Fuel
From charlesreid1
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.
IterableDataset Example
Code: https://gist.github.com/charlesreid1/eefc22defc8c6bd07c6bd0ac222c9781
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))
[
...snip...
'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)
IndexableDataset Example
Code: https://gist.github.com/charlesreid1/eefc22defc8c6bd07c6bd0ac222c9781
IndexableDataset objects do not work the same way as IterableDataset objects - there is no need to store a persistent state because all the data can be accessed randomly, in any order you please.
In [1]: from fuel.datasets import IndexableDataset
...: from collections import OrderedDict
In [2]: import numpy
...: seed = 1234
...: rng = numpy.random.RandomState(seed)
In [3]: features = rng.randint(256, size=(8, 2, 2))
...: targets = rng.randint(4, size=(8, 1))
In [4]: dataset = IndexableDataset(
...: indexables=OrderedDict([('features', features), ('targets', targets)]),
...: axis_labels=OrderedDict([('features', ('batch', 'height', 'width')),
...: ('targets', ('batch', 'index'))]))
In [5]: state = dataset.open()
In [6]: print("State is {}".format(state))
...: print("NOTE: None state returned, because there is no state to maintain!")
State is None
NOTE: None state returned, because there is no state to maintain!
In [7]: print(dataset.get_data(state=state, request=[3,1,0]))
(array([[[154, 30],
[171, 158]],
[[204, 116],
[152, 249]],
[[ 47, 211],
[ 38, 53]]]), array([[1],
[3],
[0]]))
In [8]: print(dataset.get_data(state=state, request=[1,2,4,7]))
(array([[[204, 116],
[152, 249]],
[[143, 177],
[ 23, 233]],
[[236, 124],
[ 26, 118]],
[[246, 254],
[175, 50]]]), array([[3],
[0],
[2],
[3]]))
In [9]: dataset.close(state=state)
No need to reset any iterator.
Iteration Schemes
Iteration Scheme Examples
Let's illustrate how to use iteration schemes - but first, how NOT to use iteration schemes.
Incorrect Usage
Suppose we created an IterableDataset, as in the first example, and tried to iterate over it in arbitrary order:
In [8]: dataset = IterableDataset(
...: iterables=OrderedDict([('features', features), ('targets', targets)]),
...: axis_labels=OrderedDict([('features', ('batch', 'height', 'width')),
...: ('targets', ('batch', 'index'))]))
The problem with doing this is, the get_data() function for an IterableDataset does not support any extra arguments (like request), so we can't request data out of the standard iteration order. What happens if we do? We get a ValueError...
In [23]: from fuel.schemes import ShuffledScheme
In [24]: state = dataset.open()
In [25]: scheme = ShuffledScheme(examples=dataset.num_examples, batch_size=4)
In [26]: for request in scheme.get_request_iterator():
...: data = dataset.get_data(state=state, request=request)
...: print(data[0].shape, data[1].shape)
...:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-27-24827dafdaa8> in <module>()
1 for request in scheme.get_request_iterator():
----> 2 data = dataset.get_data(state=state, request=request)
3 print(data[0].shape, data[1].shape)
4
/usr/local/lib/python3.6/site-packages/fuel-0.2.0-py3.6-macosx-10.12-x86_64.egg/fuel/datasets/base.py in get_data(self, state, request)
310 def get_data(self, state=None, request=None):
311 if state is None or request is not None:
--> 312 raise ValueError
313 return next(state)
314
ValueError:
Correct Usage
We'll need to re-create our dataset, this time using an IndexableDataset object.
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)