Fuel

Basics

Fuel is a library for creating machine learning data pipelines. There are multiple features that make it really convenient.

Find fuel on Github here: https://github.com/mila-udem/fuel

Overview of how it works: https://fuel.readthedocs.io/en/latest/overview.html

Prerequisites

Fuel uses HDF5, so you will need a copy of HDF5 header files installed locally. Use your package manager, or follow HDF5 installation instructions. On a Mac:

$ brew install hdf5

Now you can install Fuel.

Install

$ git clone git@github.com:/mila-udem/fuel.git
$ cd fuel
$ python setup.py build && python setup.py install

Basic Usage

Datasets

Datasets are the principal interface to data. Internally, they use a DataStream object to create and request iterators.

Datasets Example

Suppose we create eight (8) different 2x2 greyscale images, and put them in the variable "features", then create 4 target classes, and put them in "targets":

In [1]: import numpy

In [2]: seed = 1234

In [3]: rng = numpy.random.RandomState(seed)

In [4]: features = rng.randint(256, size=(8, 2, 2))

In [5]: targets = rng.randint(4, size=(8, 1))

Now we can create a Dataset to iterate over the data:

In [6]: from collections import OrderedDict

In [7]: from fuel.datasets import IterableDataset

In [8]: dataset = IterableDataset(
   ...: iterables=OrderedDict([('features', features), ('targets', targets)]),
   ...: axis_labels=OrderedDict([('features', ('batch', 'height', 'width')),
   ...: ('targets', ('batch', 'index'))]))

and we can access each attribute using the dataset object:

In [9]: print('Provided sources are {}.'.format(dataset.provides_sources))
Provided sources are ('features', 'targets').

In [10]: print('Sources are {}.'.format(dataset.sources))
Sources are ('features', 'targets').

In [11]: print('Axis labels are {}.'.format(dataset.axis_labels))
Axis labels are OrderedDict([('features', ('batch', 'height', 'width')), ('targets', ('batch', 'index'))]).

In [12]: print('Dataset contains {} examples.'.format(dataset.num_examples))
Dataset contains 8 examples.

In [14]: from pprint import pprint

In [15]: pprint(dir(dataset))
['__abstractmethods__',
 '__class__',
 '__delattr__',
 '__dict__',
 '__dir__',
 '__doc__',
 '__eq__',
 '__format__',
 '__ge__',
 '__getattribute__',
 '__gt__',
 '__hash__',
 '__init__',
 '__init_subclass__',
 '__le__',
 '__lt__',
 '__module__',
 '__ne__',
 '__new__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__setattr__',
 '__sizeof__',
 '__str__',
 '__subclasshook__',
 '__weakref__',
 '_abc_cache',
 '_abc_negative_cache',
 '_abc_negative_cache_version',
 '_abc_registry',
 'apply_default_transformers',
 'axis_labels',
 'close',
 'default_transformers',
 'example_iteration_scheme',
 'filter_sources',
 'get_data',
 'get_example_stream',
 'iterables',
 'next_epoch',
 'num_examples',
 'open',
 'provides_sources',
 'reset',
 'sources']

Note that the dataset is stateless, so we need to create an external object to represent the state, then pass that into the dataset when we want to iterate over/access the data:

In [17]: state = dataset.open()

In [18]: while True:
    ...:     try:
    ...:         print(dataset.get_data(state=state))
    ...:     except StopIteration:
    ...:         print('Iterator finished')
    ...:         break
    ...:
(array([[ 47, 211],
       [ 38,  53]]), array([0]))
(array([[204, 116],
       [152, 249]]), array([3]))
(array([[143, 177],
       [ 23, 233]]), array([0]))
(array([[154,  30],
       [171, 158]]), array([1]))
(array([[236, 124],
       [ 26, 118]]), array([2]))
(array([[186, 120],
       [112, 220]]), array([2]))
(array([[ 69,  80],
       [201, 127]]), array([2]))
(array([[246, 254],
       [175,  50]]), array([3]))
Iterator finished

To reset the state, use the Dataset object's reset() function. To finish, use the close() function.

In [19]: state = dataset.reset(state=state)

In [20]: print(dataset.get_data(state=state))
(array([[ 47, 211],
       [ 38,  53]]), array([0]))

In [21]: dataset.close(state=state)

Wrapping Custom Datasets with Fuel

Repo by github user dribnet illustrates how to wrap a new dataset using Fuel: https://github.com/dribnet/lfw_fuel

Advantages:

• Only takes one command to download the data and import it into fuel
• Then it only takes one command to import the library that wraps the data, and be able to turn it into training/testing X and Y

Disadvantages:

• One-size-fits-all; importing data using load_data() can take a REALLY long time, and must be done every time you run the script (not persistent in memory)
• Complicated to extend
• Removes some of the nicer options of fuel

Here is what the final payoff looks like:

from keras.models import Sequential
from lfw_fuel import lfw

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = lfw.load_data(format="deepfunneled")

# (build the perfect model here)

model.fit(X_train, Y_train, show_accuracy=True, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)