Einops v0.3.2: updated documentation and tutorials, keras -> tf.keras

• documentation and domain (#75, #76, #77, #79, #81), thanks to @cgarciae
• typos and spellcheck (thank @ollema and @GarrettMooney )
• moved away from keras to tf.keras
• adjustments to tutorials and testing
• other minor improvements

Einops v0.3: repeat + more friendly requirements + ...

• new operation: repeat (includes repeat/tiling logic, copying along a new dimension)
• anonymous axes (specified by their length not name) are allowed:

grayscale = reduce(image, 'h w 3 -> h w', 'mean')
image_with_identical_channels = repeat(grayscale, 'h w -> h w 3')

• 1 can be used to refer to all dimensions of length 1
• reduced restrictions on axes names: almost any python identified can be an axis name now
• reduction can be provided with callable not string
• tutorials were slightly updated to include these changes
• code in kernel undergone refactoring, and now more documented
• support: keras layers are deprecated in favor of tf.keras layers
• experimental layer introduced: WeightedEinsum (RFC: #71 )

Jax support + updated testing code

• experimental support for Jax framework was added
• testing code was rewritten and updated to work
• tf2 always worked with einops, but tests had to be updated. So, tests are updated for tf2
• tf readme, minor additions, comments, etc.

Thanks to contributors

Initial release (v0.1)

This release introduces einops, as well as its notion.

Initial release API:
Operations (ops)

• einops.rearrange and einops.reduce

Auxiliary

• einops.asnumpy and einops.parse_shape

Layers (for chainer, gluon, keras and torch)

• Rearrange and Reduce

Supported frameworks:

• numpy
• pytorch
• tensorflow eager
• cupy
• chainer
• gluon
• tensorflow
• mxnet (experimental)
• and keras (experimental)