Applied Machine Learning: Deep Learning for Industry/

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Protocol Buffer

Protocol Buffer

Discover the benefits of using protocol buffers in TensorFlow.

We'll cover the following...

Chapter Goals:

  • Learn how protocol buffers are used in TensorFlow
  • Implement a function to convert a Python dictionary to a tf.train.Example object

A. TensorFlow protocol buffer

Since protocol buffers use a structured format when storing data, they can be represented with Python classes. In TensorFlow, the tf.train.Example class represents the protocol buffer used to store data for the input pipeline.

Each individual tf.train.Example object describes data for a single dataset observation (e.g. a single row in a data table). We convert raw data to a protocol buffer by initializing a tf.train.Example object with the data’s values.

When we initialize a tf.train.Example object, we need to set that object’s features argument to a tf.train.Features object. The tf.train.Features class is initialized by setting the feature field to a dictionary that maps feature names to feature values.

Python 3.5
import tensorflow as tf
features = tf.train.Features(feature=f_dict)  # f_dict is a dict
ex = tf.train.Example(features=features)
print(repr(ex))

The code above initializes a tf.train.Example object. Note that f_dict is a dictionary mapping feature names to feature values. We discuss how to create the feature dictionary later in this chapter.

B. Features

Each feature value is represented by a tf.train.Featur object, which is initialized with exactly one of the following fields:

  • int64_list, for integer data values. Set with a tf.train.Int64List object.
  • float_list, for floating point data values. Set with a tf.train.FloatList object.
  • bytes_list, for byte string data values. Set with a tf.train.BytesList object.

The code below creates tf.train.Feature objects from data values. The encode function used in the last example converts the string to bytes, so the type is compatible with tf.train.BytesList.

Python 3.5
import tensorflow as tf
int_f = tf.train.Feature(
    int64_list=tf.train.Int64List(value=[1, 2]))
print(repr(int_f) + '\n')
float_f = tf.train.Feature(
    float_list=tf.train.FloatList(value=[-8.2, 5]))
print(repr(float_f) + '\n')
bytes_f = tf.train.Feature(
    bytes_list=tf.train.BytesList(value=[b'\xff\xcc', b'\xac']))
print(repr(bytes_f) + '\n')
str_f = tf.train.Feature(
    bytes_list=tf.train.BytesList(value=['joe'.encode()]))
print(repr(str_f) + '\n')

Note that the value field for the tf.train.Int64List, tf.train.FloatList, and tf.train.BytesList classes must be an iterable (e.g. list, NumPy array, or pandas Series). If a feature only has one data value, we would pass in an iterable containing the single value.

With the tf.train.Feature objects, we can create the dictionary that’s used to initialize a tf.train.Features object.

Python 3.5
import tensorflow as tf
f_dict = {
    'int_vals': int_f,
    'float_vals': float_f,
    'bytes_vals': bytes_f,
    'str_vals': str_f
}
features = tf.train.Features(feature=f_dict)
print(repr(features))

C. Bytes and text

When dealing with datasets containing bytes (e.g. images or videos) or text (e.g. articles or sentences), it is beneficial to first read all the data files and then store the read data in the bytes_list field of a tf.train.Feature. This saves us from having to open each individual file within our input pipeline, which can drastically improve efficiency.

Python 3.5
Files
import tensorflow as tf
with open('story.txt') as f:
    words = f.read().split()
encw = [w.encode() for w in words]
words_feature = tf.train.Feature(
    bytes_list=tf.train.BytesList(value=encw))
print(repr(words_feature))
with open('./img.jpg', 'rb') as f:
    img_bytes = f.read()
img_feature = tf.train.Feature(
    bytes_list=tf.train.BytesList(value=[img_bytes]))
print(repr(img_feature))

Time to Code!

In this chapter, you’ll be completing the dict_to_example function, which converts a regular dictionary into a tf.train.Example object.

import tensorflow as tf
def dict_to_example(data_dict, config):
    feature_dict = {}
    for feature_name, value in data_dict.items():
        feature_config = config[feature_name]
        shape = feature_config['shape']
        if shape == () or shape == []:
            value = [value]
        value_type = feature_config['type']
        if value_type == 'int':
            feature_dict[feature_name] = make_int_feature(value)
        elif value_type == 'float':
            feature_dict[feature_name] = make_float_feature(value)
        elif value_type == 'string' or value_type == 'bytes':
            feature_dict[feature_name] = make_bytes_feature(
              value, value_type)
    features = tf.train.Features(feature=feature_dict)
    return tf.train.Example(features=features)

The dict_to_example function uses 3 helper functions: make_int_feature, make_float_feature, and make_bytes_feature. You will be creating each of these helper functions.

The make_int_feature function returns a tf.train.Feature initialized with the int64_list field.

Return a tf.train.Feature object with the int64_list attribute set equal to a tf.train.Int64List object (initialized with value).

import tensorflow as tf
def make_int_feature(value):
    # CODE HERE
    pass

The make_float_feature function returns a tf.train.Feature initialized with the float_list field.

Return a tf.train.Feature object with the float_list attribute set equal to a tf.train.FloatList object (initialized with value).

import tensorflow as tf
def make_float_feature(value):
    # CODE HERE
    pass

The make_float_feature function returns a tf.train.Feature initialized with the bytes_list field. Note that if the value_type is 'string' we need to encode each of the elements in value.

If value_type is equal to 'string', encode each element in value (and keep the resultant list as value).

**Return a tf.train.Feature object with the bytes_list attribute set equal to a tf.train.BytesList object (initialized with value).

import tensorflow as tf
def make_bytes_feature(value, value_type):
    #CODE HERE
    pass