How to make prediction using tensorflow models?
First, you need to convert the dataframe in numpy array or tf.data dataset that the model understands. For this purpose, the tutorial provides you with a function:

# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
  dataframe = dataframe.copy()
  labels = dataframe.pop('target')
  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
  if shuffle:
    ds = ds.shuffle(buffer_size=len(dataframe))
  ds = ds.batch(batch_size)
  return ds 

test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)


Then you can get the predictions like this:

model.predict_proba(x=test_ds)


Take into account that the test-input format should be the same as training-input format, so if you have done any preprocessing (on_hot_encode, standardize, bucketize, etc) to the training dataset, you should do it also to the test dataset  

Another way, if your dataset is ready for prediction, you can just transform it to a numpy array:

model.predict_proba(x=test[feature_names].values)