data types. I explicitly specified the dtype using np.array but it was throwing the error: "TypeError: Cannot convert value dtype to a TensorFlow DType."</p>
  
  <p>cat1, cat2 and cat3 are categories and have been labelencoded during model training. </p>
  
  <p>During testing, I found that the model is returning a prediction if I pass uint16 values instead of U32 in the query np.array. But I can't figure out how to get the uint16 values for the categories. Or how to format the array to create the tensor for prediction. Any ideas?</p>