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further document usage

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Connor Olding 2018-03-15 02:36:26 +01:00
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@ -29,12 +29,32 @@ dataset = "emnist_balanced"
train_images, train_labels, test_images, test_labels = mnists.prepare(dataset)
```
the default images shape is (n, 1, 28, 28).
pass `flatten=True` to `mnists.prepare` to get (n, 784).
the default images shape is (n, 1, 28, 28) and scaled to the range [0, 1].
labels are output in [one-hot encoding.][onehot]
[onehot]: //machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/
### `prepare` arguments
pass `flatten=True` to get a flattened (n, 784) image shape.
pass `return_floats=False` to get the raw [0, 255] integer range of images.
pass `return_onehot=False` to get the raw [0, M-1] integer encoding of labels.
### why the extra dimension?
you will notice that, by default,
there is a single-dimensional entry in the shape of images:
(n, **1,** 28, 28).
this exists to obtain compatibility with programs that
expect a number of color channels in that place.
since mnist-like datasets are (as of writing) all grayscale,
there is only one color channel, and thus the size of this dimension is 1.
## datasets
in alphabetical order, using default `mnists.prepare` parameters:
in alphabetical order, using default `mnists.prepare` arguments:
[emnist]: //www.nist.gov/itl/iad/image-group/emnist-dataset
[fashion-mnist]: //github.com/zalandoresearch/fashion-mnist