Converting tensorflow dataset to numpy array

I have an autoencoder defined using tf.keras in tensorflow 1.15. I cannot upgrade to tensorflow to 2.0 for some specific reasons.

This particular autoencoder is used for anomaly detection. I currently compute the AUC score of the autoencoder as follows:

1. All anomalous inputs are labelled 1 and all normal inputs are labelled 0. This is y_true

2. I feed the autoencoder with unseen inputs and then measure the reconstruction error, like so: errors = np.mean(np.square(data - model.predict(data)), axis=-1)

3. The mean of this array is then said to the predicted label, y_pred.

4. I then compute the AUC using auc = metrics.roc_auc_score(y_true, y_pred).

This approach works well. I now need to move towards using tf.data.dataset to feed in my data. Previously, it was numpy arrays. The issue is, I am unable to convert tf.data.dataset to a numpy array and hence unable to compute the mean squared error as seen in 2.

Once I have a tf.data.Dataset, I feed it for prediction like so: results = model.predict(x_test)

This yields a numpy array, results. I want to compute the mean square error of results with x_test. However, x_test is of type tf.data.Dataset. So the question is, how can I convert a tf.data.dataset to a numpy array in tensorflow 1.15 or what is an alternative method to do this?