Fuel/Usage - Revision history

Admin: /* Correct Usage */

2017-10-15T22:46:29Z

Correct Usage

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	<pre>		<pre>
	$ py ~~iterator_example~~.py		$ py scheme_shuffled_example.py
	[7, 2, 1, 6]		[7, 2, 1, 6]
	[0, 4, 3, 5]		[0, 4, 3, 5]

2017-10-15T22:45:34Z

Iteration Schemes

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 ==Iteration Schemes==
 ===ShuffledScheme Example===
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   [2]]
 </pre>
 ==Data Streams==

2017-10-15T21:54:12Z

Transformers

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	* Streams, which combine datasets and schemes to allow normal-looking iteration through a data set		* Streams, which combine datasets and schemes to allow normal-looking iteration through a data set

	One last thing we have not covered is, how to apply transformations to our data as we load/access it.		One last thing we have not covered is, how to apply transformations to our data as we load/access it. That's where Transformer objects come in.

	=Flags=		=Flags=

	{{FuelFlag}}		{{FuelFlag}}

2017-10-15T21:53:44Z

DataStream Example: Higher Dimensional Dataset

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	[1]]		[1]]
	</pre>		</pre>


			==Transformers==

			So far we have covered:
			* Dataset objects, which are stateless objects storing the data and labels for the data axes
			* IndexableDataset objects, which allow arbitrary iteration schema (more flexible)
			* IterableDataset objects, which allow specific iteration schema (less flexible)
			* Scheme objects, which allow us to generate schema for iterating through data sets that match certain specifications that we wish to set (batch size, axes, order, etc.)
			* Streams, which combine datasets and schemes to allow normal-looking iteration through a data set

			One last thing we have not covered is, how to apply transformations to our data as we load/access it.

	=Flags=		=Flags=

	{{FuelFlag}}		{{FuelFlag}}

2017-10-15T00:43:44Z

Data Stream Documentation Links

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	When constructing the DataStream, you pass it your Dataset object, and optionally your iteration_scheme object.		When constructing the DataStream, you pass it your Dataset object, and optionally your iteration_scheme object.

	===~~Data Stream~~ Documentation Links===		===DataStream Documentation Links===

	Link to DataStream documentation: https://fuel.readthedocs.io/en/latest/api/data_streams.html		Link to DataStream documentation: https://fuel.readthedocs.io/en/latest/api/data_streams.html

2017-10-15T00:43:34Z

Stream Example: Higher Dimensional Dataset

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	</pre>		</pre>

	===~~Stream~~ Example: Higher Dimensional Dataset===		===DataStream Example: Higher Dimensional Dataset===

	Suppose we are working on a classification task, and we have a set of images of three kinds of animals (dogs, cats, and snakes). We want to pick three of each animal, and determine if these three animals could be friends - so our response is a yes/no (or, a binary 0 or 1).		Suppose we are working on a classification task, and we have a set of images of three kinds of animals (dogs, cats, and snakes). We want to pick three of each animal, and determine if these three animals could be friends - so our response is a yes/no (or, a binary 0 or 1).

2017-10-15T00:43:16Z

Data Streams

2017-10-15T00:28:13Z

Basic Usage

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 * [[#Datasets]] - wrap the data
 * [[#Iteration Schemes]] - schemes allow iterating through the data using various strategies
 ==Datasets==
 Datasets are the principal interface to data. Internally, they use a DataStream object to create and request iterators.
 ===IterableDataset Example===
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   [0]
   [2]]
 </pre>

2017-10-14T23:14:30Z

Basic Usage

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			This page covers the use of fuel, a library for easy loading of data sets for machine learning and neural network applications.

			We begin with [[#Basic Usage]] and the core classes of fuel.

			Then we move on to [[#Advanced Usage]] and how to make the most of fuel in a practical way.

			Then we cover [[#Workflows]] and how to use fuel in machine learning pipelines.

	=Basic Usage=		=Basic Usage=

			We begin with an overview of the basic types of classes in fuel:
			* [[#Datasets]] - wrap the data
			* [[#Iteration Schemes]] - schemes allow iterating through the data using various strategies

	==Datasets==		==Datasets==

2017-10-14T23:08:59Z