title: How to use tf.dataset for making predictions?
body: <p>I feel that using using tf.dataset is very intransparent how and why stuff works. I just hang out here and wonder how to make predictions now that I have a model using again a tf.dataset. It is so easy to use numpy arrays.</p>
  <p>If I use a tf.dataset in tf.keras.predict(), how does this model know what is the data data to use as input and what are the labels? Because I have labels in my data where I want to do predictions... I don't understand the way of using and understand the inner working.</p>
  <p>Thanks for enlightment.
  EDIT:</p>
  <p>Also since I need this for a custom callback I wonder if the output of predict with a datsset is aligned with the labels of the origin data structure? How could I extract the labels from the dataset of which I did predictions?</p>
  <p>Edit:</p>
  <p>I have scores of predict:</p>
  <pre><code>   scores=bert_model.predict(test)
  </code></pre>
  <p>where test is a tf.datsaet which ahs 4 column with names. I actually don't know how to display a datset. I just can iterate with take(). Then the 1. element look like this:</p>
  <pre><code>({'input_ids': &lt;tf.Tensor: shape=(15,), dtype=int32, numpy=
  array([    3, 18414,  1169,    50, 18382,   239, 26972,     4, 11909,
           333,    30,  4445, 26918,   144,     4])&gt;, 'token_type_ids': &lt;tf.Tensor: shape=(15,), dtype=int32, numpy=array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])&gt;, 'attention_mask': &lt;tf.Tensor: shape=(15,), dtype=int32, numpy=array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])&gt;}, &lt;tf.Tensor: shape=(), dtype=int64, numpy=0&gt;)
  </code></pre>
  <p>I just see that the last element has no key name? this is the label key.
  So I think that internally the label key is ignored? Hopefully?</p>
  <p>The first 3 keys should be used for predictions.</p>
  <p>Also, Is the order of the result predictions the same as of the dataset used? Such that I can extract the labels from the dataset and do custom evaluation with the scores?</p>