how about just `autoencoder` or `auto_encoder`?  Except for the buzzword
value of the word *sparse* does it really add much more? The only
non-sparse autoencoder I can think of is the bottleneck, *semantic hashing*
autoencoder which is specific enough to be called that if we ever implement
it. WDYT?


On Thu, Jul 4, 2013 at 11:52 AM, Gael Varoquaux <notifications@github.com>wrote:

> In sklearn/neural_network/sae.py:
>
> > +class SAE(BaseEstimator, TransformerMixin):
> > +    """
> > +    Sparse Autoencoder (SAE)
> > +
> > +    A Sparse Autoencoder with one hidden layer. Parameters are trained using
> > +    Limited-Memory  Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)
> > +    Parameters
> > +    ----------
> > +    n_hidden : int
> > +        Number of hidden neurons
> > +    lr : float, optional
> > +        Learning rate to use during learning. It is *highly* recommended
> > +        to tune this hyper-parameter. Possible values are 10**[0., -3.].
> > +    beta : float, optional
> > +        Weight of sparsity penalty term
> > +    sparsityParam : float, optional
>
>  In sklearn/neural_network/sae.py: > +class SAE(BaseEstimator,
> TransformerMixin):
> Neither the class name, nor the file, should be called 'SAE'. Acronyms are
> hard to follow. I can suggest 'SparseAutoEnc' and sparse_auto_enc.py.
>
> —
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> .
>