def predict_input_fn(fileNames):

    def parse_image(example_proto):
        parsed_features = tf.parse_single_example(example_proto, imageFeaturesDict)
        return parsed_features

    dataset = tf.data.TFRecordDataset(fileNames, compression_type='GZIP')
    dataset = dataset.map(parse_image)
    dataset = dataset.batch(PATCH_WIDTH * PATCH_HEIGHT)

    iterator = dataset.make_one_shot_iterator()
    return iterator.get_next()

predictions = classifier.predict(input_fn=lambda: predict_input_fn(fileNames))

for pred_dict in predictions:
   ... fill up the image array ...