How do I export a TensorFlow model as a .tflite file?

Background information:

I have written a TensorFlow model very similar to the premade iris classification model provided by TensorFlow. The differences are relatively minor:

• I am classifying football exercises, not iris species.
• I have 10 features and one label, not 4 features and one label.
• I have 5 different exercises, as opposed to 3 iris species.
• My trainData contains around 3500 rows, not only 120.
• My testData contains around 330 rows, not only 30.
• I am using a DNN classifier with n_classes=6, not 3.

I now want to export the model as a .tflite file. But according to the TensorFlow Developer Guide, I need to first export the model to a tf.GraphDef file, then freeze it and only then will I be able to convert it. However, the tutorial provided by TensorFlow to create a .pb file from a custom model only seems to be optimized for image classification models.

Question:

So how do I convert a model like the iris classification example model into a .tflite file? Is there an easier, more direct way to do it, without having to export it to a .pb file, then freeze it and so on? An example based on the iris classification code or a link to a more explicit tutorial would be very useful!

Other information:

• OS: macOS 10.13.4 High Sierra
• TensorFlow Version: 1.8.0
• Python Version: 3.6.4
• Using PyCharm Community 2018.1.3

Code:

The iris classification code can be cloned by entering the following command:

git clone https://github.com/tensorflow/models

But in case you don't want to download the whole package, here it is:

This is the classifier file called premade_estimator.py:

    #  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
    #
    #  Licensed under the Apache License, Version 2.0 (the "License");
    #  you may not use this file except in compliance with the License.
    #  You may obtain a copy of the License at
    #
    #  http://www.apache.org/licenses/LICENSE-2.0
    #
    #  Unless required by applicable law or agreed to in writing,                         software
    #  distributed under the License is distributed on an "AS IS" BASIS,
    #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    #  See the License for the specific language governing permissions and
    #  limitations under the License.
    """An Example of a DNNClassifier for the Iris dataset."""
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function

    import argparse
    import tensorflow as tf

    import iris_data

    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', default=100, type=int, help='batch size')
    parser.add_argument('--train_steps', default=1000, type=int,
                help='number of training steps')


    def main(argv):
        args = parser.parse_args(argv[1:])

        # Fetch the data
        (train_x, train_y), (test_x, test_y) = iris_data.load_data()

        # Feature columns describe how to use the input.
        my_feature_columns = []
        for key in train_x.keys():
                    my_feature_columns.append(tf.feature_column.numeric_column(key=key))

        # Build 2 hidden layer DNN with 10, 10 units respectively.
        classifier = tf.estimator.DNNClassifier(
            feature_columns=my_feature_columns,
            # Two hidden layers of 10 nodes each.
            hidden_units=[10, 10],
            # The model must choose between 3 classes.
            n_classes=3)

        # Train the Model.
        classifier.train(
            input_fn=lambda: iris_data.train_input_fn(train_x, train_y,
                                              args.batch_size),
            steps=args.train_steps)

        # Evaluate the model.
        eval_result = classifier.evaluate(
            input_fn=lambda: iris_data.eval_input_fn(test_x, test_y,
                                             args.batch_size))

        print('\nTest set accuracy:         {accuracy:0.3f}\n'.format(**eval_result))

        # Generate predictions from the model
        expected = ['Setosa', 'Versicolor', 'Virginica']
        predict_x = {
            'SepalLength': [5.1, 5.9, 6.9],
            'SepalWidth': [3.3, 3.0, 3.1],
            'PetalLength': [1.7, 4.2, 5.4],
            'PetalWidth': [0.5, 1.5, 2.1],
        }

        predictions = classifier.predict(
            input_fn=lambda: iris_data.eval_input_fn(predict_x,
                                                     labels=None,
                                                     batch_size=args.batch_size))

        template = '\nPrediction is "{}" ({:.1f}%), expected "{}"'

        for pred_dict, expec in zip(predictions, expected):
            class_id = pred_dict['class_ids'][0]
            probability = pred_dict['probabilities'][class_id]

            print(template.format(iris_data.SPECIES[class_id],
                          100 * probability, expec))


    if __name__ == '__main__':
        # tf.logging.set_verbosity(tf.logging.INFO)
        tf.app.run(main)

And this is the data file called iris_data.py:

    import pandas as pd
    import tensorflow as tf

    TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
    TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"

    CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
                        'PetalLength', 'PetalWidth', 'Species']
    SPECIES = ['Setosa', 'Versicolor', 'Virginica']


    def maybe_download():
        train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)
        test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)

        return train_path, test_path


    def load_data(y_name='Species'):
        """Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
        train_path, test_path = maybe_download()

        train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
        train_x, train_y = train, train.pop(y_name)

        test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
        test_x, test_y = test, test.pop(y_name)

        return (train_x, train_y), (test_x, test_y)


    def train_input_fn(features, labels, batch_size):
        """An input function for training"""
        # Convert the inputs to a Dataset.
        dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))

        # Shuffle, repeat, and batch the examples.
        dataset = dataset.shuffle(1000).repeat().batch(batch_size)

        # Return the dataset.
        return dataset


    def eval_input_fn(features, labels, batch_size):
        """An input function for evaluation or prediction"""
        features = dict(features)
        if labels is None:
            # No labels, use only features.
            inputs = features
        else:
            inputs = (features, labels)

        # Convert the inputs to a Dataset.
        dataset = tf.data.Dataset.from_tensor_slices(inputs)

        # Batch the examples
        assert batch_size is not None, "batch_size must not be None"
        dataset = dataset.batch(batch_size)

        # Return the dataset.
        return dataset

** UPDATE **

Ok so I have found a seemingly very useful piece of code on this page:

    import tensorflow as tf

    img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
    val = img + tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
    out = tf.identity(val, name="out")
    with tf.Session() as sess:
      tflite_model = tf.contrib.lite.toco_convert(sess.graph_def, [img], [out])
      open("test.tflite", "wb").write(tflite_model)

This little guy directly converts a simple model to a TensorFlow Lite Model. Now all I have to do is find a way to adapt this to the iris classification model. Any suggestions?